Disaggregating and Projecting Electricity Demand in China (2017-2018)


The decisions made today about the electric power sector will have profound and long-lasting implications for the economic and social well-being of citizens, and will affect the availability and prices of energy resources worldwide and the state of our natural environment.

Much of the world is in the midst of a decision about the future of electricity systems. However, there is an important element that separates the planning exercises of the developed countries from those of developing countries: projected growth of electricity demand.

Decisions about electric power development are difficult to make. Achieving energy and environmental goals while satisfying the electric power needs and supporting economic growth requires an information-driven planning process that considers the economic, environmental and reliability implications of using energy sources and key demand-side alternatives such as end-use energy efficiency and demand-side management.

Project Description

While a precise forecast of electricity demand is impossible, a projection of its many drivers and how these may lead to different consumption levels is a sine qua non for robust planning of capacity to generate, transmit and distribute electrical power. The goal of this Bass Connections project is to understand the determinants of electricity demand in order to project different electricity demand scenarios at a high temporal and spatial resolution in developing countries.

As the largest developing country in the world, China faces an even greater challenge in projecting future electricity demand. The project team will focus on a model development for China’s electricity demand projection; in the future this model can be applied to other developing countries.

The mechanistic model for estimating the uncertainty of electricity demand began in 2016 as part of the Bass Connections MOTESA project. The team collected, filtered, analyzed and synthetized high-resolution data on electricity consumption about multiple aspects, such as economic (GDP, industrial structure, income), social (urbanization rate, stocks of housing and appliances), meteorological (temperature and humidity) and technological (energy efficiency) indicators; and gathered information from other energy models for missing data.

The 2017-2018 project team seeks to understand the determinants of electricity demand in order to project different demand scenarios at a high temporal and spatial resolution based on the information gathered in 2016, and to develop a bottom-up framework for characterizing possible futures for electricity demand in China that considers the uncertainties in demand-side technologies and policies. This will be done using the concept of system dynamics, by developing dynamic energy models and then projecting changes in technologies used for different scenarios of socioeconomic conditions, regulatory framework and consumers’ behavioral trends.

Further, the team will take scenarios of projected determinants as key inputs to develop a modeling structure using a series of algorithms that will enable team members to generate a synthetic time series of hourly and sub-hourly electricity demand useful for designing end-use energy efficiency and demand side management programs for planning the future electricity system in China. 

Anticipated Outcomes

Computer-based dynamic model that projects determinants and disaggregating hourly electricity demand in multi-time-scale (short-, middle- and long-term); open-access tool based on the dynamic electricity demand model; report and corresponding database with basic information about the hourly electricity demand by province (2015-2050) and its ingredient in China; peer-reviewed journal articles that project China’s disaggregating electricity demand at a high temporal and spatial resolution under different scenarios

Student Opportunities

We are seeking approximately seven undergraduates, three master’s students and one or two additional PhD students.

Undergraduates should be pursuing a major in computer science, statistical sciences, mathematics, physics or engineering, and/or be pursuing the Energy Certificate. Summer interns will be recruited from those interested in being involved in the project as participants in a full credit independent study during the spring, or should commit to enroll in an independent study in the fall semester for full credit.

Graduate students should be willing to participate by signing up for a full-credit independent study in two semesters or working on a master’s project or thesis that is highly related to the project and using or contributing material from the project.

For all applicants, desired skills include statistical analysis; computer programming; basic knowledge of microeconomics (cost functions, supply and demand curves, market clearing) and of the electricity industry and the challenges of renewable power integration.

Summer interns and fall semester students should attend all weekly meetings on Fridays, 11:30-1:00 p.m. Besides the mentorship from leaders in the weekly meetings, the student members involved will explain disciplinary concepts, approaches and results to scientists inside and outside their field, policy makers and the public at large; share the view that tangible, useful and timely contributions to real-world problems require pursuing both disciplinary depth and a continuous fruitful interdisciplinary exchange; be divided into groups based on the principle of complementary disciplinary background; and work under the supervision of students from higher learning levels and collaborate with students from the same learning level.

All team members will develop a knowledge base and technical skills typical of their core discipline (statistics, engineering, computer science, energy and environmental science), but also typical of the “electricity consumption projection analysts” role they play in the project, including but not limited to the electricity consumption system (understanding electricity demand in different developing countries; understanding daily, monthly, yearly load profile; geographical distribution of electricity consumption; variability and uncertainties); determinants of electricity consumption (determinants of each part of Chinese electricity consumption, the relationship between determinants and electricity consumption); dynamic models (fundamentals of dynamic energy models using the concept of system dynamics, STELLA for dynamic models simulation, modeling tools for policy-based decision-making); and statistics and computer science (basics of computer programming/software development).

All students are expected to do work that advances the collective goals of this project but are also expected to be the lead authors of a paper, report or code-piece and corresponding user manual. All team members should keep a personal log updated weekly on progress made and new tasks and milestones. The log should be posted on Sakai using a template that will be designed by the group in the first meeting.

At the end of the summer and at the end of the fall semester all student team members will submit a final document including: 1) a summary of the work completed; 2) an evaluation of each of the team’s members on collaboration spirit, work and effectiveness; and 3) a reflection on the team-based learning experience and suggestions on future work to be considered by the team. This last item will be posted as part of the weekly blog posts on the project’s public website.


Summer 2017 – Fall 2017 or Spring 2018

This project is designed to be completed during Summer and Fall 2017. Involvement during the summer will be through internships, and involvement during the fall will be for academic credit (3 credit units). Students willing to continue their involvement in the project during Spring 2018 can do so as part of their senior thesis project or master’s project.

Team meetings will likely take place on Mondays from 3:00 to 4:30 p.m. 

  • Summer 2017: Literature review on China’s power system; identification of databases for location screening in China; student contributions submitted and evaluated
  • Fall 2017: Dynamic model for Chinese electric power demand projection; student contributions submitted and evaluated
  • Spring 2018: Simulations of Chinese electricity consumption under different scenarios; student contributions submitted and evaluated; theses, master’s projects and papers based on the project submitted


Independent study credit available for fall and spring semesters; summer funding

See earlier related team, Modeling Tools for Energy Systems Analysis (MOTESA) (2016-2017).


Faculty/Staff Team Members

Mark Borsuk, Pratt - Civil & Environmental Engineering
Mingquan Li, Nicholas School - Environmental Sciences & Policy
Dalia Patino Echeverri, Nicholas School - Environmental Sciences & Policy*

* denotes team leader