雾霾条件下光伏发电量预测的迭代优化与经济性分析

光伏发电易受温度、辐照度等环境因素的影响，而近年来雾霾（PM_2.5浓度较高）污染严重，大幅降低了光伏系统发电量。因此研究雾霾天气下光伏发电量预测方法对光伏市场的发展具有重要意义。通过采集上海某户用光伏屋顶的全年光伏数据，利用控制变量法及雾霾相似日原理，拟合分析PM_2.5的浓度与发电量损失指数之间的关系，通过迭代原理优化光伏发电量预测算法，并给出雾霾环境下光伏发电量预测公式，修正光伏收益预测模型。结果表明：优化后的光伏预测发电量算法可提高发电量预测结果的精确性和稳定性。通过对3种光伏经济模型进行收益分析，验证了迭代优化算法可有效提高光伏收益预测的精确性。

Iterative Optimization and Economic Analysis of Photovoltaic Power Generation Forecasting under Haze Conditions

Photovoltaic power generation is vulnerable to environmental factors such as temperature and irradiance. In recent years, haze (with high concentration of PM_2.5) has caused serious pollution, greatly reducing the power generation of the photovoltaic system. Therefore, it is of great significance for the photovoltaic market to predict the photovoltaic power generation in the haze weather. In this paper, based on the annual photovoltaic data of a household photovoltaic roof in Shanghai, the relationship between the PM_2.5 concentration and the power generation loss index is fitted and analyzed with controlled variables and the similar days for haze analysis. According to the principle of iteration, the algorithm for photovoltaic power generation prediction is optimized, and the formula for photovoltaic power generation prediction under haze is given to modify the photovoltaic revenue prediction model. The results show that the optimized algorithm can improve the accuracy and stability of the prediction results. Through the revenue analysis of three photovoltaic economic models, the iterative optimization algorithm can improve the accuracy of photovoltaic revenue forecast.