Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152736
Title: Towards cost-optimal energy procurement for cooling as a service : a data-driven approach
Authors: Zhang, Wei
Wen, Yonggang
Fang, Liu
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Zhang, W., Wen, Y. & Fang, L. (2022). Towards cost-optimal energy procurement for cooling as a service : a data-driven approach. 2021 IEEE Global Communications Conference (GLOBECOM). https://dx.doi.org/10.1109/GLOBECOM46510.2021.9685251
Project: NRF2015ENC_GBICRD001-012
BSEWWT2017_2_06
DSAIR@NTU
Abstract: Cooling as a Service (CaaS) is an emerging business that provides air conditioning services for buildings. With the rapid development of the business and the continuous increase of energy load, CaaS providers need cost-effective energy procurement to meet the service requirements. In this paper, we propose a data-driven approach for energy procurement for CaaS providers. First, we focus on two dominant variables of cooling energy cost, including outdoor temperature and electricity price. We predict their trends in the next day and accordingly, we estimate the energy usage and purchase the energy in the day- ahead energy market, one day before the actual usage. During the real-time operation, we can obtain the actual temperature and price, and we use this information to adjust the quality of service without violating the service standards. The adjustment serves as the demand response to the real-time energy market and can be cost beneficial. We conducted experimental studies to verify the performance of the proposed solution. The results show that our solution provides high-quality cooling services with minimal energy expenditure and helps improve the stability of the power grid.
URI: https://hdl.handle.net/10356/152736
ISBN: 978-1-7281-8104-2
DOI: 10.1109/GLOBECOM46510.2021.9685251
Rights: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/GLOBECOM46510.2021.9685251.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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