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 | |
| Added by: | [UNK] |
| Total mark: | 0 |
|
Attachments
|
|
|
Notes
|
|
|
|
|