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MLBEM 2021 : The first Workshop on Machine Learning for Buildings Energy Management (@ECMLPKDD2021)


When Sep 13, 2021 - Sep 17, 2021
Where Bilbao, Spain (Virtual)
Submission Deadline Jun 23, 2021
Notification Due Jul 21, 2021
Final Version Due Jul 31, 2021
Categories    machine learning   artificial intelligence   energy   buildings

Call For Papers


Machine learning is a key enabler of scalable and efficient tools for building energy assessment and for the development of services capable of dealing with the increased complexity of energy management in buildings generated by the electrification of the energy system. The aim of this workshop is to provide energy and machine learning researchers with a forum to exchange and discuss scientific contributions, open challenges, and recent achievements in machine learning and their role in the development of efficient and scalable building energy management systems.



Machine learning for:
buildings energy performance assessment
appliance and building technical equipment energy assessment
buildings occupancy assessment
energy flexibility management
buildings energy efficiency
thermal comfort estimation and control
buildings lighting control
buildings air quality control
holistic control of buildings systems and energy resources

Adversarial machine learning and the robustness of AI in BEM
Interpretability and explainability of machine learning models in BEM
Privacy preserving machine learning
Trusted machine learning
Scalable / big data approaches for BEM
Continuous and one-shot learning
Informed machine learning
User and entity behavior modeling and analysis


Submissions are accepted in two formats:

1) Regular research papers with 12 to 16 pages including references. To be published in the proceedings, research papers must be original, not published previously, and not submitted concurrently elsewhere.
2) Short research statements of at most 6 pages. Research statements aim at fostering discussion and collaboration. They may review research published previously or outline new emerging ideas.



Pedro M. Ferreira, Faculty of Sciences - University of Lisbon / LASIGE, Portugal
Guilherme Gra├ža, Faculty of Sciences - University of Lisbon / IDL, Portugal

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