基于前馈神经网络的楼宇综合能源系统日前调度

With the increase of building integrated energy systems(BIES) and its increasingly prominent role in improving energy utilization efficiency, it is necessary to study how to enhance the coupling degree of equipment within BIES and its coordination with the power grid, thereby reducing the daily comprehensive operating cost of BIES. Therefore, a BIES day‐ahead optimization scheduling model based on feedforward neural network (FNN) is proposed. Firstly, the structure of BIES with rooftop photovoltaics is analyzed. Secondly, a day-ahead PV output prediction method and process based on FNN are proposed, providing reliable PV power generation data for BIES optimization scheduling. Finally, with the objective of minimizing the daily comprehensive operating cost composed of electricity purchase cost, gas purchase cost, and equipment operation and maintenance cost, a day‐ahead scheduling model for BIES is proposed. The result of the calculation examples show that the proposed PV output prediction method can effectively improve the prediction accuracy of PV power generation output, and the propose day‐ahead optimization scheduling model can effectively improve the operational economy of BIES.