Short-term load forecasting based on AP similar days and FISOA-RBF

In order to improve the accuracy of electrical load forecasting for large public buildings, we propose a forecasting model for the short-term load of large public buildings based on affinity propagation (AP) similar days selection and the fusion improvement seeker optimization algorithm-radial basis function (FISOA-RBF) neural network by considering the weather information, date type and other influencing factors. In order to overcome the influence of external environment on the accuracy of building electrical load forecasting, AP algorithm is used to select similar days of short-term electrical load. The structural parameters of RBF neural network are optimized by FISOA which uses the fusion improvement theory to further improve the prediction accuracy and the learning speed of RBF neural network. Finally, the similar daily load data are used to train an optimized FISOA-RBF to predict the short-term electrical load of buildings. In order to validate the effectiveness of the proposed model, the exhaustive experiments are conducted in comparison with RBF, PSO-RBF and SOA-RBF methods. The experimental results indicate that the proposed model outperforms the other models: the mean absolute percentage error (MAPE) is reduced by 93.05%, 83.60% and 71.13%, and the average prediction speed is increased by 54.34%, 39.25% and 23.96%, and thus demonstrate that AP-FISOA-RBF model in prediction accuracy and speed of prediction performance is better than other three RBF-based methods.