Short Term Load Forecasting Using a Hybrid Neural Network

• Main Authors: Yap, K.S., Abidin, I.Z., Lim, C.P., Shah, M.S.
• Format: Conference Proceeding
• Language: English
• Subjects: Computational efficiency; Computational modeling; Demand forecasting; Gaussian Adaptive Resonance Theory; Generalized Regression Neural Network; Hybrid power systems; Load forecasting; Maintenance; Neural networks; Power system reliability; Power system stability; Resonance; Time Series Prediction
• DOI: 10.1109/PECON.2006.346632

Short term load forecasting (STLF) is very important from the power systems grid operation point of view. STLF involves forecasting load demand in a short term time frame. The short term time frame may consist of half hourly prediction up to weekly prediction. Accurate forecasting would benefit the utility in terms of reliability and stability of the grid ensuring adequate supply is present to meet with the load demand. Apart from that it would also affect the financial performance of the utility company. An accurate forecast would result in better savings while maintaining the security of the grid. This paper outlines the STLF using a novel hybrid online learning neural network, known as the Gaussian regression (GR). This new hybrid neural network is a combination of two existing online learning neural networks which are the Gaussian adaptive resonance theory (GA) and the generalized regression neural network (GRNN). Both GA and GRNN implemented online learning, but each of them suffers from limitation. Originally GA is used for unsupervised clustering by compressing the training samples into several categories. A supervised version of GA is available, namely Gaussian ARTMAP (GAM). However, the GAM is still not capable on solving regression problem. On the other hand, GRNN is designed for solving real value estimation (regression) problem, but the learning process would involve of memorizing all training samples, hence high computational cost. The hybrid GR is considered an enhanced version of GRNN with compression ability while still maintains online learning properties. Simulation results show that GR has comparable prediction accuracy and has less prototype as compared to the original GRNN as well as the support vector regression.