A veracity preserving model for synthesizing scalable electricity load profiles

TL;DR

This paper introduces a hierarchical multi-matrices Markov Chain model that effectively synthesizes scalable electricity load profiles across different time scales, preserving real consumption behaviors for diverse sectors.

Contribution

The paper presents a novel HMMC model that outperforms classical Markov Chain models in synthesizing realistic, scalable electricity load profiles across multiple time scales and sectors.

Findings

HMMC model better preserves real consumption behaviors.

Model performs well on residential and non-residential data.

Trained models are publicly available for research use.

Abstract

Electricity users are the major players of the electric systems, and electricity consumption is growing at an extraordinary rate. The research on electricity consumption behaviors is becoming increasingly important to design and deployment of the electric systems. Unfortunately, electricity load profiles are difficult to acquire. Data synthesis is one of the best approaches to solving the lack of data, and the key is the model that preserves the real electricity consumption behaviors. In this paper, we propose a hierarchical multi-matrices Markov Chain (HMMC) model to synthesize scalable electricity load profiles that preserve the real consumption behavior on three time scales: per day, per week, and per year. To promote the research on the electricity consumption behavior, we use the HMMC approach to model two distinctive raw electricity load profiles. 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 classical Markov Chain model. We publish two trained models online, and researchers can directly use these trained models to synthesize scalable electricity load profiles for further researches.