STATE-OF-THE-ART DEEP LEARNING APPROACHES FOR PREDICTIVE ANALYTICS IN RENEWABLE ENERGY DEMAND FORECASTING

Abstract

Sustainable functioning of renewable energy systems depends on accurate demand forecasting, yet conventional methods like regression and econometric models frequently fail to capture nonlinear, high-dimensional, and time-dependent patterns in energy consumption. Addressing this research challenge, this study explores the integration of Artificial Intelligence (AI), particularly Long Short-Term Memory (LSTM) networks, into energy demand forecasting frameworks. The objective is to improve forecasting accuracy, adaptability to dynamic loads, and operational efficiency in renewable-powered systems. The research employs a hybrid methodology—combining theoretical modeling, sector-based case studies, and empirical evaluation—to develop and validate AI-based forecasting models. Industry-relevant scenarios, including implementations by Enel, GE, and Uber, demonstrate real-world applicability. Model performance is assessed using standard metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), with the LSTM model achieving an MAE of 0.87 kW, an RMSE of 1.10 kW, and 92.3% accuracy, measured by the coefficient of determination (R² score), outperforming conventional models. Key findings highlight improvements in grid stability, cost efficiency, and responsiveness to demand variability. The study’s novelty lies in its multi-sectoral synthesis of AI forecasting applications, offering insights for developing scalable models for smart grid operations. This work provides significant implications for energy providers, engineers, and policymakers by enabling more accurate, data-driven decisions in energy planning and policy formulation. Moreover, it reinforces the transformative potential of AI in addressing operational uncertainties, environmental constraints, and technological disruptions in modern power systems. Future research may explore explainable AI models to enhance transparency and stakeholder trust.