A hybrid model for daily peak load power forecasting based on SAMBA and neural network

According to the significance of power load demand forecasting, this paper suggests a new hybrid method to reach more accurate model with fast response. The proposed model consists of two algorithms: Self Adaptive Modified Bat Algorithm (SAMBA) and Artificial Neural Network (ANN). In recent years, SAMBA has been used as a powerful tool in the optimization problems. On the other hand among the most popular methods, ANN has shown powerful performance in load prediction as the result of its ability to detect nonlinear mappings among different variables. In addition, the special ability of SAMBA in fast convergence, its low dependency to setting parameters and simple implementation make this algorithm more premiere than the other optimization algorithms. Therefore, in this paper for the first time we use SAMBA to regulate the weight matrix of ANN and optimize the degree of uncertainty which exist in load demand prediction.