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...
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ftrepec:oai:RePEc:gam:jeners:v:14:y:2021:i:4:p:798-:d:492481 2024-04-14T08:07:28+00:00 Predicting Energy Demand in Semi-Remote Arctic Locations Odin Foldvik Eikeland Filippo Maria Bianchi Harry Apostoleris Morten Hansen Yu-Cheng Chiou Matteo Chiesa https://www.mdpi.com/1996-1073/14/4/798/pdf https://www.mdpi.com/1996-1073/14/4/798/ unknown https://www.mdpi.com/1996-1073/14/4/798/pdf https://www.mdpi.com/1996-1073/14/4/798/ article ftrepec 2024-03-19T10:39:40Z 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. energy load predictions; statistical- and machine-learning-based approaches; short-term load forecasting; longer forecasting horizons; transferability predictions Article in Journal/Newspaper Arctic RePEc (Research Papers in Economics) Arctic |
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Open Polar |
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RePEc (Research Papers in Economics) |
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unknown |
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. energy load predictions; statistical- and machine-learning-based approaches; short-term load forecasting; longer forecasting horizons; transferability predictions |
format |
Article in Journal/Newspaper |
author |
Odin Foldvik Eikeland Filippo Maria Bianchi Harry Apostoleris Morten Hansen Yu-Cheng Chiou Matteo Chiesa |
spellingShingle |
Odin Foldvik Eikeland Filippo Maria Bianchi Harry Apostoleris Morten Hansen Yu-Cheng Chiou Matteo Chiesa Predicting Energy Demand in Semi-Remote Arctic Locations |
author_facet |
Odin Foldvik Eikeland Filippo Maria Bianchi Harry Apostoleris Morten Hansen Yu-Cheng Chiou Matteo Chiesa |
author_sort |
Odin Foldvik Eikeland |
title |
Predicting Energy Demand in Semi-Remote Arctic Locations |
title_short |
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_sort |
predicting energy demand in semi-remote arctic locations |
url |
https://www.mdpi.com/1996-1073/14/4/798/pdf https://www.mdpi.com/1996-1073/14/4/798/ |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
https://www.mdpi.com/1996-1073/14/4/798/pdf https://www.mdpi.com/1996-1073/14/4/798/ |
_version_ |
1796304867526967296 |