MLBEM 2022: Workshop on Machine Learning for Buildings Energy Management Online workshop co-located with ECMLPKDD 2022 Grenoble, France, September 19-23, 2022 |
Conference website | https://mlbem.lasige.di.fc.ul.pt/ |
Submission link | https://easychair.org/conferences/?conf=mlbem2022 |
Submission deadline | July 1, 2022 |
MLBEM@ECMLPKDD2022
MLBEM is co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2022).
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, and 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.
- Submissions are accepted in two formats:
- 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.
- All submissions should be made in PDF using the EasyChair platform and must adhere to the Springer LNCS style. Templates are available here.
Tentatively, all regular workshop papers will be published in a LNCS proceedings volume (to be defined).
At a minimum, a proceedings volume will be edited and published online.
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
- distributed energy resources management
- Adversarial machine learning and the robustness of AI in BEM
- Interpretability and explainability of machine learning models in BEM
- Privacy preserving machine learning in BEM
- 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
Organizing Committee
- Pedro Ferreira, University of Lisbon, Portugal
- Guilherme Graça, University of Lisbon, Portugal
Program Committee
-
António Ruano, University of Algarve, Portugal - Bratislav Svetozarevic, Swiss Federal Laboratories for Materials Science and Technology, Switzerland
- Elias Kosmatopoulos, Democritus University of Thrace | Centre for Research & Technology Hellas, Greece
- Georg Jung, Vlaamse Instelling voor Technologisch Onderzoek, Belgium
- Gerald Schweiger, Graz University of Technology, Austria
- Iakovos Michailidis, Centre for Research & Technology Hellas, Greece
- José Domingo Álvarez, University of Almería, Spain
- Nuno Mateus, EDP New, Portugal
- Per Heiselberg, Aalborg University, Denmark
Web and Publicity Chair
- Nuno Dionísio, University of Lisbon, Portugal
- Zygimantas Jasiunas, University of Lisbon, Portugal
Venue
The workshop is co-located with ECMLPKDD'22, and will be fully organized online.
There is no physical venue to attend the workshop.
Contact
All questions about submissions should be emailed to " pmf @ ciencias . ulisboa . pt " |
Sponsors
Two EU H2020 projects will sponsor MLBEM’2022:
- SATO - Self-AssessmentTowards Optimization of the building energy;
- SMART2B - Smartness to existing Buildings.