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...

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Bibliographic Details
Main Authors: Azzam-ul-Asar, ul Hassnain, S.R., Khan, A.
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Economic forecasting
Humans
Load forecasting
Neural networks
Particle swarm optimization
Power system management
Power system modeling
Power system security
Predictive models
PSO
Short term load forecasting
Swarm Intelligence
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Summary: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.
ISSN:2161-4393
2161-4407
DOI:10.1109/IJCNN.2007.4371176