Short-Term Photovoltaic Power Prediction Based on Similar Days and Improved SOA-DBN Model

Existing methods in predicting short-term photovoltaic (PV) power have low accuracy and cannot satisfy actual demand. Thus, a prediction model based on similar days and seagull optimization algorithm (SOA) is proposed to optimize a deep belief network (DBN). Fast correlation-based filter (FCBF) method is used to select a meteorological feature set with the best correlation with PV output and avoid redundancy among meteorological factors affecting PV output. In addition, a comprehensive similarity index combining European distance and gray correlation degree is proposed to select the similar day. Then, SOA is used to optimize the number of neurons and the learning rate parameters in DBN. Based on the nonuniform mutation and opposition-based learning method, an improved seagull optimization algorithm (ISOA) with higher optimization accuracy is proposed. Finally, the ISOA-DBN prediction model is established, and the experimental analysis is conducted using the actual data of PV power stations in Australia. Results show that compared with DBN, support vector machine (SVM), extreme learning machine (ELM), radial basis function (RBF), Elman, and back propagation (BP), the mean absolute percentage error indicator of ISOA-DBN is only 1.512% on a sunny day, 5.975 on a rainy day, 3.359 on a cloudy to sunny day, and 1.911% on a sunny to cloudy day. Therefore, the good accuracy of the proposed model is verified.

Abstract licence: CC BY-NC-ND

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