Research on Multi-load Short-term Forecasting Model of Integrated Energy System Based on QWCIFGLSTM

Accurate and efficient multi-load short-term forecasting is of great significance for the operation control and scheduling of integrated energy system. In order to improve the effect of load forecasting, a quantum weighted coupled input and forget gate long short-term memory(QWCIFGLSTM) neural network model is proposed. In terms of model structure, the forget gate and input gate in the long short-term memory(LSTM) neural network are combined to form a coupled input and forget gate long short-term memory(CIFGLSTM) neural network, so as to reduce the network parameters and optimize the network structure. In terms of model composition, quantum weighted neurons are used to replace traditional neurons to build QWCIFGLSTM neural network forecasting model. Quantum weighted neurons have good data processing ability and parallel computing ability, which can effectively improve the accuracy of load forecasting. The simulation results show that the proposed model has better forecasting effect than back propagation(BP) neural network forecasting model, conventional LSTM neural network forecasting model and the coupled input and forget gate long short-term memory neural network forecasting model.