Uncertainty Reduction in Power Generation Forecast Using Coupled Wavelet-ARIMA

Conference · Mon Oct 27 00:00:00 EDT 2014

OSTI ID:1163438

Hou, Zhangshuan; Etingov, Pavel V.; Makarov, Yuri V.; Samaan, Nader A.

In this paper, we introduce a new approach without implying normal distributions and stationarity of power generation forecast errors. In addition, it is desired to more accurately quantify the forecast uncertainty by reducing prediction intervals of forecasts. We use automatically coupled wavelet transform and autoregressive integrated moving-average (ARIMA) forecasting to reflect multi-scale variability of forecast errors. The proposed analysis reveals slow-changing “quasi-deterministic” components of forecast errors. This helps improve forecasts produced by other means, e.g., using weather-based models, and reduce forecast errors prediction intervals.

• Research Organization: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1163438
• Report Number(s): PNNL-SA-99910; TE1103000
• Resource Relation: Conference: IEEE PES General Meeting, Conference & Exposition, July 27-31, 2014, National Harbor, MD
• Country of Publication: United States
• Language: English

Related Subjects

Power system planning
uncertainty reduction
forecasting
wavelet transform
ARIMA