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Multi-objective optimization for thermal mass model predictive control in small and medium size commercial buildings under summer weather conditions
Journal article   Peer reviewed

Multi-objective optimization for thermal mass model predictive control in small and medium size commercial buildings under summer weather conditions

Xiwang Li and Ali Malkawi
Energy (Oxford), v 112, pp 1194-1206
01 Oct 2016

Abstract

Energy & Fuels Physical Sciences Science & Technology Technology Thermodynamics
Building thermal mass control has great potentials in saving energy consumption and cost. Optimal control schemes are able to utilize passive thermal mass storage to shift the cooling load from peak hours to off-peak hours to reduce energy costs. As such, this paper explores the idea of model predictive control for building thermal mass control. Specifically, this paper presents a study of developing and evaluating a multi-objective optimization based model predictive control framework for demand response oriented building thermal mass control. This multi-objective optimization framework takes both energy cost and thermal comfort into consideration simultaneously. In this study, the developed model predictive control framework has been applied in six commercial buildings at Boston, Chicago, and Miami, under typical summer weather conditions. Time-of-use electricity prices from these three locations are used to calculate the cooling and reheating energy costs. Pareto curves for optimal temperature setpoints under different thermal comfort requirements are calculated to show the trade-off between the cost saving and thermal comfort maintaining. Comparing with a typical "night setback" operation scheme, this model predictive control schemes are able to save energy costs from 20% to 60% at these three locations under different weather and energy pricing conditions. In addition, the Pareto curves also show that the energy cost saving potentials are highly dependent on the thermal comfort requirements, weather conditions, utility rate structures, and the building constructions. (C) 2016 Elsevier Ltd. All rights reserved.

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#11 Sustainable Cities and Communities
#7 Affordable and Clean Energy
#13 Climate Action

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Collaboration types
Domestic collaboration
Web of Science research areas
Energy & Fuels
Thermodynamics
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