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