  Availability of a validated, realistic fuel cost model is a prerequisite to
the development and validation of new optimization methods and control tools.
This paper uses an autoregressive integrated moving average (ARIMA) model with
historical fuel cost data in development of a three-step-ahead fuel cost
distribution prediction. First, the data features of Form EIA-923 are explored
and the natural gas fuel costs of Texas generating facilities are used to
develop and validate the forecasting algorithm for the Texas example.
Furthermore, the spot price associated with the natural gas hub in Texas is
utilized to enhance the fuel cost prediction. The forecasted data is fit to a
normal distribution and the Kullback-Leibler divergence is employed to evaluate
the difference between the real fuel cost distributions and the estimated
distributions. The comparative evaluation suggests the proposed forecasting
algorithm is effective in general and is worth pursuing further.
