Deep Learning for Renewable Energy Forecasting and Demand Management: A Comparative Review of Architectures, Performance, and Regional Applications
TL;DR
A comprehensive comparative analysis of the deep learning architectures such as Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Convolutional Neural Networks (CNN), Transformer models, and hybrid models based on the benchmark of four widely used renewable energy datasets revealed that the hybrid CNN-LSTM models outperformed the ARIMA baseline models.
Abstract
Accurate forecasting of renewable energy generation and electricity demand are still critical challenges because of the inherently variable nature of renewable energy sources, and the inability of traditional statistical methods to model long-term dependencies that are not linear. This paper provides a comprehensive comparative analysis of the deep learning architectures such as Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Convolutional Neural Networks (CNN), Transformer models, and hybrid models based on the benchmark of four widely used renewable energy datasets. The results revealed that the hybrid CNN-LSTM models outperformed the ARIMA baseline models with a maximum improvement of 22% in Mean Absolute Percentage Error (MAPE), whereas the GRU models provided a minimum MAPE of 0.0210 in the data-rich environments and Transformer-based models proved to be better at handling long-range dependency. The results give a systematic framework for model selection in different regional and economic conditions, and have implications for smart grid integration, optimization of energy storage, and sustainable energy policy.