A cluster-based short-term photovoltaic power forecasting method using K-means and CNN-BiLSTM-Attention
TL;DR
A clustering-based prediction framework that combines K-means clustering with a hybrid deep learning model that outperforms the baseline model on multiple error metrics is proposed, proving its effectiveness in predicting the power generation of multiple PV power plants.
Abstract
Accurately predicting the power generation of distributed photovoltaic (PV) power plants is extremely challenging due to their heterogeneity and complex temporal variations. To address these issues, this paper proposes a clustering-based prediction framework that combines K-means clustering with our proposed model. First, a multidimensional feature representation is constructed to characterize the statistical properties, dynamic fluctuations, and meteorological correlations of PV power plants. This feature representation is then used to classify the power plants into homogeneous clusters. For each cluster, a hybrid deep learning model is developed to capture local temporal patterns and long-term dependencies, with a focus on key time steps. Finally, the outputs of all clusters are aggregated to obtain the regional power generation prediction results. Experimental results demonstrate that the proposed method outperforms the baseline model on multiple error metrics, proving its effectiveness in predicting the power generation of multiple PV power plants.