Bio


I am a Stanford University doctoral candidate in the department of Energy Resources Engineering. With past experience in greenhouse gas mitigation, carbon capture and storage, and solar energy, I am building a career towards finding the balance between energy needs and environmental concerns.

Education & Certifications


  • M.S., Stanford University, Energy Resources Engineering (2015)
  • B.S., Tsinghua University, Environmental Engineering (2013)

Current Research and Scholarly Interests


My main focus is on prediction of short-term solar irradiation based on statistical learning and computer vision. Improved prediction accuracy is beneficial for both the grid operators to balance the grid, and the utility to bid for short-term market. Other interest include carbon storage and sequestration,energy systems optimization, and greenhouse gas mitigation.

Lab Affiliations


All Publications


  • Energy Return on Investment (EROI) for Forty Global Oilfields Using a Detailed Engineering-Based Model of Oil Production PLOS ONE Brandt, A. R., Sun, Y., Bharadwaj, S., Livingston, D., Tan, E., Gordon, D. 2015; 10 (12)

    View details for DOI 10.1371/journal.pone.0144141

    View details for Web of Science ID 000367092500002

    View details for PubMedID 26695068

  • Uncertainty in Regional-Average Petroleum GHG Intensities: Countering Information Gaps with Targeted Data Gathering. Environmental science & technology Brandt, A. R., Sun, Y., Vafi, K. 2015; 49 (1): 679-686

    Abstract

    Recent efforts to model crude oil production GHG emissions are challenged by a lack of data. Missing data can affect the accuracy of oil field carbon intensity (CI) estimates as well as the production-weighted CI of groups ("baskets") of crude oils. Here we use the OPGEE model to study the effect of incomplete information on the CI of crude baskets. We create two different 20 oil field baskets, one of which has typical emissions and one of which has elevated emissions. Dispersion of CI estimates is greatly reduced in baskets compared to single crudes (coefficient of variation = 0.2 for a typical basket when 50% of data is learned at random), and field-level inaccuracy (bias) is removed through compensating errors (bias of ∼5% in above case). If a basket has underlying characteristics significantly different than OPGEE defaults, systematic bias is introduced through use of defaults in place of missing data. Optimal data gathering strategies were found to focus on the largest 50% of fields, and on certain important parameters for each field. Users can avoid bias (reduced to <1 gCO2/MJ in our elevated emissions basket) through strategies that only require gathering ∼10-20% of input data.

    View details for DOI 10.1021/es505376t

    View details for PubMedID 25517046