Cong Feng is a director's postdoctoral fellow in the Sensing and Predictive Analytics Group. His research interests lie in machine learning-based renewable energy and load forecasting, energy infrastructure detection, and smart grid big data analytics. He has been working on multiple U.S. Department of Energy, Advanced Research Projects Agency–Energy (ARPA-E), and industrial projects.

Research Interests

Renewable energy and load forecasting

Energy infrastructure discovery

Smart grid sensing and analytics

Education

Ph.D., Mechanical Engineering, The University of Texas at Dallas

M.S., Mechanical Engineering, The University of Texas at Dallas

B.S., Power and Energy Systems, Wuhan University

Featured Work

A Taxonomical Review on Recent Artificial Intelligence Applications to Solar Photovoltaic System Grid Integration, International Journal of Electrical Power and Energy Systems (2021)

SolarNet: A Sky Image-based Deep Convolutional Neural Network for Intra-hour Solar Forecasting, Solar Energy (2020)

Deep Learning based Real-time Building Occupancy Detection using AMI Data, Institute of Electrical and Electronics Engineers (IEEE) Transactions on Smart Grid (2020)

Reinforced Deterministic and Probabilistic Load Forecasting via Q-Learning Dynamic Model Selection, IEEE Transactions on Smart Grid (2020)

OpenSolar: Promoting the Openness and Accessibility of Diverse Public Solar Datasets, Solar Energy (2019)

Unsupervised Clustering-Based Short-Term Solar Forecasting, IEEE Transactions on Sustainable Energy (2019)

Characterizing Forecastability of Wind Sites in the United States, Renewable Energy (2019)

A Data-Driven Multi-Model Methodology with Deep Feature Selection for Short-Term Wind Forecasting, Applied Energy (2017)

Awards and Honors

Outstanding Reviewer, IEEE Transactions on Sustainable Energy (2021)

SAS-IIF Research Award, International Institution of Forecasters (2017)

Best Paper Award, IEEE Power and Energy Society General Meeting, (2017)

Best Student Paper Award, The 4th IEEE/ACM International Conference on Big Data Computing (2017)