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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Acknowledgments
The authors would like to thank the Hellenic Artificial Intelligence Society (EETN) for covering part of their expenses to participate in AIAI 2021.
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Avramidou, A., Tjortjis, C. (2021). Predicting CO2 Emissions for Buildings Using Regression and Classification. In: Maglogiannis, I., Macintyre, J., Iliadis, L. (eds) Artificial Intelligence Applications and Innovations. AIAI 2021. IFIP Advances in Information and Communication Technology, vol 627. Springer, Cham. https://doi.org/10.1007/978-3-030-79150-6_43
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DOI: https://doi.org/10.1007/978-3-030-79150-6_43
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