This data set contains energy use data from 2009-2014 for 139 municipally operated buildings. Metrics include: Site & Source EUI, annual electricity, natural gas and district steam consumption, greenhouse gas emissions and energy cost. Weather-normalized data enable building performance comparisons over time, despite unusual weather events.

Note: This dataset has been superseded by the dataset found at "End-Use Load Profiles for the U.S. Building Stock" (submission 4520; linked in the submission resources), which is a comprehensive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

Data and Problem Description

More accurate forecasts of building energy consumption mean better planning and more efficient energy use. So The objective is to forecast energy consumption from following data: (For each data set, several test periods over which a forecast is required will be specified.)

Historical Consumption

A selected time series of consumption data for over 260 buildings.

-obs_id - An arbitrary ID for the observationaa -SiteId - An arbitrary ID number for the building, matches across datasets -ForecastId - An ID for a timeseries that is part of a forecast (can be matched with the submission file) -Timestamp - The time of the measurement -Value - A measure of consumption for that building

Building Metadata

Additional information about the included buildings.

-SiteId - An arbitrary ID number for the building, matches across datasets -Surface - The surface area of the building -Sampling - The number of minutes between each observation for this site. The timestep size for each ForecastId can be found in the separate "Submission Forecast Period" file on the data download page. -BaseTemperature - The base temperature for the building -IsDayOff - True if DAY_OF_WEEK is not a work day

Historical Weather Data

This dataset contains temperature data from several stations near each site. For each site several temperature measurements were retrieved from stations in a radius of 30 km if available. Note: Not all sites will have available weather data.

Note: Weather data is available for test periods under the assumption that reasonably accurate forecasts will be available to algorithms that the time that we are attempting to make predictions about the future.

-SiteId - An arbitrary ID number for the building, matches across datasets -Timestamp - The time of the measurement -Temperature - The temperature as measured at the weather station -Distance - The distance in km from the weather station to the building in km

Public Holidays

Public holidays at the sites included in the dataset, which may be helpful for identifying days where consumption may be lower than expected.Note: Not all sites will have available public holiday data.

-SiteId - An arbitrary ID number for the building, matches across datasets -Date - The date of the holiday -Holiday - The name of the holiday

Acknowledgements

Forecasting energy consumption data published by Schneider Electric.

Inspiration

Three time horizons and time steps are distinguished for more than 260 building sites are provided. The goal is either:

• To forecast the consumption for each quarter for the length of time specified by the submission format.
• To forecast the consumption for each hour for the length of time specified by the submission format.
• To forecast the consumption for each day for the length of time specified by the submission format.

Historical data are given at the granularity that is required for the consumption forecast. So, when historical data are given by steps of 15 minutes, forecasts are required by steps of 15 minutes. When historical data are given by steps of 1 hour, forecasts are required by steps of 1 hour. When historical data are given by steps of 1 day, forecasts are required by steps of 1 day.

#

*If you find the data useful your upvote is an explicit feedback for future works, Have fun exploring data!*

Interannual Electricity Demand Calculator

Large parts of this code were originally developed by Lieke van der Most (University of Groningen) in the EU renewable energy modelling framework and release under MIT license. The original version of the code can be found here and is referenced below as [1]. This model has been validated against historical electricity demand data reported on the ENTSO-E transparancy platform.

We have made the following adjustments to the original version:

• generate hourly instead of daily electricity consumption profiles
• use snakemake for workflow management
• trim repository to demand-related code and data
• adjust code to accept cutouts from atlite for weather data

Purpose

Variations in weather conditions affect electricity demand patterns. This workflow generates country-level electricity consumption time series based on weather data using analysis by Lieke van der Most correlating historical electricity demand to temperature. This workflow first calculates a daily electricity demand based on the regression model developed in [1]. Subsequently, cumulative daily electricity demands are disaggregated using a hourly profile sampled from a random historical day (that is the same weekday) from the Open Power System Database. The resulting output/demand_hourly.csv file is compatible with the open-source electricity system model PyPSA-Eur.

Holidays are treated like weekend days. Data on national holidays across Europe are obtained using another repository by Aleksander Grochowicz and others that similarly computes artificial electricity demand time series: github.com/aleks-g/multidecade-data. The holidays are stored at input_files/noworkday.csv.

Installation and Usage

Clone the Repository

Download the demand_calculator repository using git.

/some/other/path % cd /some/path/without/spaces
/some/path/without/spaces % git clone https://github.com/martacki/demand_calculator.git

Install Dependencies with conda/mamba

Use conda or mamba to install the required packages listed in environment.yaml.

The environment can be installed and activated using

.../demand_calculator % conda env create -f environment.yaml
.../demand_calculator % conda activate demand

Retrieve Input Data

The only required additional input files are ERA5 cutouts which can be recycled from the PyPSA-Eur weather data deposit on Zenodo. Place the file europe-2013-era5.nc in the following location (and rename!):

./input_files/cutouts/europe-era5-2013.nc

Cutouts for other weather years than 2013 can be built using the build_cutout rule from the PyPSA-Eur repository.

Run the Workflow

This repository uses snakemake for workflow management. To run the complete workflow, execute:

.../demand_calculator % snakemake -jall all

After successfully running the workflow, the output files will be located in output/energy_demand named demand_hourly_{yr}.csv.

The years to compute can be modified directly in the Snakefile.

License

The file demand_hourly.csv is released under CC-BY-4.0 license.

The file src.zip is released under MIT license.

Changelog

2024-03-15: Extended date range from 1941 to 2023.

Description

**It is presented two datasets used to train a neural network that forecasts electricity prices in the Yucatan peninsula. The first one is the Input data, which is composed of five parameters, three describing environmental conditions and two reporting the levels of operation of the electricity system in the study region. The second is the output data, corresponding to local marginal electricity prices. These prices are compound from the next three costs: energy, losses of transmission, and congestion. **

**Also, these data allow detecting the dynamics of the electricity market, which can be related to environmental conditions. Also, they allow detecting phenomena of the electricity market, i.e. negative prices of transmission losses or congestion, and the negative merit-order effect. **

**Every parameter was collected for eight load zones in hourly resolution, it is the geographic distribution according to the Mexican independent system operator. The data begins in the first hour of January 1st of 2017 and ends in the last hour of April 4th of 2019. Each parameter has 157808 observations. **

Modern buildings are complex energy systems that must be controlled for energy efficiency. The Research Support Facility (RSF) at the National Renewable Energy Laboratory (NREL) has hundreds of controllers -- computers that communicate with the building's various control systems -- to control the building based on tens of thousands of variables and sensor points. These control strategies were designed for the RSF's systems to efficiently support research activities. Many events that affect energy use cannot be reliably predicted, but certain decisions (such as control strategies) must be made ahead of time. NREL researchers modeled the RSF systems to predict how they might perform. They then monitor these systems to understand how they are actually performing and reacting to the dynamic conditions of weather, occupancy, and maintenance.

This submission includes the Weather data form the RSF systems energy model. The weather data was used to contextualize and compare energy use data like heating and cooling. The weather data was also used by the RSF Energy Model to model the energy use of the RSF.

Energy Model data and Measured Energy data related to the RSF Systems Model can be found in the "Related Datasets" section of this submission.

This dataset builds upon the publicly available work by Taranvee, who collected household energy consumption data at one-minute resolution from a smart meter monitoring multiple appliances (e.g., dishwasher, home office, fridge, kitchen). The original release also includes regional weather information (humidity, temperature, atmospheric pressure, etc.), providing a rich contextual layer for understanding how environmental factors influence residential power usage.

In this augmented version, we introduce additional consumption columns representing distinct IoT devices, Car charger,Water heater,Air conditioning,Home Theater,Outdoor lights,microwave,Laundry,Pool Pump

Each of these new columns tracks an appliance's energy usage in kilowatts ([kW]), effectively broadening the dataset’s scope for modeling complex, multi-device scenarios within a single smart home

Original work: https://www.kaggle.com/datasets/taranvee/smart-home-dataset-with-weather-information

The U.S. Climate Normals are a large suite of data products that provide information about typical climate conditions for thousands of locations across the United States. Normals act both as a ruler to compare today’s weather and tomorrow’s forecast, and as a predictor of conditions in the near future. The official normals are calculated for a uniform 30 year period, and consist of annual/seasonal, monthly, daily, and hourly averages and statistics of temperature, precipitation, and other climatological variables from almost 15,000 U.S. weather stations.

NCEI generates the official U.S. normals every 10 years in keeping with the needs of our user community and the requirements of the World Meteorological Organization (WMO) and National Weather Service (NWS). The 1991–2020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. These data allow travelers to pack the right clothes, farmers to plant the best crop varieties, and utilities to plan for seasonal energy usage. Many other important economic decisions that are made beyond the predictive range of standard weather forecasts are either based on or influenced by climate normals.

This is weather data required for use in building energy simulations, including ResStock and OpenStudio-HPXML workflows.

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.

IMPORTANT! PLEASE READ DISCLAIMER BEFORE USING DATA. This dataset backcasts estimated modeled savings for a subset of 2007-2012 completed projects in the Home Performance with ENERGY STAR® Program against normalized savings calculated by an open source energy efficiency meter available at https://www.openee.io/. Open source code uses utility-grade metered consumption to weather-normalize the pre- and post-consumption data using standard methods with no discretionary independent variables. The open source energy efficiency meter allows private companies, utilities, and regulators to calculate energy savings from energy efficiency retrofits with increased confidence and replicability of results. This dataset is intended to lay a foundation for future innovation and deployment of the open source energy efficiency meter across the residential energy sector, and to help inform stakeholders interested in pay for performance programs, where providers are paid for realizing measurable weather-normalized results. To download the open source code, please visit the website at https://github.com/openeemeter/eemeter/releases D I S C L A I M E R: Normalized Savings using open source OEE meter. Several data elements, including, Evaluated Annual Elecric Savings (kWh), Evaluated Annual Gas Savings (MMBtu), Pre-retrofit Baseline Electric (kWh), Pre-retrofit Baseline Gas (MMBtu), Post-retrofit Usage Electric (kWh), and Post-retrofit Usage Gas (MMBtu) are direct outputs from the open source OEE meter. Home Performance with ENERGY STAR® Estimated Savings. Several data elements, including, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, and Estimated First Year Energy Savings represent contractor-reported savings derived from energy modeling software calculations and not actual realized energy savings. The accuracy of the Estimated Annual kWh Savings and Estimated Annual MMBtu Savings for projects has been evaluated by an independent third party. The results of the Home Performance with ENERGY STAR impact analysis indicate that, on average, actual savings amount to 35 percent of the Estimated Annual kWh Savings and 65 percent of the Estimated Annual MMBtu Savings. For more information, please refer to the Evaluation Report published on NYSERDA’s website at: http://www.nyserda.ny.gov/-/media/Files/Publications/PPSER/Program-Evaluation/2012ContractorReports/2012-HPwES-Impact-Report-with-Appendices.pdf. This dataset includes the following data points for a subset of projects completed in 2007-2012: Contractor ID, Project County, Project City, Project ZIP, Climate Zone, Weather Station, Weather Station-Normalization, Project Completion Date, Customer Type, Size of Home, Volume of Home, Number of Units, Year Home Built, Total Project Cost, Contractor Incentive, Total Incentives, Amount Financed through Program, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, Estimated First Year Energy Savings, Evaluated Annual Electric Savings (kWh), Evaluated Annual Gas Savings (MMBtu), Pre-retrofit Baseline Electric (kWh), Pre-retrofit Baseline Gas (MMBtu), Post-retrofit Usage Electric (kWh), Post-retrofit Usage Gas (MMBtu), Central Hudson, Consolidated Edison, LIPA, National Grid, National Fuel Gas, New York State Electric and Gas, Orange and Rockland, Rochester Gas and Electric. How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.

Data Set Information:

The data set is at 10 min for about 4.5 months. The house temperature and humidity conditions were monitored with a ZigBee wireless sensor network. Each wireless node transmitted the temperature and humidity conditions around 3.3 min. Then, the wireless data was averaged for 10 minutes periods. The energy data was logged every 10 minutes with m-bus energy meters. Weather from the nearest airport weather station (Chievres Airport, Belgium) was downloaded from a public data set from Reliable Prognosis (rp5.ru), and merged together with the experimental data sets using the date and time column. Two random variables have been included in the data set for testing the regression models and to filter out non predictive attributes (parameters).

Attribute Information:

date time year-month-day hour:minute:second

Appliances, energy use in Wh

lights, energy use of light fixtures in the house in Wh

T1, Temperature in kitchen area, in Celsius

RH_1, Humidity in kitchen area, in %

T2, Temperature in living room area, in Celsius

RH_2, Humidity in living room area, in %

T3, Temperature in laundry room area

RH_3, Humidity in laundry room area, in %

T4, Temperature in office room, in Celsius

RH_4, Humidity in office room, in %

T5, Temperature in bathroom, in Celsius

RH_5, Humidity in bathroom, in %

T6, Temperature outside the building (north side), in Celsius

RH_6, Humidity outside the building (north side), in %

T7, Temperature in ironing room , in Celsius

RH_7, Humidity in ironing room, in %

T8, Temperature in teenager room 2, in Celsius

RH_8, Humidity in teenager room 2, in %

T9, Temperature in parents room, in Celsius

RH_9, Humidity in parents room, in %

To, Temperature outside (from Chievres weather station), in Celsius

Pressure (from Chievres weather station), in mm Hg

RH_out, Humidity outside (from Chievres weather station), in %

Wind speed (from Chievres weather station), in m/s

Visibility (from Chievres weather station), in km

Tdewpoint (from Chievres weather station), °C

rv1, Random variable 1, nondimensional

rv2, Random variable 2, nondimensional

Where indicated, hourly data (then interpolated) from the nearest airport weather station (Chievres Airport, Belgium) was downloaded from a public data set from Reliable Prognosis, rp5.ru. Permission was obtained from Reliable Prognosis for the distribution of the 4.5 months of weather data.

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]

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 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).

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

This project investigates the impacts on residential power demand during warm summers when air quality is compromised by smoke from wildfires. We hypothesize that the energy use increases when the air is smoky because of additional purchase and use of air conditioners and air purifiers when temperatures are warm and the air is smoky from wildfires because windows must be kept closed, eliminating the evening cooling ability practiced by homeowners. We'll focus our analysis in the Seattle area using Seattle City Light energy use data and SeaTac weather station data. U.S. Air Quality Index (AQI), EPA’s index for reporting air quality ranging from 0 to 500, will be used for air quality data. The timeframe will initially focus on June through August during 2015 through 2018.

[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/

Context

Experimental data used to create regression models of appliances energy use in a low energy building.

Content

Data Set Characteristics:

Multivariate, Time-Series, Regression

Number of Instances(Rows):

19735

Number of Attributes(Columns):

29

Associated Tasks:

Regression

Source:

Luis Candanedo, luismiguel.candanedoibarra '@' umons.ac.be, University of Mons (UMONS).

Data Set Information: Given in Metadata tab about the sources and collection methodology.

Attribute Information:

date time year-month-day hour:minute:second

Appliances, energy use in Wh (target variable for prediction)

lights, energy use of light fixtures in the house in Wh

T1, Temperature in kitchen area, in Celsius

RH_1, Humidity in kitchen area, in %

T2, Temperature in living room area, in Celsius

RH_2, Humidity in living room area, in %

T3, Temperature in laundry room area

RH_3, Humidity in laundry room area, in %

T4, Temperature in office room, in Celsius

RH_4, Humidity in office room, in %

T5, Temperature in bathroom, in Celsius

RH_5, Humidity in bathroom, in %

T6, Temperature outside the building (north side), in Celsius

RH_6, Humidity outside the building (north side), in %

T7, Temperature in ironing room , in Celsius

RH_7, Humidity in ironing room, in %

T8, Temperature in teenager room 2, in Celsius

RH_8, Humidity in teenager room 2, in %

T9, Temperature in parents room, in Celsius

RH_9, Humidity in parents room, in %

To, Temperature outside (from Chievres weather station), in Celsius

Pressure (from Chievres weather station), in mm Hg

RH_out, Humidity outside (from Chievres weather station), in %

Wind speed (from Chievres weather station), in m/s

Visibility (from Chievres weather station), in km

Tdewpoint (from Chievres weather station), °C

rv1, Random variable 1, nondimensional

rv2, Random variable 2, nondimensional

Where indicated, hourly data (then interpolated) from the nearest airport weather station (Chievres Airport, Belgium) was downloaded from a public data set from Reliable Prognosis, rp5.ru. Permission was obtained from Reliable Prognosis for the distribution of the 4.5 months of weather data.

Acknowledgements

Luis M. Candanedo, Veronique Feldheim, Dominique Deramaix, Data driven prediction models of energy use of appliances in a low-energy house, Energy and Buildings, Volume 140, 1 April 2017, Pages 81-97, ISSN 0378-7788, Web Link.

Citation

Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

Inspiration

1) This is a regression task, You should predict the "appliances" column. Column descriptions are given above. Please read them before proceeding. 2) Appropriate time series analysis with regression is preferred more. 3) Exploratory data analysis with charts and plots.

Have fun!

• W1 - Winter with high heating demand
• W2 - Winter with low heating demand
• W3 - Winter with the coldest individual day by average temperature
• S1 - Summer with the lowest cooling demand
• S2 - Summer with the highest heating demand
• S3 - Summer with the warmest individual day by average temperature

Selected years and the procedure for their selection are described in https://doi.org/10.1016/j.energy.2024.131636" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.energy.2024.131636.

Original weather data is downloaded for the selected years from Finnish Meteorological Institute's Open data repository: https://www.ilmatieteenlaitos.fi/havaintojen-lataus under CC BY 4.0 licence.

Future change in climate is based on Finnish Meteorological Institute's data used in creating Test Reference Year weather files (https://www.ilmatieteenlaitos.fi/energialaskenta-try2020) for which the climate change data is presented by Ruosteenoja et al. (2016).

The data is statistically downscaled through a method called morphing created by Belcher et al. (2005) with some parts using methods from Räisänen & Räty (2013) and Jylhä et al, (2015). Morphing was computationally conducted through created software https://github.com/japulk/Weather-Morphing-Tool For additional information please refer to original article or contact the authors.

Mini Weather Station Market Outlook

The global mini weather station market size in 2023 is estimated at USD 2.5 billion, and it is projected to reach USD 4.8 billion by 2032, growing at a CAGR of 7.2% during the forecast period. This growth is driven by advancements in sensor technologies and increasing awareness about the importance of weather monitoring in various sectors.

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Product Type Analysis

The mini weather station market is segmented into portable and fixed types. Portable mini weather stations are gaining popularity due to their ease of use and mobility. These devices are particularly useful for outdoor enthusiasts, researchers, and farmers who need to monitor weather conditions on the go. The portability factor allows users to deploy these stations in various locations, providing more comprehensive weather data across different environments. Portable weather stations are also preferred in educational settings, where stud

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This set can be used to train and validate the performance of machine learning algorithms used in regression and classification as well as modelling, simulation and optimization problems related to energy consumption and climate.