On-line optimization scheme for HVAC demand response
Abstract
A computer-implemented method of optimizing demand-response (DR) of a heating, ventilation, and air-conditioning (HVAC) system of a building, includes determining (30, 31, 32) a value of an objective function F.sub.ij of a HVAC system for each of a plurality of DR strategies j for each of a plurality of weather patterns i that is a weighted sum of an energy cost of the HVAC system and a thermal comfort loss of the HVAC system, assigning (33, 34, 35, 36) a likelihood score L.sub.i,j to each of a selected subset of near-optimal DR strategies j for each weather pattern i, and selecting (37, 38) those near-optimal DR strategies with large overall likelihood scores L.sub.j to create an optimal strategy pool of DR strategies. An optimal strategy pool can be searched (39) in real-time for an optimal DR strategy for a given weather pattern.
- Inventors:
- Issue Date:
- Research Org.:
- Siemens Corporation, Iselin, NJ (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1483372
- Patent Number(s):
- 10077915
- Application Number:
- 14/433,860
- Assignee:
- Siemens Corporation (Iselin, NJ)
- Patent Classifications (CPCs):
-
F - MECHANICAL ENGINEERING F24 - HEATING F24F - AIR-CONDITIONING
G - PHYSICS G05 - CONTROLLING G05B - CONTROL OR REGULATING SYSTEMS IN GENERAL
- DOE Contract Number:
- EE0003847
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 2013 Oct 10
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 42 ENGINEERING; 97 MATHEMATICS AND COMPUTING
Citation Formats
Lu, Yan, Shen, Ling, and Zhu, Jianmin. On-line optimization scheme for HVAC demand response. United States: N. p., 2018.
Web.
Lu, Yan, Shen, Ling, & Zhu, Jianmin. On-line optimization scheme for HVAC demand response. United States.
Lu, Yan, Shen, Ling, and Zhu, Jianmin. Tue .
"On-line optimization scheme for HVAC demand response". United States. https://www.osti.gov/servlets/purl/1483372.
@article{osti_1483372,
title = {On-line optimization scheme for HVAC demand response},
author = {Lu, Yan and Shen, Ling and Zhu, Jianmin},
abstractNote = {A computer-implemented method of optimizing demand-response (DR) of a heating, ventilation, and air-conditioning (HVAC) system of a building, includes determining (30, 31, 32) a value of an objective function F.sub.ij of a HVAC system for each of a plurality of DR strategies j for each of a plurality of weather patterns i that is a weighted sum of an energy cost of the HVAC system and a thermal comfort loss of the HVAC system, assigning (33, 34, 35, 36) a likelihood score L.sub.i,j to each of a selected subset of near-optimal DR strategies j for each weather pattern i, and selecting (37, 38) those near-optimal DR strategies with large overall likelihood scores L.sub.j to create an optimal strategy pool of DR strategies. An optimal strategy pool can be searched (39) in real-time for an optimal DR strategy for a given weather pattern.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Sep 18 00:00:00 EDT 2018},
month = {Tue Sep 18 00:00:00 EDT 2018}
}
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