Towards cost-optimal energy procurement for cooling as a service : a data-driven approach

• Main Authors: Zhang, Wei, Wen, Yonggang, Fang, Liu
• Other Authors: School of Computer Science and Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2022
• Subjects: Engineering::Computer science and engineering; Thermal Comfort; Smart Building; Machine Learning; Energy Procurement
• Online Access: https://hdl.handle.net/10356/152736
• Institution: Nanyang Technological University

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.