Predictive control methods to improve energy efficiency and reduce demand in buildings

Tim SALSBURY; Prashant MHASKAR; S. Joe QIN

doi:10.1016/j.compchemeng.2012.08.003

Predictive control methods to improve energy efficiency and reduce demand in buildings

Tim SALSBURY, Prashant MHASKAR^*, S. Joe QIN

^*Corresponding author for this work

Research output: Journal Publications › Journal Article (refereed) › peer-review

75 Citations (Scopus)

Abstract

This paper presents an overview of results and future challenges related to temperature control and cost optimization in building energy systems. Control and economic optimization issues are discussed and illustrated through simulation examples. The paper concludes with results from model predictive control solutions and identification of important directions for future work. © 2012 Elsevier Ltd.

Original language	English
Pages (from-to)	77-85
Number of pages	9
Journal	Computers and Chemical Engineering
Volume	51
Early online date	13 Aug 2012
DOIs	https://doi.org/10.1016/j.compchemeng.2012.08.003
Publication status	Published - 5 Apr 2013
Externally published	Yes

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.compchemeng.2012.08.003

Cite this

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title = "Predictive control methods to improve energy efficiency and reduce demand in buildings",

abstract = "This paper presents an overview of results and future challenges related to temperature control and cost optimization in building energy systems. Control and economic optimization issues are discussed and illustrated through simulation examples. The paper concludes with results from model predictive control solutions and identification of important directions for future work. {\textcopyright} 2012 Elsevier Ltd.",

author = "Tim SALSBURY and Prashant MHASKAR and QIN, {S. Joe}",

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AU - SALSBURY, Tim

AU - MHASKAR, Prashant

AU - QIN, S. Joe

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AB - This paper presents an overview of results and future challenges related to temperature control and cost optimization in building energy systems. Control and economic optimization issues are discussed and illustrated through simulation examples. The paper concludes with results from model predictive control solutions and identification of important directions for future work. © 2012 Elsevier Ltd.

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U2 - 10.1016/j.compchemeng.2012.08.003

DO - 10.1016/j.compchemeng.2012.08.003

M3 - Journal Article (refereed)

SN - 0098-1354

VL - 51

SP - 77

EP - 85

JO - Computers and Chemical Engineering

JF - Computers and Chemical Engineering

ER -

Predictive control methods to improve energy efficiency and reduce demand in buildings

Abstract

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