Improvement to the Prediction of Fuel Cost Distributions Using ARIMA Model

TL;DR

This paper enhances fuel cost distribution prediction accuracy using an ARIMA model combined with natural gas spot prices, validated on Texas data, aiding optimization and control in energy systems.

Contribution

It introduces a three-step-ahead ARIMA-based forecasting method incorporating spot prices, improving fuel cost distribution predictions for energy system optimization.

Findings

ARIMA model effectively predicts fuel costs

Inclusion of spot prices improves forecast accuracy

Predicted distributions closely match real data

Abstract

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.