CDT: An Effective Framework for Short-Term Photovoltaic Power Prediction

Increasing the proportion of renewable energy sources, such as photovoltaic power, in the grid can reduce fossil fuel consumption and build a low-carbon power system. However, the inherent instability of the photovoltaic power output makes it difficult to predict, thus increasing the cost of grid operation. Therefore, to improve the accuracy of power prediction and promote the development of the grid, a four-stage short-term photovoltaic power prediction framework, namely, CDT, is proposed, which includes decomposition, classification, reconstruction and forecasting. The initial power data are decomposed using complete ensemble empirical mode decomposition with adaptive noise. Next, an improved data classification and reconstruction method based on dynamic time warping is developed to process the data, which reduces the dimensionality of the data while preserving trend information. Finally, the reconstructed components are predicted using the improved TCN model. The results of the empirical study show that the proposed CDT has higher precision and scalability in processing and predicting the trend of photovoltaic power generation, compared to the other benchmark models.