Systematic Review of Deep Learning and Machine Learning for Building Energy
Problem
Building energy management needs accurate, data-driven analysis and forecasting of energy consumption and demand for smarter, sustainable cities.
Why it matters
Building energy use is central to urban sustainability, and poor forecasting wastes energy and cost.
What the AI did
A systematic review of machine learning and deep learning techniques for building energy systems, evaluating their performance and identifying promising models.
Results
Hybrid/ensemble methods ranked in high-robustness range and SVM-based methods in good robustness range for energy demand forecasting (abstract truncated)
Impact
Informs which ML/DL models are promising for building energy forecasting; no new system or deployment demonstrated.
Record
| Venue | Frontiers in Energy Research |
|---|---|
| Published | 2022 |
| Work type | review |
| Authors | Sina Ardabili, Leila Abdolalizadeh, Csaba Makó, Bernat Torok, Amir Hosein Mosavi |
| Citations | 126 (OpenAlex, at import time) |
| References | 97 |
| Model or method | SVM, hybrid and ensemble methods (reviewed) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3389/fenrg.2022.786027 |
| Open access | gold |
| Keywords | machine learning, deep learning, building energy, energy forecasting, systematic review, ensemble methods |
Importance rating
significant — thousands to millions affected
Abstract frames building energy management as essential to urban sustainability and smart cities; a review informing model choice for energy forecasting, but no quantified impact or deployment shown.
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