Note: This dataset has been superseded by the dataset found at "End-Use Load Profiles for the U.S. Building Stock" (submission 4520; linked in the submission resources), which is a comprehensive and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.

The 5-year goal of the “Model America” concept was to generate a model of every building in the United States. This data repository delivers on that goal. Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM). There were 125,714,640 buildings detected in the United States and this dataset contains 122,930,327 (97.8%) buildings which resulted in a successful simulation. Future, annual updates have been proposed that may include additional buildings, data improvements, or other algorithmic enhancements. This dataset of 122.9 million buildings includes: Models (state_county.zip) – OpenStudio (v3.1.0) and EnergyPlus (v9.4) building energy models. Please note that the download requires the free Globus Connect Personal (https://www.globus.org/globus-connect-personal); Each model has approximately 3,000 building input descriptors that can be extracted. Please see the EnergyPlus (v9.4) 2,784-page Input/Output Reference Guide (https://energyplus.net/sites/all/modules/custom/nrel_custom/pdfs/pdfs_v9.4.0/InputOutputReference.pdf) for everything that can be retrieved or simulated from these models. These models were derived from the following metadata, which is not included in this dataset: 1. ID - unique building ID 2. County - county name 3. State - state name 4. CZ - ASHRAE Climate Zone designation 5. Clim_Zone - text label of climate zone 6. est_year - estimated year of construction 7. est_commercial - estimated building type (0=residential, 1=commercial) 8. Centroid - building center location in latitude/longitude (from Footprint2D) 9. Footprint2D - building polygon of 2D footprint (lat1/lon1_lat2/lon2_...) 10. Height - building height (meters) 11. Area2D - footprint area (ft2) 12. BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards 13. WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings) 14. NumFloors - number of floors (above-grade) 15. Area - estimate of total conditioned floor area (ft2) 16. Standard - building vintage. These models are made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL) Laboratory Directed Research and Development (LDRD), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), Biological and Environmental Research (BER), and National Nuclear Security Administration (NNSA). This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. Please cite as: New, Joshua R., Adams, Mark, Bass, Brett, Berres, Anne, and Clinton, Nicholas (2021). “Model America - data and models of every U.S. building. [Data set].” Constellation, doi.ccs.ornl.gov/ui/doi/339, April 14, 2021

This data captures climate information and HVAC energy use for a baseline prototype home and for a replacement alternative energy home. The baseline home is a traditional DX cooling/gas furnace system, and the alternate system is a geothermal heat pump. Cooling degree days (CDD), heating degree days (HDD) and relative humidity were gathered from historical weather data for 12 cities across the contiguous United States. Geothermal heat pump coefficients were generated as inputs to EnergyPlus simulation software. These heat pump coefficients are generated by compiling heat pump performance data from 5 market leading, high efficiency residential geothermal heat pump manufacturers. These coefficients can be used to represent a general, market available heat pump in 2-ton, 3-ton, and 4-ton capacities. Baseline prototype home energy use by city was generated by EnergyPlus using the prototype home download file from www.energy.gov and the respective weather file for that city. This data can be interpreted as energy use per month by certain HVAC components. The GSHP home energy use by city was generated from EnergyPlus and the respective city weather file. The GSHP model was created by the authors to model the alternate closed loop, GSHP system.

This dataset contains materials from the Coalition for Community-Supported Affordable Geothermal Energy Systems (C2SAGES) project, which evaluated the techno-economic feasibility of a community geothermal system for a residential development in Hinesburg, VT. The dataset includes detailed soil conductivity test reports, energy models, borehole design reports, hourly energy loads for heating, cooling, and hot water, and design layouts. EnergyPlus was used to model building energy loads, and Modelica software was applied for geothermal loop sizing based on these loads and soil conductivity results. Python scripts for network design further refined the models. Key files include PDF reports on borehole design (with projections for 1-year, 15-year, and 30-year systems), soil conductivity test results, EnergyPlus modeling outputs, and 2D/3D design drawings in PDF, DWG, and DXF formats. Python notebooks for network design and OnePipe model files are also provided, with Modelica required for viewing certain files. Outputs and modeling data are in various formats including CSV, JPG, HTML, and IDF, with units and data clearly labeled to support understanding of system design and performance for the proposed geothermal solution.

This link presents a comprehensive 2-year-long dataset capturing Occupant Behavior (OB) of students living in residential dorms, collected both before and after a complete building envelope and energy retrofit. The dataset is categorized into three data sets: occupant behavior, indoor environmental quality, and detailed energy usage. Occupant behavior data includes window and door status, while IEQ data comprises measurements of indoor CO₂, total volatile organic compounds (TVOC), temperature, relative humidity, and lighting levels. Energy data spans 16 electrical channels in the building, covering stove usage, exhaust hood, lights, refrigerator, plug loads, HVAC energy consumption by zone, water heater, and Heat Recovery Ventilation (HRV) units (post-retrofit). This dataset offers significant value by enabling researchers to quantify key relationships among occupant behavior, building energy efficiency, and Indoor Environmental Quality (IEQ), acknowledging the influence of behavior on these outcomes. The folder named 'Before_Retrofit' contains data for all dorms prior to the retrofit, and the 'After_Retrofit' folder contains data post-retrofit.

The research elaborates various solutions using detailed economic evaluation and energy efficiency calculation and simulation technology for formulating applicable, energy and cost-efficient retrofit solutions of single-family residential buildings located in temperate climate areas. Primarily the annual energy demand for a reference existing single-family residential building was determined. The economic analysis was performed for six formulated refurbishment scenarios in order to determine which of the scenarios will demonstrate optimal performance both in energy and cost efficiency. A feasibility study was performed for the most efficient scenario, which included an economic evaluation of low temperature radiant heating systems were three energy suppliers (oil, natural gas and electricity for air to water heat pump) were compared. According to financial analyses the optimal scenario includes the replacement of windows, installation of 15 cm EPS thermal insulation, low temperature radiant floor heating, with a payback period of 10 years.

Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM). Two sets of sample data are provided for 2,555,152 buildings located within the boundary of Arizona in the United States: Data (846.3MB .csv) - minimalist list of each building (rows) for the following fields (columns) • ID - unique building ID • Centroid - building center location in latitude/longitude (from Footprint2D) • Footprint2D - building polygon of 2D footprint (lat1/lon1_lat2/lon2_...) • State_abbr - state name • Area - estimate of total conditioned floor area (ft2) • Area2D - footprint area (ft2) • Height - building height (ft) • NumFloors - number of floors (above-grade) • WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings) • CZ - ASHRAE Climate Zone designation • BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards • Standard - building vintage • Sample Models (114GB.zip by county) - OpenStudio and EnergyPlus building energy models named according to ID This data is made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), and Biological and Environmental Research (BER).

This dataset consists of synthetic buiilding data generated using EPSA algorithm and simulated in EnergyPlus under climate change scenario SSP5-8.5 (timeframes 2050 and 2080) for 30 locations in Brazil.Each location has data on the buildings' geometry, energy performance, and construction system. The locations are Manaus, Fortaleza, Teresina, Mossoró, Petrolina, Salvador, Vitória da Conquista, Cuiabá, Brasília, Goiânia, Montes Claros, Rio Verde, Belo Horizonte, Campo Grande, Franca, Ribeirão Preto, Poços de Caldas, São Carlos, Macaé, Rio de Janeiro, Campinas, Maringá, São Paulo, Santos, Curitiba, Passo Fundo, Caxias do Sul, Santa Maria, Porto Alegre, and Pelotas.The present-day weather data was retrieved from climate.onebuilding.org. The future weather was morphed using 'Future Weather Generator' software, available at http://www.adai.pt/future-weather-generator/.

For over 25 years, data exchange between architectural BIM-based designs and Building Energy Performance Simulation (BEPS) have been proposed as a solution to reduce the amount of manual and error prone rework required to create typical BEPS models. The current state of the art lacks an effective, universal and robust system of data collation, processing, quality assessment and analysis while interfacing with existing tool-chains through a streamlined data transfer process.This paper investigates the reproducibility of current BIM to BEPS transfer processes through an experiment that compares these transfer processes, as used in industry, against each other. The experiment uses five residential archetype buildings and results from BEPS models in EnergyPlus indicate that there are many barriers, both technical and methodological, to achieving reproducible results between commonly available software tools. In some cases difficulties could not be overcome as the transformation process itself did not complete, leading to inconclusive results. In cases where successful transformations occurred, variations of up to 25.89% in annual energy consumption were discovered between processes. This hints to issues and limitations of the current processes and results. Science Foundation Ireland University College Dublin ESIPP UCD

One of the homes that was part of Sacramento Municipal Utility District's (SMUD) Energy Efficiency Retrofit Demonstration (EERD) project was a 1980's era home in Fair Oaks, California, referred to as the Greenbuilt house, as Greenbuilt Construction completed the retrofit of the home. The home underwent an extensive energy efficiency retrofit with a goal of achieving a 50% reduction in energy use to demonstrate the potential for other builders and homeowners in the area. The retrofit measures included installing: ENERGY STAR appliances high efficiency light roof radiant barrier additional ceiling and wall insulation double-pane, low-e windows external motorized shading and solar tubes a 16 SEER/9.75 HSPF heat pump improved ducts a whole-house fan a heat pump water heater (HPWH) integrated collector storage solar water heater (ICS SWH) and 3.2 kW of PV. In addition, the home was air sealed to reduce infiltration. Researchers from the National Renewable Energy Laboratory (NREL) performed short-term tests on the major systems installed as part of the retrofit to ensure that they were performing as expected. The systems evaluated included the space conditioning heat pump, the air handler and ducts, the HPWH, the ICS SWH, and the PV array. Some ducts were untwisted after testing revealed that two rooms were not getting sufficient airflow. Afterwards, all systems were performing as expected. In addition to testing to confirm adequate performance of all new systems, NREL was given the opportunity to use the Greenbuilt house as a laboratory house for a year. The space conditioning system and home water systems were subjected to a series of tests to determine optimal control strategies for lowering energy consumption and reducing peak (4:00-7:00 p.m.) energy consumption during the summer. The different cooling strategies considered included two different precooling schedules, drawing the external shades during the day and using the whole-house fan at night, and combinations of those. The most effective strategy for reducing overall energy consumption was the use of external shades, which cut the daily cooling load by 34% and reduced the energy use during peak hours by 40%. The different precooling strategies eliminated the peak load entirely but actually increased daily cooling energy use. The use of shades and the advanced precooling strategy increased the daily energy use by 5% but eliminated all peak use and maintained a comfortable home. These results were verified over the entire summer using an Energy Plus model of the home. The hot water system was tested in two configurations: the HPWH alone and the ICS solar water heater paired with the HPWH. Six hot water draw profiles, varying in terms of daily hot water volume, time of day for hot water use, and the duration of the draws, were imposed on the hot water system to test their effects on performance. When operating alone in the summer, the HPWH operated with a COP around 2.2, except for a draw that used a quarter of the averaged daily hot water usage, which had an average COP of 1.6. The combination of ICS and HPWH resulted in larger COPs, but also more variability depending on the draw profile. The standard, hourly draw profile produced the highest COP of 6.4. The quarter volume draw profile had the lowest COP of 2.8 for the combined system. Relative to a standard electric water heater, the HPWH operating alone reduced the peak load by 56% and the combined ICS and HPWH system completely eliminated the peak load.

Abstract This study analyses the possibility of achieving negative energy demands in single-family housing (Plus-Energy House). Through the integration of energy efficiency measures jointly with active solar systems, it is possible to obtain high performance energy models. In order to demonstrate this, we performed energy simulations integrating active solar systems, and also possible self-shading and separations between dwellings. We analysed the deployment of BIPV, BISTw, BIPVTa and BIPVTw technologies individually and in different combinations between them with the purpose of maximizing production capability for self-consumption, comparing them with residential demands in order to identify energy deficits and surpluses in different seasons. The measurements were taken hourly on typical days in summer, winter and intermediary seasons. Finally, an annual balance was obtained, showing energy surpluses of approximately 174 % when deploying only BIPV collectors and 251 % when combining BIPVTw and BIPVTa.

OCHRE™ uses a variety of input data sources to run time-series simulations. Building models can be taken from the ResStock™ database or generated using the Building Energy Optimization Tool (BEopt™) or other OpenStudio-HPXML workflows. EV charging profiles can be taken from datasets used in NREL's 2030 National Charging Network project. Weather data can be taken from the National Solar Radiation Database or EnergyPlus® weather files. There are no public datasets with OCHRE outputs at this time. However, a recent project dataset on water heater and EV demand flexibility can be requested. OCHRE is a Python-based energy modeling tool designed to model flexible loads in residential buildings. OCHRE includes detailed models and controls for flexible devices including HVAC equipment, water heaters, EVs, solar PV, and batteries. It is designed to run in co-simulation with custom controllers, aggregators, and grid models.

This dataset comprises the performance, construction, geometry, and ventilation data of residential buildings for 16 climate regions. 500 residential buildings have different ventilation parameters for air changes per hour, and indoor minimum temperature and delta temperature for window opening, for each climate location (totalizing 1080 thousand buildings). The climate regions are CYP_Larnaca, DZA_Algiers, EGY_Alexandria, ESP_Malaga, ESP_Valencia, FRA_Marseille, GRC_Athens, ISR_TelAviv, ITA_Naples, ITA_Venice, LBY_Tripoli, MAR_Casablanca, MNE_Podgorica, TUN_Tunis, TUR_Istanbul, and TUR_Izmir, available at the Department of Energy (URL https://energyplus.net/weather). The geometric data was generated using the EPSAP algorithm (URL http://dx.doi.org/10.1016/j.cad.2013.01.001; http://dx.doi.org/10.1016/j.cad.2013.01.003; http://dx.doi.org/10.1016/j.autcon.2013.06.005); the construction elements physical properties were saved from the EPSAP algorithm database; and, the performance data was obtained using the FPOP algorithm coupled to the simulation engine EnergyPlus v8.8.0 (URL http://dx.doi.org/10.1016/j.enbuild.2014.06.016; http://dx.doi.org/10.1016/j.apenergy.2014.06.068).

This dataset consists of synthetic buiilding data generated using EPSA algorithm and simulated in EnergyPlus under climate change scenario SSP5-8.5 (timeframes 2050 and 2080) for 28 locations in Europe and Africa on the coast of the Atlantic Ocean.

Each location has data on the buildings' geometry, energy performance, and construction system. The locations are distributed over Great Britain (Bristol, Brighton, Bournemouth, Exeter, and Plymouth), France (Le Havre, Brest Bretagne, Nantes, La Rochelle, Bordeaux Merignac, and Biarritz), Spain (Gijon Musel, A Coruna, Bilbao, Vigo, and Jerez), Portugal (Viana do Castelo, Porto, Ovar, Cabo Carvoeiro, Sintra, Lisboa, Sines, and Faro), and Morocco (Tangier, Rabat, Casablanca, and Safi).

The present-day weather data was retrieved from climate.onebuilding.org. The future weather was morphed using 'Future Weather Generator' software, available at http://www.adai.pt/future-weather-generator/.

The geometric data was generated using the EPSAP algorithm; the construction elements physical properties were saved from the EPSAP algorithm database; and, the performance data was obtained using the FPOP algorithm coupled to the simulation engine EnergyPlus v8.8.0. The weather files used in the simulations were the PRT_Lisboa, PRT_Porto, ESP_Toledo, ITA_Milan, ROU_Bucharest, SWE_Kiruna, SWE_Stockholm data available at the Department of Energy (URL https://energyplus.net/weather).

This dataset consists of synthetic buiilding data generated using EPSA algorithm and simulated in EnergyPlus under climate change scenario SSP5-8.5 (timeframes 2050 and 2080) for 21 locations in Ira.

Each location has data on the buildings' geometry, energy performance, and construction system. The locations are on the coast of the Caspian Sea (Rasht, Ramsar, Nowshahr, and Sari), on the coast of the Persian Gulf (Bandar Mahshahr, Bandar Bushehr, Dayyer, Assaluyeh, Bandar Abaass, Bandar Lengeh, and Chabahar), and in the inner land (Tabriz, Karaj, Hamedan, Tehran, Semnan, Kashan, Yazd Sadooghi, Shiraz, Sirjan, and Fasa).

The present-day weather data was retrieved from climate.onebuilding.org. The future weather was morphed using 'Future Weather Generator' software, available at http://www.adai.pt/future-weather-generator/.