Tang, R;
Fan, C;
Zeng, F;
Feng, W;
(2022)
Data-driven model predictive control for power demand management and fast demand response of commercial buildings using support vector regression.
Building Simulation
, 15
pp. 317-331.
10.1007/s12273-021-0811-x.
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Abstract
Demand response (DR) of commercial buildings by directly shutting down part of operating chillers could provide an immediate power reduction for power grids. In this special fast DR event, effective control needs to guarantee expected power reduction and ensure an acceptable indoor environment. This study, therefore, developed a data-driven model predictive control (MPC) using support vector regression (SVR) for fast DR events. According to the characteristics of fast DR events, the optimized hyperparameters of SVR and shortened searching range of genetic algorithm are used to improve the control performance. Meanwhile, a comprehensive comparison with RC-based MPC is conducted based on three scenarios of power demand controls. Test results show that the proposed SVR-based MPC could fulfill the control objectives of power demand and indoor temperature simultaneously. Compared with RC-based MPC, the SVR-based MPC could alleviate the time/labor cost of model development without sacrificing the control performance of fast DR events.
| Type: | Article |
|---|---|
| Title: | Data-driven model predictive control for power demand management and fast demand response of commercial buildings using support vector regression |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s12273-021-0811-x |
| Publisher version: | https://doi.org/10.1007/s12273-021-0811-x |
| 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. |
| Keywords: | demand response, support vector regression, machine learning, building peak demand, model predictive control, smart grid |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10132136 |
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