Real-Time Predictive Analytics, Big Data &amp; Energy Market Efficiency: Key to Efficient Markets and Lower Prices for Consumers

Benjamin Schmidt; Patrick Flannery; Mark DeSantis

doi:10.4028/www.scientific.net/AMM.704.453

Paper Titles

Solar Performance of a Dome-Covered House
p.431

Contribution to Bridge Damage Analysis
p.435

Dynamic Analysis of Variable Stiffness Bracing System in Structure
p.442

Serviceability and Structural Behavior of FRP-RC Beams under Different Weathering Conditions and Fatigue
p.447

Real-Time Predictive Analytics, Big Data & Energy Market Efficiency: Key to Efficient Markets and Lower Prices for Consumers
p.453

Design and Implementation of Logistics Management Information System Based on J2EE
p.459

Privacy Preserving Reputation Based Medical Services Scheme Using a Variant of ElGamal
p.464

Designing an Engineering System for Sustainability
p.474

Optimization of District Heating Consumption Using Random Heating Scenario Generator
p.479

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 704Real-Time Predictive Analytics, Big Data &...

Real-Time Predictive Analytics, Big Data & Energy Market Efficiency: Key to Efficient Markets and Lower Prices for Consumers

Abstract:

The combination of evolving deregulation of the US and EU energy markets together with recent advances in data analytics and so called 'Big Data' technologies now offers an unprecedented opportunity for optimal real-time energy pricing for buyers and sellers alike. The main challenge to date for optimal pricing has been optimal real-time bidding and variety of traditional data analysis tools have been applied to this challenge. Yet inefficiencies remain due to the volatile nature of the real-time market. Energy data science is the best solution to protect consumers against the electricity market's inefficiencies. This field is the meeting point between computer programming, machine learning, big data, quantitative analysis and economics. Energy data science is used to help the consumer predict what price they should offer to buy at each hour and closes gaps in the electric markets.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 704)

Pages:

453-458

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.704.453

Citation:

Cite this paper

Online since:

December 2014

Authors:

Benjamin Schmidt*, Patrick Flannery, Mark DeSantis

Keywords:

Energy, Learning Algorithms, Machine Learning, Predictive Analytics, Stochastic Optimization

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

* - Corresponding Author

References

[1] Craig, J. Dean, and Scott J. Savage. Market Restructuring, Competition and the Efficiency of Electricity Generation: Plant-level Evidence from the United States 1996 to 2006., The Energy Journal 34. 1 (2013): n. page. Web.

DOI: 10.5547/01956574.34.1.1

Google Scholar

[2] Fabrizio, Kira R., Nancy L. Rose, and Catherine D. Wolfram. Do Markets Reduce Costs? Assessing the Impact of Regulatory Restructuring on US Electric Generation Efficiency., American Economic Review 97. 4 (2007): 1250-277. Web.

DOI: 10.1257/aer.97.4.1250

Google Scholar

[3] Dias, Jose G., and Sofia B. Ramos. Heterogeneous Price Dynamics in U.S. Regional Electricity Markets., Energy Economics (2014): n. page. Web.

Google Scholar

[4] Davis, Lucas W., and Catherine Wolfram. Deregulation, Consolidation, and Efficiency: Evidence from US Nuclear Power., American Economic Journal: Applied Economics 4. 4 (2012): 194-225. Web.

DOI: 10.1257/app.4.4.194

Google Scholar

[5] Bowden, Nicholas S. Measuring Efficiency in Wholesale Electricity Markets., The Electricity Journal 22. 5 (2009): 34-38. Web.

DOI: 10.1016/j.tej.2009.04.006

Google Scholar

[6] Bughin, Jacques, Michael Chui, and James Manyika. Clouds, Big Data, and Smart Assets: Ten Tech-enabled Business Trends to Watch. Tech. N. p.: McKinsey Quarterly, n. d. Web.

Google Scholar

[7] Funk, John. Grid Manager PJM Asks Consumers in Ohio to Reduce Electricity Use., Cleveland. com. N. p., n. d. Web.

Google Scholar