Energy Prediction

Energy prediction research focuses on accurately forecasting energy consumption and production across various sectors, aiming to improve efficiency, resource management, and sustainability. Current efforts utilize diverse machine learning models, including neural networks (e.g., Gaussian processes, deep learning architectures like LSTMs and CNNs), kernel methods (like quantile regression), and metaheuristic optimization to enhance prediction accuracy and handle data complexities such as missing values and distribution shifts. These advancements have significant implications for optimizing energy systems in buildings, transportation (e.g., EVs), and smart grids, enabling better resource allocation and reduced environmental impact. Explainable AI techniques are also gaining traction to improve model interpretability and trust.