XGBoost Based Bi-layer Collaborative Real-time Calibration for Ultra-short-term Photovoltaic Prediction

The accuracy of ultra-short-term photovoltaic (PV) prediction generally declines when facing sudden processing weather, and radiation value calibration through real-time meteorological monitoring has high equipment requirements and a strong dependence on refined prediction. To solve the problems above, this paper proposes a bi-layer collaborative prediction model with real-time calibration based on extreme gradient boosting (XGBoost) by the guidelines of data-driven concept. According to the continuous evolution and self-similarity of atmospheric motion, the model deduces the overall meteorological change process from the perspective of machine learning to improve the prediction accuracy. Firstly, based on numerical weather prediction (NWP), a prediction model is established on the basic layer based on highly correlated meteorological features. Secondly, on the real-time layer, the dynamic prediction of the basic layer in the adjacent time period is used to explore the potential meteorological changes, and the impact of meteorological factors on the PV output in the future prediction time period is speculated. The reference prediction values in this time period are corrected point by point. Through the case study of the measured data in a certain PV station in Binjiang District, Hangzhou, China, the results show that the proposed model has higher accuracy for ultra-short-term PV power predicition, compared with the XGBoost prediction model based on NWP feature learning, time series, error shifting, and three classical prediction model like decision tree, support vector machine (SVM), long short-term memory (LSTM) network. © 2021 Automation of Electric Power Systems Press.