MLBEM 2021: Workshop on Machine Learning for Buildings Energy Management Online Bilbao, Spain, September 13, 2021 |
Conference website | https://mlbem.lasige.di.fc.ul.pt/ |
Submission link | https://easychair.org/conferences/?conf=mlbem2021 |
Submission deadline | June 23, 2021 |
MLBEM@ECMLPKDD2021
MLBEM is co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2021).
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.
Submission Guidelines
MLBEM welcomes both research papers reporting results from mature work, recently published work, as well as more speculative papers describing new ideas or preliminary exploratory work. Papers reporting industry experiences and case studies will also be encouraged. However, it should be noticed that papers based on recently published work will not be considered for publication in the proceedings. The following paper categories are welcome:
- 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.
- 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.
List of Topics
- Machine learning for:
- buildings energy performance assessment
- appliance and building technical equipment energy assessment
- buildings occupancy assessment
- energy flexibility management
- buildings energy efficiency
- building-as-a-battery
- 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
Committees
Program Committee
To be announced
Organizing committee
- Pedro M. Ferreira, Faculty of Sciences - University of Lisbon / LASIGE, Portugal
- Guilherme Graça, Faculty of Sciences - University of Lisbon / IDL, Portugal
Publication
MLBEM 2021 proceedings will be published. Publisher and publishing method to be announced.
Contact
All questions about submissions should be emailed to Pedro Ferreira (pmf at ciencias . ulisboa . pt)
Sponsors
MLBEM is sponsored by the SATO project.