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 IEEE,Masea, OpenEI, Open Data Platform for Energy Efficiency in Buildings, the United States Department of Energy databases, the European Buildings Stock Observatory and the databases developed by the ENTRANZE and the ExcEED project. While these databases provide information relevant for, e.g. for the retrofitting of houses, databases specifically targeting electric demand due to household appliances are rare. The few examples include Open Power System Data, ECO ,GREEND, REDD, UK-DALE, OEHU, and home consumption data provided by local British communities. Data regarding energy poverty are collected at the EU Energy Poverty Observatory.

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	Example	Example	Example	Example
[3] ZEBRA 2020 nZEB buildings	Example	Example	Example	Example
[4] ZEBRA 2020 Energy efficiency trends in buildings	Example	Example	Example	Example
[5] EU Building Stock Observatory	Example	Example	Example	Example
[6] Pan-European Thermal Atlas (Peta) v4	Example	Example	Example	Example
[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	Example	Example	Example	Example
[9] CommONEergy _Economic Assessment Tool	Example	Example	Example	Example
[10] COMMONENERGY Datamapper	Example	Example	Example	Example
[11] Tabula Web Tool	Example	Example	Example	Example
[12] The FROnT project: for Fair Renewable Heating and Cooling Options and Trade	Example	Example	Example	Example
[13] Klimaaktiv building database	Example	Example	Example	Example
[14] BuildingRating	Example	Example	Example	Example

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
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