Short term photovoltaic power prediction based on transfer learning and considering sequence uncertainty

With the increasing proportion of solar grid-connected, the establishment of accurate photovoltaic(PV) power prediction model is very important for safe operation and efficient dispatching of power grid. Considering the multi-level periodicity of PV power caused by many factors such as seasons and weather, a short-term PV power prediction model based on transfer component analysis is designed by introducing the idea of transfer learning. In order to measure the uncertainty of numerical weather prediction(NWP) and power sequence, a novel algorithm considering weather similarity and power trend similarity is proposed. Fistly, the intrinsic trend is measured by extracting permutation entropy, variance and mean from the historical PV power sequence. Secondly, weighting of NWP is accomplished based on pearson correlation coefficient. And, PV power data is divided into different clusters by K-medoids clustering. At the same time, the transfer component analysis alleviates the time-varying problem of data distribution caused by multi-level time periodicity, and effectively improves the prediction accuracy of the model. Finally, simulation experiments are carried out on the public dataset PVOD. The results show that the prediction accuracy of the proposed method is better than the traditional methods, and the accuracy and applicability of the proposed method are verified.