Comprehensive Assessment and Comparative Analysis of Deep Learning Models for Large-Scale Renewable Energy Power Generation Prediction: A National Perspective

Abstract In forecasting the future energy consumption and generation at the national level, strategic planning for both the medium and long term becomes imperative. The trajectory of renewable energy contribution to the smart grid, whether in the short or long term, significantly influences the grid's operational dynamics. This study is dedicated to the development of deep learning-based power prediction models tailored for a nation characterized by extensive reliance on renewable energy sources. Specifically, four distinct deep learning methodologies—namely,Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU)—have been formulated and rigorously examined for their efficacy within this domain. These models have been individually tailored for the prediction of power generation from solar photovoltaic plants and wind turbines, leveraging the inherent advantages of architectures featuring memory cells. The outcomes of these predictive models, which encompass the entire spectrum of renewable energy sources, exhibit remarkable precision. Furthermore, an exhaustive analysis of the performance metrics derived from these models has been conducted, affording a comprehensive and nuanced comparison. The findings contribute valuable insights into the suitability and effectiveness of the aforementioned deep learning methodologies in forecasting power generation from renewable sources at a national scale.