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Energy forecasting in the big data world
Tao Hong
*
, Pierre Pinson
*
Corresponding author for this work
University of North Carolina at Charlotte
Research output
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peer-review
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Engineering
Energy Engineering
100%
Big Data
100%
Cutting Edge
25%
Information and Communication Technologies
25%
Process Forecasting
25%
High Resolution
25%
Agricultural and Biological Sciences
Communications Technology
100%
Decision Making
100%
Information Technology
100%
Economics, Econometrics and Finance
Energy Forecasting
100%
Energy Industry
100%
Specific Industry
50%
Utility Industry
50%
Computer Science
Big Data
100%
Best Practice
25%
Information and Communication Technologies
25%
Nontraditional Data
25%
Decision-Making
25%
Business Problem
25%
Energy Industry
25%
Keyphrases
Big Data World
100%
High-dimensional Processes
33%
Non-traditional Data
33%