Systematic Review of Electricity Demand Forecast Using ANN-Based Machine Learning Algorithms
The forecast of electricity demand has been a recurrent research topic for decades, due to its economical and strategic relevance. Several Machine Learning (ML) techniques have evolved in parallel with the complexity of the electric grid. This paper reviews a wide selection of approaches that have u...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-07-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/13/4544 |
| _version_ | 1797527892708556800 |
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| author | Antón Román-Portabales Martín López-Nores José Juan Pazos-Arias |
| author_facet | Antón Román-Portabales Martín López-Nores José Juan Pazos-Arias |
| author_sort | Antón Román-Portabales |
| collection | DOAJ |
| description | The forecast of electricity demand has been a recurrent research topic for decades, due to its economical and strategic relevance. Several Machine Learning (ML) techniques have evolved in parallel with the complexity of the electric grid. This paper reviews a wide selection of approaches that have used Artificial Neural Networks (ANN) to forecast electricity demand, aiming to help newcomers and experienced researchers to appraise the common practices and to detect areas where there is room for improvement in the face of the current widespread deployment of smart meters and sensors, which yields an unprecedented amount of data to work with. The review looks at the specific problems tackled by each one of the selected papers, the results attained by their algorithms, and the strategies followed to validate and compare the results. This way, it is possible to highlight some peculiarities and algorithm configurations that seem to consistently outperform others in specific settings. |
| first_indexed | 2024-03-10T09:50:16Z |
| format | Article |
| id | doaj.art-dc0e4505e403416fb2ddf8eb3caebc80 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T09:50:16Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-dc0e4505e403416fb2ddf8eb3caebc802023-11-22T02:50:52ZengMDPI AGSensors1424-82202021-07-012113454410.3390/s21134544Systematic Review of Electricity Demand Forecast Using ANN-Based Machine Learning AlgorithmsAntón Román-Portabales0Martín López-Nores1José Juan Pazos-Arias2Quobis, 36380 O Porriño, SpainatlanTTic, Universidade de Vigo, 36310 Vigo, SpainatlanTTic, Universidade de Vigo, 36310 Vigo, SpainThe forecast of electricity demand has been a recurrent research topic for decades, due to its economical and strategic relevance. Several Machine Learning (ML) techniques have evolved in parallel with the complexity of the electric grid. This paper reviews a wide selection of approaches that have used Artificial Neural Networks (ANN) to forecast electricity demand, aiming to help newcomers and experienced researchers to appraise the common practices and to detect areas where there is room for improvement in the face of the current widespread deployment of smart meters and sensors, which yields an unprecedented amount of data to work with. The review looks at the specific problems tackled by each one of the selected papers, the results attained by their algorithms, and the strategies followed to validate and compare the results. This way, it is possible to highlight some peculiarities and algorithm configurations that seem to consistently outperform others in specific settings.https://www.mdpi.com/1424-8220/21/13/4544electricity demand forecastmachine learningartificial neural networkssystematic review |
| spellingShingle | Antón Román-Portabales Martín López-Nores José Juan Pazos-Arias Systematic Review of Electricity Demand Forecast Using ANN-Based Machine Learning Algorithms Sensors electricity demand forecast machine learning artificial neural networks systematic review |
| title | Systematic Review of Electricity Demand Forecast Using ANN-Based Machine Learning Algorithms |
| title_full | Systematic Review of Electricity Demand Forecast Using ANN-Based Machine Learning Algorithms |
| title_fullStr | Systematic Review of Electricity Demand Forecast Using ANN-Based Machine Learning Algorithms |
| title_full_unstemmed | Systematic Review of Electricity Demand Forecast Using ANN-Based Machine Learning Algorithms |
| title_short | Systematic Review of Electricity Demand Forecast Using ANN-Based Machine Learning Algorithms |
| title_sort | systematic review of electricity demand forecast using ann based machine learning algorithms |
| topic | electricity demand forecast machine learning artificial neural networks systematic review |
| url | https://www.mdpi.com/1424-8220/21/13/4544 |
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