Park Energy Demand Forecasting Based on CSO Optimized Deep Belief Network

Aiming at the problems of various influencing factors, complex construction model and insufficient prediction accuracy in energy demand forecasting, a energy demand forecasting method for the multi-energy complementary system is proposed, in which the improved correlation analysis and the crisscross optimization algorithm is used to optimize deep belief network. Firstly, the influencing factors of the energy demand of the park's multi-energy complementary system are analyzed, which are then determined with the mutual information and the minimum error methods. Secondly, as for the deficiency of the traditional grey correlation analysis, the the similar day selection method based on the comprehensive similarity of the distance similarity and trend similarity is established. Finally, limited by the randomness of the deep belief initial weight of the network, the crisscross optimization algorithm is used to optimize the deep belief network to forecast the cooling and heating loads of the park. ?Taking an actual park as an example, this paper analyzes the influence of cooling, heating and power load changes on the energy demand forecasting, and verifies that the proposed forecasting method can effectively improve the prediction accuracy and has high accuracy and practicability. © 2021, Power System Technology Press. All right reserved.