10.57647/j.mjee.2024.1804.54

Short-Term Electrical Load Forecasting Through Optimally Configured Long Short-Term Memory

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  • Somasundaram Vasudevan*1,
  • Kandasamy Jothinathan1,
  1. Department of Electrical Engineering, Annamalai University, Chidambaram, India
Short-Term Electrical Load Forecasting Through Optimally Configured Long Short-Term Memory

Received: 2024-08-04

Revised: 2024-09-10

Accepted: 2024-09-17

Published 2024-12-15

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Vasudevan, S., & Jothinathan, K. (2024). Short-Term Electrical Load Forecasting Through Optimally Configured Long Short-Term Memory. Majlesi Journal of Electrical Engineering, 18(4), 1-10. https://doi.org/10.57647/j.mjee.2024.1804.54
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Abstract

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.

Keywords

  • Short Term Load Forecasting,
  • Long short-term memory,
  • Cauchy-distributed harris hawks optimization,
  • Hyperparameters tuning,
  • Uncertainties in weather forecast,
  • Power system managemen,
  • Villupuram region
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