关键词: 光伏发电, 功率预测, BP神经网络, 光伏住宅, 四季预测模型

Abstract: The residential rooftop, as an idle space that is almost free from shading, provides ideal conditions for the deployment of photovoltaic (PV) systems. However, the intermittency and volatility of photovoltaic generation, along with the mismatch between photovoltaic power generation and residential electricity demand at different times, present significant challenges for the energy management system in achieving supply-demand balance. As an essential component of energy system optimization and performance enhancement, photovoltaic generation forecasting is critical to effectively addressing these challenges. To this end, this paper proposes an improved forecasting model that combines Particle Swarm Optimization (PSO) with Backpropagation (BP) Neural Networks. In this model, PSO is employed to optimize the parameters of the BP neural network, significantly improving the accuracy and stability of photovoltaic power prediction. Experimental results demonstrate that the improved model outperforms the traditional BP neural network in forecasting accuracy across all seasons—spring, summer, autumn, and winter. The average Root Mean Square Error (RMSE) is reduced by 42.31%, and the coefficient of determination (R²) increases by 2.22%. The annual average forecasting accuracy exceeds 90%, with the highest accuracy achieved in winter, reaching 99.46%. This study provides reliable forecasting data for the optimized scheduling of photovoltaic systems in residential buildings, offering substantial practical application value.

Key words: photovoltaic power generation, power prediction, BP Neural Network, PV Residential Building, Four-Season Prediction Model