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Title: A Data Driven Pre-cooling Framework for Energy Cost Optimization in Commercial Buildings

Abstract

Commercial buildings consume significant amount of energy. Facility managers are increasingly grappling with the problem of reducing their buildings’ peak power, overall energy consumption and energy bills. In this paper, we first develop an optimization framework – based on a gray box model for zone thermal dynamics – to determine a pre-cooling strategy that simultaneously shifts the peak power to low energy tariff regimes, and reduces both the peak power and overall energy consumption by exploiting the flexibility in a building’s thermal comfort range. We then evaluate the efficacy of the pre-cooling optimization framework by applying it to building management system data, spanning several days, obtained from a large commercial building located in a tropical region of the world. The results from simulations show that optimal pre-cooling reduces peak power by over 50%, energy consumption by up to 30% and energy bills by up to 37%. Next, to enable ease of use of our framework, we also propose a shortest path based heuristic algorithmfor solving the optimization problemand show that it has comparable erformance with the optimal solution. Finally, we describe an application of the proposed optimization framework for developing countries to reduce the dependency on expensive fossil fuels, whichmore » are often used as a source for energy backup.We conclude by highlighting our real world deployment of the optimal pre-cooling framework via a software service on the cloud platform of a major provider. Our pre-cooling methodology, based on the gray box optimization framework, incurs no capital expense and relies on data readily available from a building management system, thus enabling facility managers to take informed decisions for improving the energy and cost footprints of their buildings« less

Authors:
; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1361992
Report Number(s):
PNNL-SA-125250
DOE Contract Number:
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: Proceedings of the Eighth International Conference on Future Energy Systems (e-Energy 2017), May 16-19, 2017, Shatin, Hong Kong, 157-167
Country of Publication:
United States
Language:
English

Citation Formats

Vishwanath, Arun, Chandan, Vikas, Mendoza, Cameron, and Blake, Charles. A Data Driven Pre-cooling Framework for Energy Cost Optimization in Commercial Buildings. United States: N. p., 2017. Web. doi:10.1145/3077839.3077847.
Vishwanath, Arun, Chandan, Vikas, Mendoza, Cameron, & Blake, Charles. A Data Driven Pre-cooling Framework for Energy Cost Optimization in Commercial Buildings. United States. doi:10.1145/3077839.3077847.
Vishwanath, Arun, Chandan, Vikas, Mendoza, Cameron, and Blake, Charles. Tue . "A Data Driven Pre-cooling Framework for Energy Cost Optimization in Commercial Buildings". United States. doi:10.1145/3077839.3077847.
@article{osti_1361992,
title = {A Data Driven Pre-cooling Framework for Energy Cost Optimization in Commercial Buildings},
author = {Vishwanath, Arun and Chandan, Vikas and Mendoza, Cameron and Blake, Charles},
abstractNote = {Commercial buildings consume significant amount of energy. Facility managers are increasingly grappling with the problem of reducing their buildings’ peak power, overall energy consumption and energy bills. In this paper, we first develop an optimization framework – based on a gray box model for zone thermal dynamics – to determine a pre-cooling strategy that simultaneously shifts the peak power to low energy tariff regimes, and reduces both the peak power and overall energy consumption by exploiting the flexibility in a building’s thermal comfort range. We then evaluate the efficacy of the pre-cooling optimization framework by applying it to building management system data, spanning several days, obtained from a large commercial building located in a tropical region of the world. The results from simulations show that optimal pre-cooling reduces peak power by over 50%, energy consumption by up to 30% and energy bills by up to 37%. Next, to enable ease of use of our framework, we also propose a shortest path based heuristic algorithmfor solving the optimization problemand show that it has comparable erformance with the optimal solution. Finally, we describe an application of the proposed optimization framework for developing countries to reduce the dependency on expensive fossil fuels, which are often used as a source for energy backup.We conclude by highlighting our real world deployment of the optimal pre-cooling framework via a software service on the cloud platform of a major provider. Our pre-cooling methodology, based on the gray box optimization framework, incurs no capital expense and relies on data readily available from a building management system, thus enabling facility managers to take informed decisions for improving the energy and cost footprints of their buildings},
doi = {10.1145/3077839.3077847},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue May 16 00:00:00 EDT 2017},
month = {Tue May 16 00:00:00 EDT 2017}
}

Conference:
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