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MLBEM 2021 : The first Workshop on Machine Learning for Buildings Energy Management (@ECMLPKDD2021) | |||||||||||||||
| Link: https://mlbem.lasige.di.fc.ul.pt/ | |||||||||||||||
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Call For Papers | |||||||||||||||
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OVERVIEW
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. ------------------------------------------------------------------------------------- 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 ------------------------------------------------------------------------------------ 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. ------------------------------------------------------------------------------------ ORGANIZING COMMITTEE Pedro M. Ferreira, Faculty of Sciences - University of Lisbon / LASIGE, Portugal Guilherme Graça, Faculty of Sciences - University of Lisbon / IDL, Portugal |
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