Ultra-Short-Term Prediction of Building Rooftop Photovoltaic Output Power Based on IPSO-BPNN
In the context of building-integrated photovoltaic technology,accurate prediction of rooftop photovoltaic output power is crucial for optimizing building energy management and ensuring the stable grid connection of PV electricity.Based on this,this paper proposes a rooftop PV output power ultra-short-term prediction model based on improved particle swarm optimization and backpropagation neural network(IPSO-BPNN).This model improves the basic particle swarm optimization(PSO)algorithm by introducing Sine chaotic sequence initialization and elite particle reverse learning strategy,and utilizes this algorithm to optimize the hyperparameters of the basic BPNN model,thereby achieving more accurate prediction of rooftop PV output power.Performance testing experiments of the prediction model demonstrate significant improvements in prediction accuracy and stability across different seasons.The proposed IPSO-BPNN model accurately forecasts rooftop PV electricity generation,providing a practical solution for the stable operation and energy management of building-integrated photovoltaic systems.
NETL
NSTL
万方数据
