A hybrid sparse identification and convolutional neural network framework for renewable energy forecasting - He, Junchi; Tian, Tian; Wu, Yaqing; Liu, Xiaolu; Wei, Mengli (Frontiers in Energy Research, 2024)
In the field of renewable energy, accurate long-term time series forecasting is crucial for optimizing the operation of power systems and reducing risks. Due to the intermittency of renewable energy sources, traditional data-driven deep learning methods face challenges in capturing long-term dependencies. This paper proposes a hybrid model that combines Sparse Identification (SI) with Convolutional Neural Networks (CNN) to enhance the interpretability and generalization of predictions. The SI method is utilized to extract trends, seasonality, and periodicity, while the deep neural network captures complex relationships. Experimental results demonstrate that the model exhibits high accuracy and practicality in forecasting new energy scenario data, contributing to the advancement of time series prediction methodologies.