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 [BibTeX] [Marc21]
A Machine Learning Model for the Prediction of Building Hourly Heating Demand from CityGML Files: Training Workflow and Deployment as an API
Type of publication: Conference paper
Citation: Tognoli_BS2023_2023
Publication status: Published
Booktitle: Proceedings of Building Simulation 2023: 18th Conference of IBPSA
Year: 2023
Month: September
Pages: 2932 - 2939
URL: https://publications.ibpsa.org...
DOI: 10.26868/25222708.2023.1570
Abstract: We present a workflow for the development and deployment of a data-driven model to estimate the hourly heating demand of buildings.The model is trained and tested using CityGML with the Energy ADE specification and Meteonorm CLI weather-files as the source of the input features.Through an optimization pipeline, an ensemble model including a gradient boosting algorithm presenting a RMSE of 9.5 Wh/m2 of floor area (98.7% accuracy) and low memory requirements is selected. A short-term predicting model is also developed reporting a RMSE of 4.2 Wh/m2 of floor area (99.7% accuracy).A web service providing access to a REST API deploying the data-driven model is also developed, allowing for a wide range of applications in third-party tools such as in GIS analysis.
Keywords: 3D City Models, CityGML, Decision Tree Learning, REST API
Projects Idiap
Authors Tognoli, Marco
Peronato, Giuseppe
Kämpf, Jérôme
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Total mark: 0