Short Term Load Forecasting Using Particle Swarm Optimization Based ANN Approach

This paper presents a new approach for modeling short term load forecasting (STLF) in which STLF-ANN forecaster is trained by optimizing its weights using swarm intelligence. ANN has been used successfully for STLF. However, ANN-based STLF models use backward propagation (BP) algorithm for training which does not ensure convergence and hangs in local optima more often. Moreover, BP requires much longer time for training which makes it difficult for real-time application. In this paper, we propose smaller ANN models of STLF based on hourly load data and adjust its weights through the use of particle swarm optimization (PSO) algorithm. The approach gives better trained models capable of performing well over varying time window and results fairly accurate forecasts.

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