A Hybrid Genetic Neural Network Approach for Short-Term Electrical Load Forecasting

International Journal of Computer Science and Technology | Vol. 5, No. 8, August 2003 | pp. 57–64

DOI: 10.46882/2003/IJCST/000048

Article Type: Original Research Paper

Title: A Hybrid Genetic Neural Network Approach for Short-Term Electrical Load Forecasting

Names of Authors: Ahmed Al-Mansoor¹, Yuki Tanaka²

Authors’ Affiliations: ¹Department of Computer Science, King Saud University, Riyadh, Saudi Arabia; ²Graduate School of Information Science, Kyoto University, Japan

Abstract: Accurate short-term electrical load forecasting is crucial for grid operators to manage generation allocation and prevent transmission overhead. Traditional feedforward neural networks trained with the backpropagation algorithm often get stuck in local minima, leading to higher prediction errors during sudden weather shifts. This paper introduces a hybrid forecasting model that applies Genetic Algorithms (GA) to optimize the initial weights and bias configurations of a multi-layer perceptron. The hybrid network utilizes historical temperature indexes, industrial usage indicators, and calendar data to project hourly power requirements. When validated against regional grid deployment metrics, the GA-NN model yielded a Mean Absolute Percentage Error (MAPE) of 1.42%, a notable refinement over the 2.85% baseline produced by autoregressive models. The computation engine converges in less than 45 s, enabling real-time dispatch optimizations across municipal electricity infrastructures.

Keywords: Neural Networks, Genetic Algorithms, Load Forecasting, Power Systems, Hybrid Metaheuristics, Optimization

Manuscript Timeline: Received: April 05, 2003; Revised: May 28, 2003; Accepted: June 22, 2003; Published: August 14, 2003

Citation: Al-Mansoor, A., & Tanaka, Y. (2003). A Hybrid Genetic Neural Network Approach for Short-Term Electrical Load Forecasting. International Journal of Computer Science and Technology, 5(8), 57–64.

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