Predicting Energy Demand in Semi-Remote Arctic Locations
Forecasting energy demand within a distribution network is essential for developing strategies to manage and optimize available energy resources and the associated infrastructure. In this study, we consider remote communities in the Arctic located at the end of the radial distribution network withou...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/4/798 |
| _version_ | 1797415829951741952 |
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| author | Odin Foldvik Eikeland Filippo Maria Bianchi Harry Apostoleris Morten Hansen Yu-Cheng Chiou Matteo Chiesa |
| author_facet | Odin Foldvik Eikeland Filippo Maria Bianchi Harry Apostoleris Morten Hansen Yu-Cheng Chiou Matteo Chiesa |
| author_sort | Odin Foldvik Eikeland |
| collection | DOAJ |
| description | Forecasting energy demand within a distribution network is essential for developing strategies to manage and optimize available energy resources and the associated infrastructure. In this study, we consider remote communities in the Arctic located at the end of the radial distribution network without alternative energy supply. Therefore, it is crucial to develop an accurate forecasting model to manage and optimize the limited energy resources available. We first compare the accuracy of several models that perform short-and medium-term load forecasts in rural areas, where a single industrial customer dominates the electricity consumption. We consider both statistical methods and machine learning models to predict energy demand. Then, we evaluate the transferability of each method to a geographical rural area different from the one considered for training. Our results indicate that statistical models achieve higher accuracy on longer forecast horizons relative to neural networks, while the machine-learning approaches perform better in predicting load at shorter time intervals. The machine learning models also exhibit good transferability, as they manage to predict well the load at new locations that were not accounted for during training. Our work will serve as a guide for selecting the appropriate prediction model and apply it to perform energy load forecasting in rural areas and in locations where historical consumption data may be limited or even not available. |
| first_indexed | 2024-03-09T05:55:13Z |
| format | Article |
| id | doaj.art-50ab5a4f6e1840baaf4e442c3debce04 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T05:55:13Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-50ab5a4f6e1840baaf4e442c3debce042023-12-03T12:14:31ZengMDPI AGEnergies1996-10732021-02-0114479810.3390/en14040798Predicting Energy Demand in Semi-Remote Arctic LocationsOdin Foldvik Eikeland0Filippo Maria Bianchi1Harry Apostoleris2Morten Hansen3Yu-Cheng Chiou4Matteo Chiesa5Department of Physics and Technology, UiT the Arctic University of Norway, 9037 Tromsø, NorwayDepartment of Mathematics and Statistics and NORCE, The Norwegian Research Centre, UiT the Arctic University of Norway, 9037 Tromsø, NorwayLaboratory for Energy and NanoScience (LENS), Masdar Institute Campus, Khalifa University of Science and Technology, 127788 Abu Dhabi, United Arab EmiratesIshavskraft Power Company, 9024 Tromsø, NorwayDepartment of Physics and Technology, UiT the Arctic University of Norway, 9037 Tromsø, NorwayDepartment of Physics and Technology, UiT the Arctic University of Norway, 9037 Tromsø, NorwayForecasting energy demand within a distribution network is essential for developing strategies to manage and optimize available energy resources and the associated infrastructure. In this study, we consider remote communities in the Arctic located at the end of the radial distribution network without alternative energy supply. Therefore, it is crucial to develop an accurate forecasting model to manage and optimize the limited energy resources available. We first compare the accuracy of several models that perform short-and medium-term load forecasts in rural areas, where a single industrial customer dominates the electricity consumption. We consider both statistical methods and machine learning models to predict energy demand. Then, we evaluate the transferability of each method to a geographical rural area different from the one considered for training. Our results indicate that statistical models achieve higher accuracy on longer forecast horizons relative to neural networks, while the machine-learning approaches perform better in predicting load at shorter time intervals. The machine learning models also exhibit good transferability, as they manage to predict well the load at new locations that were not accounted for during training. Our work will serve as a guide for selecting the appropriate prediction model and apply it to perform energy load forecasting in rural areas and in locations where historical consumption data may be limited or even not available.https://www.mdpi.com/1996-1073/14/4/798energy load predictionsstatistical- and machine-learning-based approachesshort-term load forecastinglonger forecasting horizonstransferability predictions |
| spellingShingle | Odin Foldvik Eikeland Filippo Maria Bianchi Harry Apostoleris Morten Hansen Yu-Cheng Chiou Matteo Chiesa Predicting Energy Demand in Semi-Remote Arctic Locations Energies energy load predictions statistical- and machine-learning-based approaches short-term load forecasting longer forecasting horizons transferability predictions |
| title | Predicting Energy Demand in Semi-Remote Arctic Locations |
| title_full | Predicting Energy Demand in Semi-Remote Arctic Locations |
| title_fullStr | Predicting Energy Demand in Semi-Remote Arctic Locations |
| title_full_unstemmed | Predicting Energy Demand in Semi-Remote Arctic Locations |
| title_short | Predicting Energy Demand in Semi-Remote Arctic Locations |
| title_sort | predicting energy demand in semi remote arctic locations |
| topic | energy load predictions statistical- and machine-learning-based approaches short-term load forecasting longer forecasting horizons transferability predictions |
| url | https://www.mdpi.com/1996-1073/14/4/798 |
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