OICC Press Majlesi Journal of Electrical Engineering 2345-3796 18 4 2024 12 15 Short-Term Electrical Load Forecasting Through Optimally Configured Long Short-Term Memory 1 10 10.57647/j.mjee.2024.1804.54 EN Somasundaram Vasudevan Department of Electrical Engineering, Annamalai University, Chidambaram, India https://orcid.org/0009-0002-6915-5900 Kandasamy Jothinathan Department of Electrical Engineering, Annamalai University, Chidambaram, India https://orcid.org/0009-0008-8029-0656 Journal Article 2024 12 15 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 researchintegrates 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.