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"Multi-features fusion for short-term photovoltaic power prediction" by Ming Ma, Xiaorun Tang et al.

In recent years, in order to achieve the goal of 'carbon peaking and carbon neutralization', many countries have focused on the development of clean energy, and the prediction of photovoltaic power generation has become a hot research topic. However, many traditional methods only use meteorological factors such as temperature and irradiance as the features of photovoltaic power generation, and they rarely consider the multi-features fusion methods for power prediction. This paper first preprocesses abnormal data points and missing values in the data from 18 power stations in Northwest China, and then carries out correlation analysis to screen out 8 meteorological features as the most relevant to power generation. Next, the historical generating power and 8 meteorological features are fused in different ways to construct three types of experimental datasets. Finally, traditional time series prediction methods, such as Recurrent Neural Network (RNN), Convolution Neural Network ....

Temporal Convolutional Network , Recurrent Neural Network , Convolution Neural Network , Northwest China , Extreme Gradient Boosting , Long Short Term Memory , Meteorological Factors , Ulti Features Fusion , Photovoltaic Power Prediction , Time Series Prediction ,