Short-Term Electrical Load Forecasting Through Optimally Configured Long Short-Term Memory
Short-term electrical load forecasting plays a pivotal role in modern energy systems, addressing the need for accurate predictions of electricity demand within a time frame ranging from a few hours to a few days. Inaccurate predictions can lead not only to operational challenges but also to economi...
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| Format: | Article |
| Language: | English |
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OICC Press
2024-12-01
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| Series: | Majlesi Journal of Electrical Engineering |
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| Online Access: | https://oiccpress.com/mjee/article/view/7992 |
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| author | Somasundaram Vasudevan Kandasamy Jothinathan |
| author_facet | Somasundaram Vasudevan Kandasamy Jothinathan |
| author_sort | Somasundaram Vasudevan |
| collection | DOAJ |
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Short-term electrical load forecasting plays a pivotal role in modern energy systems, addressing the need for accurate predictions of electricity demand within a time frame ranging from a few hours to a few days. Inaccurate predictions can lead not only to operational challenges but also to economic and environmental consequences, highlighting the critical importance of short-term electrical load forecasting in today’s energy landscape. This research aims to mitigate these issues by developing an optimally configured Long Short-Term Memory (LSTM) model for short-term electrical load forecasting in Tamil Nadu, specifically targeting the Villupuram region in India. Although LSTM models are known for their effectiveness, achieving optimal performance in short-term load forecasting requires a tailored approach. Hyperparameter optimization is essential for configuring the LSTM model for this purpose, as manual or trial-and-error hyperparameter tuning is time-consuming and computationally intensive. To address this challenge, this research
integrates the Cauchy-distributed Harris Hawks Optimization (Cd-HHO) method to optimally configure the LSTM model. The Cd-HHO-optimized LSTM consistently achieves lower Mean Squared Error (MSE) than other state-of-the-art methods, with MSE values of 0.7225 in the 2017 dataset, 0.974 in the 2018 dataset, and 0.116 in the 2019 dataset.
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| format | Article |
| id | doaj-art-e506002a67af4e7fb6518202502f6ab8 |
| institution | OA Journals |
| issn | 2345-377X 2345-3796 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | OICC Press |
| record_format | Article |
| series | Majlesi Journal of Electrical Engineering |
| spelling | doaj-art-e506002a67af4e7fb6518202502f6ab82025-08-20T01:47:42ZengOICC PressMajlesi Journal of Electrical Engineering2345-377X2345-37962024-12-0118410.57647/j.mjee.2024.1804.54Short-Term Electrical Load Forecasting Through Optimally Configured Long Short-Term MemorySomasundaram Vasudevan0https://orcid.org/0009-0002-6915-5900Kandasamy Jothinathan1https://orcid.org/0009-0008-8029-0656Department of Electrical Engineering, Annamalai University, Chidambaram, IndiaDepartment of Electrical Engineering, Annamalai University, Chidambaram, India Short-term electrical load forecasting plays a pivotal role in modern energy systems, addressing the need for accurate predictions of electricity demand within a time frame ranging from a few hours to a few days. Inaccurate predictions can lead not only to operational challenges but also to economic and environmental consequences, highlighting the critical importance of short-term electrical load forecasting in today’s energy landscape. This research aims to mitigate these issues by developing an optimally configured Long Short-Term Memory (LSTM) model for short-term electrical load forecasting in Tamil Nadu, specifically targeting the Villupuram region in India. Although LSTM models are known for their effectiveness, achieving optimal performance in short-term load forecasting requires a tailored approach. Hyperparameter optimization is essential for configuring the LSTM model for this purpose, as manual or trial-and-error hyperparameter tuning is time-consuming and computationally intensive. To address this challenge, this research integrates the Cauchy-distributed Harris Hawks Optimization (Cd-HHO) method to optimally configure the LSTM model. The Cd-HHO-optimized LSTM consistently achieves lower Mean Squared Error (MSE) than other state-of-the-art methods, with MSE values of 0.7225 in the 2017 dataset, 0.974 in the 2018 dataset, and 0.116 in the 2019 dataset. https://oiccpress.com/mjee/article/view/7992Short Term Load Forecasting Long short-term memory Cauchy-distributed harris hawks optimizationHyperparameters tuning Uncertainties in weather forecast Power system managemen |
| spellingShingle | Somasundaram Vasudevan Kandasamy Jothinathan Short-Term Electrical Load Forecasting Through Optimally Configured Long Short-Term Memory Majlesi Journal of Electrical Engineering Short Term Load Forecasting Long short-term memory Cauchy-distributed harris hawks optimization Hyperparameters tuning Uncertainties in weather forecast Power system managemen |
| title | Short-Term Electrical Load Forecasting Through Optimally Configured Long Short-Term Memory |
| title_full | Short-Term Electrical Load Forecasting Through Optimally Configured Long Short-Term Memory |
| title_fullStr | Short-Term Electrical Load Forecasting Through Optimally Configured Long Short-Term Memory |
| title_full_unstemmed | Short-Term Electrical Load Forecasting Through Optimally Configured Long Short-Term Memory |
| title_short | Short-Term Electrical Load Forecasting Through Optimally Configured Long Short-Term Memory |
| title_sort | short term electrical load forecasting through optimally configured long short term memory |
| topic | Short Term Load Forecasting Long short-term memory Cauchy-distributed harris hawks optimization Hyperparameters tuning Uncertainties in weather forecast Power system managemen |
| url | https://oiccpress.com/mjee/article/view/7992 |
| work_keys_str_mv | AT somasundaramvasudevan shorttermelectricalloadforecastingthroughoptimallyconfiguredlongshorttermmemory AT kandasamyjothinathan shorttermelectricalloadforecastingthroughoptimallyconfiguredlongshorttermmemory |