A Systematic Review of Building Energy Consumption Prediction: From Perspectives of Load Classification, Data-Driven Frameworks, and Future Directions

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(6), P. 3086 - 3086

Published: March 12, 2025

The rapid development of machine learning and artificial intelligence technologies has promoted the widespread application data-driven algorithms in field building energy consumption prediction. This study comprehensively explores diversified prediction strategies for different time scales, types, forms, constructing a framework this field. With process as core, it deeply analyzes four key aspects data acquisition, feature selection, model construction, evaluation. review covers three acquisition methods, considers seven factors affecting loads, introduces efficient extraction techniques. Meanwhile, conducts an in-depth analysis mainstream models, clarifying their unique advantages applicable scenarios when dealing with complex data. By systematically combing existing research, paper evaluates advantages, disadvantages, applicability each method provides insights into future trends, offering clear research directions guidance researchers.

Language: Английский

Renewable energies in Morocco: A comprehensive review and analysis of current status, policy framework, and prospective potential

Energy Conversion and Management X, Journal Year: 2025, Volume and Issue: unknown, P. 100967 - 100967

Published: March 1, 2025

Language: Английский