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REEDD-CR: Residential electricity end-use demand dataset from Costa Rican households
End-use demand data availability is a catalyst for improving energy efficiency measures and upgrading electricity demand studies. Nevertheless, residential end-use public datasets are limited, and end-use monitoring is costly. The lack of electricity end-use data is even more profound in Latin Ameri...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9800174/ https://www.ncbi.nlm.nih.gov/pubmed/36591381 http://dx.doi.org/10.1016/j.dib.2022.108829 |
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author | Angulo-Paniagua, Jam Victor-Gallardo, Luis Alfaro-Corrales, Ignacio Quirós-Tortós, Jairo |
author_facet | Angulo-Paniagua, Jam Victor-Gallardo, Luis Alfaro-Corrales, Ignacio Quirós-Tortós, Jairo |
author_sort | Angulo-Paniagua, Jam |
collection | PubMed |
description | End-use demand data availability is a catalyst for improving energy efficiency measures and upgrading electricity demand studies. Nevertheless, residential end-use public datasets are limited, and end-use monitoring is costly. The lack of electricity end-use data is even more profound in Latin America, where there are no public end-use datasets as far as the authors are concerned. Hence, we present the Residential Electricity End-use Demand Dataset of Costa Rica (REEDD-CR), containing the results of monitoring 51 Costa Rican households. The data set includes the aggregated and branch circuit measurements for every home with a sample time of 1 min for at least an entire week. The measurements were distributed all around the country. In addition, based on these sub-measurements, REEDD-CR includes a dataset of 197 load signatures composed of seven consumption and demand features for eight high-consuming appliances: refrigerator, stove, dryer, lighting, water heating, air conditioning, microwave, and washing machine. The features included on each load signature are average power, peak power, average daily events, average daily energy, day-use factor, night-use factor, and time of use. The single-appliance measurements used to calculate these load signatures are also part of the dataset. The release of REEDD-CR can serve as a tool for appliance modeling, demand disaggregation testing, feedback for energy demand models, and the overall upgrade of electricity supply and demand simulation studies with realistic and disaggregated data. |
format | Online Article Text |
id | pubmed-9800174 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-98001742022-12-30 REEDD-CR: Residential electricity end-use demand dataset from Costa Rican households Angulo-Paniagua, Jam Victor-Gallardo, Luis Alfaro-Corrales, Ignacio Quirós-Tortós, Jairo Data Brief Data Article End-use demand data availability is a catalyst for improving energy efficiency measures and upgrading electricity demand studies. Nevertheless, residential end-use public datasets are limited, and end-use monitoring is costly. The lack of electricity end-use data is even more profound in Latin America, where there are no public end-use datasets as far as the authors are concerned. Hence, we present the Residential Electricity End-use Demand Dataset of Costa Rica (REEDD-CR), containing the results of monitoring 51 Costa Rican households. The data set includes the aggregated and branch circuit measurements for every home with a sample time of 1 min for at least an entire week. The measurements were distributed all around the country. In addition, based on these sub-measurements, REEDD-CR includes a dataset of 197 load signatures composed of seven consumption and demand features for eight high-consuming appliances: refrigerator, stove, dryer, lighting, water heating, air conditioning, microwave, and washing machine. The features included on each load signature are average power, peak power, average daily events, average daily energy, day-use factor, night-use factor, and time of use. The single-appliance measurements used to calculate these load signatures are also part of the dataset. The release of REEDD-CR can serve as a tool for appliance modeling, demand disaggregation testing, feedback for energy demand models, and the overall upgrade of electricity supply and demand simulation studies with realistic and disaggregated data. Elsevier 2022-12-16 /pmc/articles/PMC9800174/ /pubmed/36591381 http://dx.doi.org/10.1016/j.dib.2022.108829 Text en © 2022 The Authors. Published by Elsevier Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Data Article Angulo-Paniagua, Jam Victor-Gallardo, Luis Alfaro-Corrales, Ignacio Quirós-Tortós, Jairo REEDD-CR: Residential electricity end-use demand dataset from Costa Rican households |
title | REEDD-CR: Residential electricity end-use demand dataset from Costa Rican households |
title_full | REEDD-CR: Residential electricity end-use demand dataset from Costa Rican households |
title_fullStr | REEDD-CR: Residential electricity end-use demand dataset from Costa Rican households |
title_full_unstemmed | REEDD-CR: Residential electricity end-use demand dataset from Costa Rican households |
title_short | REEDD-CR: Residential electricity end-use demand dataset from Costa Rican households |
title_sort | reedd-cr: residential electricity end-use demand dataset from costa rican households |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9800174/ https://www.ncbi.nlm.nih.gov/pubmed/36591381 http://dx.doi.org/10.1016/j.dib.2022.108829 |
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