Cong Feng

Cong Feng

Director's Fellow at NREL

Peer-Reviewed Publications

A complete list of publications, see my Google Scholar Profile.

Journal Publications

  1. Feng, C., Zhang W., Yang R., and Hodge, B.-M., “Probabilistic Solar Forecasting through Deep Learning and Occlusion-perturbed Sky Images”, IEEE Trans. Sustain. Energy, 2022. (under review)
  2. Zhang, Y., Feng, C., and Shaffery, P., Yang, R., ``BTM Visibility Gaps and Roadmap”, IEEE Power and Energy Magazine, 2022. (under review)
  3. Li, B., Feng, C., Zhang, R., Spyrou, E., Krishnan, V., Hobbs, B., and Zhang, J., ``Sizing Ramping Reserve Using Probabilistic Solar Forecasts: A Data-Driven Method”, Applied Energy, 2022. [, ]
  4. Erdener, B.C., Feng, C., Doubleday, K., Florita, A., and Hodge, B.-M., A Review of Behind-the-Meter Solar Forecasting, Renewable and Sustainable Energy Reviews, 2022. [, ]
  5. Feng, C., Zhang J., Zhang W., and Hodge, B.-M., Convolutional Neural Networks for Intra-hour Solar Forecasting based on Sky Image Sequences, Applied Energy, 2022. [, , ]
  6. Feng, C., Liu, Y., and Zhang, J., A Taxonomical Review on Recent Artificial Intelligence Applications to Solar Photovoltaic System Grid Integration, International Journal of Electrical Power and Energy Systems, 2021. [, ]
  7. Rahman, J., Feng, C., and Zhang, J., A Learning-Augmented Approach for AC Optimal Power Flow, International Journal of Electrical Power and Energy Systems, 2021. (in press). [, ]
  8. Sun, M., Feng, C., and Zhang, J., Probabilistic Solar Power Forecasting based on Weather Scenario Generation, Appl. Energy, Vol. 266, 2020. [, ]
  9. Feng, C., and Zhang, J., SolarNet: A Sky Image-based Deep Convolutional Neural Network for Intra-hour Solar Forecasting, Solar Energy, Vol. 204, 2020, pp.71–78. [, , ]
  10. Feng, C., Mehmani, A., and Zhang, J., Deep Learning based Real-time Building Occupancy Detection using AMI Data, IEEE Trans. Smart Grid, Vol. 11, 2020, pp. 4490–4501. [, ]
  11. Feng, C. and Zhang, J., Assessment of Aggregation Strategies for Machine Learning based Short-Term Load Forecasting, Electric Power Systems Research, Vol. 184, 2020. [, , ]
  12. Feng, C., Sun, M., and Zhang, J., Reinforced Deterministic and Probabilistic Load Forecasting via Q-Learning Dynamic Model Selection, IEEE Trans. Smart Grid, Vol. 11, 2020, pp.1377-1386. [, , , ]
  13. Sun, M., Feng, C., and Zhang, J., Multi-Distribution Ensemble Probabilistic Wind Power Forecasting, Renew. Energy, Vol. 148, 2020, pp. 135–149. [, ]
  14. Sun, M., Feng, C., and Zhang, J., Conditional Aggregated Probabilistic Wind Power Forecasting Based on Spatio-temporal Correlation, Appl. Energy, Vol. 256, 2019. [, ]
  15. Feng, C., Yang, D., Hodge, B.-M., and Zhang, J., OpenSolar: Promoting the Openness and Accessibility of Diverse Public Solar Datasets, Solar Energy, Vol. 188, 2019, pp.1369–1379. [, , ]
  16. Feng, C., Cui, M., Hodge, B.-M., Lu, S., Hamann, H. F. and Zhang, J., Unsupervised Clustering-Based Short-Term Solar Forecasting, IEEE Trans. Sustain. Energy, Vol. 10, 2019, pp. 2174–2185. [, ]
  17. Feng, C., Sun, M., Cui, M., Chartan, E., Zhang, J., Characterizing Forecastability of Wind Sites in the United States, Renew. Energy, Vol. 133, 2019, pp.1352–1365. [, ]
  18. Sun, M., Feng, C., Chartan, E., Hodge, B.-M., and Zhang, J., A Two-Step Short-Term Probabilistic Wind Forecasting Methodology Based on Predictive Distribution Optimization, Appl. Energy, Vol. 238, 2019, pp.1497–1505. [, ]
  19. Cui, M., Feng, C., Wang, Z., Zhang, J., Statistical Representation of Wind Power Ramps Using a Generalized Gaussian Mixture Model, IEEE Trans. Sustain. Energy, Vol. 9(1) 2018, pp.261–272. [, ]
  20. Cui, M., Zhang, J., Feng, C., Florita, A., Sun, Y., and Hodge, B.-M., Characterizing and Analyzing Ramping Events in Wind Power, Solar Power, Load, and Netload, Renew. Energy, Vol. 111, 2017, pp. 227–244. [, ]
  21. Feng, C., Cui, M., Hodge, B.-M., Zhang, J., A Data-Driven Multi-Model Methodology with Deep Feature Selection for Short-Term Wind Forecasting, Appl. Energy, Vol. 190, 2017, pp.1245–1257. [, , ]

Book Chapters

  1. Feng, C., Sun, M., Dabbaghjamanesh, M., Liu, Y., Zhang, J., Advanced Machine Learning Applications to the Modern Power Systems, Book Title: New Technologies for Power System Operation and Analysis, Elsevier, 2021, pp. 209-257. []
  2. Feng, C., Zhang, J., Wind Power and Ramp Forecasting for Grid Integration, Book Title: Advanced Wind Turbine Technology, Springer, 2018, pp. 299-315. []

Conference Publications

  1. Feng, C., Zhang, W., Hodge, B.-M., and Zhang, Y. Occlusion-perturbed Deep Learning for Probabilistic Solar Forecasting via Sky Images, IEEE Power \& Energy Society General Meeting, Denver, CO, 2022. (accepted)
  2. Zhang, W., Feng, C., and Hodge, B.-M., A Regime-Switching Spatio-temporal GARCH Method for Shor-Term Wind Forecast, IEEE Power & Energy Society General Meeting, Denver, CO, 2022. (accepted)
  3. Li, B., Feng, C., and Zhang, J., Multi-Timescale Simulation of Non-Spinning Reserve in Wholesale Power Markets, 2021 IEEE Green Technologies Conference, Virtual, April 7-9, 2021. []
  4. Rahman, J., Feng, C., and Zhang, J., Machine Learning-Aided Security Constrained Optimal Power Flow, IEEE Power & Energy Society General Meeting, Montreal, Canada, August 2-6, 2020. []
  5. Feng, C., Sun, M., Zhang, J., Doubleday, K., Hodge, B.-M., and Du, P., A Data-driven Method for Adaptive Reserve Requirements Estimation via Probabilistic Net Load Forecasting, IEEE Power & Energy Society General Meeting, Montreal, Canada, August 2-6, 2020. []
  6. Sun, M., Feng, C., and Zhang, J., Factoring Behind-the-Meter Solar into Load Forecasting: Case Studies under Extreme Weather, The 11th Conference on Innovative Smart Grid Technologies (ISGT 2020), Washington D.C., February 17-20, 2020. []
  7. Feng, C. and Zhang, J., SolarNet: A Deep Convolutional Neural Network for Solar Forecasting via Sky Images, The 11th Conference on Innovative Smart Grid Technologies (ISGT 2020), Washington D.C., February 17-20, 2020. [, ]
  8. Sun, M., Feng, C., and Zhang, J., Aggregated Probabilistic Wind Power Forecasting Based on Spatio-Temporal Correlation, IEEE Power & Energy Society General Meeting, Atlanta, GA, August 4-8, 2019. []
  9. Feng, C. and Zhang, J., Reinforcement Learning based Dynamic Model Selection for Short-Term Load Forecasting, The 10th Conference on Innovative Smart Grid Technologies (ISGT 2019), Washington D.C., February 18-21, 2019. [, , ]
  10. Feng, C. and Zhang, J., Short-Term Load Forecasting With Different Aggregation Strategies, ASME 2018 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference , Paper No. DETC2018-86084, Quebec City, Canada, August 26-29, 2018. [, ]
  11. Feng, C. and Zhang, J., Hourly-Similarity Based Solar Forecasting Using Multi-Model Machine Learning Blending, 2018 IEEE PES General Meeting, Portland, OR, August 5-10, 2018. []
  12. Sun, M., Feng, C., Zhang, J., Chartan, E., and Hodge, B.-M., Probabilistic Short-term Wind Forecasting Based on Pinball Loss Optimization, Probabilistic Methods Applied to Power Systems Conference, Boise, Idaho, June 24-28, 2018. []
  13. Feng, C., Chartan, E., Hodge, B.-M., and Zhang, J., Characterizing time series data diversity for wind forecasting, 4th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, Austin, TX, USA, Dec. 05 - 08, 2017. (Best Student Paper Award) []
  14. Feng, C., Cui, M., Lee, M., Zhang, J., Hodge, B.-M., Lu, S., and Hamann, H. F., Short-term Global Horizontal Irradiance Forecasting Based on Sky Imaging and Pattern Recognition, 2017 IEEE PES General Meeting, Chicago, IL, USA, July 16 - 20, 2017. (Best Paper Award) []
  15. Cui, M., Feng, C., Wang, Z., Wang, Q., Florita, A., and Zhang, J., Probabilistic Wind Power Ramps Forecasting Based on Massive Scenarios Generation, 2017 IEEE PES General Meeting, Chicago, IL, USA, July 16 - 20, 2017. []
  16. Cui, M., Wang, Z., Feng, C., Wang, Q., Florita, A., Zhang, J., A Truncated Gaussian Mixture Model for Statistical Analysis of Wind Power Ramping Magnitudes, 2017 IEEE PES General Meeting, Chicago, IL, USA, July 16 - 20, 2017. []