Day-ahead forecasting of residential electric power consumption for energy management using Long Short-Term Memory encoder–decoder model
Energy management in smart buildings and energy communities needs short-term load demand forecasting for optimization-based scheduling, dispatch, and real-time operation. However, producing accurate forecasting for individual residential households is more challenging compared to the forecasting of...
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| Vydané v: | Mathematics and computers in simulation Ročník 224; s. 63 - 75 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
01.10.2024
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| ISSN: | 0378-4754, 1872-7166 |
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| Abstract | Energy management in smart buildings and energy communities needs short-term load demand forecasting for optimization-based scheduling, dispatch, and real-time operation. However, producing accurate forecasting for individual residential households is more challenging compared to the forecasting of load demand at the distribution level, which is smoother and benefits from statistical compensation of errors. This paper presents a day-ahead forecasting technique for individual residential load demand that is based on the Long Short-Term Memory encoder–decoder architecture, which is extended to consider possibly differing sets of past and future exogenous variables. A novel focus is posed on the validation of the proposed approach considering that it is tailored for use by energy management systems. A publicly available dataset was used for validation, and the approach was compared with three other methods, resulting in a reduction of the Mean Absolute Scaled Error by up to 8%. |
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| AbstractList | Energy management in smart buildings and energy communities needs short-term load demand forecasting for optimization-based scheduling, dispatch, and real-time operation. However, producing accurate forecasting for individual residential households is more challenging compared to the forecasting of load demand at the distribution level, which is smoother and benefits from statistical compensation of errors. This paper presents a day-ahead forecasting technique for individual residential load demand that is based on the Long Short-Term Memory encoder–decoder architecture, which is extended to consider possibly differing sets of past and future exogenous variables. A novel focus is posed on the validation of the proposed approach considering that it is tailored for use by energy management systems. A publicly available dataset was used for validation, and the approach was compared with three other methods, resulting in a reduction of the Mean Absolute Scaled Error by up to 8%. |
| Author | La Tona, G. Di Piazza, M.C. Luna, M. |
| Author_xml | – sequence: 1 givenname: G. orcidid: 0000-0002-9097-6626 surname: La Tona fullname: La Tona, G. email: giuseppe.latona@cnr.it – sequence: 2 givenname: M. orcidid: 0000-0001-8900-9367 surname: Luna fullname: Luna, M. – sequence: 3 givenname: M.C. surname: Di Piazza fullname: Di Piazza, M.C. |
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| CitedBy_id | crossref_primary_10_1016_j_enbuild_2025_115951 crossref_primary_10_1016_j_matcom_2025_04_044 crossref_primary_10_1016_j_apenergy_2024_122722 crossref_primary_10_1016_j_aei_2025_103754 crossref_primary_10_1109_ACCESS_2024_3521010 |
| Cites_doi | 10.1109/ACCESS.2019.2963045 10.1109/INDIN.2018.8471953 10.1109/TSG.2017.2686012 10.1109/TNNLS.2016.2582924 10.1109/IECON.2016.7793413 10.1109/ICIOT.2019.00029 10.1016/j.ijforecast.2006.03.001 10.1016/j.segan.2016.02.005 10.3390/en14061598 10.3390/app9102120 10.3390/app9204237 10.1109/ICSCAN53069.2021.9526485 10.1016/j.egypro.2017.12.423 10.1109/TSG.2017.2753802 10.1016/j.matcom.2020.05.010 10.1109/ACCESS.2020.3009537 10.1016/j.ijforecast.2015.11.011 10.1109/PTC.2019.8810899 10.1109/IECON.2019.8926801 10.1016/j.energy.2019.05.230 10.1109/EAIT.2018.8470406 10.1109/ACCESS.2020.3028281 10.1109/EEEIC/ICPSEurope49358.2020.9160650 10.1016/j.apenergy.2017.12.051 10.1016/j.solener.2012.03.006 10.1109/ISIE.2017.8001465 10.1016/j.ijforecast.2019.04.014 10.1016/j.ijepes.2021.107023 |
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| Keywords | Residential electrical consumption LSTM Forecasting Energy management |
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