Energy, economic and environmental performance simulation of a hybrid renewable microgeneration system with neural network predictive control.
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| Title: | Energy, economic and environmental performance simulation of a hybrid renewable microgeneration system with neural network predictive control. |
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| Authors: | Entchev, Evgueniy1, Yang, Libing1, Ghorab, Mohamed1, Rosato, Antonio2 antonio.rosato@unina2.it, Sibilio, Sergio2 |
| Source: | Alexandria Engineering Journal. Mar2018, Vol. 57 Issue 1, p455-473. 19p. |
| Subjects: | Heat exchangers, Air conditioning, Renewable energy sources, Solar technology, Neural circuitry |
| Abstract: | The use of artificial neural networks (ANNs) in various applications has grown significantly over the years. This paper compares an ANN based approach with a conventional on-off control applied to the operation of a ground source heat pump/photovoltaic thermal system serving a single house located in Ottawa (Canada) for heating and cooling purposes. The hybrid renewable microgeneration system was investigated using the dynamic simulation software TRNSYS. A controller for predicting the future room temperature was developed in the MATLAB environment and six ANN control logics were analyzed. The comparison was performed in terms of ability to maintain the desired indoor comfort levels, primary energy consumption, operating costs and carbon dioxide equivalent emissions during a week of the heating period and a week of the cooling period. The results showed that the ANN approach is potentially able to alleviate the intensity of thermal discomfort associated with overheating/overcooling phenomena, but it could cause an increase in unmet comfort hours. The analysis also highlighted that the ANNs based strategies could reduce the primary energy consumption (up to around 36%), the operating costs (up to around 81%) as well as the carbon dioxide equivalent emissions (up to around 36%). [ABSTRACT FROM AUTHOR] |
| Copyright of Alexandria Engineering Journal 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: 128452600 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Energy, economic and environmental performance simulation of a hybrid renewable microgeneration system with neural network predictive control. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Entchev%2C+Evgueniy%22">Entchev, Evgueniy</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Yang%2C+Libing%22">Yang, Libing</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Ghorab%2C+Mohamed%22">Ghorab, Mohamed</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Rosato%2C+Antonio%22">Rosato, Antonio</searchLink><relatesTo>2</relatesTo><i> antonio.rosato@unina2.it</i><br /><searchLink fieldCode="AR" term="%22Sibilio%2C+Sergio%22">Sibilio, Sergio</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Alexandria+Engineering+Journal%22">Alexandria Engineering Journal</searchLink>. Mar2018, Vol. 57 Issue 1, p455-473. 19p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Heat+exchangers%22">Heat exchangers</searchLink><br /><searchLink fieldCode="DE" term="%22Air+conditioning%22">Air conditioning</searchLink><br /><searchLink fieldCode="DE" term="%22Renewable+energy+sources%22">Renewable energy sources</searchLink><br /><searchLink fieldCode="DE" term="%22Solar+technology%22">Solar technology</searchLink><br /><searchLink fieldCode="DE" term="%22Neural+circuitry%22">Neural circuitry</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The use of artificial neural networks (ANNs) in various applications has grown significantly over the years. This paper compares an ANN based approach with a conventional on-off control applied to the operation of a ground source heat pump/photovoltaic thermal system serving a single house located in Ottawa (Canada) for heating and cooling purposes. The hybrid renewable microgeneration system was investigated using the dynamic simulation software TRNSYS. A controller for predicting the future room temperature was developed in the MATLAB environment and six ANN control logics were analyzed. The comparison was performed in terms of ability to maintain the desired indoor comfort levels, primary energy consumption, operating costs and carbon dioxide equivalent emissions during a week of the heating period and a week of the cooling period. The results showed that the ANN approach is potentially able to alleviate the intensity of thermal discomfort associated with overheating/overcooling phenomena, but it could cause an increase in unmet comfort hours. The analysis also highlighted that the ANNs based strategies could reduce the primary energy consumption (up to around 36%), the operating costs (up to around 81%) as well as the carbon dioxide equivalent emissions (up to around 36%). [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Alexandria Engineering Journal 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=128452600 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.aej.2016.09.001 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 455 Subjects: – SubjectFull: Heat exchangers Type: general – SubjectFull: Air conditioning Type: general – SubjectFull: Renewable energy sources Type: general – SubjectFull: Solar technology Type: general – SubjectFull: Neural circuitry Type: general Titles: – TitleFull: Energy, economic and environmental performance simulation of a hybrid renewable microgeneration system with neural network predictive control. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Entchev, Evgueniy – PersonEntity: Name: NameFull: Yang, Libing – PersonEntity: Name: NameFull: Ghorab, Mohamed – PersonEntity: Name: NameFull: Rosato, Antonio – PersonEntity: Name: NameFull: Sibilio, Sergio IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 11100168 Numbering: – Type: volume Value: 57 – Type: issue Value: 1 Titles: – TitleFull: Alexandria Engineering Journal Type: main |
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