Artificial Intelligence for Advanced Building Control: Energy and GHG Savings from a Case Study
Model-based Predictive Control (MPC) is a promising advanced control strategy for the improvement of building operation. MPC uses a model of the building along with weather forecasts to optimize control strategies, such as indoor air temperature set-points, thermal storage charging and discharging c...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-08-01
|
Series: | Proceedings |
Subjects: | |
Online Access: | https://www.mdpi.com/2504-3900/23/1/7 |
id |
doaj-b1c338d8401242cdb1f34d0ce7f46f48 |
---|---|
record_format |
Article |
spelling |
doaj-b1c338d8401242cdb1f34d0ce7f46f482020-11-24T21:27:24ZengMDPI AGProceedings2504-39002019-08-01231710.3390/proceedings2019023007proceedings2019023007Artificial Intelligence for Advanced Building Control: Energy and GHG Savings from a Case StudyNunzio Cotrufo0Etienne Saloux1CanmetENERGY, Natural Resources Canada, 1615 Blvd. Lionel-Boulet, Varennes, QC J3X 1P7, CanadaCanmetENERGY, Natural Resources Canada, 1615 Blvd. Lionel-Boulet, Varennes, QC J3X 1P7, CanadaModel-based Predictive Control (MPC) is a promising advanced control strategy for the improvement of building operation. MPC uses a model of the building along with weather forecasts to optimize control strategies, such as indoor air temperature set-points, thermal storage charging and discharging cycles, etc. An obstacle to the adoption of MPC is the modelling step: developing a dedicated control-oriented model is a time-consuming process, requiring technical expertise and a large amount of information about the building and its operation. To overcome these issues, this paper proposes a new approach for the development of MPC strategies based on Artificial Intelligence (AI) techniques, aiming to map correlations among commonly available operation variables and to develop models suitable for predictive control. The proposed approach was applied in an institutional building in Varennes, QC, with the aim of reducing the natural gas consumption during the heating season. Early results show a remarkable effectiveness of the proposed approach, with a reduction of natural gas and building heating consumption of 23.9% and 6.3%, respectively.https://www.mdpi.com/2504-3900/23/1/7advanced controlArtificial Intelligencemodel-based predictive controlenergy efficiency |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Nunzio Cotrufo Etienne Saloux |
spellingShingle |
Nunzio Cotrufo Etienne Saloux Artificial Intelligence for Advanced Building Control: Energy and GHG Savings from a Case Study Proceedings advanced control Artificial Intelligence model-based predictive control energy efficiency |
author_facet |
Nunzio Cotrufo Etienne Saloux |
author_sort |
Nunzio Cotrufo |
title |
Artificial Intelligence for Advanced Building Control: Energy and GHG Savings from a Case Study |
title_short |
Artificial Intelligence for Advanced Building Control: Energy and GHG Savings from a Case Study |
title_full |
Artificial Intelligence for Advanced Building Control: Energy and GHG Savings from a Case Study |
title_fullStr |
Artificial Intelligence for Advanced Building Control: Energy and GHG Savings from a Case Study |
title_full_unstemmed |
Artificial Intelligence for Advanced Building Control: Energy and GHG Savings from a Case Study |
title_sort |
artificial intelligence for advanced building control: energy and ghg savings from a case study |
publisher |
MDPI AG |
series |
Proceedings |
issn |
2504-3900 |
publishDate |
2019-08-01 |
description |
Model-based Predictive Control (MPC) is a promising advanced control strategy for the improvement of building operation. MPC uses a model of the building along with weather forecasts to optimize control strategies, such as indoor air temperature set-points, thermal storage charging and discharging cycles, etc. An obstacle to the adoption of MPC is the modelling step: developing a dedicated control-oriented model is a time-consuming process, requiring technical expertise and a large amount of information about the building and its operation. To overcome these issues, this paper proposes a new approach for the development of MPC strategies based on Artificial Intelligence (AI) techniques, aiming to map correlations among commonly available operation variables and to develop models suitable for predictive control. The proposed approach was applied in an institutional building in Varennes, QC, with the aim of reducing the natural gas consumption during the heating season. Early results show a remarkable effectiveness of the proposed approach, with a reduction of natural gas and building heating consumption of 23.9% and 6.3%, respectively. |
topic |
advanced control Artificial Intelligence model-based predictive control energy efficiency |
url |
https://www.mdpi.com/2504-3900/23/1/7 |
work_keys_str_mv |
AT nunziocotrufo artificialintelligenceforadvancedbuildingcontrolenergyandghgsavingsfromacasestudy AT etiennesaloux artificialintelligenceforadvancedbuildingcontrolenergyandghgsavingsfromacasestudy |
_version_ |
1725974816568115200 |