Joint Seasonal ARMA Approach for Modeling of Load Forecast Errors in Planning Studies

Conference · Mon Apr 14 04:00:00 EDT 2014

DOI:https://doi.org/10.1109/TDC.2014.6863150· OSTI ID:1158496

Hafen, Ryan P.; Samaan, Nader A.; Makarov, Yuri V.; Diao, Ruisheng; Lu, Ning

To make informed and robust decisions in the probabilistic power system operation and planning process, it is critical to conduct multiple simulations of the generated combinations of wind and load parameters and their forecast errors to handle the variability and uncertainty of these time series. In order for the simulation results to be trustworthy, the simulated series must preserve the salient statistical characteristics of the real series. In this paper, we analyze day-ahead load forecast error data from multiple balancing authority locations and characterize statistical properties such as mean, standard deviation, autocorrelation, correlation between series, time-of-day bias, and time-of-day autocorrelation. We then construct and validate a seasonal autoregressive moving average (ARMA) model to model these characteristics, and use the model to jointly simulate day-ahead load forecast error series for all BAs.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1158496
• Report Number(s): PNNL-SA-98247; TE1101000
• Country of Publication: United States
• Language: English

Related Subjects

Power system planning
Operations
Probabilistic methods
Time series analysis
Forecasting
Load modeling