Economics at your fingertips  

An electricity consumption model for synthesizing scalable electricity load curves

Yunyou Huang, Jianfeng Zhan, Chunjie Luo, Lei Wang, Nana Wang, Daoyi Zheng, Fanda Fan and Rui Ren

Energy, 2019, vol. 169, issue C, 674-683

Abstract: Electricity users are the major players of the electricity systems, and electricity consumption is growing at an extraordinary rate. The research on electricity consumption behaviors are becoming increasingly important to design and deployment of the electricity systems. However, the acquisition of data related to the electricity consumption behaviors is still a major challenge. Data synthesis is among the best approaches to solving the issue, and the key is the model that preserves the real electricity consumption behaviors. In this paper, we propose a hierarchical multi-matrices Markov (HMM) model to synthesize scalable electricity load curves that preserve the real consumption behaviors on three time scales: per day, per week, and per year. To promote the research on the electricity consumption behaviors, we use the HMM approach to modeling two distinctive raw electricity load curves. One is collected from the resident sector, and the other is collected from the non-resident sectors, including different industries such as education, finance, and manufacturing. The experiments show our model performs much better than the cluster-based Markov model. We publish two trained models online, publicly available from, and researchers are allowed to directly use these trained models to synthesize scalable electricity load curves for further research.

Keywords: Data acquisition; Data models; Veracity-preserving model; Data synthesis; High-order Markov chain; Scalable electricity load curves (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
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:

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 Dana Niculescu ().

Page updated 2019-05-11
Handle: RePEc:eee:energy:v:169:y:2019:i:c:p:674-683