Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches
Author:
Publisher
Springer Science and Business Media LLC
Subject
Energy (miscellaneous),Building and Construction
Link
http://link.springer.com/content/pdf/10.1007/s12273-020-0723-1.pdf
Reference173 articles.
1. Afram A, Janabi-Sharifi F, Fung AS, Raahemifar K (2017). Artificial neural network (ANN) based model predictive control (MPC) and optimization of HVAC systems: A state of the art review and case study of a residential HVAC system. Energy and Buildings 141: 96–113.
2. Ahmad T, Chen H, Guo Y, Wang J (2018). A comprehensive overview on the data driven and large scale based approaches for forecasting of building energy demand: A review. Energy and Buildings 165: 301–320.
3. Amasyali K, El-Gohary NM (2018). A review of data-driven building energy consumption prediction studies. Renewable and Sustainable Energy Reviews 81: 1192–1205.
4. An J, Yan D, Hong T (2018). Clustering and statistical analyses of airconditioning intensity and use patterns in residential buildings. Energy and Buildings 174: 214–227.
5. Andriamamonjy A, Saelens D, Klein R (2018). An auto-deployed model-based fault detection and diagnosis approach for Air Handling Units using BIM and Modelica. Automation in Construction 96: 508–526.
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