Short Term Load Forecasting using Metaheuristic Techniques. Issue 1 (January 2021)

• Record Type: Journal Article
• Authors: Panda, Saroj Kumar; Ray, Papia; Mishra, Debani Prasad
• Is Part Of: IOP conference series. Volume 1033:Issue 1(2021)
• Publication Date: 2021-01
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/1033/1/012016
• Languages: English
• ISSNs: 1757-8981

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.