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MLBEM 2022 : 2nd Workshop on Machine Learning for Buildings Energy Management


When Sep 19, 2022 - Sep 19, 2022
Where online
Submission Deadline Jun 27, 2022
Notification Due Jul 18, 2022
Final Version Due Jul 29, 2022
Categories    machine learning   smart buildings   building energy management

Call For Papers

2nd Workshop on Machine Learning for Buildings Energy Management (MLBEM 2022)

Co-located with ECMLPKDD 2022, the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, which will be held on September 19-23 2022 in Grenoble.

The MLBEM workshop will be fully organized *online*.

*** OVERVIEW ***
Increased energy efficiency and decarbonization of the energy system are two primary objectives of the European Energy Union.
European buildings remain predominantly inefficient, accounting for 40% of final energy consumption and 36% of the total EU CO2 emissions.
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.

The workshop aims at filling a gap in the EU workshop panorama, providing researchers with a forum to exchange and discuss scientific contributions and open challenges, both theoretical and practical, related to the use of machine-learning approaches in building energy management.
We want to foster joint work and knowledge exchange between the building's energy community, researchers, and practitioners from the machine learning area, and its crossing with big data, data science, and visualization.
The workshop shall provide a forum for discussing novel trends and achievements in machine learning and their role in developing scalable BEMs. It aims to highlight the latest research trends in machine learning, privacy of data, big data, deep learning, incremental and stream learning, and adversarial learning.
In particular, it aims to promote the application of these emerging ML techniques to buildings energy management.

The workshop shall contribute to identifying new application areas as well as open and future research problems related to the application of machine-learning in the building's energy field.



Paper submission deadline: June 27, 2022
Acceptance notification: July 18, 2022
Camera ready submission: July 29, 2022

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.

Submission website:

*** 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

Pedro Ferreira, University of Lisbon
Guilherme Graça, University of Lisbon

Submission website:

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