Photovoltaic Solar Power Prediction Using iPSO-Based Data Clustering and AdaLSTM Network

Abstract

Due to the increasing integration of photovoltaic (PV) solar power into power systems, the prediction of PV solar power output plays an important role in power system planning and management. This study combines an optimized data clustering method with a serially integrated AdaLSTM network to improve the accuracy and robustness of PV solar power prediction. During the data clustering process, the Euclidean distance-based clustering centroids are optimized by an improved particle swarm optimization (iPSO) algorithm. For each obtained data cluster, the AdaLSTM network is utilized for model training, in which multiple LSTMs are serially combined together through the AdaBoost algorithm. For PV power prediction tasks, the inputs of the testing set are classified into the nearest data cluster by the K-nearest neighbor (KNN) method, and then the corresponding AdaLSTM network of this cluster is used to perform the prediction. Case studies from two real PV stations are used for prediction performance evaluation. Results based on three prediction horizons (10, 30 and 60 min) demonstrate that the proposed model combining the optimized data clustering and AdaLSTM has higher prediction accuracy and robustness than other comparison models. The root mean square error (RMSE) of the proposed model is reduced, respectively, by 75.22%, 73.80%, 67.60%, 66.30%, and 64.85% compared with persistence, BPNN, CNN, LSTM, and AdaLSTM without clustering (Case A, 30 min prediction). Even compared with the model combining the K-means clustering and AdaLSTM, the RMSE can be reduced by 10.75%.