To apply for the position, please send the required application materials to Prof. Rishee Jain (firstname.lastname@example.org). Please indicate in the subject: “IDREEM Postdoc Application”.
The Stanford Urban Informatics Lab & SLAC Grid Integration, Systems, and Mobility (GISMo) group are seeking a post-doctoral fellow to work on the Department of Energy sponsored Impact of Demand Response on short and long term building Energy Efficiency Metrics (IDREEM) project. The fellow will be primarily mentored by Prof. Rishee Jain (PI) and will also have the opportunity to work with other project faculty including: Prof. Johanna Mathieu (EECS, Michigan), Prof. Ian Hiskens (EECS, Michigan) and Prof. Jermiah Johnson (CCE, North Carolina State).
IDREEM Project Synopsis:
The goal of this project is to answer the following research questions: Does developing DR capabilities within a building generally lead to more or less efficient buildings (over periods of years)? Does implementing EE strategies within a building generally lead to more or less demand response capacity from those buildings (over periods of years)? Do buildings providing grid services via load shifting consume more energy (over the day) than they would have if not providing services? If so, what are the expected long-term energy impacts? The key outcomes are the establishment of comprehensive long-term DR/efficiency trends; assessment of the system-wide cost, efficiency, and emissions associated with DR; add-ons/extensions to commercial building software models that capture the trends; and a variety of reports, papers, and software documenting our models, methods, and results.
We will take a two-pronged approach to assess energy efficiency/demand response (DR) trends: 1) Using whole building electric load data corresponding to baseline and DR operation (900,000 customers) along with energy efficiency audits from 500,000 buildings in Northern California, we will establish long-term efficiency/DR trends associated with traditional load shedding and compare our results to the anecdotal results obtained in prior studies (including our own). Long-term trends correspond to energy efficiency/DR trends (i.e., correlations between building energy use, building peak load, and DR shed magnitudes) that occur over timeframes of years. For example, developing DR capabilities within a building helps us learn more about the building and often points to opportunities for energy efficiency, making buildings more efficient in the long-term, but possibly reducing their DR potential. 2) We will conduct experiments on multiple buildings to assess the short-term energy efficiency implications of load shifting for grid ancillary services that occur on timescales of real-time market participation to non-spinning reserve (i.e., shifting energy on timescales of 5 to 30 minutes).
Primary roles & responsibilities:
• Lead the analytical analysis of 900k customers worth of building electric load data (prong #1 above)
• Lead the deployment of a sensing system in Stanford campus buildings for experimental analysis (prong #2 above)
• Create novel data analysis methods for baselining building energy efficiency and demand-response impacts
• Mentor undergraduate and MS students (as required)
• Engage IDREEM project faculty and staff to enhance project impact
• Publish results in top energy, civil engineering and computer science proceedings/journals
The postdoc engaged in this position will also have significant opportunity for professional development and growth through structured mentorship activities.
Professional development activities:
• Create a professional development plan (PDP) with Prof. Jain and conduct bi-quarterly meetings to track overall progress towards PDP goals
• Opportunities to improve oral communication skills (e.g. seminar meetings, conference presentations); mentorship in development of “job talk” materials
• Develop proposal writing skills by engaging in brainstorming, layout and writing of major grant proposals with Prof. Jain
• Guest lecture in courses related to network analysis, urban building systems, building energy efficiency, etc
• PhD in civil engineering, environmental engineering, building science, electrical engineering, computer science or other applicable field
• Experience in designing and implementing data analytics methods for application in building energy performance and/or smart grid (i.e. strong programming skills are required)
• Already developed strong skills in: Matlab, Python, C++, R; experience in visualization (D3) is a plus
• Ability to work with large datasets, cloud computing services (e.g. AWS) and building simulation programs (e.g. EnergyPlus)
• Track record of publishing in top energy related journals (e.g. Energy, Applied Energy, Energy and Buildings), building/computing outlets (e.g. ACM BuildSys)
• Ability to structure and execute work independently
• Strong technical writing and oral communication skills
• Interest and ability to thrive in a geographically distributed and interdisciplinary team spanning power systems, building science and energy efficiency
- Short summary of your research interests/experience (<1 page)