Using Existing Datasets

Predictive analytics of near-future energy pricing

Develop predictive analytics software to forecast near-future energy pricing by extracting data from the United States Energy Information Administration (EIA) website including the historical prices of gasoline, crude oil, and electricity. Based upon these historical energy pricing trends, predicted weather patterns, location, and Monte-Carlo analysis that predicts future geopolitical events, extreme weather events, discovery of new fuel reserves, and increased energy demand, the software would be able to predict future energy costs for petroleum and electricity.

 

The following datasets from EIA would be used:

 

1. Average Retail Price of Electricity to Ultimate Customers by End-Use Sector

 

2. EIA's latest weekly petroleum analysis

 

This type of predictive analysis software would benefit the following stakeholders:

 

1. Specific types of businesses: Allowing them to better predict energy costs so as to optimize times to purchase gasoline (i.e. - aircraft, truck fleet, and large mail/delivery companies)

 

2. Consumers: Allow consumers to better predict when to purchase a tank of gas from one week to another. (i.e. – Develop a mobile app that suggests to the user which day during a particular week is best to fill-up the gas tank).

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Idea No. 94