TY - EJOUR
AU - Vasudevan, Somasundaram
AU - Jothinathan, Kandasamy
PY - 2024
DA - June
TI - Short-Term Electrical Load Forecasting Through Optimally Configured Long Short-Term Memory
T2 - Majlesi Journal of Electrical Engineering
L1 - https://oiccpress.com/Majlesi-Journal-of-Electrical-Engineering/article/short-term-electrical-load-forecasting-through-optimally-configured-long-short-term-memory/
DO - 10.57647/j.mjee.2024.1802.35
N2 - 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. The implications of inaccurate predictions extend beyond operational challenges to potential economic and environmental consequences, emphasizing the critical role that short-term electrical load forecasting plays in the modern energy landscape. The purpose of this research is to address the aforementioned consequences by developing an optimally configured Long Short-Term Memory (LSTM) model for predicting short-term electrical load forecasting in Tamil Nadu, specifically focusing on India’s Villupuram region. While LSTM models are recognized for their overall effectiveness, their performance in short-term electrical load forecasting necessitates a tailored approach. Hyperparameter optimization is the appropriate choice for configuring the LSTM model for short-term electrical load forecasting. The manual or trial-and-error process in hyperparameter tuning is time-consuming and complex to compute. To address this, the research integrates the Cauchy-distributed Harris Hawks Optimization (Cd-HHO) approach for the optimal configuration of the LSTM model. The optimally configured LSTM through Cd-HHO consistently achieves lower Mean Squared Error (MSE) compared to other state-of-the-art methods, which is 0.7225 in the 2017 database, 0.974 in the 2018 database, and 0.116 in the 2019 database.
PB - OICC Press
KW - Cauchy-distributed Harris Hawks Optimization, hyperparameters tuning, Uncertainties in weather forecasts. Power system management, Villupuram region, Short Term Load Forecasting, Long Short-Term Memory 
EN -