Nonlinear model predictive control for energy efficient housing with modern construction materials

B. Novoselnik, Josip Cesic, M. Baotic, I. Petrović
{"title":"Nonlinear model predictive control for energy efficient housing with modern construction materials","authors":"B. Novoselnik, Josip Cesic, M. Baotic, I. Petrović","doi":"10.1109/SAS.2015.7133614","DOIUrl":null,"url":null,"abstract":"The reduction of energy consumption in residential buildings and houses represents a major societal and environmental challenge. A huge portion of energy consumption in houses and buildings goes towards comfort control - heating, ventilation and air conditioning. Modern architecture tackles these problems by considering (i) passive exploitation of solar energy and (ii) use of special construction materials that often have quite complex characteristics. Therefore, a smart energy management control system should be able to take both of these considerations into account. This paper proposes a model predictive control (MPC) strategy for energy efficient management of heating and cooling of a house that can cope with the nonlinearities in the system induced by the use of modern construction materials. Furthermore, the proposed controller takes into account the weather forecast information, in particular the prediction of solar irradiance and air temperature. The nonlinear behavior of the system is approximated by a piecewise affine model and further incorporated into the mixed logical dynamical framework. The performance of the proposed nonlinear MPC is demonstrated in simulation experiments, which show applicability and usefulness of the MPC algorithm for energy management.","PeriodicalId":384041,"journal":{"name":"2015 IEEE Sensors Applications Symposium (SAS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE Sensors Applications Symposium (SAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SAS.2015.7133614","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

The reduction of energy consumption in residential buildings and houses represents a major societal and environmental challenge. A huge portion of energy consumption in houses and buildings goes towards comfort control - heating, ventilation and air conditioning. Modern architecture tackles these problems by considering (i) passive exploitation of solar energy and (ii) use of special construction materials that often have quite complex characteristics. Therefore, a smart energy management control system should be able to take both of these considerations into account. This paper proposes a model predictive control (MPC) strategy for energy efficient management of heating and cooling of a house that can cope with the nonlinearities in the system induced by the use of modern construction materials. Furthermore, the proposed controller takes into account the weather forecast information, in particular the prediction of solar irradiance and air temperature. The nonlinear behavior of the system is approximated by a piecewise affine model and further incorporated into the mixed logical dynamical framework. The performance of the proposed nonlinear MPC is demonstrated in simulation experiments, which show applicability and usefulness of the MPC algorithm for energy management.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
现代建筑材料节能住宅的非线性模型预测控制
减少住宅建筑和房屋的能源消耗是一项重大的社会和环境挑战。房屋和建筑物的很大一部分能源消耗用于舒适控制——供暖、通风和空调。现代建筑通过考虑(1)被动利用太阳能和(2)使用通常具有相当复杂特性的特殊建筑材料来解决这些问题。因此,智能能源管理控制系统应该能够兼顾这两个方面。本文提出了一种模型预测控制(MPC)策略,用于住宅供暖和制冷的节能管理,该策略可以应对由于使用现代建筑材料而引起的系统非线性。此外,所提出的控制器考虑了天气预报信息,特别是太阳辐照度和空气温度的预测。系统的非线性行为由分段仿射模型近似,并进一步纳入混合逻辑动力框架。仿真实验验证了所提出的非线性MPC算法的性能,证明了该算法在能量管理中的适用性和实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Microimmune algorithm for sensor network localization Empirical evaluation of OI-MAC: Direct interconnection between wireless sensor networks for collaborative monitoring DiverNet — A network of inertial sensors for real time diver visualization Sensor fusion for intrusion detection under false alarm constraints Fault tolerant and scalable IoT-based architecture for health monitoring
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1