Datong Paul Zhou
Ph.D. Student, Hybrid Systems Laboratory
University of California, Berkeley
Department of Mechanical Engineering/
Electrical Engineering and Computer Sciences
307 Cory Hall, Desk 10
Berkeley, CA 94720-1770
datong.zhou [at] berkeley [dot] edu
I am a third year graduate student in Mechanical Engineering, working with Professor Claire Tomlin with a focus on Systems and Controls. My primary research interests lie in the field of energy markets, particularly in the interaction between end-users of electricity and load serving entities.
I graduated from Technische Universität München, Germany in 2011 with a major in Mechanical Engineering (with distinction). In the fall of 2016, I was a visiting student at the Laboratory for Information and Decision Systems (LIDS) of MIT, supervised by Prof. Munther A. Dahleh and Dr. Mardavij Roozbehani.
During the summer of 2015, I worked as a Consulting Intern for Siemens AG in Munich, Germany.
In my free time I enjoy doing triathlon, and I have achieved the title of Ironman 70.3 in July 2016.
Demand-side management has emerged as a vital tool to align electricity demand and supply closer to an equilibrium. My goal is to understand and model the potential for eliciting a desired consumer behavior through Demand Side Response (DR), which intentionally seeks to modify electricity consumption from expected patterns with the help of monetary incentives. It is my ambition to utilize tools at the intersection of mathematics, statistics, operations research, and control theory with an eye towards data-driven analysis to answer the following questions:
- How much do people reduce their electricity consumption in response to monetary incentives? How can we estimate such reductions?
- Given the inherently volatile nature of wholesale electricity prices and uncertainty of user demand, how can a load-serving entity optimally hedge against price and demand uncertainty?
- Can we observe peer effects in electricity consumption, and if yes, is it possible to utilize such social effects to (a) improve energy efficiency, and/or (b) maximize utility profit through price discrimination of end-users?
- Does real-time pricing of electricity, taking into account dynamic consumption models with memory, result in a stable feedback loop in terms of prices?
Preprints / Working Papers
- D. Zhou, M. Dahleh, and C. Tomlin. Eliciting Private User Information for Demand Response.
- D. Zhou, M. Dahleh, and C. Tomlin. How Peer Effects Influence Energy Consumption.
- D. Zhou, M. Dahleh, and C. Tomlin. Hedging Strategies in Wholesale Electricity Markets.
- D. Zhou, M. Balandat, and C. Tomlin. Estimating Treatment Effects of a Residential Demand Response Program Using Non-Experimental Data.
- D. Zhou, M. Roozbehani, M. Dahleh, and C. Tomlin. Stability Analysis of Wholesale Electricity Markets under Dynamic Consumption Models and Real-Time Pricing. To appear in 2017 American Control Conference.
- D. Zhou*, Q. Hu*, and C. Tomlin. Quantitative Comparison of Data-Driven and Physics-Based Models for Commercial Building HVAC Systems. To appear in 2017 American Control Conference.
- D. Zhou, M. Balandat, and C. Tomlin. Residential Demand Response Targeting Using Observational Data. 55th Conference on Decision and Control, December 2016.
- D. Zhou, M. Balandat, and C. Tomlin. A Bayesian Perspective on Residential Demand Response Using Smart Meter Data. 54th Annual Allerton Conference on Communication, Control, and Computing, September 2016.
- Q. Hu, F. Oldewurtel, M. Balandat, E. Vrettos, D. Zhou, and C. Tomlin. Building Model Identification during Regular Operation - Empirical Results and Challenges. American Control Conference (ACC), July 2016.
* Equal contribution among authors.
- EE 222: Nonlinear Systems: Analysis, Stability, and Control (Spring 2017)
- Engineering Thermodynamics, Technische Universität München (Spring 2014)
- Engineering Mechanics, Technische Universität München (Spring 2014)
- ME 232: Advanced Control Systems I
- ME 233: Advanced Control Systems II
- ME C231A: Experiential Advanced Control Design
Electrical Engineering and Computer Sciences
- EE 221a: Linear System Theory
- EE 222: Nonlinear Systems: Analysis, Stability, and Control
- CS C281A: Statistical Learning Theory
- CS 289A: Introduction to Machine Learning
Industrial Engineering and Operations Research
- IEOR 165: Engineering Statistics, Quality Control, and Forecasting
- IEOR 263A: Applied Stochastic Processes
- MATH 170: Mathematical Methods for Optimization
- MATH 204: Ordinary Differential Equations and Dynamical Systems
- ENG 231: Mathematical Methods in Engineering
Current GPA: 3.97/4.0