volume | PIER Journals

Abstract

The inherent volatility and uncertainty associated with photovoltaic (PV) power generation present significant challenges to maintaining grid stability. As the level of PV integration into the grid continues to rise, the importance of accurately predicting its power output becomes increasingly critical. This study presents a new PV power prediction model utilizing the K-means++-BiLSTM-Transformer framework. Initially, the Pearson correlation coefficient is computed to determine the key factors influencing the prediction of PV power significantly. Following this, the K-means++ clustering algorithm is applied to analyze historical power data, categorizing it into three distinct groups corresponding to different weather conditions. Finally, the BiLSTM-Transformer architecture is employed to develop a power output prediction model tailored for the three weather scenarios. The prediction model is subsequently optimized using Bayesian methods to determine the optimal model configuration for each specific weather condition. Experimental findings demonstrate that the proposed K-means++-BiLSTM-Transformer similar day PV power prediction model exhibits superior accuracy, enhanced generalization, and increased robustness compared to alternative prediction models.

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Dr. Jianwei Liang

Dr. Liying Yin

Dr. Sichao Li

Dr. Xiubin Zhu

Dr. Zhangsheng Liu

Dr. Yanli Xin