  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.
