Volume 31: Clean Energy Technologies towards Carbon Neutrality

Day-ahead Hourly Photovoltaic Power Prediction Based on Multivariate Data Driven Hybrid Physical and Deep Learning Model Boheng Chen, Zhicong Chen, Lijun Wu, Peijie Lin, Shuying Cheng



In order to overcome the negative impact of the discontinuity and fluctuation of photovoltaic (PV) power generation on the power grid, in this study, a multi-variate data driven hybrid method for day-ahead hourly PV power curve prediction based on physical model and deep learning model is proposed. The physical model includes Ineichen clear sky model and PV performance model, while the deep learning model is a hybrid model combining two-dimensional grey relational analysis and bi-directional long short-term memory network model (2DGRA-BiLSTM). Firstly, the ideal clear sky global tilted radiation is calculated through the clear sky model, which is used as the input of PV performance model to obtain the ideal PV power under clear sky conditions. Secondly, the improved 2DGRA algorithm is proposed to obtain the best similar day from historical data. Thirdly, under the guidance of ideal clear sky power, the BiLSTM is trained with similarity-physics-informed data to obtain the difference between actual power and ideal clear sky power which is defined as RES-power. Compared with the other methods, results show that the accuracy of the deep learning model combined with physical method is the highest, followed by the deep learning model without physical method, and finally the simple physical model, whether it’s in clear sky condition or not.

Keywords photovoltaic power prediction, clear sky model, PV performance model, grey relational analysis, bi-directional long short-term memory

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