WS1UC1

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This page provides space for notes from workshop discussions of use case 1 during Day 2 of WS1.

Interactive elements

The discussions can also benefit from considering the EERAdata storyboard and/or slido comments. See WS1#Interactive_elements

Notes from the morning session

The morning session is dedicated to database discussions with the aim to select 3-5 databases from below's list of databases for further examination. The first table of the WIKI page is filled out by noon.

The morning session is dedicated to database discussions with the aim to select 3-5 databases from below's list of databases for further examination. The workshop, in which 9 participants actively worked, has started with the introduction of participants and a guiding presentation by UC1 leader. UC1 workshop aims:

  • Discuss the related literature, gaps, best practices, FAIR/O- fairification, Metadata-DC and non-DC
  • Discuss the state of FAIR/O for the Buildings Efficiency related Databases from the pre-selected databases
  • Select 3-5 Buildings Efficiency related Databases
  • Discuss the state of FAIR/O metadata for selected databases
  • Derive lessons learned

The limitations regarding data and metadata were listed as follows:

  • 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

14 databases were preliminarily assessed and analyzed according to following criteria:

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

Each pre-selected database was discussed in terms of its strengths and weaknesses. Accordingly, recommendations were made for potential candidate databases for further research.

Draft list of databases


Discussion on the choice of databases:

Based on the preliminary work performed by partners, each partner provided the following brief information about the databases analyzed:

[1.] Global Buildings Performance Network (GBPN)

  • Description of database:

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 criterion. 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.

  • Rationale for choosing this database:

This is a database which covers the use case theme, buildings efficiency

It involves both qualitative data regarding building energy efficiency codes, and quantitative assessments of these codes

It also has an extensive coverage, including Europe, North America, and China

[2.] ZEBRA 2020 Data Mapper

  • Description of database:

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.

  • Rationale for choosing this database:

The database presents data visualizations, which may be an important aspect of presentation of data
It covers a significant aspect of energy efficiency in buildings, that is, nearly Zero-Energy Buildings (nZeb)s

[3.] ZEBRA 2020 nZEB buildings

  • Description of database:

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.

  • Rationale for choosing this database:

The database covers a significant aspect of energy efficiency in buildings, that is, nearly Zero-Energy Buildings (nZeb)s

[4.] ZEBRA 2020 Energy efficiency trends in buildings

  • Description of database:

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. Rationale for choosing this database:

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

[5.] EU Building Stock Observatory

  • Description of database:

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).

  • Rationale for choosing this database:

It is a EC initiative and needs to be analyzed to identify to what extend it conforms to EU perspective on data
• 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

[6.] Pan-European Thermal Atlas (Peta) v4

  • Description of database:

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.

  • Rationale for choosing this database:

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 in line with EERAdata's focus

[7.] ExCEED - European Energy Efficient building district Database

  • Description of 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 analyze 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.

  • Rationale for choosing this database:

The ExcEED cloud-based platform supports a portfolio of data integration mechanisms

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

  • The discussion on the database also reveals that some providers are adding a lot of data but no metadata.
  • There are a lot of info about energy consumption, 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. Moreover, 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. The anonymized data are available to everyone for analysis and benchmarking
  • But the problem is data is limited because people do not upload their data.

Expert views are key to determine, assess and analyze data and metadata. For this reason, participants from EURAC, which is a private research center, contributed a lot in the light of their views and experience in ExCEED database. They provide information about the aims and scope of ExCEED database. The objective of EXCEED is 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 provide data. Therefore, ExCEED is a good example for database selection in terms of choose of indicators and metadata.

[8.] ENTRANZE database and web tool

  • Description of database:

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.

  • Rationale for choosing this database:

EU-27 + Croatia; quite accessible; various EU project used for Data sources

[9.] CommONEergy - Economic Assessment Tool

  • Description of database:

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.

  • Rationale for choosing this database:

The database includes data on a significant category of buildings, that is, commercial buildings

[10.] COMMONENERGY Datamapper

  • Description of database:

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.

  • Rationale for choosing this database:

The database involves a visual tool to demonstrate buildings-related data, which may be interesting in terms of data presentation requirements of databases or platforms

  • Regarding COMMONENERGY databases, the information is accessible, and structure and indicators are set up very clear
  • Definition of indicators were clear
  • 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
  • Besides, 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

[11.] Tabula Web Tool

  • Description of database:

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.

  • Rationale for choosing this database:

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

  • Tabula might be suggested for the second phase.
  • There are some sources and references. Data they look is fine from an engineering perspective. There are references on standards, but still there is more room to work on. But the problem is that some people are lacking information in the database. All government offices have some data. Every federal state has its own different data, but you cannot access to them.

[12.] The FROnT project: for Fair Renewable Heating and Cooling Options and Trade

  • Description of database:

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.

  • Rationale for choosing this database

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

[13.] Klimaaktiv building database

  • Description of 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.

  • Rationale for choosing this database

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

[14.] BuildingRating

  • Description of database:

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.

  • Rationale for choosing this database

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-users

  • One problem with many databases is that 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

[15] Hotmaps

  • While the databases were selected for preliminary assessment, Hotmaps Project was not included in the list.
  • During the workshop session in UC1, Simon Pezzutto presented Hotmaps Project 2020.
  • Accordingly, it was designed as an open source mapping and planning tool for heating and cooling.
  • In the project, a unique database named as Building Stock Analysis database was used.
  • The database covers 28 EU countries with respect to building stock country by country.
  • The data is available in Excel and csv formats.
  • There is data for different sectors such as residential sector and service sector.
  • Residential sector is sub-divided into single family houses, multifamily houses etc.
  • Service sector includes offices, hotels, health, and other non-residential buildings (buildings for transportation, airports etc.)
  • The data covers a huge range of information including construction periods, covered area, number of dwellings, number of buildings, etc.
  • Moreover, The dataset consists of quantitative information and expert interviews.
  • In overall, Hotmaps Project was selected for further considertiaon owing to its structure, metadata and content.

Notes from the afternoon session

The afternoon session is dedicated to discussing metadata for the selected databases. The aim of the afternoon session is to fill out table 2 of the wiki page for the use case and to decide what to report back from the use case to the plenary session the next day. Thus, at the end of the day, the WIKI page for the use case is complete.

The afternoon session is dedicated to discuss metadata for the selected databases. The aim of the afternoon session is to fill out table 2 of the wiki page for the use case and to decide what to report back from the use case to the plenary session the next day. Thus, at the end of the day, the WIKI page for the use case is complete.

  • Assessment of metadata for databases

In total, 5 databases were decided for metadata assessment.

[1.] EU Building Stock Observatory [2.] Global Buildings Performance Network (GBPN) [3.] Pan-European Thermal Atlas (Peta) v4 [4.] ExCEED - European Energy Efficient building district Database [5.] Hotmaps


[1.] EU Building Stock Observatory

  • Type of metadata provided: Provenance (sources), descriptive (axis and time series descriptions), partially provenance (only sources).
  • Extend of metadata provided: Descriptions, definitions, factsheet. However loosely attached to the data itself.
  • Level of implementation of FAIR/O principles: 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
  • Frameworks for metadata used: Controlled vocabulary and thesaurus
  • Technical implementation of metadata: Plain text and html

[2.] Global Buildings Performance Network (GBPN)

  • Type of metadata provided: Mostly descriptive, partially provenance (only sources) and administrative (copyright and ownership).
  • Extend of metadata provided: 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.
  • Level of implementation of FAIR/O principles: 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
  • Frameworks for metadata used: Mainly controlled vocabulary
  • Technical implementation of metadata: Plain text and html

[3.] Pan-European Thermal Atlas (Peta) v4

  • Type of metadata provided: Mostly descriptive, sources (not totally sufficient to trace back to the source)
  • Extend of metadata provided: Metadata contains rich descriptive information and sources
  • Level of implementation of FAIR/O principles: 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
  • Frameworks for metadata used: Controlled vocabulary, taxonomy, and thesaurus
  • Technical implementation of metadata: html and plain text

[4.] ExCEED - European Energy Efficient building district Database

  • Type of metadata provided: Provenance (Sources), descriptive, type, units , definition
  • Extend of metadata provided: Metadata contains rich descriptive information and sources
  • Level of implementation of FAIR/O principles: F1: YES; F2: YES; F3: YES A1.1: YES; A1.2: YES; I1.: YES I3: YES R1.2: YES
  • Frameworks for metadata used: Controlled vocabulary, and thesaurus
  • Technical implementation of metadata: html and plain text

[5.] Hotmaps

  • Type of metadata provided: 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
  • Extend of metadata provided: 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.
  • Level of implementation of FAIR/O principles: 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
  • Frameworks for metadata used: Controlled Vocabulary
  • Technical implementation of metadata: Machine-readable JSON

What to report back to the plenary on Day 3?

  • Databases selected

1. EU Building Stock Observatory 2. Global Buildings Performance Network (GBPN) 3. Pan-European Thermal Atlas (Peta) v4 4. ExCEED - European Energy Efficient building district Database 5. Hotmaps

  • Main insights from discussions

Based on the discussions in morning and afternoon sessions of UC1, the participants derived lessons learned from the selected databases.

Main insights from discussions are as follows:

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, there are more likely to be issues with consistency and reliability

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

The data in repositories and databases are usually not up-to-date. There is Lack of live/fresh data

Interface design is crucial for the end user

Databse designs usually do not consider DC standards or FAIR principles at the start, as design requirements

The main focus is on "getting the job done"

The FAIRness assessment is usually subjective, even if done by machines

  • Suggested next steps

Evaluate the data profiles of the selected databases in order to seek for recipes

Define recipes to provide database-specific implementation of the Fairification

Assess the metadata quality of the selected databases (TOOL STANDARD COMMUNITY)

Accept the fact that “%100 FAIR is not really possible!”

Evaluate before and after for each selected databases

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