Hourly water demand forecasting using a hybrid model based on mind evolutionary algorithm. Issue 1 (16th July 2021) - Huang, Haidong; Zhang, Zhixiong; Lin, Zhenliang; Liu, Shitong
Abstract: A hybrid model based on the mind evolutionary algorithm is proposed to predict hourly water demand. In the hybrid model, hourly water demand data are first reconstructed to generate appropriate samples so as to represent the characteristics of time series effectively. Then, the mind evolutionary algorithm is integrated into a back propagation neural network (BPNN) to improve prediction performance. To investigate the application potential of the proposed model in hourly water demand forecasting, real hourly water demand data were applied to evaluate its prediction performance. In addition, the performance of the proposed model was compared with a traditional BPNN model and another hybrid model where the genetic algorithm (GA) is used as an optimization algorithm for BPNN. The results show that the proposed model has a satisfactory prediction performance in hourly water demand forecasting. On the whole, the proposed model outperforms all other models involved in the comparisons in both prediction accuracy and stability. These findings suggest that the proposed model can be a novel and effective tool for hourly water demand forecasting. HIGHLIGHTS: A unified framework is developed to select input variables for an hourly water demand forecasting model. Mind evolutionary algorithm, a novel and powerful optimization algorithm, is used to obtain the optimal initial weights and thresholds for the back propagation neural network. A hybrid model coupling mind evolutionary algorithm and back propagation neural network is proposed to predict hourly water demand.