Modeling Energy Demand—A Systematic Literature Review
In this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing l...
| Những tác giả chính: | , , , , |
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| Định dạng: | Bài viết |
| Ngôn ngữ: | English |
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MDPI AG
2021-11-01
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| Loạt: | Energies |
| Những chủ đề: | |
| Truy cập trực tuyến: | https://www.mdpi.com/1996-1073/14/23/7859 |
| _version_ | 1827675030746562560 |
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| author | Paul Anton Verwiebe Stephan Seim Simon Burges Lennart Schulz Joachim Müller-Kirchenbauer |
| author_facet | Paul Anton Verwiebe Stephan Seim Simon Burges Lennart Schulz Joachim Müller-Kirchenbauer |
| author_sort | Paul Anton Verwiebe |
| collection | DOAJ |
| description | In this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing literature reviews, in this comprehensive study all of the following aspects of energy demand models are analyzed: techniques, prediction accuracy, inputs, energy carrier, sector, temporal horizon, and spatial granularity. Readers benefit from easy access to a broad literature base and find decision support when choosing suitable data-model combinations for their projects. Results have been compiled in comprehensive figures and tables, providing a structured summary of the literature, and containing direct references to the analyzed articles. Drawbacks of techniques are discussed as well as countermeasures. The results show that among the articles, machine learning (ML) techniques are used the most, are mainly applied to short-term electricity forecasting on a regional level and rely on historic load as their main data source. Engineering-based models are less dependent on historic load data and cover appliance consumption on long temporal horizons. Metaheuristic and uncertainty techniques are often used in hybrid models. Statistical techniques are frequently used for energy demand modeling as well and often serve as benchmarks for other techniques. Among the articles, the accuracy measured by mean average percentage error (MAPE) proved to be on similar levels for all techniques. This review eases the reader into the subject matter by presenting the emphases that have been made in the current literature, suggesting future research directions, and providing the basis for quantitative testing of hypotheses regarding applicability and dominance of specific methods for sub-categories of demand modeling. |
| first_indexed | 2024-03-10T04:55:30Z |
| format | Article |
| id | doaj.art-a399a23a54aa45c3b63c571437ed422c |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T04:55:30Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-a399a23a54aa45c3b63c571437ed422c2023-11-23T02:18:52ZengMDPI AGEnergies1996-10732021-11-011423785910.3390/en14237859Modeling Energy Demand—A Systematic Literature ReviewPaul Anton Verwiebe0Stephan Seim1Simon Burges2Lennart Schulz3Joachim Müller-Kirchenbauer4Chair of Energy and Resource Management, Technische Universität Berlin, H69, Straße des 17. Juni 135, 10623 Berlin, GermanyChair of Energy and Resource Management, Technische Universität Berlin, H69, Straße des 17. Juni 135, 10623 Berlin, GermanyInstitute of Energy and Climate Research, Systems Analysis and Technology Evaluation (IEK-STE), Forschungszentrum Jülich, 52428 Jülich, GermanyChair of Energy and Resource Management, Technische Universität Berlin, H69, Straße des 17. Juni 135, 10623 Berlin, GermanyChair of Energy and Resource Management, Technische Universität Berlin, H69, Straße des 17. Juni 135, 10623 Berlin, GermanyIn this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing literature reviews, in this comprehensive study all of the following aspects of energy demand models are analyzed: techniques, prediction accuracy, inputs, energy carrier, sector, temporal horizon, and spatial granularity. Readers benefit from easy access to a broad literature base and find decision support when choosing suitable data-model combinations for their projects. Results have been compiled in comprehensive figures and tables, providing a structured summary of the literature, and containing direct references to the analyzed articles. Drawbacks of techniques are discussed as well as countermeasures. The results show that among the articles, machine learning (ML) techniques are used the most, are mainly applied to short-term electricity forecasting on a regional level and rely on historic load as their main data source. Engineering-based models are less dependent on historic load data and cover appliance consumption on long temporal horizons. Metaheuristic and uncertainty techniques are often used in hybrid models. Statistical techniques are frequently used for energy demand modeling as well and often serve as benchmarks for other techniques. Among the articles, the accuracy measured by mean average percentage error (MAPE) proved to be on similar levels for all techniques. This review eases the reader into the subject matter by presenting the emphases that have been made in the current literature, suggesting future research directions, and providing the basis for quantitative testing of hypotheses regarding applicability and dominance of specific methods for sub-categories of demand modeling.https://www.mdpi.com/1996-1073/14/23/7859energy demand modelingenergy forecasting techniquessystematic literature reviewenergy demand driverslevel of detailelectricity load forecasting |
| spellingShingle | Paul Anton Verwiebe Stephan Seim Simon Burges Lennart Schulz Joachim Müller-Kirchenbauer Modeling Energy Demand—A Systematic Literature Review Energies energy demand modeling energy forecasting techniques systematic literature review energy demand drivers level of detail electricity load forecasting |
| title | Modeling Energy Demand—A Systematic Literature Review |
| title_full | Modeling Energy Demand—A Systematic Literature Review |
| title_fullStr | Modeling Energy Demand—A Systematic Literature Review |
| title_full_unstemmed | Modeling Energy Demand—A Systematic Literature Review |
| title_short | Modeling Energy Demand—A Systematic Literature Review |
| title_sort | modeling energy demand a systematic literature review |
| topic | energy demand modeling energy forecasting techniques systematic literature review energy demand drivers level of detail electricity load forecasting |
| url | https://www.mdpi.com/1996-1073/14/23/7859 |
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