A novel hybrid model for short-term prediction of PV power based on KS-CEEMDAN-SE-LSTM

Abstract

Accurate prediction of photovoltaic (PV) power plays a pivotal role in ensuring safe and stable grid operation, enhancing power supply quality, and facilitating proactive grid management to mitigate power fluctuations. This paper introduces a novel hybrid model named KS-CEEMDAN-SE-LSTM, which combines K-Shape (KS) clustering, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Sample entropy (SE), and Long Short-Term Memory (LSTM) techniques. To improve the accuracy of PV power prediction, three advanced preprocessing techniques, KS clustering, CEEMDAN and SE, are employed for feature extraction before prediction. In the proposed approach, KS clustering is employed to capture the similarity features of PV power. Subsequently, CEEMDAN decomposes the PV power into multiple intrinsic mode functions (IMFs) to reduce the influence of non-stationary and noisy features in power prediction. To further enhance prediction accuracy and streamline computational complexity, SE methods and K-means clustering are utilized to reconstruct the decomposed IMFs into new sub-sequences with refined granularity features. Finally, the LSTM model is applied to predict each reconstructed sub-sequence, and the ultimate prediction results are obtained by aggregating the predictions from all sub-sequences. The effectiveness of this method is demonstrated using real-world PV power data collected from China. Comprehensive experiments substantiate that the proposed KS-CEEMDAN-SE-LSTM model outperforms benchmark methods in short-term PV power prediction.