UC1

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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 European Buildings Stock Observatory, 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.

Databases of Interest

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 into 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 an 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. F1:Yes; F2:Yes; F3: Yes; A1.1:Yes; A1.2:Yes; I1:Yes; I3:Yes; R1.2: Yes Full FAIR level can be achieved, maybe
[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 projects are used as Data sources F1: yes; F2: Yes; F3:Yes; A1: Yes; I1: Yes; R1.2: Yes F4 and A2 in addition
[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 Metadata contains rich descriptive information and sources F1: YES; F2: YES; F3: YES A1.1: YES; A1.2: YES; I1.: YES I3: YES R1.2: YES Controlled vocabulary, and thesaurus 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, [1]

WS1 Minutes of the Work Group of Use Case 1 (3 June 2020)

The main aim of USE CASE 1 Workshop is to have a discussion about the selected databases and possible candidate databases. Use case leader designed templates to allow all partners involved to conduct preliminary analyses of the selected databases. In addition, we aimed to select 3-5 databases from the list. The metadata status of the selected databases and lessons learned were also discussed in this session. There are pre-selected databases that are relevant databases that can be used in UC1. An initial assessment has been completed to have a general idea in the building's efficiency domain.

There are severe limitations to the:

  • Findability of buildings efficiency data- local kept data
  • Accessibility—maintenance of the data repositories
  • Interoperability— setbacks regarding data structures (such as interview transcripts)
  • Reusability-- Privacy issues
  • Openness—restrictions from commercial and security concerns

An initial assessment conducted via template below:

  • Descriptive analysis of the selected databases
  • Classification of Meta Data
  • Identification of Database Framework (DC and non-DC)
  • Assessing the database via Wilkonson’s principles

Consequently, 14 databases were preliminarily analyzed.

Assessments of the selected projects:

CommONEnergy (Economic Assessment Tool and Datamapper)

  • FP7 programme 2007.
  • It was mainly dedicated to the development of technologies and methodologies for the innovation of shopping centers.
  • Some of its tools are older while some are new.
  • Accessible information, structure, and indicators are set up very clear.
  • Definition of indicators were clear.
  • Newest databases were built upon older ones.
  • They were well explained and accessible.
  • However, the barrier is that EU projects end up at the end, and the data is no longer there anymore.
  • Metadata rating was very difficult (foreseeing what was behind the data was difficult).
  • These data were accessible, but there were some problems regarding the Accessibility of Energy Performance Certificates, which could be a huge source of information.

EXCEED

  • H2020 project: Disappointed with the collection of data.
  • Overall framework was not easy at the beginning from an IT point of view in terms of technical issues. Yet, it achieved a reliable IT infrastructure.
  • The objective of EXCEED was to transform data and metadata into information and knowledge for final users.
  • To achieve this target, a big amount of data is required, and the data providers are needed to be convinced to provide data.— A complicated market.
  • Some providers are adding a lot of data but no metadata. There is a lot of info about energy consumption, the temperature in buildings; but you don’t know which kind of building is concerned, where the building is, etc. This is a constraint.
  • On the other hand, there are some repositories. You can find the metadata, information related to the data; but you don’t have any information about energy consumption or indoor conditions.
  • It is a professional platform with many users, but there is difficulty to convince the people to upload their data. They are scared of uploading their data into a platform. There are some tools to analyze these data and make the data anonymous.
  • It is easy to visualize and qualify the data because it is open. (analysis and benchmark).
  • But the problem is data is limited because people do not upload their data.
  • Data can be extracted in excel file format.
  • It is public.

Building Rating

  • You don’t see how old the data is. It is a setback.
  • In terms of building rating, it is not so much customized.
  • It is mainly focusing on renewable energy and consumer attendance to costs and technologies.

Klimaaktiv

  • A very big database in Austria, but it might not be an effective candidate for the second phase.
  • It is based on free certification systems for buildings.
  • There are criteria and catalog mostly on the behavior in the buildings, ventilation, and energy efficiency in the buildings. (Big database for buildings).

Tabula Web Tool

  • Tabula might be suggested for the second phase.
  • Sources are referenced.
  • Data they look is fine from an engineering perspective. There are references on standards, but still, there is more room to work on.
  • Every federal state has its own different data, but you cannot access them.
  • Another issue is that what is the advantage of uploading data on a web page for customers.
  • On the PV side, customers can get a reflection on the functions of their PVs.
  • More interestingly, there is no database for monitoring the data.

GBPN

  • A database of building energy efficiency codes.
  • There is a visual tool where you score and compare the information in the database.
  • It includes both qualitative and quantitative data.
  • Part of the visuals can be downloaded or saved as JPG. Therefore, in terms of reusability, there are some problems.
  • In terms of interoperability, the score is between red and yellow, which means it is not that much interoperable.

ZEBRA2020 Databases

  • They are the parts of ZEBRA 2020 project.
  • Aim is to increase nearly zero energy buildings. The goal is to have more of these buildings in Europe.
  • Several databases and tools are created.
  • One is similar to GBPN in terms of interface and use. There is a similar visual tool.
  • Interoperability issues: the tool has also the similar issues.
  • Other databases: one includes panel data—analyzable and customizable database. You can easily select countries, time series, etc.
  • In the panel, you can download your results.
  • Looking at metadata, it has a kind of explanation and vocabulary that defines the data. But these are not regular statements.
  • At some places, there is only the name of the date or some other, there is only source.
  • There are also problems regarding data availability. Some data are not available for all countries or all-time spans.
  • This might be a candidate if there is not any possible candidate for further research.

Hotmaps

  • Building Stock Analysis database is used.
  • Excel and CSV formats are available.
  • EU 28 countries are included in the data (Building stock country by country).
  • There are data for different sectors like the residential sector and service sector.
  • Residential sector is sub-divided into single-family houses, multifamily houses, etc.
  • Service sector includes offices, hotels, health, other non-residential buildings (buildings for transportation, airports, etc.).
  • There is information about construction periods. Construction engineers prepared this long and detailed data.
  • Metadata is available online in a machine-actionable format.

Lessons Learned

  • One of the major barriers is about uploading the data to an online platform. As consumers and energy managers are not volunteer to share their data, the databases remain restricted.
  • When the data is collected from different and high number of sources, its reliability decreases.
  • Issues related to DC are not considered while creating the database. This is also a setback.
  • Privacy issue constitute a matter for the reusability of the data.
  • Deficiencies in metadata make the research complicated and difficult.
  • There is generally either a lack of information in metadata or in data itself.
  • Usability could arise as a problem. It is sometimes difficult to observe in detail.
  • Lack of live data is generally missing.
  • Interface design is crucial for the end user.
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