Multi-dimensional data processing and intelligent forecasting technologies for renewable energy generation

Abstract

As the global demand for renewable energy continues to rise, efficient and intelligent management and forecasting of renewable energy generation have become a critical area of research. This study comprehensively explores the application of artificial intelligence (AI) technologies in multidimensional data processing and intelligent forecasting of renewable energy generation. By examining meteorological and spatiotemporal perspectives, it investigates multidimensional data processing techniques in the renewable energy sector, including data preprocessing, feature extraction, and multi-source data fusion, to address challenges posed by the complexity and variability of renewable energy generation data. Subsequently, the research investigates intelligent forecasting technologies across temporal and spatial scales through two complementary paradigms: deterministic forecasting models and probabilistic forecasting frameworks. The analysis specifically emphasizes how machine learning (ML) and deep learning (DL) architectures enhance deterministic prediction accuracy by effectively handling nonlinear relationships in high-dimensional data, while novel probabilistic approaches leveraging neural networks and ensemble techniques demonstrate superior capabilities in quantifying prediction uncertainty—a critical advancement for operational risk management. Finally, the study summarizes the advantages and limitations of current technologies, discusses future research directions, and emphasizes the importance of enhancing the robustness and real-time performance of intelligent forecasting models. The objectives of this study are twofold: (i) to promote intelligent development in the renewable energy sector, and (ii) to provide new insights into renewable energy generation through data processing and forecasting.

Topics & Concepts