This dataset contains the weather files described in in the DEEP Report 6.01 Improved Weather Files for Building Simulation which is available here: https://assets.publishing.service.gov.uk/media/6717bfcbe319b91ef09e385a/6.01_DEEP_Weather_Report.pdf.The dataset is made publicly available here. This dataset includes:1. README.txt: A Read Me file with more details of the dataset.2. Dataset_descriptor.pdf: A guidance document containing information on how to use the weather files.3. London_RWD.epw: A multi-year weather that depicts the weather conditions of London for the 2010-2019 decade.4. Manchester_RWD.epw: A multi-year weather that depicts the weather conditions of Manchester for the 2010-2019 decade.5. Glasgow_RWD.epw: A multi-year weather that depicts the weather conditions of Glasgow for the 2010-2019 decade.6-17. London_FWD_Sc.1.epw - London_FWD_Sc.12.epw : Multi-year weather files that depict future weather conditions of London (considering twelve scenarios in UKCP18 projections and modifying the RWD file).18-29. Manchester_FWD_Sc.1.epw - Manchester_FWD_Sc.12.epw : Multi-year weather files that depict future weather conditions of Manchester (considering twelve scenarios in UKCP18 projections and modifying the RWD file).30-41. Glasgow_FWD_Sc.1.epw - Glasgow_FWD_Sc.12.epw : Multi-year weather files that depict future weather conditions of Glasgow (considering twelve scenarios in UKCP18 projections and modifying the RWD file).

The dataset comprises three file categories: Multiyear (MY), Typical meteorological year (TMY) and Heatwave year (HWY). The MY files in .CSV format contain the hourly values of the bias-corrected climate projections for three 20-year reference periods: 2001-2020, 2041-2060 and 2081-2100. The TMYs files represent typical city meteorological conditions corresponding to historical (2001-2020), medium-term future (2041-2060) and long-term future (2081-2100) periods. The TMYs are provided in EPW format, a weather file format commonly used in building energy simulation tools such as EnergyPlus and similar. The HWYs, also provided in EPW format, are weather files with extreme heatwaves, i.e. the years with the most intense, most severe and longest heatwaves experienced in the three reference periods.

This is an hourly future weather dataset for energy modeling applications. The dataset is primarily based on the output of a regional climate model (RCM), i.e., the Weather Research and Forecasting (WRF) model version 3.3.1. The WRF simulations are driven by the output of a general circulation model (GCM), i.e., the Community Climate System Model version 4 (CCSM4). This dataset is in the EPW format, which can be read or translated by more than 25 building energy modeling programs (e.g., EnergyPlus, ESP-r, and IESVE), energy system modeling programs (e.g., System Advisor Model (SAM)), indoor air quality analysis programs (e.g., CONTAM), and hygrothermal analysis programs (e.g., WUFI). It contains 13 weather variables, which are the Dry-Bulb Temperature, Dew Point Temperature, Relative Humidity, Atmospheric Pressure, Horizontal Infrared Radiation Intensity from Sky, Global Horizontal Irradiation, Direct Normal Irradiation, Diffuse Horizontal Irradiation, Wind Speed, Wind Direction, Sky Cover, Albedo, and Liquid Precipitation Depth. This dataset provides future weather data under two emissions scenarios - RCP4.5 and RCP8.5 - across two 10-year periods (2045-2054 and 2085-2094). It also includes simulated historical weather data for 1995-2004 to serve as the baseline for climate impact assessments. We strongly recommend using this built-in baseline rather than external sources (e.g., TMY3) for two key reasons: (1) it shares the same model grid as the future projections, thereby minimizing geographic-averaging bias, and (2) both historical and future datasets were generated by the same RCM, so their differences yield anomalies largely free of residual model bias. This dataset offers a spatial resolution of 12 km by 12 km with extensive coverage across most of North America. Due to the enormous size of the entire dataset, in the first stage of its distribution, we provide weather data for the centroid of each Public Use Microdata Area (PUMA), excluding Hawaii. PUMAs are non-overlapping, statistical geographic areas that partition each state or equivalent entity into geographic areas containing no fewer than 100,000 people each. The 2,378 PUMAs as a whole cover the entirety of the U.S. The weather data can be utilized alongside the large-scale energy analysis tools, ResStock and ComStock, developed by National Renewable Energy Laboratory, whose smallest resolution is at the PUMA scale. The authors observed an anomalous warming signal over the Great Plains in the end-of-century (2085 - 2094) RCP4.5 time slice. This anomaly is absent in the mid-century slice (2045 - 2054) under RCP4.5 and in both the mid- (2045 - 2054) and end-of-century (2085 - 2094) slices under RCP8.5. Consequently, we recommend that users exercise particular caution when using the RCP4.5 2085-2094 data, especially for analyses involving the Great Plains region.

As global emissions and temperatures continue to rise, global climate models offer projections as to how the climate will change in years to come. These model projections can be used for a variety of end-uses to better understand how current systems will be affected by the changing climate. While climate models predict every individual year, using a single year may not be representative as there may be outlier years. It can also be useful to represent a multi-year period with a single year of data. Both items are currently addressed when working with past weather data by a using Typical Meteorological Year (TMY)methodology. This methodology works by statistically selecting representative months from a number of years and appending these months to achieve a single representative year for a given period. In this analysis, the TMY methodology is used to develop Future Typical Meteorological Year (fTMY) using climate model projections. The resulting set of fTMY data is then formatted into EnergyPlus weather (epw) fi les that can be used for building simulation to estimate the impact of climate scenarios on the built environment.

This dataset contains fTMY files for 3281 US Counties in the continental United States. The data for each county is derived from six different global climate models (GCMs) from the 6th Phase of Coupled Models Intercomparison Project CMIP6-ACCESSCM2, BCC-CSM2-MR, CNRM-ESM2-1, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-MM. The six climate models were statistically downscaled for 1980–2014 in the historical period and 2015–2100 in the future period under the SSP585 scenario using the methodology described in Rastogi et al. (2022). Additionally, hourly data was derived from the daily downscaled output using the Mountain Microclimate Simulation Model (MTCLIM; Thornton and Running, 1999). The shared socioeconomic pathway (SSP) used for this analysis was SSP 5 and the representative concentration pathway (RCP) used was RCP 8.5. More information about SSP and RCP can be referred to O'Neill et al. (2020).

More information about the six selected CMIP6 GCMs:

ACCESS-CM2 -
http://dx.doi.org/10.1071/ES19040
BCC-CSM2-MR -
https://doi.org/10.5194/gmd-14-2977-2021
CNRM-ESM2-1-
https://doi.org/10.1029/2019MS001791
MPI-ESM1-2-HR -
https://doi.org/10.5194/gmd-12-3241-2019
MRI-ESM2-0 -
https://doi.org/10.2151/jmsj.2019-051
NorESM2-MM -
https://doi.org/10.5194/gmd-13-6165-2020

Additional references:
O'Neill, B. C., Carter, T. R., Ebi, K. et al. (2020). Achievements and Needs for the Climate Change Scenario Framework.
Nat. Clim. Chang. 10, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0
Rastogi, D., Kao, S.-C., and Ashfaq, M. (2022). How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections? Earth's Future, 10, e2022EF002734. https://doi.org/10.1029/2022EF002734
Thornton, P. E. and Running, S. W. (1999). An Improved Algorithm for Estimating Incident Daily Solar Radiation from Measurements of Temperature, Humidity and Precipitation, Agricultural and Forest Meteorology, 93, 211-228.

Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (West and Midwest)." ORNL internal Scientific and Technical Information (STI) report, doi:10.5281/zenodo.8338549, Sept 2023. [Data]

Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (East and South)." ORNL internal Scientific and Technical Information (STI) report, doi:10.5281/zenodo.8335815, Sept 2023. [Data]

Bass, Brett, New, Joshua R., Rastogi, Deeksha and Kao, Shih-Chieh (2022). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation (1.0) [Data set]." Zenodo, doi.org/10.5281/zenodo.6939750, Aug. 2022. [Data]

These files were created in response to growing demand for weather data suitable for exploring the impact of climate change on the built environment.

These datasets consist of 996 text files. The files contain hourly weather data for 83 Australian locations under 3 future climate scenarios (RCP2.6, RCP4.5, and RCP8.5) and for 4 future years (2030, 2050, 2070, and 2090).

The dataset is available in two formats:

• In .epw format that can be used by building simulation software such as EnergyPlus, ESP-r, and IESVE.

• In a weather file format suitable for building simulations using Nationwide House Energy Rating Scheme (NatHERS) software such as AccuRate, BERSPro, FirstRate5, and HERO in non-regulatory mode. Lineage: The predictive weather data is based on a typical meteorological year of historical weather data drawn from Bureau of Meteorology weather data from the years 1990 to 2015. Global Climate Models and morphing were applied to this data to predict the future values under each climate scenario at each location.

As global emissions and temperatures continue to rise, global climate models offer projections as to how the climate will change in years to come. These model projections can be used for a variety of end-uses to better understand how current systems will be affected by the changing climate. While climate models predict every individual year, using a single year may not be representative as there may be outlier years. It can also be useful to represent a multi-year period with a single year of data. Both items are currently addressed when working with past weather data by a using Typical Meteorological Year (TMY)methodology. This methodology works by statistically selecting representative months from a number of years and appending these months to achieve a single representative year for a given period. In this analysis, the TMY methodology is used to develop Future Typical Meteorological Year (fTMY) using climate model projections. The resulting set of fTMY data is then formatted into EnergyPlus weather (epw) fi les that can be used for building simulation to estimate the impact of climate scenarios on the built environment.

This dataset contains the cross-climate-model version fTMY files for 3281 US Counties in the continental United States. The data for each county is derived from six different global climate models (GCMs) from the 6th Phase of Coupled Models Intercomparison Project CMIP6-ACCESSCM2, BCC-CSM2-MR, CNRM-ESM2-1, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-MM. The six climate models were statistically downscaled for 1980–2014 in the historical period and 2015–2100 in the future period under the SSP585 scenario using the methodology described in Rastogi et al. (2022). Additionally, hourly data was derived from the daily downscaled output using the Mountain Microclimate Simulation Model (MTCLIM; Thornton and Running, 1999). The shared socioeconomic pathway (SSP) used for this analysis was SSP 3 and the representative concentration pathway (RCP) used was RCP 7.0. More information about SSP and RCP can be referred to O'Neill et al. (2020).

Please be aware that in cases where a location contains multiple .EPW files, it indicates that there are multiple weather data collection points within that location.

More information about the six selected CMIP6 GCMs:

ACCESS-CM2 -
http://dx.doi.org/10.1071/ES19040
BCC-CSM2-MR -
https://doi.org/10.5194/gmd-14-2977-2021
CNRM-ESM2-1-
https://doi.org/10.1029/2019MS001791
MPI-ESM1-2-HR -
https://doi.org/10.5194/gmd-12-3241-2019
MRI-ESM2-0 -
https://doi.org/10.2151/jmsj.2019-051
NorESM2-MM -
https://doi.org/10.5194/gmd-13-6165-2020

Additional references:
O'Neill, B. C., Carter, T. R., Ebi, K. et al. (2020). Achievements and Needs for the Climate Change Scenario Framework.
Nat. Clim. Chang. 10, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0
Rastogi, D., Kao, S.-C., and Ashfaq, M. (2022). How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections? Earth's Future, 10, e2022EF002734. https://doi.org/10.1029/2022EF002734Thornton, P. E. and Running, S. W. (1999). An Improved Algorithm for Estimating Incident Daily Solar Radiation from Measurements of Temperature, Humidity and Precipitation, Agricultural and Forest Meteorology, 93, 211-228.

Please cite the following if this data is used in any research or project:

Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New (2023). “Multi-Model Future Typical Meteorological (fTMY) Weather Files for nearly every US County.” The 3rd ACM International Workshop on Big Data and Machine Learning for Smart Buildings and Cities and BuildSys '23: The 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Istanbul, Turkey, November 15-16, 2023. DOI: 10.1145/3600100.3626637

Cross-Model Version:

Representative Cities Version:

Bass, Brett, New, Joshua R., Rastogi, Deeksha and Kao, Shih-Chieh (2022). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation (1.0) [Data set]." Zenodo, doi.org/10.5281/zenodo.6939750, Aug. 2022. [<a

A set of TMY3 EPW weather files for each county of the U.S.

A set of TMY3 EPW weather files for each county of the U.S. NOTE: A file was uploaded on 2022/10/05 with additions to address known missing data for Nolan County, TX (FIPS: 48353), Fisher County, TX (FIPS: 48151), and Stonewall County, TX (FIPS: 48433).

This experiment collects the datasets created by the "Weather Data" group of the IEA EBC Annex 80 “Resilient Cooling for Buildings” project. These are datasets of current and future weather files for building energy performance simulation covering 15 locations in ten climate zones worldwide. The datasets contain ambient air temperature, relative humidity, atmospheric pressure, direct and diffuse solar irradiance, and wind speed at hourly resolution, which are essential climate elements needed to undertake building simulations. The datasets include typical and extreme weather years in the EnergyPlus weather file (EPW) format and multi-year projections in comma-separated value (CSV) format for three periods: historical (2001-2020), future mid-term (2041-2060), and future long-term (2081-2100). The weather files were generated based on the climate projections from the Regional Climate Model (RCM) MPI-RCA4, then bias-corrected using multiyear observational data for each city. The weather files are ready to be used in building energy simulations and systems design for adaptation and resilience studies.

This dataset contains future weather files for energy simulation of buildings of the state capitals of Brazil plus the Federal District. The weather data is provided in EPW format, commonly used in EnergyPlus inputs. The periods of 2010, 2050, and 2090 are considered for generating the Typical Meteorological Years (TMY) for each location. Additionally, different climate model projections were used to enable an analysis of uncertainties in the simulation results. In this updated version, we have included the complete individual years used to develop the TMYs. This data is provided in CSV format and has been bias-corrected.

The future climate data was derived from various regional climate model projections from the Coordinated Regional Downscaling Experiment (CORDEX) project. These projections are part of the CORDEX-CORE experiment, which includes three GCMs (HadGEM2, MPI-ESM, and NorESM1) as driving models, and two nested RCMs (regcm and remo) for dynamical downscaling, totaling an ensemble of six members at a spatial resolution of approximately 25km. The representative concentration pathways RCP 8.5 and RCP 2.6 were the available scenarios for the region, and both were considered for developing the weather files. The future climatic variable values were interpolated and formatted to an hourly resolution, commonly used in building energy simulation tools. Subsequently, different bias correction methods were applied to specific climate variables based on historical weather series. These data series consist of hourly data measured at weather stations located in each city between the years 2001 and 2021. The historical series files were made available by Dru Crawley and Linda Lawrie and served as the basis for developing the TMYx weather files available on the OneClimate Building website.

It is crucial to recognize that the historical data is based on measurements taken at airports, which are often situated far from urban centers. As a result, urban overheating was not factored into these developed weather files. It is also important to note that the developed weather files represent a typical meteorological year for each period, and do not include the most extreme periods, such as heatwaves and atypical summers.

The weather files were developed specifically for use with the EnergyPlus engine. Therefore, climatic variables not used by the engine (e.g., precipitation and ceiling height), even if available in EPW format, should not be considered for other studies.

It is important to emphasize the higher internal operative temperature results obtained when using the REGCM model compared to the REMO model in Building Energy Simulations. Caution is recommended when using files developed using a single combination of climate models, especially with weather files based on the REGCM model.

Suggestions for corrections can be sent to matheus.bracht@posgrad.ufsc.br

The EPW is a weather file format used to run simulations in EnergyPlus. EPWs are text files and can be opened and edited in any text editor, spreadsheet tools or open-source software tools for creating and editing customised weather files, such as the Element software developed by Big Ladder Software (https://bigladdersoftware.com/projects/elements/). The EnergyPlus Auxiliary Programmes document (attached) describes EPW weather data and provides general information on weather data for energy simulations and weather file conversion.

Converted Weather Data in EPW format for Toronto Pearson Airport from CWEEDs weather data in WYEC3 format.

Future design weather data (epw. files) for evaluating the building thermal-energy performance in Hong Kong using the downscaled data from 24 general circulation models (GCMs) in the CMIP5. It includes six sets of future design weather data under three time slices (2035s, 2065s, 2090s) of two climate change scenarios (RCP4.5 and RCP8.5) for Hong Kong.

The dataset comprises three file categories: Multiyear (MY), Typical meteorological year (TMY) and Heatwave year (HWY). The MY files in .CSV format contain the hourly values of the bias-corrected climate projections for three 20-year reference periods: 2001-2020, 2041-2060 and 2081-2100. The TMYs files represent typical city meteorological conditions corresponding to historical (2001-2020), medium-term future (2041-2060) and long-term future (2081-2100) periods. The TMYs are provided in EPW format, a weather file format commonly used in building energy simulation tools such as EnergyPlus and similar. The HWYs, also provided in EPW format, are weather files with extreme heatwaves, i.e. the years with the most intense, most severe and longest heatwaves experienced in the three reference periods.

Background

This dataset includes future-shifted versions of the Canadian Weather Year for Energy Calculation 2020 weather files (CWEC2020; [1], [2]) produced using future climate projections from CMIP6 climate models. CWEC2020 weather files in Canada offer a summary of typical climatic conditions at specific locations to analyze and simulate building energy use. These files contain hourly weather station-based observations encompassing a single year that is meant to be representative of average conditions from the recent past, defined as a Typical Meteorological Year. Ongoing climate change means the information summarized in these weather files may not reflect present day conditions and is expected to become increasingly unrepresentative over typical building lifespans. Future-shifted files can be used with energy modelling software to compare building energy use between past and future conditions.

These future-shifted files are produced using a “morphing” procedure that adjusts the mean and variance of the hourly series [3] with morphing factors calculated from CMIP6 climate model projections for five future periods (2040s to 2080s). Projected changes used to calculate morphing factors are taken from 18 CMIP6 global climate models following low, medium and high future emissions pathways (SSP1 2.6, SSP2 4.5, SSP5 8.5). Existing hourly series in the weather files are modified using additive or multiplicative morphing factors that are derived from the climate projections.

Dataset Format

This dataset consists of 15 future-shifted weather files at each of the 564 CWEC2020 locations in Canada. Future-shifted files employ the same EPW file structure formatting as the current CWEC2020 files. Within each future-shifted file, the hourly time series of dry bulb and dew point temperature, relative humidity, and surface pressure are adjusted using the morphing method to incorporate future climate change. The hourly time series for all other variables remain unchanged.

The uploaded files are organized into separate zip files for each Canadian province or territory. Each zip file contains separate directories for all CWEC2020 locations within the province or territory. These location directories each contain the 15 future-shifted weather files generated using the current CWEC2020 file at that site.

Data Portal

All future-shifted CWEC2020 files are also accessible via a purpose-built data portal with search options to select files from specific locations: https://www.pacificclimate.org/data/weather-files

References

1) Engineering Climate Services Unit. CWEEDS/CWEC/TDY Files - 2020 Release. Technical report, Toronto, ON, Canada, 2020.

2) Robert Morris. Final Report - Updating CWEEDS Weather Files. Technical Report EC3000607888, Environment and Climate Change Canada, Toronto, ON, Canada, 2016.

3) S.E. Belcher, J.N. Hacker, and D.S. Powell. Constructing design weather data for future climates. Building Services Engineering Research and Technology, 26(1):49–61, February 2005.

This repository contains weather data files for four cities in the main climatic zones of Catalunya, Spain — Barcelona, Lleida, Manresa and La Seu d’Urgell representing the coastal, continental, pre-coastal and pre- and Pyrenean Mediterranean climates, respectively. For each location, datasets represent typical and extreme meteorological conditions for the present (2020s), mid-term future (2050s), and long-term future (2090s). For each city there are several sub-folders: - BiasCorrected_CSV folder contains three comma-separated value (CSV) files, each comprising hourly weather data over 20-year periods representing the 2020s, 2050s, and 2090s. These datasets are derived from climate projections obtained through the COordinated Regional Downscaling EXperiment (CORDEX), aligned with the RCP 8.5 scenario outlined in the IPCC Fifth Assessment Report (AR5). To improve reliability, the raw projections have been adjusted for bias using historical meteorological records from local weather stations (in Barcelona - Raval, Lleida - Raimat, Manresa - Sant Salvador de Guardiola and La Seu d'Urgell) Servei Meteorològic de Catalunya. (2025, June). Metadades estacions meteorològiques automàtiques. Anàlisi Dades Obertes – Transparència Catalunya. https://analisi.transparenciacatalunya.cat/Medi-Ambient/Metadades-estacions-meteorol-giques-autom-tiques/yqwd-vj5e/about_data. - The TMY_EPW folder includes weather files in the EnergyPlus EPW format, representing Typical Meteorological Years (TMYs). Each file provides hourly climate data for a full year, covering all key variables required for building thermal simulations, such as air temperature, relative humidity, atmospheric pressure, wind speed and direction, and global horizontal irradiance. These files are constructed from 20-year climate series corresponding to the present (2020s), mid-century (2050s), and end-century (2090s) periods, using a standardized method that selects the most representative months across the time spa. - The Heatwaves_EPW folder contains annual weather files in EPW format for EnergyPlus simulations, specifically representing years with significant heat wave (HW) events. For each 20-year period and city, selected years include those marked by the most intense, longest, and most intense heat waves identified within the dataset. One representative file is provided for each time slot: Present (2006–2025), Mid-century (2041–2060), and End-century (2081–2100).

This dataset comprises Typical Solar Years (TSYs) and Typical Wind Years (TWYs) for the efficient assessment of PV system and wind turbine performance for over 2,000 locations across the U.S. TSYs and TWYs are single synthetic years generated from the National Aeronautics and Space Administration (NASA) Prediction of Worldwide Energy Resources (POWER) data spanning from 2001 to 2022. These synthetic years represent the long-term average solar and wind resource conditions of a _location, respectively. The POWER solar data is derived from satellite observations and has a spatial resolution of 1 degree * 1 degree (latitude/longitude). The meteorological variables are sourced from NASA's Goddard Earth Observing System (GEOS) Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) assimilation model, which features a spatial resolution of 1/2 degree * 5/8 degree (latitude/longitude). The methods for creating TSYs and TWYs are adapted from the Sandia method. Specifically, the weights assigned to different weather parameters have been adjusted, and the final selection step has been omitted. For TSYs, a weight of 0.7 is assigned to daily cumulative GHI, and 0.3 is assigned to daily cumulative DNI. For TWYs, weights of 0.2, 0.2, and 0.6 are assigned to daily median zonal wind speed, daily median meridional wind speed, and daily 0.75 quantile wind speed, respectively. These weights have been optimized based on the simulated solar PV system and wind turbine outputs. 12 representative months are then selected based on their Finkelstein-Schafer (FS) statistics and concatenated into a synthetic year. The paper describing our methodology has been published in Applied Energy and is available via the "Project Publication" resource link below. The TSYs and TWYs are provided for the centroids of all Public Use Microdata Areas (PUMAs) in the U.S. PUMAs are non-overlapping statistical geographic areas that partition each state or equivalent entity into regions containing no fewer than 100,000 people each. The 2,378 PUMAs collectively cover the entire U.S. A file named "PUMA information.csv" is included with the dataset, containing the PUMA number, PUMA name, latitude, longitude, elevation, and time zone of all PUMA centroids. Users can reference this file to find the PUMAs corresponding to their locations of interest. To accommodate different user communities, the data is provided in three formats. The TSYs are available in EPW and SAM weather file formats, while the TWYs are available in EPW, SAM weather file, and CSV formats. The EPW format, developed by the U.S. Department of Energy, is a de facto standard for weather data in building energy modeling and is compatible with various building energy modeling programs, including EnergyPlus, ESP-r, and IESVE. The SAM weather file format is designed for the System Advisor Model (SAM), a renewable energy project evaluation tool developed by the National Renewable Energy Laboratory (NREL). If you use this dataset in your research, please consider citing our paper: Zeng, Z., Stackhouse, P., Kim, J.-H. (Jeannie), & Muehleisen, R. T. (2025). Development of typical solar years and typical wind years for efficient assessment of renewable energy systems across the U.S. Applied Energy, 377, 124698. https://doi.org/10.1016/j.apenergy.2024.124698.

Weather data file of ISTANBUL, KARS and ANTALYA

This layer contents specific IEQ data sets provided which are rereferred to the closest possible locations of the Cultural-E demo cases. Specific locations and coordinates are included for each one in the IEQ related files that have been made available for downloading. Each geo-referred location contains IEQ related information and graphics, in the specific: 1. Reference year -.epw file-. 2. Climatic statistical data -.txt file-. Weather data plots, elaborated with Climate consultant and merged in a unique document -.pdf file-. 4. Weather data summary elaborated -.xls file-. Relevant information is provided in relation to: data source, used tool, implemented comfort tool, weather stations spec, software download links. This aims to illustrate the accuracy and "data fairness" of the data provided.

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.