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Learning Energy Consumption and Demand Models through Data Mining for Reverse Engineering


Abstract The estimation of energy demand (by power plants) has traditionally relied on historical energy use data for the region(s) that a plant produces for. Regression analysis, artificial neural network and Bayesian theory are the most common approaches for analysing these data. Such data and techniques do not generate reliable results. Consequently, excess energy has to be generated to prevent blackout; causes for energy surge are not easily determined; and potential energy use reduction from energy efficiency solutions is usually not translated into actual energy use reduction. The paper highlights the weaknesses of traditional techniques, and lays out a framework to improve the prediction of energy demand by combining energy use models of equi... (more)
Created Date 2015-12-09
Contributor Naganathan, Hariharan (ASU author) / Chong, Oswald (ASU author) / Ye, Long (ASU author) / Ira A. Fulton School of Engineering / School of Sustainable Engineering and the Built Environment / Ira A. Fulton Schools of Engineering / School of Computing, Informatics and Decision Systems Engineering
Series PROCEDIA ENGINEERING
Type Text
Extent 6 pages
Language English
Identifier DOI: 10.1016/j.proeng.2015.11.392 / ISSN: 1877-7058
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Citation Naganathan, H., Chong, W. K., & Ye, N. (2015). Learning Energy Consumption and Demand Models through Data Mining for Reverse Engineering. Procedia Engineering, 118, 1319-1324. doi:10.1016/j.proeng.2015.11.392
Collaborating Institutions ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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