A Comparative Study of AI-Driven Forecasting Models for Renewable Energy Resource Management in Different Climate Zones

Abstract

This study presents a comprehensive comparative analysis of artificial intelligence (AI)-driven forecasting models applied to renewable energy resource management across diverse climate zones. Utilizing a qualitative methodology based on an extensive literature review and library research, the paper evaluates the performance, applicability, and adaptability of various AI techniques—including machine learning, deep learning, and hybrid models—in forecasting renewable energy outputs such as solar, wind, and hydroelectric power. The research highlights how climate zone characteristics influence model accuracy and operational efficiency, emphasizing the need for tailored forecasting solutions aligned with local environmental factors. By synthesizing findings from multiple peer-reviewed studies and industry reports, the paper identifies strengths and limitations of prevalent AI models and explores their integration into energy management systems to enhance reliability and sustainability. The results underscore that while models such as artificial neural networks (ANNs) and support vector machines (SVMs) demonstrate robust performance in temperate climates, deep learning approaches tend to excel in complex, highly variable tropical and arid zones due to their ability to capture nonlinear patterns and temporal dependencies. Furthermore, hybrid models combining physical and data-driven approaches offer promising avenues for improving forecast precision across heterogeneous climatic conditions. This qualitative assessment contributes valuable insights into optimizing renewable energy forecasting by advocating for climate-specific AI model selection and encouraging future research on adaptive, hybrid forecasting frameworks to support the global transition to sustainable energy systems.