A software framework for model predictive control with GenOpt

被引：81

作者：

Coffey, Brian ^{[1
]}

Haghighat, Fariborz ^{[2
]}

Morofsky, Edward ^{[3
,4
]}

Kutrowski, Edward ^{[3
,4
]}

机构：

[1] Univ Calif Berkeley, Berkeley, CA 94720 USA

[2] Concordia Univ, Montreal, PQ, Canada

[3] Publ Works Serv, Ottawa, ON, Canada

[4] Govt Serv, Ottawa, ON, Canada

来源：

ENERGY AND BUILDINGS | 2010年 / 42卷 / 07期

关键词：

Building simulation; Optimization; Model predictive control; Demand response; REINFORCEMENT LEARNING CONTROL; ENERGY SIMULATION PROGRAMS; OPTIMIZATION; CALIBRATION;

D O I：

10.1016/j.enbuild.2010.01.022

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

There is a growing interest in integrated control strategies for building systems with numerous responsive elements, such as solar shading devices, thermal storage and hybrid ventilation systems, both for energy efficiency and for demand response. Model predictive control is a promising way of approaching this challenge. This paper presents a flexible software framework for model predictive control using GenOpt, along with a modified genetic algorithm developed for use within it, and applies it to a case study of demand response by zone temperature ramping in an office space. Various areas for further research and development using this framework are discussed. (C) 2010 Elsevier B.V. All rights reserved.

引用

页码：1084 / 1092

页数：9

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