SHORT-TERM PHOTOVOLTAIC POWER PREDICTION BASED ON TIMESTAMP FEATURE EXTRATION AND CatBoost-LSTM MODEL
To solve the problem that the short-term power prediction effect is poor due to the insufficient input feature dimensions of the prediction model and the low prediction accuracy of a single model,a short-term prediction model of photovoltaic power generation combining with timestamp feature extraction and based on CatBoost and long short-term memory(LSTM)neural network is proposed.Firstly,the traditional grey correlation analysis is improved by using the information entropy weighting,and the improved method is used to analyze the correlation between the meteorological features such as irradiance,temperature,rainfall and power,and select the key features as the input features;Then,new temporal features such as year,month,day,hour,minute,second,timestamp-power are extracted from timestamp and power;On this basis,key meteorological features and extracted new temporal features are used for combined model training;Finally,an example analysis was conducted on the proposed method and combination model using real operating data of photovoltaic power plants.The results show that both the extracted new time series feature and the combined model can help improve the prediction accuracy.Under the non-sunny conditions,the prediction error of the combined model can be reduced by about 12%-23%compared with the single model,and it has higher prediction accuracy compared with other combined models.
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