基于ARIMA模型的燃料费用分布预测的改进
Improvement to the Prediction of Fuel Cost Distributions Using ARIMA Model
摘要：
有效的、现实的燃料成本模型的可用性是开发和验证新的优化方法和控制工具的先决条件。本文采用自回归综合移动平均（ARIMA）模型与历史燃料成本数据的发展，提前三步燃料成本分布预测。首先，表格EIA-923的数据特征进行了探索，并使用得克萨斯州发电设施的天然气燃料成本来开发和验证预测算法的得克萨斯州的例子。此外，与得克萨斯州的天然气枢纽的现货价格被用来提高燃料成本预测。将预测数据拟合成正态分布，并采用库尔贝克-莱布勒散度来评估实际燃料成本分布与估计分布之间的差异。对比评估表明，所提出的预测算法是有效的，在一般情况下，值得进一步研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
