浙江桐乡光伏电站临近区域净太阳辐射变化特征及预测技术研究

针对目前对太阳辐射变化特征及预测研究相对较少的现状，且太阳辐射对光伏发电具有最直接的影响，因此研究小区域太阳辐射时间变化规律、预测等，对光伏发电量准确估算缺乏一定指导意义。本文利用桐乡光伏电站临近区域1950-2020年净太阳辐射资料，分别采用最小二成分、M-K突变、EEMD周期分解、BP神经网络模型，对其整体、年代际时间变化特征、周期演变规律，并对比多种预测模型对该区域净太阳辐射预测的适用性。研究表明：净太阳辐射整体上呈递减趋势，每10年单位m2减少12.125MJ，且80年代、00年代递增趋势最为显著，1956年、2011年为净太阳辐射突变的年份；净太阳辐射存在多个时间尺度周期变化规律，分别为2-3年(短期)、10年(中期)、50年(长期)；所建立BP神经网络模型，预测较LSTM、DNN等算法均较高，能较好的应用于对该光伏电站净太阳辐射预测业务中。本文研究结论，能够较好的揭示小区域净太阳辐射演变规律，对光伏电站发电量精准测算以及预测工作均具有重要意义。

Research on the Characteristics and Prediction Techniques of Net Solar Radiation Changes in the Adjacent Area of Tongxiang Photovoltaic Power Station in Zhejiang Province

Due to the relatively limited research on the characteristics and prediction of solar radiation changes, and the fact that solar radiation has the most direct impact on photovoltaic power generation, studying the temporal changes and predictions of solar radiation in small areas lacks certain guiding significance for accurately estimating photovoltaic power generation. This article uses net solar radiation data from 1950 to 2020 in the vicinity of Tongxiang Photovoltaic Power Station. The minimum two component, M-K mutation, EEMD period decomposition, and BP neural network models are used to analyze the overall and interdecadal temporal variation characteristics and periodic evolution patterns, and to compare the applicability of various prediction models for predicting net solar radiation in this area. Research has shown that the overall net solar radiation shows a decreasing trend, with a decrease of 12.125MJ per square meter every 10 years. The most significant increasing trend was observed in the 1980s and 2000s, with 1956 and 2011 being years of sudden changes in net solar radiation; The net solar radiation exhibits multiple time scale periodic variations, namely 2-3 years (short-term), 10 years (medium-term), and 50 years (long-term); The BP neural network model established has a higher prediction accuracy compared to algorithms such as LSTM and DNN, and can be well applied in predicting the net solar radiation of the photovoltaic power plant. The research conclusion of this article can better reveal the evolution law of net solar radiation in small areas, which is of great significance for accurate calculation and prediction of photovoltaic power generation in photovoltaic power stations.