Next Article in Journal
Utilising Open Geospatial Data to Refine Weather Variables for Building Energy Performance Evaluation—Incident Solar Radiation and Wind-Driven Infiltration Modelling
Previous Article in Journal
Experimental and Numerical Study of a Microcogeneration Stirling Unit under On–Off Cycling Operation
 
 
Article

Predicting Energy Demand in Semi-Remote Arctic Locations

1
Department of Physics and Technology, UiT the Arctic University of Norway, 9037 Tromsø, Norway
2
Department of Mathematics and Statistics and NORCE, The Norwegian Research Centre, UiT the Arctic University of Norway, 9037 Tromsø, Norway
3
Laboratory for Energy and NanoScience (LENS), Masdar Institute Campus, Khalifa University of Science and Technology, 127788 Abu Dhabi, United Arab Emirates
4
Ishavskraft Power Company, 9024 Tromsø, Norway
*
Author to whom correspondence should be addressed.
Energies 2021, 14(4), 798; https://doi.org/10.3390/en14040798
Received: 29 December 2020 / Revised: 21 January 2021 / Accepted: 27 January 2021 / Published: 3 February 2021
(This article belongs to the Section F: Electrical Engineering)
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. View Full-Text
Keywords: energy load predictions; statistical- and machine-learning-based approaches; short-term load forecasting; longer forecasting horizons; transferability predictions energy load predictions; statistical- and machine-learning-based approaches; short-term load forecasting; longer forecasting horizons; transferability predictions
Show Figures

Figure 1

MDPI and ACS Style

Foldvik Eikeland, O.; Bianchi, F.M.; Apostoleris, H.; Hansen, M.; Chiou, Y.-C.; Chiesa, M. Predicting Energy Demand in Semi-Remote Arctic Locations. Energies 2021, 14, 798. https://doi.org/10.3390/en14040798

AMA Style

Foldvik Eikeland O, Bianchi FM, Apostoleris H, Hansen M, Chiou Y-C, Chiesa M. Predicting Energy Demand in Semi-Remote Arctic Locations. Energies. 2021; 14(4):798. https://doi.org/10.3390/en14040798

Chicago/Turabian Style

Foldvik Eikeland, Odin, Filippo Maria Bianchi, Harry Apostoleris, Morten Hansen, Yu-Cheng Chiou, and Matteo Chiesa. 2021. "Predicting Energy Demand in Semi-Remote Arctic Locations" Energies 14, no. 4: 798. https://doi.org/10.3390/en14040798

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop