Probabilistic Spatial Load Forecasting Based on Hierarchical Trending Method

Vasileios Evangelopoulos, Panagiotis Karafotis and Pavlos Georgilakis
Additional contact information
Vasileios Evangelopoulos: School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Zografou, Greece
Panagiotis Karafotis: School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Zografou, Greece
Pavlos Georgilakis: School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Zografou, Greece

Energies, 2020, vol. 13, issue 18, 1-25

Abstract: The efficient spatial load forecasting (SLF) is of high interest for the planning of power distribution networks, mainly in areas with high rates of urbanization. The ever-present spatial error of SLF arises the need for probabilistic assessment of the long-term point forecasts. This paper introduces a probabilistic SLF framework with prediction intervals, which is based on a hierarchical trending method. More specifically, the proposed hierarchical trending method predicts the magnitude of future electric loads, while the planners’ knowledge is used to improve the allocation of future electric loads, as well as to define the year of introduction of new loads. Subsequently, the spatial error is calculated by means of root-mean-squared error along the service territory, based on which the construction of the prediction intervals of the probabilistic forecasting part takes place. The proposed probabilistic SLF is introduced to serve as a decision-making tool for regional planners and distribution network operators. The proposed method is tested on a real-world distribution network located in the region of Attica, Athens, Greece. The findings prove that the proposed method shows high spatial accuracy and reduces the spatial error compared to a business-as-usual approach.

Keywords: distribution networks; hierarchical trending method; prediction interval; probabilistic forecasting; spatial load forecasting (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/18/4643/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/18/4643/ (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:13:y:2020:i:18:p:4643-:d:410051

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 ().