| Author: | Wong, Chun Ho |
| Title: | Development of a novel data-driven chiller sequence control strategy |
| Advisors: | Xiao, Fu Linda (BSE) |
| Degree: | M.Eng. |
| Year: | 2021 |
| Subject: | Air conditioning -- Efficiency Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Building Services Engineering |
| Pages: | [61] pages : color illustrations |
| Language: | English |
| Abstract: | This thesis studies the optimization of chiller sequencing control in the multi-chiller system in commercial buildings using the data-driven model. The model can be described as following parts: the chiller performance model and the cooling load prediction model. A control strategy is proposed and validated using site data from Hong Kong Electric Headquarter. For the chiller performance model, the operation data of chillers are collected, including ambient air temperature, cooling load, and power consumption of the chiller. First, the data collected is pre-processed using different Python library including pandas, NumPy, and sci-kit learn. Secondly, the pre-processed data is used to train a linear regression model. The chiller performance model developed is used to determine thresholds of the chiller switching actions. For the cooling load prediction model, the operation data of the whole chiller plant is collected to investigate the cooling load profile of the building. Features identified for developing the cooling load prediction model include months and date, time, outdoor air temperature, outdoor humidity, and cooling load from direct measurement. The optimal sequencing strategy is determined by the two models developed in the optimal sequencing control and validated through case studies with significant energy savings. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 5741.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 6.38 MB | Adobe PDF | View/Open |
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