RESEARCH ON PHOTOVOLTAIC OUTPUT COMBINATION PREDICTION MODEL BASED ON SIMILAR DAY SELECTION AND PCA-LSTM
In this paper,a PV output portfolio forecasting model is constructed by integrating principal component analysis(PCA),an improved K-means clustering method,dynamic time warping(DTW),and a long-short term memory(LSTM)neural network.Based on the PCA method to extract the principal component factors of meteorological elements,the improved K-means clustering method and DTW algorithm are innovatively used to generate a set of historical day samples with a high degree of internal correlation and similar weather characteristics to the day to be predicted.Then,the LSTM neural network is combined to build a PV power prediction model based on the selection of similar days,which finally achieves the accurate prediction of power generation of a PV plant in Yunnan.The comparison results with other prediction models show that the combined prediction model constructed in this paper has better prediction performance and broad application prospects.
PV power stationprincipal component analysislong-short term memoryprediction modelimproved K-meansdynamic time warping
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