Division of Building Physics
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- 2024
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Mark
Data-driven Approaches for Predicting Hazardous Substances in the Building Stock
2024)(
- Thesis › Doctoral thesis (compilation)
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Mark
Enhancing climate resilience in buildings using Collective Intelligence : A pilot study on a Norwegian elderly care center
(
- Contribution to journal › Article
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Mark
Mold Odor from Wood Treated with Chlorophenols despite Mold Growth That Can Only Be Seen Using a Microscope
(
- Contribution to journal › Article
- 2023
-
Mark
Evidence of Unrecognized Indoor Exposure to Toxic Chlorophenols and Odorous Chloroanisoles in Denmark, Finland, and Norway
(
- Contribution to journal › Article
-
Mark
Evaluating the Indoor Radon Concentrations in the Swedish Building Stock Using Statistical and Machine Learning
(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
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Mark
CIRLEM : a synergic integration of Collective Intelligence and Reinforcement Learning in Energy Management for enhanced climate resilience and lightweight computation
(
- Contribution to journal › Article
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Mark
How to develop fifth-generation district heating and cooling in Sweden? Application review and best practices proposed by middle agents
(
- Contribution to journal › Article
-
Mark
Designing climate resilient energy systems in complex urban areas considering urban morphology : A technical review
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- Contribution to journal › Scientific review
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Mark
Indoor radon interval prediction in the Swedish building stock using machine learning
(
- Contribution to journal › Article
-
Mark
Machine learning models for the prediction of polychlorinated biphenyls and asbestos materials in buildings
(
- Contribution to journal › Article