Grammenos, R;
Karagiannis, K;
Escalante Ruiz, M;
(2022)
Analysis and Optimisation of Building Efficiencies through Data Analytics and Machine Learning.
In:
Proceedings of the IAQ 2020: Indoor Environmental Quality Performance Approaches.
(pp. pp. 1-11).
ASHRAE
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Abstract
Productivity of workers is greatly affected by their comfort in the workplace. Research has shown that thermal comfort is one of the most influential parameters on worker productivity, and that the running costs of a Heating, Ventilation and Air Conditioning (HVAC) system could be up to ten times lower compared to productivity losses that would be incurred in a free-runing building. With the increased availability of Internet of Things (IoT) devices, it is now possible to continuously monitor multiple variables that influence a user’s thermal comfort and to act pre-emptively to prevent discomfort situations. Smart buildings make use of technology that enable them to become more efficient, reduce costs and emissions and become more transparent in terms of operation. To this end, this work has the following aims; develop a machine learning model to predict setpoint temperatures in an HVAC system; use exploratory data analysis techniques to evaluate the current operation and energy performance of an HVAC system in an office block; and finally, identify and compare patterns and trends between BMS parameters and thermal comfort standards.
| Type: | Proceedings paper |
|---|---|
| Title: | Analysis and Optimisation of Building Efficiencies through Data Analytics and Machine Learning |
| Event: | IAQ 2020: Indoor Environmental Quality Performance Approaches |
| Location: | Athens, Greece |
| Dates: | 4th-6th May 2022 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://www.ashrae.org/conferences/topical-confere... |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10122385 |
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