Short-Term Load Forecasting Using A Hybrid Of Genetic Algorithm (Ga) And Particle Swarm Optimization (Pso) For An Optimized Neural Network

Abstract

Short-term load forecasting (STLF) has emerged as one of the most important fields of study for power system operation for system efficiency and reliability. It plays a significant role in load flow analysis, contingency analysis, planning, scheduling and maintenance of power systems facilities; therefore, the system cost-effectiveness is determined by accurate load forecast. Numerous researchers have been done to improve the accuracy of the conventional methods such as time series, regression analysis or autoregressive moving average (ARMA) and the use of Artificial Neural Networks (ANNs) in load forecasting. ANN has shown more accurate results than the others. But the training of ANNs, with a back-propagation algorithm or gradient algorithms, requires long processing time has the difficulty in selecting the optimal order of the components and trapping in local minima. This research aimed at solving this problem by proposing a hybrid method based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for training and optimizing the weights of ANN. The proposed hybrid method enables a reduction in the search space and the iteration time. The proposed algorithm was tested in MATLAB 2016® software using 24 hourly load data of different days (i.e. weekdays and weekends) from Juba Power Plant (JPP), South Sudan. PSO, GA and a hybrid of genetic algorithm with particle swarm optimization (HGAPSO) and ANN were studied and the resulting mean absolute percentage error (MAPE) found to be range from 1.9% to 3.40%, 2.23% to 3.65% and 1.47% to 1.98% respectively. The results obtained were compared and it was observed that HGAPSO-ANN method has a better performance in reducing and improving forecast error compared to PSO-ANN and GA-ANN methods. Therefore, a hybridized HGAPSO algorithm with ANN improves forecast accuracy.