基于小波变换和支持向量机的短期光伏发电功率预测
罗毅;邢校萄
【期刊名称】《新能源进展》 【年(卷),期】2014(000)005
【摘要】Photovoltaic power prediction is an effective way to reduce adverse effects caused by the large-scale photovoltaic power connected to grid, and it is of great significance for power grid scheduling and optimal operation of the photovoltaic power station. Considering the cyclical and non-stationary of photovoltaic power sequence, this paper provides a prediction method based on wavelet transform and support vector machine (SVM). By wavelet decomposition and single refactoring, photovoltaic power sequence is converted to the low frequency trend signal and high frequency random signal. In consideration of strong small sample learning ability and small amount of calculation which SVM has, every wavelet signal are separately forecasted with support vector machine models. Finally, the predicted results of original photovoltaic power sequence are achieved by merging every single forecasted value. The actual data simulation validation of a photovoltaic power station shows the feasibility and effectiveness of this prediction method.%光伏发电功率预测是减小大规模光伏发电并网对电网造成不良影响的有效手段，对电网调度及光伏电站的优化运行具有重要意义。针对光伏发电功率序列的周期性和非平稳性，本文提出了基于小波变换和支持向量机
基于小波变换和支持向量机的短期光伏发电功率预测
