A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Building occupancy number prediction: A Transformer approach
https://orcid.org/0000-0003-1648-3409
https://orcid.org/0009-0002-6163-5571
https://orcid.org/0000-0003-0528-2644
https://orcid.org/0000-0002-7952-5621
Abstract Buildings substantially impact global energy usage and emit significant carbon dioxide. Building occupancy is crucial to enabling energy conservation in buildings and achieving zero emissions by 2050. While existing occupancy prediction methods have made remarkable progress, their analysis is limited to complex practical scenes. In addition, the expectations of Transformers are high for predicting building occupancy. To address these problems, we introduce Transformer algorithms for building occupancy number prediction. We publicly provide an actual operating dataset for 2 weeks in 6 zones, including multi-sensor sensing information (e.g., temperature, occupancy, relative humidity, FCU control, FCU fan feedback, and FCU on/off feedback). To evaluate the performance, we provide an experimental analysis and comparison among our Transformer-based occupancy prediction algorithm and different machine learning methods (e.g., random forest, decision trees, XGBoost, and long short-term memory networks). Our Transformer-based occupancy prediction algorithm performs better on our dataset than other existing algorithms. The code, dataset, and demo are available at https://github.com/kailaisun/occprediction.
Highlights A Transformer-based building occupancy number prediction algorithm was proposed. A 2-week real operating multi-sensor occupancy dataset is available publicly. Quantitative analyses and comparisons were conducted on our dataset. Transformer-based algorithm achieved superior performance than 4 ML methods.
Building occupancy number prediction: A Transformer approach
https://orcid.org/0000-0003-1648-3409
https://orcid.org/0009-0002-6163-5571
https://orcid.org/0000-0003-0528-2644
https://orcid.org/0000-0002-7952-5621
Abstract Buildings substantially impact global energy usage and emit significant carbon dioxide. Building occupancy is crucial to enabling energy conservation in buildings and achieving zero emissions by 2050. While existing occupancy prediction methods have made remarkable progress, their analysis is limited to complex practical scenes. In addition, the expectations of Transformers are high for predicting building occupancy. To address these problems, we introduce Transformer algorithms for building occupancy number prediction. We publicly provide an actual operating dataset for 2 weeks in 6 zones, including multi-sensor sensing information (e.g., temperature, occupancy, relative humidity, FCU control, FCU fan feedback, and FCU on/off feedback). To evaluate the performance, we provide an experimental analysis and comparison among our Transformer-based occupancy prediction algorithm and different machine learning methods (e.g., random forest, decision trees, XGBoost, and long short-term memory networks). Our Transformer-based occupancy prediction algorithm performs better on our dataset than other existing algorithms. The code, dataset, and demo are available at https://github.com/kailaisun/occprediction.
Highlights A Transformer-based building occupancy number prediction algorithm was proposed. A 2-week real operating multi-sensor occupancy dataset is available publicly. Quantitative analyses and comparisons were conducted on our dataset. Transformer-based algorithm achieved superior performance than 4 ML methods.
Building occupancy number prediction: A Transformer approach
https://orcid.org/0000-0003-1648-3409
https://orcid.org/0009-0002-6163-5571
https://orcid.org/0000-0003-0528-2644
https://orcid.org/0000-0002-7952-5621
Abstract Buildings substantially impact global energy usage and emit significant carbon dioxide. Building occupancy is crucial to enabling energy conservation in buildings and achieving zero emissions by 2050. While existing occupancy prediction methods have made remarkable progress, their analysis is limited to complex practical scenes. In addition, the expectations of Transformers are high for predicting building occupancy. To address these problems, we introduce Transformer algorithms for building occupancy number prediction. We publicly provide an actual operating dataset for 2 weeks in 6 zones, including multi-sensor sensing information (e.g., temperature, occupancy, relative humidity, FCU control, FCU fan feedback, and FCU on/off feedback). To evaluate the performance, we provide an experimental analysis and comparison among our Transformer-based occupancy prediction algorithm and different machine learning methods (e.g., random forest, decision trees, XGBoost, and long short-term memory networks). Our Transformer-based occupancy prediction algorithm performs better on our dataset than other existing algorithms. The code, dataset, and demo are available at https://github.com/kailaisun/occprediction.
Highlights A Transformer-based building occupancy number prediction algorithm was proposed. A 2-week real operating multi-sensor occupancy dataset is available publicly. Quantitative analyses and comparisons were conducted on our dataset. Transformer-based algorithm achieved superior performance than 4 ML methods.
Building occupancy number prediction: A Transformer approach
Sun, Kailai (author) / Qaisar, Irfan (author) / Khan, Muhammad Arslan (author) / Xing, Tian (author) / Zhao, Qianchuan (author)
Building and Environment ; 244
2023-09-03
Article (Journal)
Electronic Resource
English
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