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Title: Uncertainty Reduction in Power Generation Forecast Using Coupled Wavelet-ARIMA

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.
Authors:
; ; ;
Publication Date:
OSTI Identifier:
1163438
Report Number(s):
PNNL-SA-99910
TE1103000
DOE Contract Number:
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: IEEE PES General Meeting, Conference & Exposition, July 27-31, 2014, National Harbor, MD
Publisher:
IEEE, Piscataway, NJ, United States(US).
Research Org:
Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
Power system planning, uncertainty reduction, forecasting, wavelet transform, ARIMA