Abstract:In view of the strong randomness and low prediction accuracy of short-term forecasting, a short-term load forecasting model based on fuzzy grey clustering and bat optimization neural network is proposed. Fuzzy clustering method was used to select rough sets of similar days, and then improved grey correlation analysis method was used to select the similar days. In order to overcome the problem that the traditional BP algorithm is prone to fall into local extremum and slow convergence speed, BP neural network prediction model optimized by training bats with samples of similar daily concentration is used. Taking the historical data of a certain area as a practical example, comparing the proposed algorithm with the common BP neural network, traditional gray correlation and bat optimized BP neural network prediction results, the results show that the proposed method has high prediction accuracy and Stability has certain application value in practice.