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Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting

The development of kernel methods and hybrid evolutionary algorithms (HEAs) to support experts in energy forecasting is of great importance to improving the accuracy of the actions derived from an energy decision maker, and it is crucial that they are theoretically sound. In addition, more accurate or more precise energy demand forecasts are required when decisions are made in a competitive environment. Therefore, this is of special relevance in the Big Data era. These forecasts are usually based on a complex function combination. These models have resulted in over-reliance on the use of informal judgment and higher expense if lacking the ability to catch the data patterns.

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Detail Information
Series Title
-
Call Number
624 KER
Publisher
Basel : MDPI - Multidisciplinary Digital Publishing Institute.,
Collation
-
Language
English
ISBN/ISSN
978-3-03897-293-8
Classification
624
Content Type
text
Media Type
computer
Carrier Type
online resource
Edition
-
Subject(s)
General and Civil Engineering
Specific Detail Info
-
Statement of Responsibility
Other Information
Cataloger
Arin
Source
-
Validator
maya
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