Joint Seasonal ARMA Approach for Modeling of Load Forecast Errors in Planning Studies
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.
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- Conference: IEEE PES Transmission and Distribution Conference and Exposition, April 14-17, 2014, Chicago, Illinois
- IEEE, Piscataway, NJ, United States(US).
- Research Org:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
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- Country of Publication:
- United States
- Power system planning, Operations, Probabilistic methods, Time series analysis, Forecasting, Load modeling