(752e) Towards a Novel Energy Price Predictive Framework: The Texas ? & ? Energy Price Index

Baratsas, S. G., Texas A&M Energy Institute
Niziolek, A. M., Princeton University
Onel, O., Princeton University
Matthews, L. R., Texas A&M University
Floudas, C. A., Texas A&M University
Hallermann, D. R., Mays Business School
Sorescu, S. M., Mays Business School
Pistikopoulos, E. N., Texas A&M Energy Institute, Texas A&M University
The energy markets are sensitive and volatile to technological breakthroughs, changes in monetary policy, or major global challenges. Moreover, various governmental agencies, political and commercial organizations, think tanks as well as researchers and academics around the globe, consider various energy policies and their effects in an attempt to deal with the increasing concerns in energy independence, energy scarcity, energy sustainability, and pollution caused by the utilization of energy. Also, in the current environment in which all strategic political and commercial decisions and policies are assessed in economic terms, it is of outmost importance to determine the cost of energy precisely so as to evaluate their effectiveness. Considering the fact that energy affects every single individual and entity in the world, it is crucial to quantify “the unit price of energy” accurately, and study how it evolves with respect to time, major breakthroughs, challenges, energy and monetary policies. Given the lack of such a tool, we develop a novel framework, the Texas A&M Energy Price Index (EPI) that can be used as a benchmark to calculate the average price of energy to the end-use consumers in the United States.

The complicated energy landscape of the United States is carefully depicted to determine the products that are directed to the end-use sectors of the U.S. economy [1-5]. The total energy demand of these products, together with their prices, serve as the backbone of the Energy Price Index [6-10]. Since several key components (e.g. prices, demands) of the Energy Price Index have a lag between two and three months, we introduce a rolling horizon based parameter estimation model to approximate the current value of the Energy Price Index. The predictive methodology is tested on 120 months to show the accuracy of the developed framework in the prediction of the current value of the Energy Price Index. Energy price sub-indices for the transportation, residential, commercial, and industrial sectors are also developed. Some of the potential applications of the proposed index in the areas of economics, finance, engineering, law, and policy are demonstrated.


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