Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network.
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| Title: | Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network. |
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| Authors: | Zeng, Yu-Rong1,2 zyrhbue@gmail.com, Zeng, Yi1 zengy200810@126.com, Choi, Beomjin3 choib@csus.edu, Wang, Lin1 wanglin982@gmail.com |
| Source: | Energy. May2017, Vol. 127, p381-396. 16p. |
| Subjects: | Energy consumption, Back propagation, Artificial neural networks, Decision making, Energy policy, Gross domestic product |
| Abstract: | Reliable energy consumption forecasting can provide effective decision-making support for planning development strategies to energy enterprises and for establishing national energy policies. Accordingly, the present study aims to apply a hybrid intelligent approach named ADE–BPNN, the back-propagation neural network (BPNN) model supported by an adaptive differential evolution algorithm, to estimate energy consumption. Most often, energy consumption is influenced by socioeconomic factors. The proposed hybrid model incorporates gross domestic product, population, import, and export data as inputs. An improved differential evolution with adaptive mutation and crossover is utilized to find appropriate global initial connection weights and thresholds to enhance the forecasting performance of the BPNN. A comparative example and two extended examples are utilized to validate the applicability and accuracy of the proposed ADE–BPNN model. Errors of the test data sets indicate that the ADE–BPNN model can effectively predict energy consumption compared with the traditional back-propagation neural network model and other popular existing models. Moreover, mean impact value based analysis is conducted for electrical energy consumption in U.S. and total energy consumption forecasting in China to quantitatively explore the relative importance of each input variable for the improvement of effective energy consumption prediction. [ABSTRACT FROM AUTHOR] |
| Copyright of Energy is the property of Pergamon Press - An Imprint of Elsevier Science 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: 123078702 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zeng%2C+Yu-Rong%22">Zeng, Yu-Rong</searchLink><relatesTo>1,2</relatesTo><i> zyrhbue@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Zeng%2C+Yi%22">Zeng, Yi</searchLink><relatesTo>1</relatesTo><i> zengy200810@126.com</i><br /><searchLink fieldCode="AR" term="%22Choi%2C+Beomjin%22">Choi, Beomjin</searchLink><relatesTo>3</relatesTo><i> choib@csus.edu</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Lin%22">Wang, Lin</searchLink><relatesTo>1</relatesTo><i> wanglin982@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energy%22">Energy</searchLink>. May2017, Vol. 127, p381-396. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Back+propagation%22">Back propagation</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+policy%22">Energy policy</searchLink><br /><searchLink fieldCode="DE" term="%22Gross+domestic+product%22">Gross domestic product</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Reliable energy consumption forecasting can provide effective decision-making support for planning development strategies to energy enterprises and for establishing national energy policies. Accordingly, the present study aims to apply a hybrid intelligent approach named ADE–BPNN, the back-propagation neural network (BPNN) model supported by an adaptive differential evolution algorithm, to estimate energy consumption. Most often, energy consumption is influenced by socioeconomic factors. The proposed hybrid model incorporates gross domestic product, population, import, and export data as inputs. An improved differential evolution with adaptive mutation and crossover is utilized to find appropriate global initial connection weights and thresholds to enhance the forecasting performance of the BPNN. A comparative example and two extended examples are utilized to validate the applicability and accuracy of the proposed ADE–BPNN model. Errors of the test data sets indicate that the ADE–BPNN model can effectively predict energy consumption compared with the traditional back-propagation neural network model and other popular existing models. Moreover, mean impact value based analysis is conducted for electrical energy consumption in U.S. and total energy consumption forecasting in China to quantitatively explore the relative importance of each input variable for the improvement of effective energy consumption prediction. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Energy is the property of Pergamon Press - An Imprint of Elsevier Science 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.energy.2017.03.094 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 381 Subjects: – SubjectFull: Energy consumption Type: general – SubjectFull: Back propagation Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Decision making Type: general – SubjectFull: Energy policy Type: general – SubjectFull: Gross domestic product Type: general Titles: – TitleFull: Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zeng, Yu-Rong – PersonEntity: Name: NameFull: Zeng, Yi – PersonEntity: Name: NameFull: Choi, Beomjin – PersonEntity: Name: NameFull: Wang, Lin IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 05 Text: May2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 03605442 Numbering: – Type: volume Value: 127 Titles: – TitleFull: Energy Type: main |
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