Short-term Prediction of Photovoltaic Power based on Meteorological Features and Improved Transformer
Photovoltaic(PV)output is susceptible to meteorological factors,thus showing intermittency and randomness.Accurate and reliable prediction of PV power can not only alleviates the impact of high percentage of PV grid-connectedness on the power grid,but also provides data reference for grid schedu-ling decision makers.In this paper,we proposed a short-term prediction method of PV power based on meteorological features and improved Transformer.Firstly,incremental features,statistical features and time-varying features were extracted for PV-related meteorological factors;then,the extracted features and PV output data were input into the BOA-iTransformer model,and each variable was embedded inde-pendently,which was convenient for the model to capture the key meteorological features and the correla-tion of multivariate data;subsequently,Bayesian optimal tuning was used for feature selection to obtain the optimal feature combinations,which was used to build the BOA-iTransformer PV prediction model;fi-nally,the actual data of photovoltaic power stations in a region of China were used for comparative experi-ments.The experimental results show that the prediction accuracy of this model can be improved by 3.54%,7.24%and 14.2%compared with iTransformer,Transformer and LSTM models,respectively.
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