SMART CITIES INFORMATION SYSTEM Key Performance Indicator Guide

Author: Sebastian Möller, AIT Reviewer: Cordelia Wilson, GOPA Com.

TABLE OF CONTENTS 1.

2.

3.

Introduction ................................................................................................................................................... 5 1.1

Objectives ..................................................................................................................................................... 5

1.2

Sources for KPIs............................................................................................................................................. 6

1.3

Structure ....................................................................................................................................................... 7

Objects of assessment ................................................................................................................................... 8 2.1

Buildings ....................................................................................................................................................... 9

2.2

Set of buildings ............................................................................................................................................. 9

2.3

Energy supply units ..................................................................................................................................... 10

2.4

Set of energy supply units ........................................................................................................................... 10

2.5

Neighbourhood / city .................................................................................................................................. 10

Data requirements and comparability ......................................................................................................... 12 3.1 Data requirements ...................................................................................................................................... 12 3.1.1 Baseline scenario .................................................................................................................................... 12 3.1.2 Design data ............................................................................................................................................. 13 3.1.3 Monitoring data ..................................................................................................................................... 13 3.2 Data comparability ..................................................................................................................................... 13 3.2.1 Climate corrections ................................................................................................................................ 13 3.2.2 Comparability between objects of assessment...................................................................................... 16 3.2.3 Economic corrections ............................................................................................................................. 16

4.

General performance indicators .................................................................................................................. 18 4.1 Environmental performance indicators ...................................................................................................... 18 4.1.1 Energy performance indicators .............................................................................................................. 18 4.1.2 Emission indicators................................................................................................................................. 22 4.2 Technical performance indicators............................................................................................................... 25 4.2.1 Efficiency indicators ............................................................................................................................... 25 4.3 Economic performance indicators .............................................................................................................. 30 4.3.1 Operation cost related to their specific unit .......................................................................................... 30 4.3.2 Life-cycle costs ....................................................................................................................................... 31 4.3.3 Payback .................................................................................................................................................. 33 4.3.4 Annuity ................................................................................................................................................... 36 4.4 Eco-environmental performance indicators ............................................................................................... 36 4.4.1 Mitigation costs ...................................................................................................................................... 36 4.5 Macro-economic performance indicators................................................................................................... 38 4.5.1 Stimulation of local economy ................................................................................................................. 38 4.5.2 Triggered positive effect per grant......................................................................................................... 38

5.

Specific performance indicators ................................................................................................................... 40 5.1 ICT performance indicators......................................................................................................................... 40 5.1.1 Reliability ................................................................................................................................................ 40

2

5.1.2 5.1.3 5.1.4 5.1.5 5.1.6 5.1.7

Demand-side management .................................................................................................................... 40 Energy savings ........................................................................................................................................ 41 Integration of RES ................................................................................................................................... 42 Consumer engagement .......................................................................................................................... 43 Data generation...................................................................................................................................... 43 Economic indicators – ICT ...................................................................................................................... 44

5.2 Mobility performance indicators ................................................................................................................ 44 5.2.1 Energy consumption............................................................................................................................... 44 5.2.2 Pollution and nuisance ........................................................................................................................... 46 5.2.3 Modal split.............................................................................................................................................. 47 5.2.4 Alternative fuel vehicles ......................................................................................................................... 47 5.2.5 Smart mobility ........................................................................................................................................ 48 5.2.6 Logistics .................................................................................................................................................. 49 5.2.7 Smart traffic – ICT ................................................................................................................................... 49 5.2.8 Infrastructure ......................................................................................................................................... 50 5.2.9 Economic indicators – Mobility .............................................................................................................. 51 5.3 Prefabrication performance indicators ....................................................................................................... 51 5.3.1 Improvement compared to standard construction................................................................................ 51 5.3.2 Economic indicators – Prefabrication .................................................................................................... 52 5.4 Other measure-specific performance indicators......................................................................................... 53 5.4.1 Temperature .......................................................................................................................................... 53 5.4.2 Gas distribution system .......................................................................................................................... 53 6.

Overview of KPIs .......................................................................................................................................... 54

7.

Glossary for SCIS .......................................................................................................................................... 57

References ........................................................................................................................................................... 59

3

This document has been elaborated by the Smart Cities Information System (SCIS) following a thorough analysis of different initiatives and projects that work on the development of a key performance indicator (KPI) framework for Smart Cities. It is complementary to other initiatives and is focused on the energy aspects of Smart Cities. At the time of the document’s publication, not all of the projects in the scope of SCIS have been completed. Thus this guide does not provide a final list of KPIs and will be updated accordingly. The KPI framework has been developed under consideration of the appropriate elaboration of the following initiatives, standardisation committees, institutions and projects: • • • • • • • • • •

CONCERTO Premium – predecessor of SCIS European Commission – Eurostat European Innovation Partnership The Covenant of Mayors CEN – European Committee for Standardisation DIN – German Institute for Standardisation ISO – International Organisation for Standardisation ITU – International Telecommunication Union CITYKeys – Project funded under Horizon 2020 (H2020) CELSIUS– Project funded under the Seventh Framework Programme (FP7)

4

1. INTRODUCTION 1.1 Objectives The objective of this guide is to give a description of the SCIS key performance indicators and their application to the different objects of assessment, identify the data requirements for their calculation and describe the methodology for the selection of these indicators. SCIS focuses on the development of indicators to measure technical and economic aspects of energyrelated measures. These should be applicable to European-funded demonstration projects for Smart Cities and Communities (SCC), energy-efficient buildings (EeB) and designated projects funded under the calls for energy efficiency (EE). Due to the complexity and variety of the projects in the scope of SCIS, the indicators will be calculated for different aggregation levels (building, set of buildings, energy supply unit, set of energy supply units, neighbourhood, etc.).

Figure 1: SCIS KPI framework development

Figure 1 displays how the SCIS KPI framework has been developed through an alignment with other initiatives and projects at European level. SCIS will contribute to a general Smart Cities KPI framework through the definition of energy-level indicators. Further indicators are being developed by other 5

initiatives focusing on additional city aspects such as governance, people, safety and prosperity. These are not the focus of SCIS. In addition, this guide does not cover the social aspects; these are described in the SCIS Social Monitoring Guide.

1.2 Sources for KPIs The implementation of SCIS indicators has been done through an alignment with other initiatives and already existing indicators. Different frameworks for KPIs have been analysed and compared. Indicators focusing on energy and environmental aspects from different projects have been collected and additional ones have been included through the analysis of the selected demonstration projects. The main aim of this indicator list is to allow for comparability between projects. The following sources have been used: •

CONCERTO Premium Indicator Guide



CONCERTO Premium Guidebook for Assessment



CIVITAS



European Innovation Partnership on Smart Cities and Communities – Operational Implementation Plan (EIP-OIP)



Covenant of Mayors (CoM)



European Environment Agency (EEA)



European Energy Award (eea)



International Telecommunication Union – Focus Group on Smart Sustainable Cities: Key performance indicators related to the use of information and communication technology in smart sustainable cities (ITU-T)



ISO 37151: Smart community infrastructures – principles and requirements for performance metrics (ISO_37151)



ISO 37120: 2014_Sustainable development of communities – Indicators for city services and quality of life (ISO 37120)



CELSIUS project



CITYKeys project

The definition of indicators is still an ongoing process. In order to cover all technologies, applications and measures implemented in the projects within the scope of SCIS, assessment and discussions with project experts need to be conducted. However, some of the selected projects, as well as certain H2020 calls, 6

have not yet started or been published, so collaboration between SCIS and the new projects will need to be established.

1.3 Structure The structure of the guide starts from the general objectives of the definition of a SCIS KPI framework to a detailed description of each indicator. In section 2 the objects of assessment are presented. The indicators can then be calculated for each one of the levels of aggregation. Data requirements are presented in section 3 together with the necessary climate and economic corrections to allow for comparability between different project locations. Sections 4 and 5 provide a description and the calculation methodology for general and specific indicators. General indicators are those that can be used to define every project; specific indicators apply only to particular measures. A first set of specific indicators has been defined and will be further implemented in collaboration with the projects in scope. At the end of the document, in section 6, an overview of the SCIS indicators and their application to the different objects of assessment is provided.

7

2. OBJECTS OF ASSESSMENT In the projects to be assessed there are different levels of spatial aggregation that go from single entities to a whole neighbourhood or city. In order to allow the assessment of these projects, a classification of different typologies has been carried out. The two main entities are buildings and energy supply units. Additionally, and due to the special characteristics of these measures, information and communication technologies (ICT) and mobility have been defined as one entity.

Figure 2: Classification of assessment typologies and clustering

The different levels of aggregation (city, district, neighbourhood, implementation area, etc.) are then defined by the combination and clustering of these typologies. The following combinations are possible: -

Building (construction or refurbishment Set of buildings Energy supply unit Set of energy supply units Buildings + energy supply units ICT measures at the building level ICT measures at the energy supply unit level ICT measures at the neighbourhood / city level Mobility measures at the building level Mobility measures at the neighbourhood / city level Combination of measures of the different themes (energy, ICT and mobility) on the different levels (e.g. building, neighbourhood).

For additional information regarding these classification groups and definitions please refer to the Technical Monitoring Guide.

8

2.1 Buildings The assessment boundary at the building level is depicted in figure 3. According to EN 15603, the energy performance of the building is the balance of: -

the delivered energy, required to meet the energy needs; the exported energy.

Figure 3: Energy flows and terminology at the building level

The delivered energy is to be expressed per energy carrier. If part of this delivered energy is allocated to energy export, it also needs to be specified in the data collection, for example gas-fired combined heat and power (CHP) where the electricity produced is not used in the building. In this case the corresponding amount of gas allocated to electricity production shall be specified in order to be able to calculate the energy performance of the building. At the building level, the data required is: -

energy needs per area of application (heating, cooling, domestic hot water, etc.); energy technologies supplying these energy needs; energy storage units; delivered energy to each energy supply units expressed per energy carrier.

(The calculation procedure goes from the energy needs to the primary energy.)

2.2 Set of buildings The assessment for a set of buildings is done by an aggregation of building units. The indicators can then be calculated for the sum of the buildings as a group.

9

2.3 Energy supply units At the energy supply unit level, the approach followed is similar to that for the building level. The delivered energy per energy carrier and the output energy allocated to the energy carrier need to be specified. In addition, and depending on the energy supply unit, different indicators can be calculated. This assessment object refers to the building of integrated energy supply units (ESUs) as well as to largescale energy supply units.

2.4 Set of energy supply units The assessment for a set of ESUs is achieved by aggregating the energy supply units. The indicators can then be calculated for the sum of the energy supply units.

2.5 Neighbourhood / city The level of an implementation area or neighbourhood is calculated by the aggregation of different entities.

Figure 4: Objects of assessment and boundary conditions

10

The energy flows at this point also need to be defined. The following information is required to define the energy system: -

energy carriers used at the implementation area level and the primary energy factors corresponding to the area; demonstration units involved (buildings, energy supply units, storage units and distribution systems); delivered energy to each ESU and building allocated to the corresponding energy carrier; output energy of each ESU and, if applicable, output energy exported out of the boundary allocated to the corresponding energy carrier; energy flows between technologies and buildings (i.e. which ESU is supplying which building or ESU).

Additional data is also needed to define the measures applied. For more information regarding this please refer to the SCIS Technical Monitoring Guide. Due to the complexity of these systems, indicators can only be calculated if a full set of data is available.

11

3. DATA REQUIREMENTS AND COMPARABILITY 3.1 Data requirements For the calculation of indicators and the assessment of the energy performance, different sets of data are needed. These include the baseline scenario, design data and monitoring data. The division into these three data sets will allow the comparison between: •

design data and baseline scenario: improvement compared to the typical solution;



monitoring data and baseline scenario: real improvement compared to the typical solution;



monitoring data and design data: comparison of achieved performance against prediction, this can also be defined as a separate indicator (quality of prediction).

Figure 5: Comparison of data on energy performance

The indicators defined in this guide can also be calculated as a reduction or increase of, for example, the energy performance in comparison with the baseline or the designed data. A detailed explanation of each of the cases can be found below. For additional information regarding the monitoring data please refer to the SCIS Technical Monitoring Guide.

3.1.1 Baseline scenario When defining a baseline, it is important to differentiate between new projects and retrofitting projects. Both project types require the definition of a baseline in order to further compare the performance of the different systems involved in the demonstration project: 12

1. Projects dealing with existing systems: If the demonstration project is a refurbishment / retrofit, an improvement to the existing technology or building, or is a substitution of the previous system for a highly efficient one, it is important to collect data on all the energy consumption in the building before the refurbishment works start. At least one year of monitoring data should be available for the existing system: final energy demands for heating, DHW, cooling, electrical appliances in kWh/month. If metering was not possible, data from energy bills can be used to define the status before refurbishment. 2. New projects: Since there is no real data to compare the performance of new systems, it is important to define a baseline. SCIS recommend to provide at least one year of synthetic data, reflecting the typical scenario: This data has to be calculated according to regulations, technical guides or similar projects.

3.1.2 Design data In the first phase of the monitoring it is also important to calculate, via modelling and simulation tools, the energy performance that is expected from the design of the system. Both the baseline and the design data will be later used to compare the actual energy performance of the building. In this way the energy-efficiency improvements can be demonstrated and the deviations from the design can be detected.

3.1.3 Monitoring data The purpose of the monitoring is to demonstrate the energy performance of the implementation area. It is therefore important to collect all the sampled data for the same time period in a consistent manner. Monthly metered values of energy consumption and energy generation should be provided. Once the construction is finished and the systems start to work under real conditions, the first year of monitoring will support the implementation progress of the energy system. This process is important for the analysis and optimisation of the operating system. Afterwards it is possible to check the actual consumption against expected, calculated data, and to analyse and evaluate the energy performance. In the case of refurbishments it is possible to compare the data collected/metered before refurbishment against the data metered after refurbishment.

3.2 Data comparability 3.2.1 Climate corrections It is essential to record outdoor conditions to compare data from different times and places. Comparing actual energy consumption figures of a given building in different years or energy-efficient buildings in warm and cold climates requires normalising the energy performance figures by means of a factor characterising the climate conditions. Traditionally, climate conditions are characterised by heating degree days (HDD) and cooling degree days (CDD), which indicate how many days or hours of heating or cooling energy is required to heat or cool buildings. As European countries traditionally use different definitions of HDD and CDD, unprocessed data without weather correction is required in order to allow for comparison.

13

Figure 6: Climate correction

Heating degree days (HDD) (this is an auxiliary indicator) Definition For normalising heating energy consumption in different climate conditions, the so-called ‘heating degree days’ (HDD) are used and well established. However, the definition differs but two main algorithms are known: one implementing the building’s threshold heating temperature alone; the other implementing the targeted set temperature of the building as well. Both methods calculate the sum of a temperature difference on all days when the heating has to be turned on (heating day). On non-heating days the temperature difference is not included in the sum. When looking at European countries, different applications can be found for the methodology, with different thresholds and different set temperatures. This hampers a unified calculation. In 1996, the European Commission asked for an assessment of the climatic correction methods applied in various Member States. Eurostat (European Commission – Eurostat, 2007) presented the findings to the Energy Statistics Committee and the Member States, in principle, approved a common method for correcting the heating temperature. It employs the first described formula and defines 15°C as the heating threshold temperature and 18°C as the heating set temperature. The average daily temperature is defined as the arithmetic mean of the minimum and maximum air temperature for that specific day. The reference HDD value will be calculated by using an arithmetical mean value of the long-term values for the different communities. Typically, the annual HDD for the last 10 years is used for the calculation.

14

Input parameters Name

Symbol

Unit

Number of heating days in time period

z

-

Daily average ambient temperature

ta

[°C]

Qactual

[kWh/a]

Qnormalised

[kWh/a]

HDD for a reference climate

HDDreference

[K.d/a]

HDD for the actual climate

HDDactual

[K.d/a]

Heating energy demand before correction Heating energy demand after correction

Calculation With z being the number of days in a time period when the average daily temperature is equal or below the heating threshold of 15°C, the HDD is calculated where a minimum temperature tmin and a maximum temperature tmax is available for each relevant day. /

=

(18° − !

" #$% =

,

)



!$'$!$ ($

&

()* "

=

,

+∙

+ 2



()* "

Cooling degree days (CDD) (this is an auxiliary indicator) Definition There is neither a standardised method for cooling degree days available, nor has Eurostat proposed a procedure. However, in the literature and in different projects a method has become commonly accepted. The calculation is analogous to the heating degree days and as it is applied to air-conditioning systems, there is very often no distinction between the ambient air temperature and the set room temperature. The supply air with a specific set temperature has to be cooled down exactly at the time when the temperature of the ambient air temperature exceeds that value. According to common use, the base temperature is defined as 18°C, with a cooling day again defined as a day when the outdoor temperature is equal or higher than the cooling threshold. Input parameters Name

Symbol

Unit

Number of cooling days in time period

z

-

Daily average ambient temperature

ta

[°C]

Qactual

[kWh/a]

Cooling energy demand before correction 15

Cooling energy demand after correction CDD for a reference climate CDD for the actual climate Calculation #) =

(

− 18° )

Qnormalised

[kWh/a]

CDDreference

[K.d/a]

CDDactual

[K.d/a]



,

=

+ 2

Solar radiation Definition The total solar radiation that hits a horizontal surface is called global radiation and consists of direct radiation and diffuse radiation. Diffuse radiation emerges from the reflection on clouds and dust or water particles. The unit is W/m2 for instant radiation power, or kWh/m2 for radiation energy in a time period. Solar radiation is a factor that influences the heating and cooling energy consumption as well as the ambient temperature, but a standardised normalisation method does not exist. However, for the calculation of climate-corrected outputs by solar energy systems (solar thermal collectors or photovoltaic cells) and for their comparison, the monthly or annual solar radiation is the appropriate measure. If comparing systems with each other, the output is normalised to one of the systems; when assessing a number of systems, a reference value is chosen according to the approach used for heating degree days.

3.2.2 Comparability between objects of assessment Buildings To enable the comparability between buildings, the performance indicator is related to the size of the building (e.g. gross floor area or net floor area, heated floor area) and the considered time interval (e.g. year) Energy supply units To enable the comparability between energy supply units, the total energy performance indicator is related to the energy output of the energy supply unit (e.g. electricity, heat, cold). In the case of cogeneration, the input is matched to the output using an exergy-based approach. This indicator represents the reciprocal efficiency of the energy supply unit.

3.2.3 Economic corrections Construction costs Definition Construction costs are figures that underlie temporal and spatial price levels. Therefore, the comparability of construction costs requires a correction regarding time and space: •

corrections for temporal dispersions to a common price level can be performed using price indices of official statistics; 16



corrections for spatial dispersions to a common price level can be performed using factors accounting for differences of local price levels. In Germany, the Baukosteninformationszentrum Deutscher Architektenkammern (BKI) annually publishes so-called regional factors. Furthermore, factors for EU-wide corrections are given at the country level.

Input parameters Name Costs in year t corrected to base year 0 in country j Invoiced costs in year t in country j Construction cost index of country j that corrects costs of year t to costs of base year 0 Costs in year t corrected from country j to reference country i Invoiced costs in year t in country j BKI factor that corrects costs of country j to costs of reference country I in year t

Symbol

Unit

K0,t,j

[€/a]

Kt,j

[€/a]

P0,t,j

[-]

Ki,j,t

[€/a]

Kt,j

[€/a]

RFi,j,t

[K.d/a]

Calculation Temporal dispersions -.,),/ =

-),/ 0.,),/

Spatial dispersions - ,/,) =

-),/ 12 ,/,)

Combined correction - ,/,.,) =

-),/ 12 ,/,) ∙ 0.,),/

17

4. GENERAL PERFORMANCE INDICATORS The general performance indicators have been clustered into five groups: • • • • •

Environmental performance indicators Technical performance indicators Economic performance indicators Eco-environmental performance indicators Macro-economic performance indicators

4.1 Environmental performance indicators 4.1.1 Energy performance indicators Delivered energy Applicability for objects of assessment Building

X

Set of energy supply units

X

Set of buildings

X

Neighbourhood

X

Energy supply unit

X

City

X

Building level Definition Delivered energy is energy, expressed per energy carrier, that is supplied to the technical building systems through the system boundary, to satisfy the uses taken into account (heating, cooling, ventilation, domestic hot water, lighting, etc.) or to produce electricity (EN 15603:2008). Often comparability with respect to electricity can only be achieved if just lighting and auxiliary energy are considered. This means that user-dependant electricity consumers (computer, refrigerator, etc.) are not considered. To enable the comparability between buildings, the delivered energy is related to the size of the building (e.g. gross floor area or net floor area, heated floor area) and the considered time interval (e.g. year). Unit: [kWh/m²a] Input parameters Name

Symbol

Unit

Edel,EC

[kWh/a]

Floor area of the building

A

[m²]

Reference time period

tref

[year]

Delivered energy per energy carrier

18

Calculation 3′%$",56 =

∑ 3%$",56 8 ∙ !$'

ESU level Definition The delivered energy of a large-scale or building-integrated energy supply unit corresponds to the energy entering the energy supply unit (e.g. the energy content of light oil, electricity, district heat). To enable the comparability between energy supply units, the total delivered energy is related to the energy output of the energy supply unit (e.g. electricity, heat, cold). In the case of cogeneration, the input is matched to the output using an exergy-based approach. This indicator represents the reciprocal efficiency of the energy supply unit. Unit: [kWhin/kWhout] Input parameters Name

Symbol

Unit

Delivered energy per energy carrier (EC)

Edel,EC

[kWh/a]

Output per EC

OutEC

[kWhout]

Exergy factor for the EC’s output

EXEC

-

Calculation 9: 56 ∙ 3;56 ∑ 3%$",56 ∑(9: 56 ∙ 3;56 ) 3′%$",56 = 9: 56 Exergy factor In the case of polygeneration, the raw energy used as input has to be allocated to the different outputs. The exergy-based approach only considers that part of energy that can be converted into mechanical work. If, for example, a CHP plant produces heat and power, the exergy of 1 kWh of electricity is higher than the exergy of the same amount of thermal energy. Therefore the major part of the input can be assigned to the generated electricity and the smaller portion to the generated heat. This approach thus considers how useful the forms of energy are for the final consumer. Unit: [kWh], [kWh/ m²], [kWh/ m²a]

19

Input parameters Name

Symbol

Unit

EXEC

[kWh/a]

Tambient

[year]

Average temperature of the heat output

Theat

[year]

Average temperature of the cold output

Tcold

[year]

Exergy factor for the EC’s output Annual average ambient temperature

Calculation 3;56

$ )

=$ )

Primary energy Definition The primary energy approach makes the simple addition of different types of energies (e.g. thermal and electrical) possible because primary energy includes the losses of the whole energy chain, including those located outside the building system boundary. These losses (and possible gains) are included in a primary energy factor. The energy performance of a building is the balance of the delivered energy and the exported energy. The annual amount of primary energy (net delivered primary energy) is calculated as the difference between the weighed delivered energy, totalled across all energy carriers and the weighed exported energy, totalled across all energy carriers (EN 15603:2008). Applicability for objects of assessment Building

X

Set of energy supply units

X

Set of buildings

X

Neighbourhood

X

Energy supply unit

X

City

X

Building level Unit: [kWh/m²a]

20

Input parameters Name

Symbol

Unit

Delivered energy per EC

Edel,EC

[kWh/a]

Exported energy per EC

Eexp,EC

[kWh/a]

Primary energy factor for the delivered EC

fdel,EC

-

Primary energy factor for the exported EC

fexp,EC

-

Floor area of the building

A

[m²]

Reference time period

tref

[year]

Calculation 3′A =

∑BC%$",56 ∙ 3%$",56 D − ∑BC$ 8 ∙ !$'

E,56

∙ 3$

E,56 D

ESU level Unit: [kWhin/kWhout] Input parameters Name

Symbol

Unit

Delivered energy per EC

Edel,EC

[kWh/a]

Primary energy factor for the delivered EC

fdel,EC

-

Exergy factor for the EC’s output

EXEC

[kWh/a]

Output per EC

OutEC

[kWhout]

Exergy factor for the EC’s output

EXEC

-

Calculation 3A =

BC%$",56 ∙ 3%$",56 D

9: 56 ∙ 3;56 ∙3 ∑(9: 56 ∙ 3;56 ) A 3′A,56 = 9: 56

21

Density of energy demand Definition The indicator is defined as a ratio of the final energy demand (for heating or cooling) of a cohesive set of buildings and a simple figure representing the effort that a district heating or cooling network operator would make in order to supply these buildings. For the latter, the territory area or the number of buildings is chosen in order to represent the length of the network and the number of connections that are required. Applicability for objects of assessment Building Set of buildings

Set of energy supply units X

Energy supply unit

Neighbourhood

X

City

X

Unit: [kWh/m²a]

4.1.2 Emission indicators Greenhouse gas / particulate matter / NOx / SO2 emissions Applicability for objects of assessment Building

X

Set of energy supply units

X

Set of buildings

X

Neighbourhood

X

Energy supply unit

X

City

X

Building level Definition The greenhouse gas, particulate matter (PM), nitrogen oxide (NOx) and sulphur dioxide (SO2) emissions of a building correspond to the emissions that are caused by different areas of application (i.e. space heating, space cooling, domestic water heating, electrical appliances). In different variants of this indicator, the emissions caused by the production of the building components can be either included or excluded. To enable the comparability between buildings, the emissions relate to the size of the building (e.g. gross floor area or net floor area, heated floor area) and the considered interval of time (e.g. year). The greenhouse gases are considered as t of carbon dioxide (CO2) or a CO2 equivalent (CO2e). Unit: [t/m²a]

22

Input parameters Name

Symbol

Unit

Delivered energy per EC

Edel,EC

[kWh/a]

Exported energy per EC Emission coefficient for the delivered EC (CO2, CO2e, PM, NOx, SO2) Emission coefficient for exported EC (CO2, CO2e, PM, NOx, SO2) Floor area of the building

Eexp,EC

[kWh/a]

Kdel,EC

-

Kexp,EC

-

A

[m²]

tref

[year]

Reference time period

Calculation 3′A =

∑B-%$",56 ∙ 3%$",56 D − ∑B-$ 8 ∙ !$'

E,56

∙ 3$

E,56 D

ESU level Definition The greenhouse gas, particulate matter, NOx and SO2 emissions of a large-scale or building-integrated energy supply unit correspond to the emissions that are caused by the energy output. In different variants of this indicator the emissions caused by the production of the energy supply unit components can be either included or excluded. To enable the comparability between energy supply units, the total energy demand is related to the energy output of the energy supply unit (e.g. electricity, heat, cold). In the case of cogeneration, the input is matched to the output using an exergy-based approach Unit: [t/m²a] Input parameters Name

Symbol

Unit

Delivered energy per EC Emission coefficient for the delivered EC (CO2, CO2e, PM, NOx, SO2) Exergy factor for the EC’s output

Edel,EC

[kWh/a]

Kdel,EC

-

EXEC

[kWh/a]

Output per EC

OutEC

[kWhout]

23

Calculation 3A =

B-%$",56 ∙ 3%$",56 D

9: 56 ∙ 3;56 ∙3 ∑(9: 56 ∙ 3;56 ) A 3′A,56 = 9: 56

24

4.2 Technical performance indicators 4.2.1 Efficiency indicators Overall heat transfer coefficient of building envelope Definition This indicator gives an overall rating of the insulation quality of the building’s envelope. It can simply be named ‘averaged U-value’, whereas the average is surface-weighted and elements with lower influence on heat transmission (like walls to ground or walls to a space with less heating) are rated with additional reduction factors. The U-value describes the required heating power transmitted to the outside per Kelvin temperature difference between inside and outside of a building element (in [W/m²K]). The overall insulation standard of different buildings can be compared by this indicator, as well as the improvement to one single building before and after a retrofit. This allows the comparison of different insulation standards (either defined per national regulation or achieved through building construction) by country or climate zone. Applicability for objects of assessment Building

X

Set of energy supply units

Set of buildings

Neighbourhood

Energy supply unit

City

Unit: [W/m².K] Input parameters Name

Symbol

Unit

Surface area of element

A

[m²]

U-value

U

[W/m²K]

Total surface area

Atotal

[m²]

Reduction factors

fx

-

Calculation 3A =

∑(8 ∙ F ∙ C ) 8) ) "

The following table shows the suggested fx values to be applied, according to the type of building element. It presents a simplification of DIN V 4108-6:2003. Type of element

fx value 25

Element to ambient air

1.0

Ceiling to unheated attic

0.8

Walls and ceilings to unheated rooms

0.5

Ground floor to soil

0.3

Ground floor to unheated basement

0.55

Specific yield Definition The specific yield is the calculated or metered output energy of a supply system related to the size (capacity) of the system. It is often provided as an annual or monthly value; a higher resolution is adequate for in-depth studies. All energy supply units have a peak power load and all heat exchangers have a surface area, so these are taken as the related size of the system. The system size is either described by the surface area (e.g. the collector area of solar thermal systems) or by the peak power (e.g. the electrical power of a wind turbine). Applicability for objects of assessment Building

Set of energy supply units

X

Set of buildings

Neighbourhood

X

City

X

Energy supply unit

X

Unit: [W/m².K] Input parameters Name

Symbol

Unit

Eout

[kWh]

Size (system area)

A

[m²]

Size (peak power)

Ppeak

[kWpeak]

Annual output energy

Calculation Size of technology measured in installed area:

G#E$( =

installed power:

3 *) 3 *) G#E$( = 8 0E$ H

26

Efficiency factors Definition Each energy supply unit can be considered as a converter of energy: input energy of one type is transformed into output energy of another type, accompanied by energy losses, which are not usable. The ratio between the input energy and the (usable) output energy shows the efficiency of an energy supply unit. The lower heating value of the energy carrier is used to define the input energy; it is possible to have boilers with an efficiency factor larger than 1. As regards cogeneration units (CHP), the efficiency factors are calculated for heat and electricity separately and as a total value according to the described formula. Applicability for objects of assessment Building

Set of energy supply units

X

Set of buildings

Neighbourhood

X

City

X

Energy supply unit

X

Unit: [%] Input parameters Name

Symbol

Unit

Annual output energy

Eout

[kWh]

Annual input energy

Ein

[kWh]

Calculation 32 =

3 *) 3

Whereas the principle of calculating the efficiency is the same, the name differs with different types of energy supply units (see table below). Application

Indicator

Boiler

Efficiency factor

Chiller

EER (energy efficiency ratio)

Heat pump

COP (coefficient of performance) 27

Description Describes overall efficiency of an actual installed system Describes how much cooling energy can be produced by 1 kWh of electricity (or other energy source) Describes overall efficiency of an actual installed system. The temperature level needs to be provided

EER Seasonal performance factor Thermal collectors

Efficiency factor

Photovoltaic

Performance ratio Heat efficiency factor

CHP

Power efficiency factor Overall efficiency factor

Storage systems

Efficiency factor

Describes the cooling efficiency of the heat pump Describes the overall heating/cooling efficiency of a heat pump during a season Describes the percentage of solar radiation turned into usable energy Compares the real performance of a system to a calculated one, including the efficiency factor of the cells Heating energy output related to input energy Electric energy output related to input energy Sum of heating and electric energy related to energy input Relation of input energy to output energy for a specific time period

Coverage fraction / Degree of energetic self-supply / Share of renewable energy sources Definition This describes how large the share is that an energy supply system contributes to a total energy supply (usually over a year). This indicator is often used for thermal collectors but it is also interesting for every combined energy supply system. The coverage fraction can also be used to express the total share of renewable energy sources (RES) in a complex energy supply system. Also the share of self-sufficiency of a district can be expressed as a coverage fraction, in order to discover how large the coverage fraction of self-generated energy is compared to the total amount of energy. Applicability for objects of assessment Building

X

Set of energy supply units

X

Set of buildings

X

Neighbourhood

X

Energy supply unit

X

City

X

Unit: [%] Input parameters Name Energy provided by ESU / by RES / selfproduced Total energy amount

28

Symbol

Unit

Epart

[kWh]

Etotal

[kWh]

Calculation 32 =

3E !) 3) ) "

Average power in operation Definition The power of an energy supply unit or a set of energy supply units in operation can be determined as the maximum or as the average power over a month or a year. The average power is defined as energy output of the energy supply over a period of time and the length of this period (month or year). In order to determine the maximum power within a period of time, a high temporal resolution is required. Therefore the maximum power in operation will not be assessed in SCIS. The average or maximum power in operation can be compared to the maximum capacity (design value) in order to determine the degree of utilisation of the energy supply unit. Applicability for objects of assessment Building

Set of energy supply units

Set of buildings

Neighbourhood

Energy supply unit

X

X

City

Unit: [kW/kWpeak] Input parameters Name

Symbol

Unit

Energy output

Eout

[kWh]

Power peak

Ppeak

[kWpeak]

Calculation 0 =

3 *) 8760 ∙ 0E$

H

Full load hours Definition Full load hours – sometimes called equivalent full load hours – show how many hours during the year a system would have run at maximum load generating the same amount of energy. This calculation is theoretical and neglects the different efficiencies at different loads. The indicator is calculated by dividing the annual amount of energy by the peak load. It is often used during the planning stage to rate the economical sense of implementing the particular energy supply 29

unit. Comparing the real full load hours to planned value or reference values can be an indicator of an oversized system or incorrect operation. Applicability for objects of assessment Building

Set of energy supply units

X

Set of buildings

Neighbourhood

X

City

X

Energy supply unit

X

Unit: [h/a] Input parameters Name

Symbol

Unit

Energy output

Eout

[kWh]

Power peak

Ppeak

[kWpeak]

Calculation 2K =

3 *) 0E$ H

4.3 Economic performance indicators

4.3.1 Operation cost related to their specific unit Specific costs Definition The total costs of different objects are hard to compare and interpret because they significantly depend on the size of a building or the energy supply installation. Thus the costs will be related to a reference unit, for example m2, kWpeak. Applicability for objects of assessment Building

X

Set of energy supply units

X

Set of buildings

X

Neighbourhood

X

Energy supply unit

X

City

X

Unit: [€/m2, €/kWpeak] 30

Input parameters Name

Symbol

Unit

Costs under consideration

CCon

[€]

Floor area of the building

A

[m²]

Ppeak

[kW]

Power peak

Calculation L#E$( =

(

8

L#E$( =

(

0E$

H



4.3.2 Life-cycle costs Life-cycle cost of building / ESU operation Definition The indicator ‘life-cycle costs of building / ESU operation’ contain the total cost of owning, operating and maintaining the building / ESU. Therefore it is taken into account that higher initial investments/construction costs can result in, for example, lower energy and/or maintenance costs over a lifetime, which might induce lower overall costs. Generally this concept can also be applied for (extensive) refurbishments. In the case of (extensive) refurbishments it is assumed that the building enters a new life cycle so the assessment will start again. Applicability for objects of assessment Building

X

Set of energy supply units

X

Set of buildings

X

Neighbourhood

X

Energy supply unit

X

City

X

Unit: [€/m2, €/kWpeak]

Input parameters Name

Symbol

Unit

Construction costs

Ctotal

[€]

Costs in use

Cuse,x

[€]

Replacement investments

Ireplace

[kW]

E

[year]

Cend

[€]

Time of replacement End-of-life costs 31

Reference unit

Uref

[m2]

Discount rate

i

[%]

Price increase rate

r

[%]

Reference study period

T

[years]

Calculation K

=M

) )

"+

Q )

N

∙ (1 + O)) 1 !$E" ($ $ % + R∙ P+ ) 5 Q (1 + ) (1 + ) (1 + ) F!$'

*#$

Life-cycle cost of energy generation Definition The life-cycle cost of energy generation (LCOE) can be applied to any energy system. The LCOE contains the total cost of owning, operating and maintaining an energy supply unit. The indicator includes the investments and the prospective cash flows over the technical/economic lifetime of the facility, including the identification of the least-cost alternatives of energy-generation technologies. The LCOE considers only expenditures and does not take the investment grants and revenues for energy sales into account. In the case of polygeneration, the energy input has to be allocated to the respective outputs where the exergy-based approach is used. Applicability for objects of assessment Building

Set of energy supply units

X

Set of buildings

Neighbourhood

X

City

X

Energy supply unit

X

Unit: [€/MWh] Input parameters Name

Symbol

Unit

Output energy flow of EC in year t

OutEC,t

[kWh/a]

Exergy factor for the output of EC

EXEC

-

Investments in t0

I0

[€]

Total grants in year t0 Total annual cost in year t including fixed and variable operating costs Total revenues for energy sales in year t

G0

[€]

At

[€]

Rt

[€]

Discount rate

i

[%]

Reference study period of energy system

n

[a]

32

Year of available monitoring data

j

-

Reference study period

T

[years]

Calculation

K 9356 =

∑/)

∑56 ∑/)

9:

56,)

9:

∙ 3;56

∙ &L. + ∑)

∙ 3;56 9: 56,) ∑) (1 + ))

56,)

8) + (1 + ))

4.3.3 Payback Economic payback period Definition The payback period is the time it takes to recover investment costs. It can be calculated from the number of years elapsed between the initial investment and the time at which cumulative savings offset the investment. Simple payback takes real (non-discounted) values for future income. Discounted payback uses present values. Payback in general ignores all the costs and savings that occur after payback has been reached. The payback period is usually considered as an additional criterion to assess the investment, especially to assess the risks. Investments with a short payback period are considered safer than those with a longer payback period. As the invested capital is repaid slower, the risk that the market will change and the invested capital only recovered later or not at all increases. On the other hand, costs and savings that occur after the investment has paid back are not considered. This is why decisions that are based on payback periods are sometimes not optimal and it is recommended that other indicators be consulted. Applicability for objects of assessment Building

X

Set of energy supply nits

X

Set of buildings

X

Neighbourhood

X

Energy supply unit

X

City

X

Unit: [years]

Input parameters

33

Name

Symbol

Unit

Ienergy

[€]

Average annual costs in use savings

m

[€/a]

Discount rate

i

[%]

Energy price increase rate

p

[%]

Energy-related investment

Calculation Type A: static 300 =

L$

Type B: dynamic 300 =

$!S?

T

lnBT ∙ (1 + )D − lnBL$ $!S? − L$ ln(1 + )

$!S?

∙ (1 + ) + TD

−1

Type C: dynamic with energy price increase rate 300 =

lnBT ∙ (1 + )D − lnBL$

∙ (1 + W) − L$ $!S? ∙ (1 + ) + (1 + W) ∙ TD −1 ln(1 + ) − ln(1 + W)

$!S?

Net present value Definition The net present value of an investment is defined as the sum of the discounted annual incoming cash flows related to the investment less the discounted annual outgoing cash flows over a period of time. The issue of cash flow streams must be addressed for all dynamic considerations. Cash flow streams are payments that occur at different times. Generally, it is argued that payments at different time periods cannot be compared directly. This is why, initially, all payments must be referred to a common time period. By discounting the payments to the agreed time, in most cases t = 0, the comparability is established. In contrast to static considerations, the absolute value is no longer the only influence but also the timely structure of the cash flow stream. Applicability for objects of assessment Building

X

Set of energy supply units

X

Set of buildings

X

Neighbourhood

X

Energy supply unit

X

City

X

Unit: [€] Input parameters 34

Name

Symbol

Unit

Initial investment in t0

I0

[€]

Cash inflow in t

Et

[€]

Cash outflow in t

At

[€]

Discount rate

i

[%]

Reference study period

T

[years]

Calculation X0Y = L. +

Q )

3) − 8) (1 + ))

Internal rate of return Definition The internal rate of return (IRR) on an investment resulting in energy savings or energy production in comparison to a baseline is defined as the interest rate that results in a net present value of zero. Unit: [-] Applicability for objects of assessment Building

X

Set of energy supply units

X

Set of buildings

X

Neighbourhood

X

Energy supply unit

X

City

X

Input parameters Name

Symbol

Unit

Initial investment in t0

I0

[€]

Cash inflow in t

Et

[€]

Cash outflow in t

At

[€]

Reference study period

T

[years]

Calculation The internal rate of return i is met when the following formula is fulfilled: 35

X0Y = −L. +

Q )

3) − 8) =0 (1 + ))

Typically interpolation procedures are used for the calculation of the IRR.

4.3.4 Annuity Annuity gain Definition The indicator ‘annuity gain’ gives an impression of how much money can be saved or must be paid annually when implementing energy efficiency or renewable energy measures. Contrary to the economic payback period, which suggests that an investment only gets profitable after x years, the annuity gain states that, from the beginning, a profitable investment yields positive monetary effects. If the annuity gain is negative the investment is not profitable under the given conditions (reference study period, discount rate). Applicability for objects of assessment Building

X

Set of energy supply units

X

Set of buildings

X

Neighbourhood

X

Energy supply unit

X

City

X

Unit: [-] Input parameters Name

Symbol

Unit

Ienergy

[€]

Average annual costs in use savings

m

[€/a]

Discount rate

i

[%]

Reference study period

T

[years]

Energy-related investment

Calculation 8 = T − L$

$!S? ∙

∙ (1 + )Q (1 + )Q − 1

4.4 Eco-environmental performance indicators 4.4.1 Mitigation costs Emissions / energy mitigation costs 36

Definition Mitigation costs compare the object of interest to a baseline (or reference object). The considered indicators encompass the following categories: • • • • •

delivered energy primary energy demand greenhouse gas emissions particulate matter emissions NOx and SO2 emissions.

A common characteristic is that the mitigation costs are determined as a ratio of the difference of a sum of discounted costs and the difference of a cumulated environmental performance (over a period of time). The environmental performance represents the reduction in (final) energy demand and consumption, primary energy demand and consumption, greenhouse gas emissions, particulate matter emissions, NOx emissions or SO2emissions over a period of time. The mitigation costs illustrate how expensive it is to save, for example, one ton of CO2 emissions. The figure can be compared to, for example, the price of CO2 emission certificates. As mentioned before, this performance indicator accounts for the interlinkage between economic and environmental aspects. The economic effort in the form of an investment in energy efficiency or renewable energy measures is contrasted with the environmental benefit of saving CO2 emissions. Applicability for objects of assessment Building

X

Set of energy supply units

X

Set of buildings

X

Neighbourhood

X

Energy supply unit

X

City

X

Unit: [€/t, €/kWh] Input parameters Name

Symbol

Unit

Life-cycle costs of option i

LCCi

[€]

Life-cycle costs of reference case

LCCref

[€]

Total savings (emissions / energy)

Esaving

[t] or [kWh]

Calculation )S )

=

K

37

−K

3#

Z

S

!$'

4.5 Macro-economic performance indicators 4.5.1 Stimulation of local economy Employment effects Definition The indicator ‘employment effects’ gives an impression of the employment market’s stimulation of a national economy induced by energy efficiency and renewable energy measures. Since the calculation of employment effects is quite complex, the indicator described here only provides a rough estimation. Applicability for objects of assessment Building Set of buildings

X

Energy supply unit

Set of energy supply units

X

Neighbourhood

X

City

X

Unit: [number of jobs created] Input parameters Name Total investment in t Gross domestic product of national economy in t

Percentage of assessed sector (e.g. construction) on gross domestic product of national economy in t Total number of employees in assessed sector in t

Symbol

Unit

Itotal,t

[€]

GDPt

[€]

r

[%]

W

-

Calculation 3TW[\]T^_ ^CC^` a) =

L) ) ",) ∙G b 0) ∙ O

4.5.2 Triggered positive effect per grant Triggered investment / energy savings / emission savings Definition The performance indicator ‘triggered positive effect’ per grant gives an impression of the positive effects of a grant scheme. The indicator can be calculated in different variations: 38

• • • • • •

triggered (final) energy reduction per grant; triggered primary energy reduction per grant; triggered greenhouse gas emissions reduction per grant; triggered particulate matter emissions reduction per grant; triggered NOx and SO2 emissions reduction per grant; triggered investment per grant provided.

Applicability for objects of assessment Building

X

Set of energy supply units

X

Set of buildings

X

Neighbourhood

X

Energy supply unit

X

City

X

Unit: [€/€], [kWh/€], [kg/€] Input parameters Name

Symbol

Unit

Total investment

Itotal

[€]

Primary energy non-renewable saved

Esaved

[kWh]

Emsaved

[kg] or [t]

Gtotal

[€]

Emissions saved Total grants provided

Calculation =O cc^O^d _e^a T^_ =

L) ) b) )

=O cc^O^d ^_^Oc] afe _ca =

3# b)

=O cc^O^d ^T aa \_a afe _ca =

39

"

"

Z$% ) "

3T# Z$% b) ) "

5. SPECIFIC PERFORMANCE INDICATORS 5.1 ICT performance indicators 5.1.1 Reliability Failures avoided Definition Avoiding failures reverts to a higher reliability, meaning fewer stops in the normal operation of the building and associated systems. With the application of ICT measures it is possible to correct a potential misbehaviour of the system and avoid unexpected stops. In SCIS, the indicator will be measured as: • • •

ratio of power interruptions avoided in a year; ratio of power quality issues avoided in a year; number of fines avoided in a year.

Applicability for objects of assessment Building

X

Set of energy supply units

X

Set of buildings

X

Neighbourhood

X

Energy supply unit

X

City

X

Unit: [number of failures avoided; %]

5.1.2 Demand-side management Peak-load reduction Definition The peak load is the maximum power to a building or group of buildings within a certain period (e.g. year). With the correct application of ICT systems, the peak load can be reduced to a large extent and therefore so too the dimensions of the supply system. In SCIS, the indicator ‘peak-load reduction’ is used to assess the reduction of the maximum power rate of a system. For the assessment of the impact of ICT measures, the peak loads, both before and after the implementation, need to be estimated and monitored. Applicability for objects of assessment Building

X

Set of energy supply units

Set of buildings

X

Neighbourhood

X

City

X

Energy supply unit

40

Unit: [kW; MW] Input parameters Name

Symbol

Unit

Ppeak reduction

[kW; MW]

Peak load before implementation of measure

Ppeak before

[kW; MW]

Peak load after implementation of measure

Ppeak after

[kW; MW]

Peak-load reduction achieved by measure

Calculation 0E$

H !$%*()

= 0E$

H >$' !$

− 0E$

H ')$!

Load profile Definition The load profile describes the consumption pattern of a certain system on a time basis. This load profile is an indicator of the variation in the power load versus time. Load profiles with a wide variance present more disadvantages than profiles with a small variation between maximum and minimum loads. A proper use of ICT allows shifting and flattening the load profile in a convenient way. High temporal data resolution is needed to define this indicator. In SCIS, a typical or average value will be asked. Applicability for objects of assessment Building

X

Set of energy supply units

Set of buildings

X

Neighbourhood

X

City

X

Energy supply unit

Unit: [kW or MW in time series]

5.1.3 Energy savings Reduction on primary energy use Definition The correct use of ICT increases the energy efficiency of the building and its associated systems, with a reduction on the primary energy use. In SCIS, the indicator is used to analyse the reduction on the total energy demand of a system, with and without the measures to provide the same comfort levels. Applicability for objects of assessment Building

X

Set of energy supply units

Set of buildings

X

Neighbourhood 41

X

Energy supply unit

City

X

Unit: [%] Input parameters Name Total primary energy without ICT Total primary energy with ICT

Symbol

Unit

EP,B

[kWh; MWh]

EP,TOT

[kWh; MWh]

Calculation % =

3A,h ∙ 100 3A,QiQ

5.1.4 Integration of RES Fossil energy/ CO2 savings (related to the use of RES) Definition Renewable energy systems are very dependent on weather factors and are sometimes unpredictable. Backup systems based on storage or fossil fuel technologies have to be implemented alongside RES to provide continuous energy to a city. A proper prediction and coupling of RES generation and demand can maximise the use of RES and reduce the use of fossil fuels, with the consequent savings on CO2 emissions. With the present indicator, SCIS evaluates the reduction on CO2 emission thanks to the better management and coupling of RES production with the load profile by using proper ICT measures. Applicability for objects of assessment Building

X

Set of energy supply units

X

Set of buildings

X

Neighbourhood

X

Energy supply unit

X

City

X

Unit: [%; t/a] Input parameters Name

Symbol

Unit

Delivered energy per RES

Edel,RES

[kWh/a]

Total delivered energy Emission coefficient for delivered EC (CO2, CO2e, PM, NOx, SO2)

Edel,TOT

[kWh/a]

Kdel,EC

-

42

Calculation 3A =

-%$",56 ∙ 3%$",56,QiQ −

-%$",j5k ∙ 3%$",j5k

5.1.5 Consumer engagement Involvement of users Definition The implementation of ICT solutions can also be related to the users’ involvement in controlling the energy use in the building. A variety of measures can be implemented, from the installation of metering systems to provide user feedback, to the involvement of the user in managing their energy consumption. Where these measures can be allocated to an energy demand reduction, this indicator will be shown. Additionally, the following information will be requested as indicators of the impact on the implemented measures: • • • •

number of final users involved; number of people with increased capacity; increase of demand response capacity of the final user; capability of consumers to participate in demand response solutions.

Applicability for objects of assessment Building

X

Set of energy supply units

X

Set of buildings

X

Neighbourhood

X

Energy supply unit

X

City

X

Unit: [number; %]

5.1.6 Data generation

Data availability Definition Data is generated through the implementation of ICT measures; this data can be used to produce different impacts on the energy consumption. This impact can be difficult to monitor so, where available, data on the reduction of energy consumption is to be provided. Other indicators to describe the measure are: • •

number of buildings/ESU managed by the ICT; data resolution in time; 43

• • • •

amount of data generated; number of apps developed (this only applies to the city/neighbourhood level); number of users; level of usage.

Applicability for objects of assessment Building

X

Set of energy supply units

X

Set of buildings

X

Neighbourhood

X

Energy supply unit

X

City

X

Unit: [number; resolution (weekly, daily, hourly, minute, etc.); Mbytes; number of apps; number of users; interactions per day]

Use of information generated by end consumers Definition The use of the information generated by the end consumers regarding occupancy, behaviour patterns, etc. can be used to automatise the systems, which would have an impact on energy consumption. Through behaviour prediction, the use of energy can be optimised (e.g. lighting). In SCIS, this indicator will be calculated as a reduction of energy demand. Applicability for objects of assessment Building

X

Set of energy supply units

Set of buildings

X

Neighbourhood

X

City

X

Energy supply unit Unit: [yes/no; kWh/m2a]

5.1.7 Economic indicators – ICT The economic indicators defined in section 4 can also be used for the implementation of ICT measures. The investments, grants, costs and revenues correspond in this case to the implemented ICT measure/s.

5.2 Mobility performance indicators 5.2.1 Energy consumption Vehicle fuel efficiency 44

Definition Vehicle fuel efficiency is defined as the energy consumption per unit of transport activity. This should be derived by vehicle type and fuel type. In SCIS, the indicator is used to compare vehicle fuel efficiency with and without the measures. Higher vehicle fuel efficiency means less fuel consumption and lower emissions, i.e. at the same level of traffic demand. Many measures will have impacts on fuel efficiency including clean vehicles (freight and passenger transport), alternative fuels, car-pooling and increased public transport (PT) use, which will result in higher PT occupancy, reduced private car use and reduced congestion. Applicability for objects of assessment Building

Set of energy supply units

Set of buildings

Neighbourhood

X

Energy supply unit

City

X

Unit: [MJ/vkm, l/100km, kWh/100km, km/l, km/kg] Input parameters Name Total energy consumed for the vehicle(s) (by type and fuel) Total amount of vehicle kilometres completed by the vehicle(s) (by type and fuel)

Symbol

Unit

Evehicle

[MJ]

Vdist

[vkm]

Calculation Y^ℎ `[^ 2:^[ ^CC ` ^_`] =

3Z$< ("$ Y% #)

Fuel mix Definition Fuel mix is the percentage of the market share of transport fuel for each type of fuel used in a given period. Fuel mix can be measured at the transport operator level or at a wider level (city). Many measures will have impacts on fuel use including clean vehicles (freight and passenger transport), alternative fuels, car-pooling and increased PT use, which will result in higher PT occupancy, reduced private car use and reduced congestion. Applicability for objects of assessment Building

Set of energy supply units 45

Set of buildings

Neighbourhood

X

Energy supply unit

City

X

Unit: [%]

5.2.2 Pollution and nuisance

Air quality Definition Air quality can be measured as: • • •

level of carbon monoxide (CO); level of NOx; level of particulate matter.

It is defined as the average hourly (or peak/off-peak) CO/NOx/PM10/PM2.5 concentration over a full year. Many of the measures aim either directly (through incentives to promote the use of cleaner fuels or vehicles or more environmentally friendly behaviours) or indirectly (e.g. congestion reduction and access restriction measures) at reducing the emissions and levels of air pollutants. In such a context, the success or failure of the measures must be assessed by taking into account air quality indicators. Applicability for objects of assessment Building

Set of energy supply units

Set of buildings

Neighbourhood

X

Energy supply unit

City

X

Unit: [ppm or g/m3]

Emissions Definition CO2/CO/NOx/PM10/PM2.5 emissions are defined as the average emissions per vehicle km by vehicle and fuel types. Many of the measures will have impacts on the emissions directly (through incentives to promote the use of cleaner fuels or vehicles or more environmentally friendly behaviours) or indirectly (e.g. congestion reduction and access restriction measures). This indicator can be used to assess the impacts of such measures. 46

Applicability for objects of assessment Building

Set of energy supply units

Set of buildings

Neighbourhood

X

Energy supply unit

City

X

Unit: [g/vkm]

5.2.3 Modal split Average modal split Definition The average modal split (vehicle km / passenger km) is defined as the percentage of vehicle km or passenger km by transport mode over the year. The modes are walking, bicycle, bus, tram, metro, train, car (driver and passenger) and motorcycle. Many measures will have impacts on the modal split including: access and parking control, promotion of PT, bicycle use and walking, etc. These indicators are quite widely used since it gives insight to the entire travel picture and enables easy comparisons (among target groups, different areas, etc.). Applicability for objects of assessment Building

Set of energy supply units

Set of buildings

Neighbourhood

X

Energy supply unit

City

X

Unit: [% of vehicle km] or [passenger km] or [trips]

5.2.4 Alternative fuel vehicles Infrastructure growth Definition Infrastructure growth can be assessed through different indicators, such as: • •

number of e-car/e-bike charging points available; increase in the use of electric / plug-in hybrid vehicles.

From the point of view of the integration of the electric car fleet into the electric grid, the indicator ‘electrical storage capacity’ is used to assess the measure. 47

Applicability for objects of assessment Building

X

Set of energy supply units

Set of buildings

X

Neighbourhood

X

City

X

Energy supply unit

Unit: [number of e-car/e-bike] or [kWh]

5.2.5 Smart mobility Average occupancy Definition Average occupancy is defined as the average number of passengers per vehicle per trip. Many measures will have impacts on occupancy, including car-pooling, access control, pricing schemes and the promotion of PT use by improving service quality. Applicability for objects of assessment Building

Set of energy supply units

Set of buildings

Neighbourhood

X

Energy supply unit

City

X

Unit: [number of passengers per vehicle]

Traffic flow Definition Traffic flow (peak / off-peak) is the average daily vehicle flow during the peak and off-peak hours. The peak and off-peak hours must be defined by each city in order to correspond with the local conditions. Many measures will have impacts on traffic levels, including road pricing, access control, parking control, promotion of PT, bicycle use and walking. The indicator can be used together with peak and off-peak average vehicle speeds to indicate traffic levels on city/neighbourhood road networks. Applicability for objects of assessment Building

Set of energy supply units

Set of buildings

Neighbourhood

X

Energy supply unit

City

X

48

Unit: [vehicles/hour]

5.2.6 Logistics Efficiency of freight deliveries Definition The delivery of goods makes up a significant share of traffic in European cities and is a major contributor to deteriorating air quality, rising carbon emissions and congestion. Measures in neighbourhoods/cities encourage the use of cleaner freight vehicles and develop solutions to better coordinate freight logistics. More efficient freight deliveries can reduce congestion, lower emissions and free up space for sustainable modes. This can be measured using the following indicators: • •

reduction of delivery times; freight movement.

Applicability for objects of assessment Building

Set of energy supply units

Set of buildings

Neighbourhood

X

Energy supply unit

City

X

Unit: [time], [%], [number of movements per day]

5.2.7 Smart traffic – ICT Average vehicle speed (peak / off-peak) Definition Average vehicle speed is defined as the average network or route speed by vehicle type. The peak and off-peak hours must be defined by each city to correspond with the local conditions. Many measures will have impacts on traffic levels, including road pricing, access control, parking control, promotion of PT, bicycle use and walking. The indicator can be used together with peak and off-peak average vehicle flows to indicate traffic levels on city road networks. Applicability for objects of assessment Building

Set of energy supply units

Set of buildings

Neighbourhood

X

Energy supply unit

City

X

49

Unit: [km/h]

Accuracy of time keeping Definition The indicator ‘accuracy of time keeping’ is defined as the number and percentage of public transport services that arrive within an acceptable interval around the planned times provided by timetables. This indicator accounts for the real (not the perceived) reliability of arrival times of public transport services at PT stops and stations. Many measures will have impacts on public transport timekeeping, including PT priority, bus lane control, using telematics for PT monitoring and control, etc. This indicator provides an objective measure of public transport service quality. It may also be used as a measure of reliability of just-in-time freight deliveries. Applicability for objects of assessment Building

Set of energy supply units

Set of buildings

Neighbourhood

X

Energy supply unit

City

X

Unit: [number and % of the total arrival times per year that are within a given interval around the time shown in the timetable]

5.2.8 Infrastructure

Context information on the city/neighbourhood infrastructure Definition In order to assess the measures implemented at the infrastructure level of a city or neighbourhood, the following context information is required to assess the improvements: • • • • •

capacity of the infrastructure network; transport final energy consumption by mode; size of the vehicle fleet; average age of the vehicle fleet; proportion of vehicle fleet meeting certain emissions standards.

Applicability for objects of assessment Building

Set of energy supply units

Set of buildings

Neighbourhood 50

X

Energy supply unit

City

X

5.2.9 Economic indicators – Mobility The economic indicators that were defined in section 4 can also be used for the implementation of mobility measures. The investments, grants, costs and revenues correspond in this case to the implemented mobility measure/s.

5.3 Prefabrication performance indicators 5.3.1 Improvement compared to standard construction Improvement of thermal performance Definition The thermal performance of a building envelope is characterised by the U-value. In this case the comparison of the achieved U-Value in the standardised module Is to be compared to the standard values. In addition, an improvement in the quality and finishing of the construction is achieved through the use of prefabricated modules, thus improving the infiltration rate. In order to measure this parameter a Bower Door Test is to be performed before and after refurbishment. Comparisons with new buildings is done using standardised and typical values. Applicability for objects of assessment Building

X

Set of energy supply units

Set of buildings

X

Neighbourhood

Energy supply unit

City

Unit: [W/m².K] and [1/h] Input parameters Name

Symbol

Unit

Air changes per hour at 50 pascal

n50

[1/h]

U-Value

U

[W/m².K]

Reduction of construction site workload 51

Definition The use of prefabrication in construction has different impacts on the reduction of the construction site workload, due to the faster building time, the increase in precision and quality, and the reduction of delays. In order to assess the impacts of these measures, the following indicators have been chosen: • • •

installation time of prefabricated modules; reduction of the total installing time on site; reduction of delays due to weather conditions.

Applicability for objects of assessment Building

X

Set of energy supply units

Set of buildings

X

Neighbourhood

Energy supply unit

City

Unit: [%] and [time]

5.3.2 Economic indicators – Prefabrication The economic indicators that were defined in section 4 can also be used for the implementation of prefabrication. The investments, grants, costs and revenues correspond in this case to the implemented mobility measure/s. In addition, the following indicators are specific for prefabrication related measures: Prefabrication-related reduction of costs Definition Similar to the indicator ‘reduction of construction site workload’, this also has an impact on the economic indicators. The use of prefabricated modules also has an impact on the reduction of the costs in the production lines. It can be measured as: • • •

reduction of construction failure costs; reduction of workload costs; cost/output reduction in production lines.

Applicability for objects of assessment Building

X

Set of energy supply units

Set of buildings

X

Neighbourhood

Energy supply unit

City

Unit: [€/m2, %] 52

5.4 Other measure-specific performance indicators 5.4.1 Temperature Impact on the indoor temperature Definition This indicator is to be used by projects dealing with measures focusing on the thermal mass of the building, measures focusing on maintaining certain indoor comfort levels with the main aim of monitoring the indoor temperature, or those quantifying the rebound effect of energy-efficiency measures on comfort levels desired by the tenants. To measure the impact of the measures and to allow for comparability with other projects, the following indicators have been defined (baseline, design and monitoring data can be compared): • • •

average indoor temperature (seasonal); variation of indoor temperature (∆T); average outdoor temperature.

Applicability for objects of assessment Building

X

Set of energy supply units

Set of buildings

X

Neighbourhood

Energy supply unit

City

Unit: [°C]

5.4.2 Gas distribution system Gas flow rate Definition This indicator refers to specific measures implemented on the gas distribution system, focusing on the modification of the gas flow rate. An example of this kind of measure is found in the project CELSIUS. Applicability for objects of assessment Building

X

Set of energy supply units

Set of buildings

X

Neighbourhood

Energy supply unit

City

Unit: [Nm3/h] 53

6. OVERVIEW OF KPIS SCIS KPIs

Energy performance indicators Environmental performance indicators

Emission indicators

TECHNICAL

Technical performance indicators

Efficiency

Investments and grants

Operation and maintenance (annually) Economic performance indicators

GENERAL KPIs

Revenues (annually)

Annuity Life-cycle costs ECONOMIC

Payback Ecoenvironmental performance indicators

Mitigation costs

Stimulation of local economy Macro-economic performance indicators Triggered positive effect per grant

Unit Delivered energy Primary energy Density of energy demand Greenhouse gas emissions Particulate matter emissions NOx and SO₂ emissions Reduction of greenhouse gas emissions Reduction of particulate matter emissions Reduction of NOx and SO₂ emissions Overall heat transfer coefficient of building envelope (U-value) Specific yield Efficiency factors (average/maximum) Coverage fraction Degree of energetic self-supply Share of renewable energy Average power in operation Full load hours Total investments Specific measure investment Specific construction cost figures (detailed) Grants Total O&M Costs Energy costs (use/production) Inspection and servicing Other (insurance) Maintenance and replacement Total revenues Energy production revenues Energy production grants Other revenues (rental, etc.) Annuity gain (average) Life-cycle cost of building / ESU operation Life-cycle costs of energy generation Economic payback period Net present value Internal rate of return Mitigation costs of (final) energy demand Mitigation costs of primary energy demand Mitigation costs of greenhouse gas emissions Mitigation costs of particulate matter emissions Mitigation costs of NOx and SO₂ emissions Number of jobs created Number of new businesses created Number of trainings/persondays for trainings offered in the project Increase in real estate and apartments value Changes in community demographics - neighbourhood growth Triggered (final) energy reduction per grant Triggered primary energy reduction per grant Triggered greenhouse gas emissions reduction per grant Triggered particulate matter emissions reduction per grant Triggered NOx and SO₂ emissions reduction per grant Triggered investment per grant provided

54

kWh/(m² a), kWhin/kWhout kWh/(m² a), kWhin/kWhout kWh/m² t/(m² a), t/kWh t/(m² a), t/kWh t/(m² a), t/kWh %, t/(m² a), t/kWh %, t/(m² a), t/kWh %, t/(m² a), t/kWh W/(m².K) kWh/kWpeak, kWh/m² % % % % kW/kWp h/a €, €/m², €/kWp €, €/m², €/kWp €/m², €/kWp €, €/m², €/kWp €/a €/a, €/kWh €/a €/a €/a €/a €/a €/a €/a €/a €/m² €/MWh(el, th) years € €/kWh €/t €/t €/t €/t number number number % inhabitants kWh/€ kWh/€ t/€ t/€ t/€ €/€

Building x x x x x x x x x

Area of application of the KPI Set of Neighbou ESU Set of ESU buildings rhood x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x

x x

x x

x x x x x x x x x x x x x x x

x x x x x x x x x x x x x x x

x x x x x x x x

x x x x x x x x x x x x

x x x x x x

x x x x x x

x x x

x x x

x x x x x x x x x x x x x x x x x x x x x x x x x x x

x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x

x x x x x x

x x x x x x

City x x x x x x x x x

x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x

x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x

x x x x x x x x

x x x x x x x x

Measure-specific KPIs Measure dealing with thermal mass/indoor temperature in Gas network

Load TECHNICAL

TECHNICAL

Temperature Flow rate Reliability

Demand-side management Energy savings TECHNICAL

Integration of RE

ICT Consumers engagement

Data generation

ECONOMIC

TECHNICAL Prefabrication ECONOMIC

Improvement compared to standard construction

Peak load reduction Average indoor temperature (seasonal) Variation of indoor temperature (∆T) Average outdoor temperature Gas flow rate compared to the baseline situation Ratio of power interruptions avoided in a year Ratio of power quality issues avoided in a year Number of fines avoided in a year Peak load reduction Difference between maximum and minimum power demand) Load profile Reduction on primary energy use Delivered energy reduction Fossil energy savings (related to the use of RE, not demand reduction) CO₂ equivalent savings (related to the use of RE, not demand reduction) Number of final users involved Number of people with increased capacity Increase of demand response capacity of final user Capability of consumers to participate in demand-response solutions Number of buildings/ESU managed by ICT Data time resolution Amount of data generated Apps developed (for smart phones and tablets, etc.) Use of information generated ( behaviour prediction ) Cost reduction vs cost of the new project Reduction of primary energy consumption Improvement of thermal performance Reduction of installation time of prefab panels Reduction of total installing time on site Reduction of delays due to weather conditions Overall cost reduction Reduction of construction failure costs Reduction of workload cost Cost/output reduction in production lines

55

kW, % °C °C °C Nm3/h number, % number, % number, % kW, % kW kW timeseries %, kWh/(m² a), kWhin/kWhout %, kWh/(m² a), kWhin/kWhout

t/a, % t/a, % number number % % number weekly, daily, hourly, minute… Mbytes number yes/no, kWh/m²a € kWh/m² U-Value/infiltration rate % % % €/m², % €/m², % €/m², % €/m², %

x x x x

x x x x

x x

x x

x x x x x x x x x x x

x x x x x x x x x x x

x x x x x x x x x x x

x x x x x x x x x x x

x x x x

x x x x

x x x x

x x x x

x x x

x x x

x

x

x

x

x x x x x

x x x x x

x x x x x x x x x x x x x x

x x x x x x x x x x x x x x

Energy consumption

Fuel consumption Air quality / savings, baseline/ objective

Pollution and nuisance

Modal split TECHNICAL Alternative fuel vehicles

Mobility

Smart mobility

Logistics

Emissions / savings

Noise Average modal split - passengers Average modal split - vehicles Average modal split - trips Infrastructure E-car as storage Average occupancy Traffic flow by vehicle type - peak Traffic flow by vehicle type - off-peak Efficiency of freight deliveries

Accuracy of timekeeping Smart traffic - ICT Average vehicle speed - peak Average vehicle speed - off-peak Operating revenues ECONOMIC

Costs

Fossil energy savings Vehicle fuel efficiency ( before / after; increase ) Fuel mix CO levels NOx levels Particulate levels CO₂ emissions CO emissions NOx emissions Particulate emissions Noise perception Percentage of passenger-km for each mode Percentage of vehicle-km for each mode Percentage of trips for each mode Number of e-car/e-bike charging points available Increase in use of electric / plug-in hybrid vehicles Electrical storage capacity Reduction in number of cars per person/household Mean number persons per vehicle/day Average vehicles per hour by vehicle type - peak Average vehicles per hour by vehicle type – off-peak Reduction of delivery times Number of urban distribution centres Number of pollution-free or environmental zones Number and percentage of services arriving / departing on time Average vehicle speed over total network Average vehicle speed over total network Fuel prices Operating revenues Capital costs Operating costs

56

t/a, % MJ/vkm % ppm, g/m3 ppm, g/m4 ppm, g/m5 g/vkm g/vkm g/vkm g/vkm dB % % % number ?? (%, number) kWh % person/vehicle v/h v/h time, % number/km² number number, % km/h km/h €/l €/pkm, €/vkm € €/pkm, €/vkm

x x x x

x x x x

x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x

x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x

7. GLOSSARY FOR SCIS Term

Description

Comment

CONCERTO-plus

Predecessor of CONCERTO Premium

CONCERTO-Premium

Predecessor of SCIS project

SCIS

Smart Cities and Communities Information System

Self-reporting

Refers in the SCIS to the development of a user-friendly and flexible input mask that enables project managers to provide the data

Auto-analysis

Refers to the provision of general data and automated default detection during the self-reporting. The goal is to support the project manager during the self-reporting and get complete and plausible datasets

Measure

A measure is defined as an implementation of technologies

Technology group

A technology group is defined as a cluster of technologies of the same characteristics

Indicator

An indicator is a calculated parameter

EeB

energy-efficient building

SCC

Smart Cities and Communities

EII

European Industrial Initiative

EIP

European Innovation Partnership

PPP

public-private partnership

KPI

key performance indicators

TMD

technical monitoring database

EED

Energy Efficiency Directive

EPBD

Energy Performance of Buildings Directive

57

CEN

European Committee for Standardisation (Comité Européen de Normalisation)

PT

passenger transport

vkm

vehicle kilometre

pkm

passenger kilometre

58

REFERENCES Applied framework for evaluation in CIVITAS PLUS II, n.d. CELSIUS D4 1. Report on KPI values, n.d. CEN, 2008. EN 15603:2008. Energy performance of buildings – Overall energy use and definition of energy ratings. See: http://standards.cen.eu/dyn/www/f?p=204:110:0::::FSP_PROJECT:27654&cs=1AF4D76BF8CF676 FF1309B6875F21B1EB (accessed 3.27.15). CONCERTO_Premium_Guidebook_for_Assessment_Part I-Methodology.pdf, n.d. CONCERTO_Premium_Indicator-Guide_v4_working-version.pdf, n.d. Digest of EEA indicators 2014, n.d. DIN V 4108-6:2003-06. Thermal protection and energy economy in buildings – Part 6: Calculation of annual heat and energy use, n.d. See: http://www.beuth.de/de/vornorm/din-v-41086/63939447 (accessed 10.9.15). European Commission – Eurostat (2007). Panorama of Energy – Energy Statistics to support EU policies and solutions, n.d. European Innovation Partnership on Smart Cities and Communities – Operational Implementation Plan: First Public Draft, n.d. ISO, 2014. ISO 37120:2014. Sustainable development of communities – Indicators for city services and quality of life. See: http://www.iso.org/iso/home/store/catalogue_tc/catalogue_detail.htm?csnumber=62436 (accessed 2.26.15). ISO_37151_Smart-community-infrastructures-principles-and-requirements-for-performance-metrics.pdf, n.d. ITU-T Focus Group on Smart Sustainable Cities: Key performance indicators related to the use of information and communication technology in smart sustainable cities, n.d. The Covenant of Mayors in Figures and Performance Indicators: 6-year Assessment, n.d.

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