Photovoltaic power generation prediction method based on dynamic time warping and transformer

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original abstract

Abstract To address the issue of low prediction accuracy caused by the strong randomness of photovoltaic (PV) power generation, this paper proposes a PV power forecasting method based on Dynamic Time Warping (DTW) and the Transformer model. Firstly, preprocess the data and use pearson correlation coefficient to select several meteorological factors that have a significant impact on PV power generation. Secondly, the training data is divided into three weather types: sunny, cloudy, and rainy using the DTW algorithm. Finally, based on the Transformer model, a PV power generation prediction model was established to predict the PV power generation under three different weather conditions. The verification results of the examples show that the method proposed in this paper achieves higher predictive accuracy compared to Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) prediction methods.

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