Photovoltaic Power Prediction Considering Multifactorial Dynamic Effects: A Dynamic Locally Featured Embedding-Based Broad Learning System

Abstract

Accurate photovoltaic power (PVP) prediction is a prerequisite for the efficient and stable operation of new power systems. While existing research has extensively explored the relationship between global factors such as temperature, irradiance, and photovoltaic power, the local dynamic impacts of these factors are often overlooked, which may reduce the accuracy of predictions. To address this issue, this paper considers the dynamic interrelationships among multiple factors and proposes a dynamic locally featured embedding-based broad learning system (DLFE-BLS) algorithm for PVP prediction. Firstly, a novel dynamic phase space reconstruction method (DPSR) is proposed to characterize the dynamic properties of multivariate data. Furthermore, a dynamic local featured embedding (DLFE) algorithm is introduced to extract local dynamic features from multivariate data. Finally, by integrating the dynamic reconstruction and dynamic feature extraction processes into the broad learning system (BLS) framework, we propose the DLFE-BLS algorithm to improve the accuracy of PVP prediction. Case studies have shown that DLFE-BLS outperforms other models in terms of prediction accuracy. Additionally, it has the highest accuracy when applied to transfer prediction.