Applied Energy

A total of 54 Geotiffs in EPSG:4326 (can easily be opened with GIS software such as ArcGIS or QGIS) is provided . These maps are the results of 18 scenarios (S01-S18) proposed to evaluate technical requirements of electricity self-sufficient single family houses in low population density areas in Germany and the Czech Republic. The non-data values inside of the territory of the countries correspond either to pixels with no population or population beyond 1,500 inhabitants per square kilometre (The classification was made using population data from the LUISA project of the Joint Research Centre of the European Commission). The file names can be interpreted in the same way as the following example: S01_Battery_min_cost_no_sc.tif where S01 is the scenario number (01 to 18 are possible), Battery is the type of technology presented in the map (there are also optimally tilted photovoltaic panels named "PV1" and photovoltaic panels with 70° inclination named "PV2"), “min” stands for minimizing and the following word stands for the minimization objective. In this case with “cost” the objective of the scenario is to minimize cost (“battery” for battery size and “pv” for photovoltaic size are also possible). Additionally, there is “no_sc” for case studies that do not consider snow cover and "sc" in case snow cover is considered. Finally some of the files include a year at the end of the file name. This stands for the year of the irradiation and temperature data sets that were used to run the scenario. All files without a year correspond to scenarios calculated with average weather data (Average hours calculated from two decades of data from the COSMO-REA6 regional reanalysis).

Data for table 1-5 in the paper. Data for table 1 contains extended data

This repository contains the office plug load dataset that was collected in the paper titled "Near-Real-Time Plug Load Identification using Low-frequency Power Data in Office Spaces: Experiments and Applications". This paper was submitted on 27th April 2020 to the Journal of Applied Energy and accepted on 9th June 2020. Please include the following citation if you are interested in using this dataset: Tekler ZD, Low R, Zhou Y, Yuen C, Blessing L, Spanos C. Near-real-time plug load identification using low-frequency power data in office spaces: Experiments and applications. Applied Energy 2020;275:115391. https://doi.org/10.1016/j.apenergy.2020.115391 The dataset was the result of a three-week data collection effort that was conducted in a typical office environment between February 2020 to March 2020. The dataset contains the power consumption data of several plug loads that are commonly found on the occupants' desks, including 31 laptops, 9 desktops, 35 monitors, 13 fans, and 11 task lamps. A total of 36 occupants participated in this study consisting of a mixture of researchers and administrative staff. Each entry in the dataset contains four fields, including 1) the timestamp information, 2) the instantaneous power value of the connected plug load recorded up to two decimal places, 3) a unique ID indicating the smart power plug that recorded the information, and 4) the label of the corresponding plug load type that was provided post-data collection. The data was also collected with a sampling frequency of 1/60 Hz (equivalent to 1 sample every minute). This dataset has also been uploaded at the following sites: GitHub: https://github.com/zeynepduygutekler/plug-load-dataset

The excel file includes the simulation results of cam optimization. The simulation results is from the cam optimization and separated in four tabs, 1. Base 1500rpm 13bar 2. Base 3500rpm 20bar 3. Atk 1500rpm 13bar 4. Atk 3500rpm 20bar

Excel spreadsheet file with input data for the OSeMOSYS energy system optimization model of the U.S. electricity sector featured in the paper "U.S. electricity infrastructure of the future: Generation and transmission pathways through 2050" by Gopika Jayadev, Benjamin D. Leibowicz, and Erhan Kutanoglu of The University of Texas at Austin. All input data come from publicly available data sources, as indicated on the first sheet titled "Data Source."

Table S1: Overview of studies compiled for analysis, stating ‘Type of function’, ‘Energy performance class’, ‘World region’ and ‘Climate zone’. The ‘Status’ column indicates which studies were contained in the final sample (FINAL) or why studies were excluded (EXC_1 = excluded due to general lack of information; EXC_2 = Excluded because information on building area or Reference Study Period (RSP) not available; EXC_3 = Excluded because no embodied GHG emission values were reported.

These data describe how the energy usage of a large, diverse residential sector would change if all space heating was electrified. Using the actual weather data from 2016 for 17 locations in Texas, thousands of building models representative of the building stock in the residential sector of the Texas electric grid were simulated using the open-source dynamic energy modeling tool, EnergyPlus. Four total scenarios are examined in this study: a base scenario representative of current building stock and three electrification scenarios. In each electrification scenario, building models with fossil fuel heating sources had their heating units replaced with reversible heat pumps (i.e., heat pumps that provide both heating and cooling). The three electrification scenarios reflected the efficiency of the reversible heat pump being installed: standard efficiency, high efficiency, and ultra-high efficiency. Data reflect a residential system peak shifting from summer to winter and reduced summer consumption due to efficiency improvements. 1. Energy Consumption Data ~/hourly_energy_consumption: Hourly energy usage data for each building model was collected from EnergyPlus and multiplied by a scaling factor sized to reflect the actual dimensions of the Texas grid's residential sector. In the case of this study, that scaling factor is 230. The scaled hourly energy usage was summed over all modeled dwellings to give the Texas grid's residential sector hourly energy consumption. Each scenario's hourly consumption data are in a separate csv file in this directory, noted by the filename. 2. Daily Peak Demand ~/daily_peak_demand: The maximum hour of electricity consumption (kWh) for each day is divided by the change in time (one hour) to create an absolute peak hourly demand value (kW) for the day. These values for maximum hourly demand on each day are referred as daily peak demand values in the associated journal article. Each scenario's daily peak demand data are in a separate .csv file in this directory, noted by the filename. 3. Building Stock Details ~/building_stock_details The .csv file "base_scenario_building_stock_data.csv" includes housing information (e.g., insulation details, setpoint data, geometry data) about every building modeled in this study. It also includes annual energy and end-use consumption values for each building. Note: data from approximately 38,000 dwellings of the total 41,000 were used in our study, because some of the locations covered areas not served by the Texas electric grid. The remaining .csv files contain energy consumption data for the buildings that had heating units replaced by heat pumps. The files are organized by electrification scenario.

CO2 emissions in Central Plains urban agglomeration

Data set of hourly QCLCD for nine low wind sites in Florida is extracted from the NOAA data set cited below. National Oceanic Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) formerly National Climatological Data Center (NCDC), “QCLCD ASCII Files,” Data Set. [Online]. Available: https://www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-datasets/quality-controlled-local-climatological-data-qclcd. [Accessed: 21-Aug-2019]

The folder contains data related to manuscript: "Electrical system architectures for building-integratedphotovoltaics (BIPV): A comparative analysis using amodelling framework in Modelica". Specifically, it contains: 1) Power electronics efficiency curves 2) Input meteorological data per location (TMY) 3) Results (KPI) in pandas dataframe csv format. Feel free to use the any data, provided that you respect our authorship and you cite the dataset and/or the associated paper that provides detailed explanations on them.