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Title: Data analytics and optimization of an ice-based energy storage system for commercial buildings

Ice-based thermal energy storage (TES) systems can shift peak cooling demand and reduce operational energy costs (with time-of-use rates) in commercial buildings. The accurate prediction of the cooling load, and the optimal control strategy for managing the charging and discharging of a TES system, are two critical elements to improving system performance and achieving energy cost savings. This study utilizes data-driven analytics and modeling to holistically understand the operation of an ice–based TES system in a shopping mall, calculating the system’s performance using actual measured data from installed meters and sensors. Results show that there is significant savings potential when the current operating strategy is improved by appropriately scheduling the operation of each piece of equipment of the TES system, as well as by determining the amount of charging and discharging for each day. A novel optimal control strategy, determined by an optimization algorithm of Sequential Quadratic Programming, was developed to minimize the TES system’s operating costs. Three heuristic strategies were also investigated for comparison with our proposed strategy, and the results demonstrate the superiority of our method to the heuristic strategies in terms of total energy cost savings. Specifically, the optimal strategy yields energy costs of up to 11.3%more » per day and 9.3% per month compared with current operational strategies. A one-day-ahead hourly load prediction was also developed using machine learning algorithms, which facilitates the adoption of the developed data analytics and optimization of the control strategy in a real TES system operation.« less
Authors:
 [1] ; ORCiD logo [2] ;  [3] ;  [4] ;  [5]
  1. Tsinghua Univ., Beijing (China); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  3. Shenzhen SECOM Technolgy Ltd., Shenzhen (China)
  4. Univ. of California, Berkeley, CA (United States)
  5. Tsinghua Univ., Beijing (China)
Publication Date:
Grant/Contract Number:
AC02-05CH11231
Type:
Accepted Manuscript
Journal Name:
Applied Energy
Additional Journal Information:
Journal Volume: 204; Journal Issue: C; Journal ID: ISSN 0306-2619
Publisher:
Elsevier
Research Org:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
OSTI Identifier:
1436610

Luo, Na, Hong, Tianzhen, Li, Hui, Jia, Ruoxi, and Weng, Wenguo. Data analytics and optimization of an ice-based energy storage system for commercial buildings. United States: N. p., Web. doi:10.1016/j.apenergy.2017.07.048.
Luo, Na, Hong, Tianzhen, Li, Hui, Jia, Ruoxi, & Weng, Wenguo. Data analytics and optimization of an ice-based energy storage system for commercial buildings. United States. doi:10.1016/j.apenergy.2017.07.048.
Luo, Na, Hong, Tianzhen, Li, Hui, Jia, Ruoxi, and Weng, Wenguo. 2017. "Data analytics and optimization of an ice-based energy storage system for commercial buildings". United States. doi:10.1016/j.apenergy.2017.07.048. https://www.osti.gov/servlets/purl/1436610.
@article{osti_1436610,
title = {Data analytics and optimization of an ice-based energy storage system for commercial buildings},
author = {Luo, Na and Hong, Tianzhen and Li, Hui and Jia, Ruoxi and Weng, Wenguo},
abstractNote = {Ice-based thermal energy storage (TES) systems can shift peak cooling demand and reduce operational energy costs (with time-of-use rates) in commercial buildings. The accurate prediction of the cooling load, and the optimal control strategy for managing the charging and discharging of a TES system, are two critical elements to improving system performance and achieving energy cost savings. This study utilizes data-driven analytics and modeling to holistically understand the operation of an ice–based TES system in a shopping mall, calculating the system’s performance using actual measured data from installed meters and sensors. Results show that there is significant savings potential when the current operating strategy is improved by appropriately scheduling the operation of each piece of equipment of the TES system, as well as by determining the amount of charging and discharging for each day. A novel optimal control strategy, determined by an optimization algorithm of Sequential Quadratic Programming, was developed to minimize the TES system’s operating costs. Three heuristic strategies were also investigated for comparison with our proposed strategy, and the results demonstrate the superiority of our method to the heuristic strategies in terms of total energy cost savings. Specifically, the optimal strategy yields energy costs of up to 11.3% per day and 9.3% per month compared with current operational strategies. A one-day-ahead hourly load prediction was also developed using machine learning algorithms, which facilitates the adoption of the developed data analytics and optimization of the control strategy in a real TES system operation.},
doi = {10.1016/j.apenergy.2017.07.048},
journal = {Applied Energy},
number = C,
volume = 204,
place = {United States},
year = {2017},
month = {7}
}