CNN-based Quick Energy Prediction Model using Image Analysis for Shape Information

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung

Autoren

  • Philipp Florian Geyer
  • Manav Mahan Singh

Externe Organisationen

  • KU Leuven
  • Technische Universität Berlin
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Titel des SammelwerksProceedings of Building Simulation 2021
Untertitel17th Conference of IBPSA
Herausgeber/-innenDirk Saelens, Jelle Laverge, Wim Boydens, Lieve Helsen
Seiten1311-1316
Seitenumfang6
ISBN (elektronisch)978-1-7750520-2-9
PublikationsstatusVeröffentlicht - Sept. 2021
Extern publiziertJa
Veranstaltung 17th IBPSA Conference - Bruges, Belgien
Dauer: 1 Sept. 2021 → 3 Sept. 2021

Publikationsreihe

NameBuilding Simulation Conference Proceedings
ISSN (Print)2522-2708

Abstract

Data-driven approaches are useful to substitute computationally expensive tools used for energy prediction. These approaches allow developing quick energy prediction models, essential to promote energy analysis at the early stages. However, developing a wellgeneralising model is challenging due to varying building shape. This article develops such a model using a deep learning approach. A convolutional neural network (CNN) with modified architecture is used to capture the shape and technical specifications. The model predicts energy use intensity with a mean-absolute-percentageerror of 1.51% and root-mean-square-error of 1.06 kWh/m2a. Integrated with building information modelling (BIM), the model predicts probabilistic energy performance for 5000 samples in 65 seconds to support informed decision-making under uncertain scenario.

ASJC Scopus Sachgebiete

Zitieren

CNN-based Quick Energy Prediction Model using Image Analysis for Shape Information. / Geyer, Philipp Florian; Singh, Manav Mahan.
Proceedings of Building Simulation 2021: 17th Conference of IBPSA. Hrsg. / Dirk Saelens; Jelle Laverge; Wim Boydens; Lieve Helsen. 2021. S. 1311-1316 (Building Simulation Conference Proceedings).

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung

Geyer, PF & Singh, MM 2021, CNN-based Quick Energy Prediction Model using Image Analysis for Shape Information. in D Saelens, J Laverge, W Boydens & L Helsen (Hrsg.), Proceedings of Building Simulation 2021: 17th Conference of IBPSA. Building Simulation Conference Proceedings, S. 1311-1316, 17th IBPSA Conference, Belgien, 1 Sept. 2021. https://doi.org/10.26868/25222708.2021.30250
Geyer, P. F., & Singh, M. M. (2021). CNN-based Quick Energy Prediction Model using Image Analysis for Shape Information. In D. Saelens, J. Laverge, W. Boydens, & L. Helsen (Hrsg.), Proceedings of Building Simulation 2021: 17th Conference of IBPSA (S. 1311-1316). (Building Simulation Conference Proceedings). https://doi.org/10.26868/25222708.2021.30250
Geyer PF, Singh MM. CNN-based Quick Energy Prediction Model using Image Analysis for Shape Information. in Saelens D, Laverge J, Boydens W, Helsen L, Hrsg., Proceedings of Building Simulation 2021: 17th Conference of IBPSA. 2021. S. 1311-1316. (Building Simulation Conference Proceedings). doi: 10.26868/25222708.2021.30250
Geyer, Philipp Florian ; Singh, Manav Mahan. / CNN-based Quick Energy Prediction Model using Image Analysis for Shape Information. Proceedings of Building Simulation 2021: 17th Conference of IBPSA. Hrsg. / Dirk Saelens ; Jelle Laverge ; Wim Boydens ; Lieve Helsen. 2021. S. 1311-1316 (Building Simulation Conference Proceedings).
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title = "CNN-based Quick Energy Prediction Model using Image Analysis for Shape Information",
abstract = "Data-driven approaches are useful to substitute computationally expensive tools used for energy prediction. These approaches allow developing quick energy prediction models, essential to promote energy analysis at the early stages. However, developing a wellgeneralising model is challenging due to varying building shape. This article develops such a model using a deep learning approach. A convolutional neural network (CNN) with modified architecture is used to capture the shape and technical specifications. The model predicts energy use intensity with a mean-absolute-percentageerror of 1.51% and root-mean-square-error of 1.06 kWh/m2a. Integrated with building information modelling (BIM), the model predicts probabilistic energy performance for 5000 samples in 65 seconds to support informed decision-making under uncertain scenario. ",
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