Local Law 84 of 2009 (LL84) requires annual energy and water benchmarking data to be submitted by owners of buildings with more than 50,000 square feet. This data is collected via the Environmental Protection Agency's (EPA) Portfolio Manager website

Each property is identified by it's EPA assigned property ID, and can contain one or more tax lots identified by one or more BBLs (Borough, Block, Lot) or one or more buildings identified by one or more building identification numbers (BIN)

Please visit DOB's Benchmarking and Energy Efficiency Rating page for additional information.

Note: This dataset has been superseded by the dataset found at "End-Use Load Profiles for the U.S. Building Stock" (submission 4520; linked in the submission resources), which is a comprehensive and validated representation of hourly load profiles in the U.S. commercial and residential building stock. The End-Use Load Profiles project website includes links to data viewers for this new dataset. For documentation of dataset validation, model calibration, and uncertainty quantification, see Wilson et al. (2022). These data were first created around 2012 as a byproduct of various analyses of solar photovoltaics and solar water heating (see references below for are two examples). This dataset contains several errors and limitations. It is recommended that users of this dataset transition to the updated version of the dataset posted in the resources. This dataset contains weather data, commercial load profile data, and residential load profile data. Weather The Typical Meteorological Year 3 (TMY3) provides one year of hourly data for around 1,000 locations. The TMY weather represents 30-year normals, which are typical weather conditions over a 30-year period. Commercial The commercial load profiles included are the 16 ASHRAE 90.1-2004 DOE Commercial Prototype Models simulated in all TMY3 locations, with building insulation levels changing based on ASHRAE 90.1-2004 requirements in each climate zone. The folder names within each resource represent the weather station _location of the profiles, whereas the file names represent the building type and the representative city for the ASHRAE climate zone that was used to determine code compliance insulation levels. As indicated by the file names, all building models represent construction that complied with the ASHRAE 90.1-2004 building energy code requirements. No older or newer vintages of buildings are represented. Residential The BASE residential load profiles are five EnergyPlus models (one per climate region) representing 2009 IECC construction single-family detached homes simulated in all TMY3 locations. No older or newer vintages of buildings are represented. Each of the five climate regions include only one heating fuel type; electric heating is only found in the Hot-Humid climate. Air conditioning is not found in the Marine climate region. One major issue with the residential profiles is that for each of the five climate zones, certain _location-specific algorithms from one city were applied to entire climate zones. For example, in the Hot-Humid files, the heating season calculated for Tampa, FL (December 1 - March 31) was unknowingly applied to all other locations in the Hot-Humid zone, which restricts heating operation outside of those days (for example, heating is disabled in Dallas, TX during cold weather in November). This causes the heating energy to be artificially low in colder parts of that climate zone, and conversely the cooling season restriction leads to artificially low cooling energy use in hotter parts of each climate zone. Additionally, the ground temperatures for the representative city were used across the entire climate zone. This affects water heating energy use (because inlet cold water temperature depends on ground temperature) and heating/cooling energy use (because of ground heat transfer through foundation walls and floors). Representative cities were Tampa, FL (Hot-Humid), El Paso, TX (Mixed-Dry/Hot-Dry), Memphis, TN (Mixed-Humid), Arcata, CA (Marine), and Billings, MT (Cold/Very-Cold). The residential dataset includes a HIGH building load profile that was intended to provide a rough approximation of older home vintages, but it combines poor thermal insulation with larger house size, tighter thermostat setpoints, and less efficient HVAC equipment. Conversely, the LOW building combines excellent thermal insulation with smaller house size, wider thermostat setpoints, and more efficient HVAC equipment. However, it is not known how well these HIGH and LOW permutations represent the range of energy use in the housing stock. Note that on July 2nd, 2013, the Residential High and Low load files were updated from 366 days in a year for leap years to the more general 365 days in a normal year. The archived residential load data is included from prior to this date.

[v2 update] weather data correction

The data describes an electrical energy community, containing photovoltaic (PV) production profiles and end-user consumption profiles, desegregated by individual appliances used.

A dataset of a residential community was constructed based on real data, where sample consumption and photovoltaic generation profiles were attributed to 50 residential households and a public building (municipal library), a total of 51 buildings. The data concerns a full year.

The overall power consumption of these houses was desegregated into the consumption of 10 commonly used appliances using real energy profiles.

This work has been published in Elsevier's Data in Brief journal: Calvin Goncalves, Ruben Barreto, Pedro Faria, Luis Gomes, Zita Vale, Dataset of an energy community's generation and consumption with appliance allocation, Data in Brief, Volume 45, 2022, 108590, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2022.108590 (https://www.sciencedirect.com/science/article/pii/S2352340922007971)

We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Data in Brief publication to cite this work.

Reference data used to create this dataset:

Renewable energy production profiles: https://site.ieee.org/pes-iss/data-sets/

End-user profiles:

https://data.london.gov.uk/dataset/smartmeter-energy-use-data-in-london-households

https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption

https://site.ieee.org/pes-iss/data-sets/

Seattle’s Building Energy Benchmarking Program (SMC 22.920) requires owners of non-residential and multifamily buildings (Greater than 20,000 square feet) to track energy performance and annually report to the City of Seattle. Annual benchmarking, reporting and disclosing of building performance are foundational elements of creating more market value for energy efficiency. Per Ordinance (125000), starting with 2015 energy use performance reporting, the City of Seattle is making the data for all buildings greater than 20,000 SF available annually. This dataset contains all 2022 buildings required to report. If you have questions or comments on the data, email us at energybenchmarking@seattle.gov and include Open Data in the subject line.

This dataset includes the heating energy consumption of buildings in the city of Grenoble since 2012, by year.

Analysis of ‘DCAS Managed Building Energy Usage’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/223b0b2f-ae62-4b70-989d-5a5ce2b93ab2 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

City Building Energy Usage Data.

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Monroe County Single Family Residential Building Assets and Energy Consumption: 2017-2019’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/fd9c640d-6e47-4a51-9b9b-0cc6304517e1 on 12 February 2022.

--- Dataset description provided by original source is as follows ---

PLEASE DOWNLOAD THE FULL REPORT UNDER THE ATTACHMENT SECTION IN THE 'ABOUT THIS DATASET' SECTION BELOW.

This aggregated and anonymized dataset of single-family residential building asset attributes and observed average annual energy consumption over the 2-year period from August 2017 through July 2019 is available for Monroe County. The dataset includes more than 55,000 properties from the study’s matched residential dataset that had sufficient data for calculation of average annual energy consumption and could not be uniquely identified in the larger dataset of Monroe County residential parcels or Infogroup data. The data were anonymized by removing all property identifying information including address, parcel identifiers, and parcel size. Attributes such as square footage, building age, and assessed value were then grouped such that no groupings contained fewer than three properties in the Monroe County parcel dataset. This dataset with average annual energy consumption for gas, electric, and total consumption can be used by those interested in further analysis and energy modeling.

In response to the New York State Department of Public Service (DPS) Order Adopting Accelerated Energy Efficiency targets, issued December, 18, 2018, the New York State Energy Research and Development Authority (NYSERDA) contracted with Stone Environmental, Inc to conduct an Asset Data Matching Pilot in Monroe County to analyze building asset data, utility usage data, and NYSERDA program data for single family residential buildings. The objective of the study was to analyze publicly available data along with two years of utility usage data provided by Rochester Gas and Electric (RG&E) to provide information and data to the market to help reduce customer acquisition costs for adoption of energy efficiency measures and to better understand the ability to use building asset data to determine energy efficiency.

See the final report from the analysis under the attachments section.

NYSERDA offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and accelerate economic growth. reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on Twitter, Facebook, YouTube, or Instagram.

--- Original source retains full ownership of the source dataset ---

Understanding the residential energy consumption patterns across multiple income groups under decarbonization scenarios is crucial for designing equitable and effective energy policies that address climate change while minimizing disparities. This dataset is developed using an integrated human-Earth system model, supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment at Pacific Northwest National Laboratory (PNNL).

GCAM-USA operates within the Global Change Analysis Model, which represents the behavior of, and interactions between, different sectors or systems, including the energy system, the economy, agriculture and land use, water, and the climate. GCAM is one of only a few integrated global human-Earth system models, also known as Integrated Assessment Models (IAMs), which address key processes in inter-linked human and earth systems and provide insights into future global environmental change under alternative scenarios (IAMC, 2022).

GCAM has global coverage with varying spatial disaggregation depending on the type of system being modeled. For energy and economy systems, 32 regions across the globe, including the USA as its own region, are modeled in GCAM. GCAM-USA advances with greater spatial detail in the USA region, which includes 50 States plus the District of Columbia (hereinafter “state”). The core operating principle for GCAM and GCAM-USA is market equilibrium. The model solves every market simultaneously at each time step where supply equals demand and prices are endogenous in the model. The official documentation of GCAM and GCAM-USA can be found at: https://jgcri.github.io/gcam-doc/toc.html

The dataset included in this repository is based on an improved version of GCAM-USA v6, where multiple consumer groups, differentiated by the average income level for 10 population deciles, are represented in the residential building energy sector. As of May 15, 2023, the latest officially released version of GCAM-USA has a single consumer (represented by average GDP per capita) in the residential sector and thus does not include this feature. This multiple-consumer feature is important because (1) demand for residential floorspace and energy are non-linear in income, so modeling more income groups improves the representation of total demand and (2) this feature allows us to explore the distributional effects of policies on these different income groups and the resulting disparity across the groups in terms of residential energy security. If you need more information, please contact the corresponding author.

Here, we ran GCAM-USA with the multiple-consumer feature described above under four scenarios over 2015-2045 (Table 1), including two business-as-usual scenarios and two decarbonization scenarios (with and without the impacts of climate change on heating and cooling demand). This repository contains the key output variables related to the residential building energy sector under the four scenarios, including:

• income shares by consumer groups at each state over 2015-2045 (Casper et al. 2022)
• residential energy consumption per capita by service and fuel, by state and income group, 2015-2045
• residential energy service output (energy consumption * technology efficiency) per capita by service, fuel, and technology, by state and income group, 2015-2045
• estimated energy burden (Eq.1), by state and income group, 2015-2045
• residential heating service inequality (Eq.2), by state, 2015-2045

Table 1

Eq. 1

\(Energy\ burden_i = \dfrac{\sum_j (service\ output_{i,j} * service\ cost_j)}{GDP_i}\)

for income group i and service j

Eq. 2

\(Residential\ heating\ service\ inequality = \dfrac{S_{d10}}{(S_{d1} +S_{d2} + S_{d3} + S_{d4})}\)

where S is the residential heating service output per capita of the highest income group (d10) divided by the sum of that of the lowest four income groups (d1, d2, d3, and d4), similar to the Palma ratio often used for measuring income inequality. A higher Palma ratio indicates a greater degree of inequality.

Reference

Casper, Kelly, Narayan, Kanishka B., O'Neill, Brian C., & Waldhoff, Stephanie. 2022. State level income distributions for net income deciles for the US for historical years (2011-2014) and projections for different SSP scenarios (2015-2100) (latest version obtained from the authors on April 6, 2023) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7227128

IAMC. 2022. The common Integrated Assessment Model (IAM) documentation [Online]. Integrated Assessment Consortium. Available: https://www.iamcdocumentation.eu/index.php/IAMC_wiki [Accessed May 2023].

This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).

PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.

The data stock of electricity consumption in Berlin for 2022, aggregated by building blocks, districts and zip code areas. The data does not take into account self-consumption or network losses. For individual building blocks, the consumption is not indicated for data protection reasons

Abstract

"The Building Performance Database (BPD) is the nation's largest dataset of information about the energy-related characteristics of commercial and residential buildings. The BPD combines, cleanses and anonymizes data collected by federal, state and local governments, utilities, energy efficiency programs, building owners and private companies, and makes it available to the public" (Lawrence Berkeley National Laboratory, 2022). Data curated by Carnegie Mellon University Libraries.

Methodology

Data were combined across the datasets listed on the BPD website (Menu button -%3E Public Datasets -%3E List of Files).

Usage

• The raw data were collected from public sources by the BPD.

%3C!-- --%3E

• The BPD-curated data are for public use.
• Data from multiple years are included.

%3C!-- --%3E

• The building identifiers in the id column may be duplicated across data sources.

%3C!-- --%3E

This data is collected annually via EPA Portfolio Manager. The data collection requires building owners to measure their energy and water consumption and compare it against that of similar buildings in the city and country. The data is useful for policy analysts as it provides transparency into energy and water consumption for the city's largest buildings. Please visit https://www1.nyc.gov/site/buildings/codes/benchmarking.page for additional information.

The district of Treptow-Köpenick of Berlin operates a variety of different buildings that create a significant energy consumption. As part of energy management, this energy consumption is regularly monitored and published annually in accordance with the Berlin Energy Transition Act. The consumption data are available for download here. It is noted that not every object has its own supply and thus a separate heat or power meter. This can lead to no or only a pro rata consumption shown in the overview for individual properties. Other properties, on the other hand, may also contain the (partial) heat or power consumption of adjacent objects. In addition, an area-based breakdown of the total consumption was made in individual cases. The specific boundary conditions of individual objects are not taken into account. The present presentation also excludes other temporary special effects (such as the limited use of a property in the course of a refurbishment). The consumption data shown correspond to the current state of knowledge and may change in individual cases (e.g. by subsequent billing corrections of the suppliers).

Analysis of ‘ Steel Industry Energy Consumption’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/csafrit2/steel-industry-energy-consumption on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Content

This company produces several types of coils, steel plates, and iron plates. The information on electricity consumption is held in a cloud-based system. The information on energy consumption of the industry is stored on the website of the Korea Electric Power Corporation (pccs.kepco.go.kr), and the perspectives on daily, monthly, and annual data are calculated and shown.

Attribute Information:

Date Continuous-time data taken on the first of the month Usage_kWh Industry Energy Consumption Continuous kWh Lagging Current reactive power Continuous kVarh Leading Current reactive power Continuous kVarh CO2 Continuous ppm NSM Number of Seconds from midnight Continuous S Week status Categorical (Weekend (0) or a Weekday(1)) Day of week Categorical Sunday, Monday : Saturday Load Type Categorical Light Load, Medium Load, Maximum Load

Acknowledgements

This dataset is sourced from the UCI Machine Learning Repository Relevant Papers:

1. Sathishkumar V E, Changsun Shin, Youngyun Cho, “Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city†, Building Research & Information, Vol. 49. no. 1, pp. 127-143, 2021.
2. Sathishkumar V E, Myeongbae Lee, Jonghyun Lim, Yubin Kim, Changsun Shin, Jangwoo Park, Yongyun Cho, “An Energy Consumption Prediction Model for Smart Factory using Data Mining Algorithms†KIPS Transactions on Software and Data Engineering, Vol. 9, no. 5, pp. 153-160, 2020. Transactions on Software and Data Engineering, Vol. 9, no. 5, pp. 153-160, 2020.
3. Sathishkumar V E, Jonghyun Lim, Myeongbae Lee, Yongyun Cho, Jangwoo Park, Changsun Shin, and Yongyun Cho, “Industry Energy Consumption Prediction Using Data Mining Techniques†, International Journal of Energy Information and Communications, Vol. 11, no. 1, pp. 7-14, 2020.

Inspiration

Which times of the year is the most energy consumed? What patterns can we identify in energy usage?

--- Original source retains full ownership of the source dataset ---

The dataset contains hourly Anthropogenic heat (AH) from buildings in Los Angeles County, based on weather data from 2018. The hourly AH is aggregated at three spatial resolutions: 450m x 450m grid, 12km x 12km grid, and census tract. The AH is broken down into three components: building envelope surface convection, heating, ventilation, and air conditioning (HVAC) system heat release, and zone exfiltration and exhaust air heat loss. The dataset is created with the physics-based EnergyPlus building energy models to calculate individual buildings' AH considering WRF-UCM simulated microclimate conditions. Please refer to the paper "A multi-scale time-series dataset of anthropogenic heat from buildings in Los Angeles County" for more information about the data generation workflow and the data validation procedure. The data set contains two folders: the "output_data" folder holds the simulation results (EP_output and EP_output_csv), building metadata (building_metadata.geojson and building_metadata.csv), aggregated heat emission and energy consumption time-series data (hourly_heat_energy), and geographical data (geo_data) associated with the GEOID referenced in heat and energy consumption data. The "input_data" folder contains the raw data used to generate files in the "output_data" folder as well as data sets used in the validation. The code repository (https://github.com/IMMM-SFA/xu_etal_2022_sdata) holds the processing scripts for data curation, validation, and visualization.

Abstract

City Building Energy Usage Data This dataset was created by EIDC on Tue, 14 Jun 2022 14:40:23 GMT.

Czech Republic Electricity Consumption: BC: Buildings Construction data was reported at 38,283.010 GJ in 2023. This records a decrease from the previous number of 39,608.355 GJ for 2022. Czech Republic Electricity Consumption: BC: Buildings Construction data is updated yearly, averaging 49,103.115 GJ from Dec 2007 (Median) to 2023, with 17 observations. The data reached an all-time high of 484,889.000 GJ in 2008 and a record low of 38,283.010 GJ in 2023. Czech Republic Electricity Consumption: BC: Buildings Construction data remains active status in CEIC and is reported by Czech Statistical Office. The data is categorized under Global Database’s Czech Republic – Table CZ.RB005: Energy Consumption: Electricity: by Industry: Statistical Classification of Economic Activities Rev. 2.

Forecast: Electricity Consumption in Construction in China 2022 - 2026 Discover more data with ReportLinker!

This is grouped and aggregated electricity consumption data from the EtudELEC study conducted by the Observatoire du Transition Energétique Grenoble (OTE-UGA). If you find this dataset useful and like different groupings to be published or have any questions, please feel free to comment on the discussion dedicated to this dataset on the OTE forum . The EtudELEC study only studies electricity consumption from residential dwellings around France. The study involved over 400 homes (individual houses and apartments) and spanned ~11 months from 25th October 2022 to 1st October 2023. The data is collected from the smart meter data (Linky data) which is only available with a time-step of 30 minutes. The data is in average watts consumed in a half-hour period. For reasons for privacy in line with GDPR laws, personal data such as individual home consumption will be shared as aggregated datasets as opposed to individual data points. The data from the participants were aggregated based on the following groupings: - Type of heating used and type of residence (stand-alone house vs apartment) This dataset is best viewed in the "Tree" view below. A folder is created for each of the groupings and sub-folders exist for all the subsequent groups. Each group folder contains: - a table of the minimum, mean, and maximum of the average power consumed for each 30-minute period (W), and - a JSON file with aggregated demographics information (number of inhabitants in different age backets, socio-professional category, year of construction etc.) of the group The datasets will be updated on a yearly basis following the renewal of consent of the panel members. Il s'agit de données de consommation électriques groupées et agrégées issues de l'étude EtudELEC menée par l'Observatoire de la Transition Energétique (OTE-UGA). Si vous trouvez ce jeu de données utile et souhaitez que différents regroupements soient publiés, n'hésitez pas à écrire dans le topic sur le forum OTE. L'étude EtudELEC est une étude sur la consommation d'électricité des logements résidentiels en France. L'étude porte sur plus de 400 logements (maisons individuelles et appartements) et s'étend sur 11 mois du 25 octobre 2022 au 1 octobre 2023. Les données sont collectées à partir des données des compteurs intelligents (données Linky) qui ne sont disponibles qu'avec un pas de temps de 30 minutes. Les données sont exprimées en watts consommés en moyenne sur une période d'une demi-heure. Pour des raisons de confidentialité conformes aux lois RGPD, les données personnelles telles que la consommation individuelle des maisons seront partagées sous forme d'ensembles de données agrégées plutôt que de points de données individuels. Les données des participants ont été agrégées sur la base des regroupements suivants : - Type de chauffage utilisé et type de résidence (maison individuelle ou appartement). Cet ensemble de données est mieux visualisé dans l'arborescence ci-dessous. Un dossier est créé pour chaque groupe et des sous-dossiers existent pour tous les groupes suivants. Chaque dossier de groupe contient : - un tableau du minimum, de la moyenne et du maximum de la puissance moyenne consommée pour chaque période de 30 minutes (W), et - un fichier JSON avec des informations démographiques agrégées (nombre d'habitants dans différentes tranches d'âge, catégorie socioprofessionnelle, année de construction, etc. Les jeux de données seront mis à jour chaque année après le renouvellement du consentement des membres du panel.

The 2022 Energy and Water consumption sent to the City by owners of buildings 50,000 SQFT or greater using the Energy Star Portfolio Manager tool. Data is required by the Energy Empowerment Ordinance in Kansas City, Missouri. The data was collected in 2023 and might be appended as new submissions come in.