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Predicting CO2 Emissions for Buildings Using Regression and Classification

Abstract : This paper presents the development of regression and classification algorithms to predict greenhouse gas emissions caused by the building sector, and identify key building characteristics, which lead to excessive emissions. More specifically, two problems are addressed: the prediction of metric tons of CO2 emitted annually by a building, and building compliance to environmental laws according to its physical characteristics, such as energy, fuel, and water consumption. The experimental results show that energy use intensity and natural gas use are significant factors for decarbonizing the building sector.
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https://hal.inria.fr/hal-03287715
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Submitted on : Thursday, July 15, 2021 - 6:12:41 PM
Last modification on : Friday, August 13, 2021 - 4:29:53 PM
Long-term archiving on: : Saturday, October 16, 2021 - 7:11:51 PM

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Alexia Avramidou, Christos Tjortjis. Predicting CO2 Emissions for Buildings Using Regression and Classification. 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2021, Hersonissos, Crete, Greece. pp.543-554, ⟨10.1007/978-3-030-79150-6_43⟩. ⟨hal-03287715⟩

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