WindView: An Open Platform for Wind Energy Forecast Visualization (funded by DOE EERE)

This project is to create an open situational awareness and decision support platform “WindView”, that provides grid operators with knowledge on the state and performance of their power system, with an emphasis on wind energy. The focus will be on utilizing advanced visualization to display pertinent information, extracted through computational techniques, from wind power forecasts for high-wind penetration systems.

Related Journal Publications:

1. Mucun Sun, Cong Feng, Jie Zhang Conditional Aggregated Probabilistic Wind Power Forecasting Based on Spatio-temporal Correlation, Applied Energy, Vol. 256, 2019, pp. 113842.

2. Mucun Sun, Cong Feng, Erol Kevin Chartan, Bri-Mathias Hodge, Jie Zhang, A Two-Step Short-Term Probabilistic Wind Forecasting Methodology Based on Predictive Distribution Optimization, Applied Energy, Vol. 238, 2019, pp. 1497-1505.

3. Cong Feng, Mucun Sun, Mingjian Cui, Erol Kevin Chartan, Bri-Mathias Hodge, Jie Zhang, Charactering Forecastability of Wind Sites in the United States, Renewable Energy, Vol. 133, 2019, pp. 1352-1365.

4. Mucun Sun, Cong Feng, Jie Zhang, Multi-Distribution Ensemble Probabilistic Wind Power Forecasting, Renewable Energy. (under review)

Related Conference Publications:

1. Mucun Sun, Cong Feng, Jie Zhang, Aggregated Probabilistic Wind Power Forecasting Based on Spatio-Temporal Correlation, IEEE Power & Energy Society General Meeting, Atlanta, GA, August 4-8, 2019.

2. Mucun Sun, Cong Feng, Erol Kevin Chartan, Bri-Mathias Hodge, Jie Zhang, Probabilistic Short-term Wind Forecasting Based on Pinball Loss Optimization, Probabilistic Methods Applied to Power Systems Conference (PMAPS), Boise, Idaho, June 24-28, 2018.