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.
Highlights
Accurate load forecasting systems can reduce additional costs related to inaccurate prediction of the energy demand and provide a better understanding of the dynamics of existing power systems [1,2]
The global energy market is evolving from centralized systems with large power stations connected to a single electricity grid which support the area of interest, towards the inclusion of more decentralized energy systems where the area of interest may be supplied by multiple energy sources, such as local renewable distributed generation (DG) technologies and battery storage systems [5,6,7,8,9,10,11]
We evaluate approaches to forecasting energy demand based on both statistical and machine learningbased approaches to project future energy demand from historical data
Summary
Accurate load forecasting systems can reduce additional costs related to inaccurate prediction of the energy demand and provide a better understanding of the dynamics of existing power systems [1,2]. If the forecasts overestimate the demand, the result will be excess power supply. This will result in increased costs and contract curtailments for the energy market participants. The energy load profiles are typically represented by time series that describe the dynamics of the underlying energy distribution system and are characterized by typical human-based seasonal and cyclic consumption patterns. We examine several approaches to predict energy consumption on both short and longer forecasting horizons. Two different forecasting methodologies are used, one statistical-based method, and one method based on neural networks
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
