Short Term Load Forecasting using Metaheuristic Techniques

Author:

Panda Saroj Kumar,Ray Papia,Mishra Debani Prasad

Abstract

Abstract The power systems are important by using short term load forecasting (STLF) because it predicts the load in 24 hours ahead or a week ahead. The artificial neural network (ANN) using short term load forecasting brings good result in the predicted load because of its accurateness, easiness in the processing of data, construction of the model as well as excellent performances. The optimization value of ANN is found by different methods which consist of some weights. This manuscript explains the work of ANN with back propagation (BP), genetic algorithm (GA) as well as particle swarm optimization (PSO) for the STLF. The detailed work of the GA and PSO based BP is presenting in this paper which helps for its utilization in the STLF and also able to find the good result in the predicted load. Finally, the result of GA and PSO are compared by simulation and after that, it concluded, the PSO-BP is a good method for STLF using ANN.

Publisher

IOP Publishing

Subject

General Medicine

Reference30 articles.

1. An Implementation of a Neural Network Based Load Forecasting Model for the EMS;Papalexopoulos;Trans. on Power Syst.,1994

2. Practical On-line Predicting System for Short-Term Load;Chen;East China Electric Power,1996

3. An Implementation of Power System Short-Term Load Forecasting;Chen,1997

4. Market Participants Gain Energy Trading Tools;Slutsker;Computer Appllication in Power,1998

5. Analysis and Evaluation of Five Short-Term Load Forecasting Techniques;Moghram;Trans. on Power Syst.,1989

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