In wind integration studies, accurate representations of the wind power output from potential wind power plants and corresponding representations of wind power forecasts are needed, and typically used in a production cost simulation. Two methods for generating “synthetic” wind power forecasts that capture the statistical trends and characteristics found in commercial forecasting techniques are presented. These two methods are based on auto-regressive moving average (ARMA) models and the Markov random walk method. Statistical criteria are suggested for evaluation of wind power forecast performance, and both synthetic forecast methods proposed are evaluated quantitatively and qualitatively. The forecast performance is then compared with a commercial forecast used for an operational wind power plant in the Northwestern United States evaluated using the same statistical performance measures. These quantitative evaluation parameters are monitored during specific months of the year, during rapid ramping events, and at all times. The best ARMA based models failed to replicate the auto-regressive decay of forecast errors associated with commercial forecasts. A modification to the Markov method, consisting of adding a dimension to the state transition array, allowed the forecast time series to depend on multiple inputs. This improvement lowered the artificial variability in the original time series. The overall performance of this method was better than for the ARMA based models, and provides a suitable technique for use in creating a synthetic wind forecast for a wind integration study.
Skip Nav Destination
ASME 2010 4th International Conference on Energy Sustainability
May 17–22, 2010
Phoenix, Arizona, USA
Conference Sponsors:
- Advanced Energy Systems Division and Solar Energy Division
ISBN:
978-0-7918-4395-6
PROCEEDINGS PAPER
Modeling of Wind Power Production Forecast Errors for Wind Integration Studies
Jason J. Kemper,
Jason J. Kemper
Northern Arizona University, Flagstaff, AZ
Search for other works by this author on:
Mark F. Bielecki,
Mark F. Bielecki
Northern Arizona University, Flagstaff, AZ
Search for other works by this author on:
Thomas L. Acker
Thomas L. Acker
Northern Arizona University, Flagstaff, AZ
Search for other works by this author on:
Jason J. Kemper
Northern Arizona University, Flagstaff, AZ
Mark F. Bielecki
Northern Arizona University, Flagstaff, AZ
Thomas L. Acker
Northern Arizona University, Flagstaff, AZ
Paper No:
ES2010-90441, pp. 885-894; 10 pages
Published Online:
December 22, 2010
Citation
Kemper, JJ, Bielecki, MF, & Acker, TL. "Modeling of Wind Power Production Forecast Errors for Wind Integration Studies." Proceedings of the ASME 2010 4th International Conference on Energy Sustainability. ASME 2010 4th International Conference on Energy Sustainability, Volume 2. Phoenix, Arizona, USA. May 17–22, 2010. pp. 885-894. ASME. https://doi.org/10.1115/ES2010-90441
Download citation file:
3
Views
Related Proceedings Papers
Related Articles
Operation and Simulation of Hybrid Wind and Gas Turbine Power System Employing Wind Power Forecasting
J. Eng. Gas Turbines Power (December,2012)
Wind Power Deterministic Prediction and Uncertainty Quantification Based on Interval Estimation
J. Sol. Energy Eng (December,2021)
A Message From the Special Issue Editor
J. Sol. Energy Eng (November,2002)
Related Chapters
An Efficient Approach to Power Coefficient and Tip Speed Ratio Relationship Modeling in Maximum Power Point Tracking of Wind Power Generation
International Conference on Software Technology and Engineering (ICSTE 2012)
Role of Wind Energy Technology in India and Neighboring Countries
Wind Energy Applications
A Utility Perspective of Wind Energy
Wind Turbine Technology: Fundamental Concepts in Wind Turbine Engineering, Second Edition