An improved fuzzy neural networks approach for short-term electrical load forecasting

To solve the Short-Term Load Forecasting (STLF) tasks, this paper proposes to use a new method, namely, Quantum Genetic Algorithm (QGA) merged with Fuzzy Neural Networks (FNNs), here after called the QGA-FNN method. With the QGA method, we encode all the networks´ weights and biases into several Artificial Neural Network (ANN) system particle swarms, and then we train the network parameter values using the QGA method proposed in this paper to locate the networks´ optimal parameter solution. Next, we resolve the optimal STLF with the FNNs derived. The results by the proposed method are compared with that by other commonly-used load forecasting methods, such as the Artificial Neural Network, the Evolutionary Programming combined with ANN (EP-ANN) and the Genetic Algorithm combined with ANN (GA-ANN). The comparisons indicate that the proposed method renders smaller load forecasting discrepancies, with significant improvement rates ranging from 6.2% to 43.4%, signifying the proposed method´s advantage in load forecasting.