AI-BASED ENERGY CONSUMPTION FORECASTING AND SUSTAINABLE OPTIMIZATION: A REVIEW OF INTELLIGENT FORECASTING MODELS AND GREEN AI APPROACHES

Abstract

The rapid growth of urbanization, industrialization, digital technologies, and connected devices has resulted in increasing energy demand and greater complexity in modern energy systems. Accurate energy consumption forecasting has therefore become an important component of efficient energy management, smart-grid operation, renewable-energy integration, and demand-response optimization. Artificial intelligence (AI) and machine learning (ML) have emerged as powerful approaches for modeling the nonlinear and dynamic relationships between energy consumption and influencing factors such as historical demand, weather conditions, seasonal patterns, occupancy, socioeconomic characteristics, and renewable-energy availability. Existing research demonstrates the application of artificial neural networks, support vector machines, decision trees, regression models, convolutional neural networks, recurrent neural networks, LSTM architectures, attention mechanisms, federated learning, and hybrid deep-learning models for energy forecasting.