Micro-genetic algorithm embedded multi-population differential evolution based neural network for short-term load forecasting

The load of a power system usually presents a certain range of nonlinear fluctuation with time. Even then, the load characteristics still follow certain rules which can be exploited to optimise and improve the accuracy of computer-based Short-Term Load Forecasting (STLF) models. Therefore, this paper presents a mGA (micro-Genetic Algorithm) embedded multi-population DE (Differential Evolution) to optimise an Artificial Neural Network (ANN) STLF model. Firstly, the mGA embedded multi-population DE is proposed, to improve and balance the global and local search. Then the proposed DE is applied to optimise the weights during the training of the ANN. The overall model's performance is evaluated using publicly available Panama electricity load dataset against four state-of-the-art machine learning algorithms. The evaluation results show that the proposed DE based NN STLF model has higher prediction accuracy compared to the other selected machine learning algorithms.