Short-Term Photovoltaic Output Prediction Method Based on Data Decomposition and Error Correction

Considering the limited availability of meteorological data in practice, this paper investigates the short-term photovoltaic output prediction problem based on data decomposition and error correction to further improve prediction accuracy. Firstly, according to the analysis of the variation characteristics of photovoltaic output data, the Seasonal and Trend decomposition using Loess (STL) method is used to decompose the original data into three components: seasonal term, trend term, and residual term. Considering that the variation patterns of different components are different, based on the division of the dataset, temporal convolutional network (TCN)-based prediction models for each component are constructed separately, and the prediction results are superimposed to obtain the predicted value of the photovoltaic output. Secondly, an error dataset is constructed based on the prediction errors of the training set and validation set, and a TCN error prediction model is established. The error prediction value is used as compensation to correct the photovoltaic output prediction value, and the final photovoltaic output prediction value is obtained. Finally, based on the measured photovoltaic output data of a certain region in China, the effectiveness and advancement of the proposed method are demonstrated through the ablation and comparative experiments.