Machine learning-based cooling load prediction and optimal control for mechanical ventilative cooling in high-rise buildings.
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| Title: | Machine learning-based cooling load prediction and optimal control for mechanical ventilative cooling in high-rise buildings. |
|---|---|
| Authors: | Sha, Haohan1 (AUTHOR), Moujahed, Majd1,2 (AUTHOR), Qi, Dahai1 (AUTHOR) dahai.qi@usherbrooke.ca |
| Source: | Energy & Buildings. Jul2021, Vol. 242, pN.PAG-N.PAG. 1p. |
| Subjects: | Cooling loads (Mechanical engineering), Tall buildings, Office buildings, Climate change, Cooling, Machine learning |
| Geographic Terms: | Montréal (Québec) |
| Abstract: | • A machine learning model is selected to predict cooling load. • Optimal control based on machine learning model and energy models is developed. • Potential ventilative cooling hours decrease due to global warming climate change. Ventilation has proved to be an effective solution for reducing building cooling load in high-rise buildings, i.e. ventilative cooling (VC), especially in cold climates. Mechanical ventilation system can achieve VC (i.e. mechanical VC) in high-rise buildings, but it needs appropriate control to reduce cooling related energy consumption and to consider the impact of climate change. This study aims to develop an optimal control method for mechanical VC and evaluate its energy performance, based on an advanced model. In this advanced model, a machine learning model was applied to predict building cooling load and energy models were developed to predict energy consumption. A case study was conducted on a real high-rise building located in Montreal (Canada). Using the measured data from the Building Automation System (BAS), the machine learning model, generated by an algorithm called Gradient tree boosting (GTB), was found to be the most accurate and was used in the optimal control method. The energy models that couple mechanical ventilation and chiller cooling were validated with the BAS measured data. Then, the optimal control method was applied to study the energy performance of mechanical VC under long-term climate conditions. The results indicate that the energy savings of mechanical VC will decrease around 16% during the summer but increase around 105% during the shoulder season due to climate change. The optimal nominal ventilation rate for mechanical VC in the typical meteorological year is twice of that in the 2080 s A1FI weather scenario. [ABSTRACT FROM AUTHOR] |
| Copyright of Energy & Buildings is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 150574209 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Machine learning-based cooling load prediction and optimal control for mechanical ventilative cooling in high-rise buildings. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sha%2C+Haohan%22">Sha, Haohan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Moujahed%2C+Majd%22">Moujahed, Majd</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qi%2C+Dahai%22">Qi, Dahai</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> dahai.qi@usherbrooke.ca</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energy+%26+Buildings%22">Energy & Buildings</searchLink>. Jul2021, Vol. 242, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Cooling+loads+%28Mechanical+engineering%29%22">Cooling loads (Mechanical engineering)</searchLink><br /><searchLink fieldCode="DE" term="%22Tall+buildings%22">Tall buildings</searchLink><br /><searchLink fieldCode="DE" term="%22Office+buildings%22">Office buildings</searchLink><br /><searchLink fieldCode="DE" term="%22Climate+change%22">Climate change</searchLink><br /><searchLink fieldCode="DE" term="%22Cooling%22">Cooling</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Montréal+%28Québec%29%22">Montréal (Québec)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: • A machine learning model is selected to predict cooling load. • Optimal control based on machine learning model and energy models is developed. • Potential ventilative cooling hours decrease due to global warming climate change. Ventilation has proved to be an effective solution for reducing building cooling load in high-rise buildings, i.e. ventilative cooling (VC), especially in cold climates. Mechanical ventilation system can achieve VC (i.e. mechanical VC) in high-rise buildings, but it needs appropriate control to reduce cooling related energy consumption and to consider the impact of climate change. This study aims to develop an optimal control method for mechanical VC and evaluate its energy performance, based on an advanced model. In this advanced model, a machine learning model was applied to predict building cooling load and energy models were developed to predict energy consumption. A case study was conducted on a real high-rise building located in Montreal (Canada). Using the measured data from the Building Automation System (BAS), the machine learning model, generated by an algorithm called Gradient tree boosting (GTB), was found to be the most accurate and was used in the optimal control method. The energy models that couple mechanical ventilation and chiller cooling were validated with the BAS measured data. Then, the optimal control method was applied to study the energy performance of mechanical VC under long-term climate conditions. The results indicate that the energy savings of mechanical VC will decrease around 16% during the summer but increase around 105% during the shoulder season due to climate change. The optimal nominal ventilation rate for mechanical VC in the typical meteorological year is twice of that in the 2080 s A1FI weather scenario. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Energy & Buildings is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.enbuild.2021.110980 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Cooling loads (Mechanical engineering) Type: general – SubjectFull: Tall buildings Type: general – SubjectFull: Office buildings Type: general – SubjectFull: Climate change Type: general – SubjectFull: Cooling Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Montréal (Québec) Type: general Titles: – TitleFull: Machine learning-based cooling load prediction and optimal control for mechanical ventilative cooling in high-rise buildings. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sha, Haohan – PersonEntity: Name: NameFull: Moujahed, Majd – PersonEntity: Name: NameFull: Qi, Dahai IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 03787788 Numbering: – Type: volume Value: 242 Titles: – TitleFull: Energy & Buildings Type: main |
| ResultId | 1 |
