Load prediction in short-term implementing the multivariate quantile regression

Yazhou Xing, Su Zhang, Peng Wen, Limin Shao and Babak Daneshvar Rouyendegh

Energy, 2020, vol. 196, issue C

Abstract: Probability-based interim demand prediction plays and important role in managing the grid and optimizing the transmitted power through lines. Improved prediction techniques able to offer precise forecasting are supposed to be compatible with their own implementational situations in interim operation and must be highly efficient and fast. A lot of prediction techniques based on data are excessively verbose and not very suitable. The mentioned challenge emerges when the numerous demands are supposed to be forecasted at the same time, for example assessing and optimizing the energy delivery network. Here, a novel hybrid prediction framework is suggested, which improves the probability-based prediction of each load in real-time. The improvement approach uses the multi-variable quantile regression that is implemented on each prediction in real-time when a new observational data is inputted to the system. The proposed procedure is assessed using the demand data released by the Independent System Operator-new England for eight areas, which is composed of six states of the U.S. The performance of the probability-based prediction are compared to that of three other benchmarks with respect to reliability and accuracy. The suggested approach shows better accuracy compared to the highest-ranked benchmark.

Keywords: Hybrid prediction method; Multi-variable regression; Interim probability-based prediction; Quantile regression forest (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544220301420
Full text for ScienceDirect subscribers only

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:eee:energy:v:196:y:2020:i:c:s0360544220301420

DOI: 10.1016/j.energy.2020.117035

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().