A range of LSTM, GP and STS models (provided as Jupyter notebooks) and yearly data (provided as excel files) used for developing explainable approaches for building electricity demand forecasting. A index of the models is also provided.

Detailed household load and solar generation in minutely to hourly resolution. This data package contains measured time series data for several small businesses and residential households relevant for household- or low-voltage-level power system modeling. The data includes solar power generation as well as electricity consumption (load) in a resolution up to single device consumption. The starting point for the time series, as well as data quality, varies between households, with gaps spanning from a few minutes to entire days. All measurement devices provided cumulative energy consumption/generation over time. Hence overall energy consumption/generation is retained, in case of data gaps due to communication problems. Measurements were conducted 1-minute intervals, with all data made available in an interpolated, uniform and regular time interval. All data gaps are either interpolated linearly, or filled with data of prior days. Additionally, data in 15 and 60-minute resolution is provided for compatibility with other time series data. Data processing is conducted in Jupyter Notebooks/Python/pandas.

A Global Database on Whole Life Carbon, Energy and Material Intensity of Buildings (CarbEnMats-Buildings)

• Data descriptor article (Preprint): https://doi.org/10.5281/zenodo.8378939
• Data records / database (this repo): https://doi.org/10.5281/zenodo.8363895
• Latest version: Available via https://github.com/mroeck/carbenmats-buildings (main)

Abstract

Globally, interest in understanding the life cycle related greenhouse gas (GHG) emissions of buildings is increasing. Robust data is required for benchmarking and analysis of parameters driving resource use and whole life carbon (WLC) emissions. However, open datasets combining information on energy and material use as well as whole life carbon emissions remain largely unavailable – until now.

We present a global database on whole life carbon, energy use, and material intensity of buildings. It contains data on more than 1,200 building case studies and includes over 300 attributes addressing context and site, building design, assessment methods, energy and material use, as well as WLC emissions across different life cycle stages. The data was collected through various meta-studies, using a dedicated data collection template (DCT) and processing scripts (Python Jupyter Notebooks), all of which are shared alongside this data descriptor.

This dataset is valuable for industrial ecology and sustainable construction research and will help inform decision-making in the building industry as well as the climate policy context.

Background & Summary

The need for reducing greenhouse gas (GHG) emissions across Europe require defining and implementing a performance system for both operational and embodied carbon at the building level that provides relevant guidance for policymakers and the building industry. So-called whole life carbon (WLC) of buildings is gaining increasing attention among decision-makers concerned with climate and industrial policy, as well as building procurement, design, and operation. However, most open buildings datasets published thus far have been focusing on building’s operational energy consumption and related parameters 1,2,2–4. Recent years furthermore brought large-scale datasets on building geometry (footprint, height) 5,6 as well as the publication of some datasets on building construction systems and material intensity 7,8. Heeren and Fishman’s database seed on material intensity (MI) of buildings 7, an essential reference to this work, was a first step towards an open data repository on material-related environmental impacts of buildings. In their 2019 descriptor, the authors present data on the material coefficients of more than 300 building cases intended for use in studies applying material flow analysis (MFA), input-output (IO) or life cycle assessment (LCA) methods. Guven et al. 8 elaborated on this effort by publishing a construction classification system database for understanding resource use in building construction. However, thus far, there is a lack of publicly available data that combines material composition, energy use and also considers life cycle-related environmental impacts, such as life cycle-related GHG emissions, also referred to as building’s whole life carbon.

The Global Database on Whole Life Carbon, Energy Use, and Material Intensity of Buildings (CarbEnMats-Buildings) published alongside this descriptor provides information on more than 1,200 buildings worldwide. The dataset includes attributes on geographical context and site, main building design characteristics, LCA-based assessment methods, as well as information on energy and material use, and related life cycle greenhouse gas (GHG) emissions, commonly referred to as whole life carbon (WLC), with a focus on embodied carbon (EC) emissions. The dataset compiles data obtained through a systematic review of the scientific literature as well as systematic data collection from both literature sources and industry partners. By applying a uniform data collection template (DCT) and related automated procedures for systematic data collection and compilation, we facilitate the processing, analysis and visualization along predefined categories and attributes, and support the consistency of data types and units. The descriptor includes specifications related to the DCT spreadsheet form used for obtaining these data as well as explanations of the data processing and feature engineering steps undertaken to clean and harmonise the data records. The validation focuses on describing the composition of the dataset and values observed for attributes related to whole life carbon, energy and material intensity.

The data published with this descriptor offers the largest open compilation of data on whole life carbon emissions, energy use and material intensity of buildings published to date. This open dataset is expected to be valuable for research applications in the context of MFA, I/O and LCA modelling. It also offers a unique data source for benchmarking whole life carbon, energy use and material intensity of buildings to inform policy and decision-making in the context of the decarbonization of building construction and operation as well as commercial real estate in Europe and beyond.

Files

All files related to this descriptor are available on a public GitHub repository and related release via Zenodo (https://doi.org/10.5281/zenodo.8363895). The repository contains the following files:

• README.md is a text file with instructions on how to use the files and documents.
• CarbEnMats_attributes.XLSX is a table with the complete attribute description.
• CarbEnMats_materials.XLSX is the table of material options and mappings.
• CarbEnMats_dataset.XLSX is the building dataset in MS Excel format.
• CarbEnMats_dataset.txt is the building dataset in tab-delimited TXT format.

Further information

Please consult the related data descriptor article (linked at the top) for further information, e.g.:

• Methods (Data collection; data processing)
• Data records (Files; Sources; Attributes)
• Technical validation (Data overview; Data consistency)
• Usage Notes (Attribute priority; Scope summary, Missing information)

Code availability (LICENSE)

The dataset, the data collection template as well as the code used for processing, harmonization and visualization are published under a GNU General Public License v3.0. The GNU General Public License is a free, copyleft license for software and other kinds of works. We encourage you to review, reuse, and refine the data and scripts and eventually share-alike.

Contributing

The CarbEnMats-Buildings database is the results of a highly collaborative effort and needs your active contributions to further improve and grow the open building data landscape. Reach out to the lead author (email, linkedin) if you are interested to contribute your data or time.

Cite as

When referring to this work, please cite both the descriptor and the dataset:

• Descriptor: RÖCK, Martin, SORENSEN, Andreas, BALOUKTSI, Maria, RUSCHI MENDES SAADE, Marcella, RASMUSSEN, Freja Nygaard, BIRGISDOTTIR, Harpa, FRISCHKNECHT, Rolf, LÜTZKENDORF, Thomas, HOXHA, Endrit, HABERT, Guillaume, SATOLA, Daniel, TRUGER, Barbara, TOZAN, Buket, KUITTINEN, Matti, ALAUX, Nicolas, ALLACKER, Karen, & PASSER, Alexader. (2023). A Global Database on Whole Life Carbon, Energy and Material Intensity of Buildings (CarbEnMats-Buildings) [Preprint]. Zenodo. https://doi.org/10.5281/zenodo.8378939
• Dataset: Martin Röck. (2023). mroeck/carbenmats-buildings: Pre-release (0.1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8363895

This dataset consists of the excel based Local Energy Emissions (LEE) model. The LEE model is a simulator to estimate the annual emissions of local energy-related activities including electricity, heating, cooking, and transport. The emissions calculation principle follows the international guidance, i.e., emissions=activity*emission factor. Users can alter inputs to see how the local emissions would vary. The default values in the model are based on 2020 inputs of the local area in Wales.The LEE model associated the article published at https://doi.org/10.1016/j.adapen.2022.100088

This dataset is part of a research project on the Kuujjuaq Forum, a multi-activity building in Kuujjuaq (Nunavik). The first objective of this study was to analyze unprecedented energy consumption data for a building with a solar power generation system in subarctic conditions. The second objective was to develop an energy model to assess the feasibility, potential and benefits of a solar-assisted ground-coupled heat pump (SAGCHP) installation. The 'Forum_General_Information' folder contains Excel files detailing the building's heating oil consumption, mechanical equipment and operational information, pictures and simplified building plans. 'GHE_Sizing' folder contains an Excel file summarizing a list of GHE designs generated to supply different heat loads (air preheating, air terminal heating, and total ventilation heating). The 'TRNSYS_Model' folder includes TRNSYS files (.b18, .bld, .dck, etc.) of the building model, GHE model, and photovoltaic panels model, along with Excel files summarizing results such as annual heat loads profiles, solar production profiles, and a 25-year simulation of the GHE system. This folder also includes an analysis of SAGCHP performance. The 'Economic_and_GHG_Analysis' folder includes Excel files summarizing results on greenhouse gas (GHG) and energy cost savings and a 25-year economic analysis. In this folder, the Excel files 'analysis_eco_calculsheet' and 'analysis_eco_resultsheet' are called by the Python script 'NPC_sensibility_analysis_capex' (Python 3.12 version). All these documents were used to prepare the paper entitled 'Sustainable Heating For A Non-Residential Building In Kuujjuaq: Field Data Analysis And Development Of An Energy Model' (Thermo, MDPI, to be submitted).

This excel file includes three sheets. The first sheet contains the consumption power of the uncontrollable load of one-day energy planning of a residential end-user. The second sheet contains the local photovoltaic power generation of a residential building. The third sheet contains the local wind power generation of a residential building.

This submission includes an excel workbook containing propane energy logs for the UIUC Energy Farm from March 2013 to March 2016. It also includes heating degree day information for the region from the period October 1 to March 31, for the years 2008 to 2013.

The propane logs are for use in parameterizing the demand and life-cycle assessments associated with the project. This data provides information about energy loads for the buildings being included in the DDU applications. Propane energy use logs for UIUC Energy farm for period 2013-2016.

Supplementary data for: N.F. Jensen, M. Morelli, L.S. Sørensen, 2021. "Fire safety evaluation of different internal insulation measures in Danish and European context"Abstract:In about the last 10 years there has been an increased focus on energy upgrading the existing building stock. This have included several international and national projects dealing with internal insulation. Many of the studies have considered the internal insulation as a measure to achieve a specific energy consumption of buildings. Later, the focus has been on the durability of the ‘new’ structure with additional insulation on the internal side of walls, i.e. if the measure is moisture safe. These measures have been applied in both theoretical studies, laboratory and real buildings. None of the studies has reported whether or not the suggested retrofit measures fulfil respective fire regulations. The height of the building is also considered in fire regulations, and therefore, measures that are applicable in e.g. single-family houses might not be applicable in apartment buildings. This study includes a review of a number of different insulation materials and – systems used for internal insulation. These measures are evaluated against the EU-harmonized and Danish fire regulations, as many countries might have adapted national requirements. The study evaluates, whether the measure are applicable at all floor levels or not.The dataset comprises an Excel file containing a review and evaluation of a number of case projects where internal insulation was installed in buildings with preservation worth façade walls, to determine if the studies met the fire requirements. Detailed information and references to the scientific publications for each of the examined case projects are provided.

Database prepared in Excel including four elements, as background information for RIBuild Deliverable D1.1 about the historic building stock:

Historic buildings stock energy consumption (1)

Historic building stock description (2)

Building construction elements (3)

Case studies (4)

Element (1)-(3) are referring to the historic building stock in RIBuild partner countries in general, while element (4) contains examples of carried out renovation projects, involving internal insulation of a historic building.

If available, the case study sheets contain information about the floor area, present use, the building envelope (thickness, materials), renovation history, pre- and post-energy usage and renovation cost. Further, information about typical defects and the main driving forces for the renovation project, planning or design tools used, whether the goal with the renovation was achieved and the satisfaction of the users.

Overview of data files to be found in 'RIBuild data WP1' as part of this dataset.

The data of this dataset comes from simulations performed in the software TRNSYS. The simulations were performed for the study case of a multi-family building situated in the climate of Tarragona, Spain, where the HVAC systems comprise a centralized dual source heat pump system.

The simulations were done for 3 representative weeks selected for different seasons (winter, spring, summer). Each time, the simulation is performed twice: once with a standard reference control, and a second time with the advanced energy management system (AEMS) in control, programmed in GAMS and coupled with the TRNSYS simulation. Hence there are 6 tests in total, one per Excel sheet in the dataset file. The description of the columns is shown in the following table:

Name	Unit	Description
Tamb	ºC	Ambient outdoor temperature
Irr	kJ/h.m2	Solar irradiation horizontal
Troom	ºC	Room temperature
Troom_set	ºC	Room set-point temperature
Pel_FCU	kW	Electrical power consumption of the FCU
Qth_cool_FCU	kW	Thermal cooling power of the FCU
Qth_heat_RadFl	kW	Thermal heating power of the radiant floor
TDHW_up	ºC	Temperature at the top of the DHW tank
TDHW_lo	ºC	Temperature at the bottom of the DHW tank
TSHC_up	ºC	Temperature at the top of the SHC tank
TSHC_lo	ºC	Temperature at the bottom of the SHC tank
Pel_HH_kW	kW	Electrical power consumption of the appliances
Pel_PV_kW	kW	Electrical power generation of the PV
Pel_HVAC_kW	kW	Electrical power consumption from the HVAC incl. HP
Pel_Grid_kW	kW	Electrical exchange with the grid
Pel_Bat_kW	kW	Charging/discharging power of the battery
SOC_Bat	%	State of charge of the battery
Qth_SH_kW	kW	Thermal heating power produced by the HP for space heating
Qth_SC_kW	kW	Thermal cooling power produced by the HP for space cooling
Qth_DHW_kW	kW	Thermal heating power produced by the HP for DHW
Pel_HP_kW	kW	Electrical consumption of heat pump

These results were extensively described in the deliverable D6.5 of the TRI-HP project.

London Heat Map The London Heat Map is a tool designed to help you identify areas of high heat demand, explore opportunities for new and expanding district heat networks and to draw potential heat networks and assess their financial feasibility. The new version of the London Heat Map was created for the Greater London Authority by the Centre for Sustainable Energy (CSE) in July 2019. The London Heat Map is regularly updated with new network data and other datasets. Background datasets such as building heat demand was last updated on 26/06/2023. The London Heatmap is a map-based web application you can use to find and appraise opportunities for decentralised energy (DE) projects in London. The map covers the whole of Greater London, and provides very local information to help you identify and develop DE opportunities, including data such as: Heat demand values for each building Locations of potential heat supply sites Locations of existing and proposed district heating networks A spatial heat demand density map layer The map also includes a user-friendly visual tool for heat network design. This is intended to support preliminary techno-economic appraisal of potential district heat networks. The London Heat Map is used by a wide variety of people in numerous ways: London Boroughs can use the new map to help develop their energy master plans. Property developers can use the map to help them meet the decentralised energy policies in the London Plan. Energy consultants can use the map to gather initial data to inform feasibility studies. More information is available here, and an interactive map is available here. Building-level estimated annual and peak heat demand data from the London Heat Map has been made available through the data extracts below. The data was last updated on 26/06/2023. The data contains Ordnance Survey mapping and the data is published under Ordnance Survey's 'presumption to publish'. © Crown copyright and database rights 2023. The Decentralised Energy Master planning programme (DEMaP) The Decentralised Energy Master planning programme (DEMaP), was completed in October 2010. It included a heat mapping support package for the London boroughs to enable them to carry out high resolution heat mapping for their area. To date, heat maps have been produced for 29 London boroughs with the remaining four boroughs carrying out their own data collection. All of the data collected through this process is provided below. Carbon Calculator Tool Arup have produced a Carbon Calculator Tool to assist projects in their early estimation of the carbon dioxide (CO2) savings which could be realised by a district heating scheme with different sources of heating. The calculator's estimates include the impact of a decarbonising the electrical grid over time, based on projections by the Department for Energy and Climate Change, as well as the Government's Standard Assessment Procedure (SAP). The Excel-based tool can be downloaded below. Borough Heat Maps Data and Reports (2012) In March 2012, all London boroughs did a heat mapping exercise. The data from this includes the following and can be downloaded below: Heat Load for all boroughs Heat Supplies for all boroughs Heat Network LDD 2010 database Complete GIS London Heat Map Data The heat maps contain real heat consumption data for priority buildings such as hospitals, leisure centres and local authority buildings. As part of this work, each of the boroughs developed implementation plans to help them take the DE opportunities identified to the next stages. The implementation plans include barriers and opportunities, actions to be taken by the council, key dates, personnel responsible. These can be downloaded below. Other Useful Documents Other useful documents can be downloaded from the links below: Energy Masterplanning Manual Opportunities for Decentralised Energy in London - Vision Map London Heat Network Manual London Heat Network Manual II

Materials to support paper

CEB_Electricity_Consumption_Data:
CEB_Data.xlsx (Microsoft Excel Worksheet) contains Ceylon Energy Board (CEB) electricity consumption data for 2017 Dec and Jan-Mar 2018 in the East (E), West (W), North (N) and South (S) regions of Colombo.
Data obtained upon request from the CEB. The E, W, N and S region polgons are contained in CEB_regions.shp (shapefile).

CMC_Building_Footprint:
CMC_bldg_footprint.shp (shapefile) contains the plan area (m^2) and number of storeys of individual buildings within the CMC for 2014.

Divisional_Secretariat_Population_Densities:
Div_Sec_Pop_Dens_Day_Night.shp (shapefile) contains daytime and night time 2012 population densities (capita per hectare) in divisional secretariats within the Western Province of Sri Lanka.

Gridded_100m_2020_QF-av-wk-we_Pop-Dens_Land-Cover_LCZs_Building-Geom:
gridded_100m_2020_averages.shp (shapefile) and gridded_100m_2020_averages.pkl (Python pickle file) both contain 100 m gridded 2020 average QF, QFm, QFv, QFb (for all days, workdays and non-workdays),
daytime and night time population density (capita per hectare), land cover fractions, Local Climate Zone classifications and average building height (m).