Scheduled System Maintenance:
Some services will be unavailable Sunday, March 29th through Monday, March 30th. We apologize for the inconvenience.
By Topic

Short Term Load Forecasting Using Particle Swarm Optimization Based ANN Approach

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
3 Author(s)
Azzam-ul-Asar ; NWFP Univ. of Eng. & Technol., Peshawar ; ul Hassnain, S.R. ; Khan, A.

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.

Published in:

Neural Networks, 2007. IJCNN 2007. International Joint Conference on

Date of Conference:

12-17 Aug. 2007