UC1

Buildings efficiency

General description of use case

With 40% of total energy consumption, buildings are major consumers of energy causing 36% of the total CO2 emissions. Thereby, the building sector has a large untapped potential for the reduction of CO2 emissions by optimizing in construction and management. The EU has issued a Directive on the Energy Performance of Buildings (EPBD) as the main EU policy instrument to improve in this regard (Directive 2002/91/EC). The directive involves a framework for assessing the energy performance of buildings through Energy Performance Certificates (EPCs) that need to include reference values, such as current legal standards. Hence, the relationship between buildings and energy, impacts a wide spectrum from strategic to operational concerns, including energy efficiency investment decisions, buildings energy performance management, energy efficiency policies, smart buildings, and energy disaggregation. This makes data on energy efficiency in buildings crucial for the discussion and the decision-making in many practical contexts by households, academicians, or policymakers. Moreover, a wide variety of information is needed, starting from the existing building stock to solutions in ventilation and air conditioning, socio-demographic information, cultural perception of thermal comfort to climatic and weather information. To some extent, this data is available; however, databases are unorganized and not interlinked. A comprehensive collection of European buildings and urban stock data has been identified by the EFFESUS project, serving as a starting point for the use case.

A specific part of energy consumption in residential buildings is due to the use of household appliances. Time series of corresponding demand profiles on this scale are typically not open, but constitute business assets of utilities. A wealth of information on such data is produced due to the massive rollout of smart meters across Europe. Access to this data raises important concerns regarding privacy issues (Véliz and Grunewald 2018). Thus, legal restrictions as well as commercial interests have to be accounted for. Finally, analyzing this information by e.g. geolocation or social strata is key to mitigate energy poverty making the energy transition inclusive for all.

To this end, the use case on buildings efficiency has three main goals: 1) identifying the available metadata on buildings efficiency and constructing a metadata repository of available data, 2) assessing the level of FAIRness and openness of the available data with the contribution of experts from the field in the planned workshops, and 3) contributing to the FAIRification and opening of the available data respecting privacy concerns. In doing so, it will utilize existing databases such as the European Buildings Stock Observatory and the databases developed by the ENTRANZE and the ExcEED project. Data regarding energy poverty are collected at the EU Energy Poverty Observatory.

This use case will focus on the metadata assessments of EU Building Stock Observatory, Global Buildings Performance Network (GBPN), Pan-European Thermal Atlas (Peta) v4, ExCEED - European Energy Efficient building district Database, and Hotmaps

to analyze further.

List of selected databases

During the first workshop (see notes from Day 2), the following databases were selected to analyze and improve their compliance with FAIR and Open data principles:

Name of database	Short description	Reasoning of choice	Current state of FAIR/O principles	Target of FAIR/O to achieve within EERAdata
[1] Global Buildings Performance Network (GBPN)	The database (The Policy Comparative Tool) includes information on 25 building energy efficiency codes (BEECs) that are identified as the world's best practice policies for new residential and commercial buildings. The database involves a scoring and comparison of the 25 building energy efficiency codes based on 14 criteria and their sub-criteria. A scoring and ranking is done for every criteria.The database also includes detailed information regarding each building energy efficiency code. The Global Buildings Performance Network (GBPN), founded in 2010 aims to contribute to knowledge and expertise regarding building energy performance, and to enhance the building sector towards energy transition and climate change related goals. GBPN partners with a wide range of institutions including IEA, UNFCCC, UNEP-SBCI, The World Bank, SE4ALL Buildings Efficiency Accelerator, BPIE, and NREL.	This is a database which covers the use case theme, buildings efficiency. It involves both qulitative data regarding building energy efficiency codes, and quantitative assessments of these codes. It also has an extensive coverage, including Europe, North America, and China.	F1:Yes,F2:Yes,F3:No,F4:No A1:No,A1.1:Yes,A1.2:Yes,A2:No I1:Yes,I2:Yes,I3:No R1:Yes,R1.1:No,R1.2:No,R1.3:No	F3: No --> Yes A2:No --> Yes I3: No --> Yes R1.3: No --> Yes
[2] ZEBRA 2020 Data Mapper	The database includes visual data on final energy demand for space heating, hot water and cooling, market penetration of nZEB - new building construction, building renovation activities, total yearly investments in renewable heating systems (RES-H) and renovation of the building envelope including expenses for public support, national policies supporting the market development for nZEB, and total yearly public budget spent for financial support of renewable heating systems (RES-H) and renovation of the building envelope. The database is constructed as part of the Zebra2020 Project, that started in 2014 and is co-funded by the Intelligent Energy Europe Programme of the European Union. The main goal of the Zebra2020 Project is to investigate the current situation of nearly Zero-Energy Buildings (nZeb)s and formulate strategies and recommendations in an attempt to accelerate the spread of nZEBs.	The database presents data visualisations, which may be an important aspect of presentation of data. covers a significant aspect of energy efficiency in buildings, that is, nearly Zero-Energy Buildings (nZeb)s	F1: Yes, F2: Yes, F3: No, F4: No A1: No, A1.1: Yes, A1.2: Yes, A2: No I1: Yes, I2: No, I3: No R1: Yes, R1.1: No, R1.2: No, R1.3: No	F3: No --> Yes A2: No --> Yes R1.3: No --> Yes
[3] ZEBRA 2020 nZEB buildings	The database involves data on nZEB or near-nZEB buildings. The data is classified under residential and non-residential buildings and involves the following indicators: building energy performance, passive energy efficient solutions, active energy efficient solutions, and use of renewable energies. The database is constructed as part of the Zebra2020 Project, that started in 2014 and is co-funded by the Intelligent Energy Europe Programme of the European Union. The main goal of the Zebra2020 Project is to investigate the current situation of nearly Zero-Energy Buildings (nZeb)s and formulate strategies and recommendations in an attempt to accelerate the spread of nZEBs	The database covers a significant aspect of energy efficiency in buildings, that is, nearly Zero-Energy Buildings (nZeb)s	F1: Yes, F2: Yes, F3: No, F4: No A1: Yes, A1.1: Yes, A1.2: Yes, A2: No I1: Yes, I2: No, I3: No R1: Yes, R1.1: No, R1.2: No, R1.3: No	F3: No --> Yes I2: No --> Yes R1.3: No --> Yes
[4] ZEBRA 2020 Energy efficiency trends in buildings	The database includes information on Energy efficiency trends in buildings (time series for 2010-2014), and demonstrates indicators on the status of building stock development in selected European countries in 4 main sections: new construction, renovation activities, sales of energy-efficient equipment, and energy performance certificates (EPC). The database is constructed as part of the Zebra2020 Project, that started in 2014 and is co-funded by the Intelligent Energy Europe Programme of the European Union. The main goal of the Zebra2020 Project is to investigate the current situation of nearly Zero-Energy Buildings (nZeb)s and formulate strategies and recommendations in an attempt to accelerate the spread of nZEBs	The database covers energy efficiency trends in buildings, which is a significant aspect of the use case theme.The geographical coverage also fits well with EERAdata's scope	F1: Yes, F2: Yes, F3: No, F4: No A1: Yes, A1.1: Yes, A1.2: Yes, A2: No I1: Yes, I2: No, I3: No R1: Yes, R1.1: No, R1.2: No, R1.3: No	F3: No --> Yes I2: No --> Yes R1.3: No --> Yes
[5] EU Building Stock Observatory	The database involves information (2015-2019) about building stock characteristics, building shell performance, technical building systems, nearly zero-energy buildings (nZEB), building renovation, energy consumption, certification, financing, energy poverty, and energy market. BSO is a European Commission initiative established in 2016 as part of the Clean energy for all Europeans package, to monitor the energy performance of buildings across Europe. The EU BSO aims to provide a snapshot of the energy performance of the EU built stock in a consistent and comparable manner and set a framework for the continuous monitoring of the EU built stock (and of EPBD and RED implementation)	The database is important in two aspects: first, it is a EC initiative and needs to be analyzed to identify to what extend it conforms to EU perspective on data. Second, the database is merely a platform that contains data from various sources. Hence, it may be analyzed as a platform similar to the one to be developed in EERAdata	F1: Yes, F2: Yes, F3: Yes, F4: No A1: Yes, A1.1: Yes, A1.2: Yes, A2: No I1: Yes, I2: Yes, I3: No R1: Yes, R1.1: No, R1.2: Yes, R1.3: No	F4: No --> Yes A2: No --> Yes I3: No --> Yes R1.3: No --> Yes
[6] Pan-European Thermal Atlas (Peta) v4	The Pan-European Thermal Atlas (v4.3), is a geographic representation of heating and cooling demands in the fourteen european countries with the highest building and industrial heat demands in the EU28. Some layers concerning Denmark have been added. The Pan-European Thermal Atlas (Peta) has been developed as part of the work of the fourth Heat Roadmap Europe project (HRE4), quantifying and mapping the spatial distribution of significant elements that constitute the European heat and cold market	The database aligns with the use case theme. It also pertains to the significant aspect of the demand side of buildings. The geographical coverage is Europe, which is also inline with EERAdata's focus	F1: Yes, F2: Yes, F3: Yes, F4: No A1: Yes, A1.1: Yes, A1.2: No, A2: No I1: Yes, I2: Yes, I3: No R1: Yes, R1.1: No, R1.2: Yes, R1.3: No	F4: No --> Yes A1.2: No --> Yes R1.1: No --> Yes
[7] ExCEED - European Energy Efficient building district Database	The ExcEED platform is designed to integrate measurements from meters, Building Management Systems, head end systems, databases and other data providers. The platform transforms user’s monitored data into knowledge using energy performance indicators and air quality surveys. It provides a front-end dashboard with integrated tools: geo-clustered, statistical and knowledge analysis of building data; benchmarking function to analyse building interaction (energy, IEQ). Information coming from building monitoring systems can be divided in two levels: Private data, thus data uploaded by the user and visible only by himself; Public, aggregated, geo-cluster tool (which displays data on the aggregate level) still enables data comparison with other buildings in the platform. The database is a combination of metadata and measured data, with complex KPI algorithms. The measured data can be imported from a number of data sources including utility meters (usually provided by data collectors/aggregators), grid data (e.g. electricity market data from market operators), monitoring data stored in csv files	The ExcEED cloud-based platform supports a portfolio of data integration mechanisms and ensures that all data uploaded is seamlessly incorporated with industry-standard communication interfaces. Exceed allows and online analysis against continuously updated datasets from many EU MS (28). Interoperability with data from meters is assured. The platform is still alive (managed by EURAC) and FAIR principles can be improved.	Example	Example
[8] ENTRANZE database and web tool	The data mapper displays indicators and analysis on EU residential and non-residential building stock. It is a support to policy makers to achieve a fast and strong penetration of nZEB and RES-H/C within the existing national building stocks. It provides trends (dynamics) about technologies for better performance in the sector	EU-27 + Croatia; quite accessible; various EU project used for Data sources	Example	Example
[9] CommONEergy _Economic Assessment Tool	The tool allows users (managers and owners of the shopping centre) to enter (input) relevant information on these buildings, providing: quick information on energy consumption, estimate of energy saving and CO2 emission reduction potential, economic benefits of retrofitting. The database includes information on EU-28 and Norway.	The database includes data on a significant category of buildings, that is, commercial buildings	F1: Yes, F2: No, F3: No, F4: No A1: Yes, A1.1: No, A1.2: No, A2: No I1: No, I2: Yes, I3: No R1: No, R1.1: No, R1.2: No, R1.3: No	F2: No --> Yes I1: No --> Yes R1.3: No --> Yes
[10] COMMONENERGY Datamapper	Data mapper displays indicators on the commercial building stock (retail sector, shopping malls) including the current status indicators (EU shopping centres building final energy demand), and scenarios of future final energy demand, renovation, development until 2030. The coverage is EU-28 and Norway.	The database involves a visual tool to demonstrate buildings-related data, which may be interesting in terms of data presentaiton requirements of databases or platforms	F1: Yes, F2: No, F3: No, F4: No A1: Yes, A1.1: No, A1.2: No, A2: No I1: No, I2: No, I3: Yes R1: No, R1.1: No, R1.2: Yes, R1.3: No|| F2: No --> Yes I1: No --> Yes R1.3: No --> Yes
[11] Tabula Web Tool	The database was developed within the framework of the Intelligent Energy Europe projects TABULA and EPISCOPE and includes data representing the residential building stock in different Euopean countries. The typologies consist of the following elements: a classification concept for existing residential buildings according to age, size and further parameters, a set of example buildings which represent specific building types of the national stocks, typical energy consumption values for the example buildings, showcase calculations of the possible energy savings, statistical data for buildings and supply systems	The database was chosen because one of the benefits of building typologies is to provide a basis for the analysis of the national building stocks, e.g. for energy balance and scenario calculations.	F1: Yes, F2: Yes, F3: No, F4: No A1: Yes, A1.1: Yes, A1.2: Yes, A2: No I1: Yes, I2: No, I3: No R1: Yes, R1.1: No, R1.2: No, R1.3: Yes	F3: No --> Yes I2: No --> Yes
[12] The FROnT project: for Fair Renewable Heating and Cooling Options and Trade	The database is constructed as part of the FROnT project was to promote a level playing field for Renewable Heating and Cooling (RHC) in Europe, that started in 2014 and is co-funded by the Intelligent Energy Europe Programme of the European Union. It provided a better understanding about how to deploy RHC in the market. It improved transparency about costs of heating and cooling options (using RHC or fossil fuels), RHC support schemes and end-user key decision factors. This knowledge has helped towards developing Strategic Policy Priorities for RHC to be used by public authorities in designing and implementing better support mechanisms. It also supported the industry in engaging more effectively their prospective clients	The database was chosen as it represents an example of a database which supports customers in their decision of choosing an efficient heating system based on renewable energies and which provides the possibility to make a rough system dimensioning and to evaluate costs compared the standard systems	F1: No, F2: No, F3: Yes, F4: Yes A1: Yes, A1.1: No, A1.2: Yes , A2: No I1: Yes, I2: No, I3: No R1: Yes, R1.1: No, R1.2: No, R1.3: Yes	F1; F2: No --> Yes A1.1: No -> Yes I2: No --> Yes
[13] Klimaaktiv building database	The database shows a classification of the klimaaktiv building standard and represents a summary of around 1000 buildings (residential and non-residential) all over Austria	The database was chosen because in addition to energy efficiency, the climate-active building standard also assesses and evaluates the quality of planning and execution, the quality of building materials and construction as well as central aspects of comfort and indoor air quality are evaluated and classified from a neutral side	F1: Yes, F2: Yes, F3: No, F4: Yes A1: Yes, A1.1: Yes, A1.2: No, A2: No I1: Yes, I2: No, I3: No R1: Yes, R1.1: No, R1.2: No, R1.3: Yes	F3: No --> Yes A1.2: No -> Yes I2: No --> Yes
[14] BuildingRating	The database was generated by the ENTRANZE project which objective was to actively support policy making by providing the required data, analysis and guidelines to achieve a fast and strong penetration of nZEB and RES-H/C within the existing national building stocks. The project has intended to connect building experts from European research and academia to national decision makers and key stakeholders with a view to build ambitious, but reality proof, policies and roadmaps	The database was chosen because the data tool is an interactive user-friendly data mapping tool which is accessible. It contains an in-depth description of the characteristics of buildings and related energy systems in EU-28 and Serbia. It provides data on the thermal quality, size, age, type, ownership structure of buildings, on the heating and cooling systems and on the energy consumption by end-use	F1: No, F2: Yes, F3: No, F4: Yes A1: Yes, A1.1: Yes, A1.2: No, A2: No I1: Yes, I2: No, I3: No R1: Yes, R1.1: No, R1.2: No, R1.3: Yes	F1; F3: No --> Yes A1.2: No -> Yes I2: No --> Yes

Metadata assessments

Databases above were assessed with respect to their current meta practices. The table belows summarizes the current state and issues identified during WS 1:

Name of database	Type of metadata provided	Extend of metadata provided	Level of implementation of FAIR/O principles	Frameworks for metadata used	Technical implementation of metadata
[1] EU Building Stock Observatory	Provenance (sources), descriptive (axis and time series descriptions), partially provenance (only sources)	Descriptions, definitions, factsheet. However loosely attached to the data itself	F1: Yes, F2: Yes, F3: Yes, F4: No A1: Yes, A1.1: Yes, A1.2: Yes, A2: No I1: Yes, I2: Yes, I3: No R1: Yes, R1.1: No, R1.2: Yes, R1.3: No	Controlled vocabulary and thesaurus	Plain text and html
[2] Global Buildings Performance Network (GBPN)	Mostly descriptive, partially provenance (only sources) and administrative (copyright and ownership)	For the graphs regarding comparison of variables for countries/BEECs or multiple variables, sources of data are always listed under the graph. There is also data regarding the descriptions of the BEECs, however, these are more of a plain-text descriptive format and contain almost no metadata. Other than these, there are no other metadata attached.	F1: Yes, F2: Yes, F3: No, F4: No A1: No, A1.1: Yes, A1.2: Yes, A2: No I1: Yes, I2: Yes, I3: No R1: Yes, R1.1: No, R1.2: No, R1.3: No	Mainly controlled vocabulary	Plain text and html
[3] Pan-European Thermal Atlas (Peta) v4	Mostly descriptive, sources (not totally sufficient to trace back to the source)	Metadata contains rich descriptive information and sources	F1: Yes, F2: Yes, F3: Yes, F4: No A1: Yes, A1.1: Yes, A1.2: No, A2: No I1: Yes, I2: Yes, I3: No R1: Yes, R1.1: No, R1.2: Yes, R1.3: No	Controlled vocabulary, taxonomy, and thesaurus	html and plain text
[4] ExCEED - European Energy Efficient building district Database	Provenance (Sources), descriptive, type, units , definition	Example	F1: YES; F2: YES; F3: YES A1.1: YES; A1.2: YES; I1.: YES I3: YES R1.2: YES || Controlled vocabulary, and …. || html and plain text
[5] Hotmaps	Descriptive (name, title, description, version, date, profile, keywords, license), sources (adress and contributors), resources (description of data in more detail, temporal, schema, datatype), unique id	Detailled description of dataset, how it was created and sources. The level of detail is different for each dataset, basic description exists for each one.	F1: Yes, F2: Yes, F3: Yes, F4: No, A1: Yes, A1.1: Yes, A1.2: No, A2: Yes, I1: Yes, I2: Yes, I3: No, R1: Yes, R1.1: Yes, R1.2: Yes, R1.3: No	Controlled Vicabulary (?)	Machine-readable JSON, Example