Machine learning in building energy management: A critical review and future directions

2022 ◽  
Author(s):  
Qian Shi ◽  
Chenyu Liu ◽  
Chao Xiao
Keyword(s):  
Machine Learning ◽  
Energy Management ◽  
Critical Review ◽  
Building Energy ◽  
Future Directions ◽  
Building Energy Management

Smart Building Energy Management: Load Profile Prediction using Machine Learning

2021 ◽  
Author(s):  
G. Revati ◽  
J. Hozefa ◽  
S. Shadab ◽  
A. Sheikh ◽  
S. R. Wagh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Management ◽  
Building Energy ◽  
Load Profile ◽  
Smart Building ◽  
Building Energy Management

scholarly journals Forecasting Algorithms and Optimization Strategies for Building Energy Management & Demand Response

Proceedings ◽  
2018 ◽  
Vol 2 (15) ◽  
pp. 1133 ◽  
Author(s):  
Fanlin Meng ◽  
Kui Weng ◽  
Balsam Shallal ◽  
Xiangping Chen ◽  
Monjur Mourshed
Keyword(s):  
Machine Learning ◽  
Energy Management ◽  
Demand Response ◽  
Learning Algorithms ◽  
Building Energy ◽  
Machine Learning Algorithms ◽  
Future Research ◽  
Research Directions ◽  
Building Energy Management ◽  
Future Research Directions

In this paper, we look at the key forecasting algorithms and optimization strategies for the building energy management and demand response management. By conducting a combined and critical review of forecast learning algorithms and optimization models/algorithms, current research gaps and future research directions and potential technical routes are identified. To be more specific, ensemble/hybrid machine learning algorithms and deep machine learning algorithms are promising in solving challenging energy forecasting problems while large-scale and distributed optimization algorithms are the future research directions for energy optimization in the context of smart buildings and smart grids.

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