Predicting Energy Demand in Semi-Remote Arctic Locations

Odin Foldvik Eikeland, Filippo Maria Bianchi, Harry Apostoleris, Morten Hansen, Yu-Cheng Chiou and Matteo Chiesa
Additional contact information
Odin Foldvik Eikeland: Department of Physics and Technology, UiT the Arctic University of Norway, 9037 Tromsø, Norway
Filippo Maria Bianchi: Department of Mathematics and Statistics and NORCE, The Norwegian Research Centre, UiT the Arctic University of Norway, 9037 Tromsø, Norway
Harry Apostoleris: Laboratory for Energy and NanoScience (LENS), Masdar Institute Campus, Khalifa University of Science and Technology, 127788 Abu Dhabi, United Arab Emirates
Morten Hansen: Ishavskraft Power Company, 9024 Tromsø, Norway
Yu-Cheng Chiou: Department of Physics and Technology, UiT the Arctic University of Norway, 9037 Tromsø, Norway
Matteo Chiesa: Department of Physics and Technology, UiT the Arctic University of Norway, 9037 Tromsø, Norway

Energies, 2021, vol. 14, issue 4, 1-17

Abstract: 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.

Keywords: energy load predictions; statistical- and machine-learning-based approaches; short-term load forecasting; longer forecasting horizons; transferability predictions (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/4/798/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/4/798/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:4:p:798-:d:492481

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().