Export citation

Show simple item record

dc.contributor.authorWei-Chiang Hong (Ed.)*
dc.date.accessioned2021-02-11T17:06:20Z
dc.date.available2021-02-11T17:06:20Z
dc.date.issued2018*
dc.date.submitted2018-10-22 10:01:53*
dc.identifier29155*
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/51077
dc.description.abstractThe 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. The novel applications of kernel methods and hybrid evolutionary algorithms can provide more satisfactory parameters in forecasting models. We aimed to attract researchers with an interest in the research areas described above. Specifically, we were interested in contributions towards the development of HEAs with kernel methods or with other novel methods (e.g., chaotic mapping mechanism, fuzzy theory, and quantum computing mechanism), which, with superior capabilities over the traditional optimization approaches, aim to overcome some embedded drawbacks and then apply these new HEAs to be hybridized with original forecasting models to significantly improve forecasting accuracy.*
dc.languageEnglish*
dc.subjectQA75.5-76.95*
dc.subjectTA1-2040*
dc.subject.otherhybrid models*
dc.subject.otherenergy forecasting*
dc.subject.otherempirical mode decomposition*
dc.subject.otherevolutionary algorithms*
dc.subject.otherwavelet transform*
dc.subject.otherquantum computing mechanism*
dc.subject.othersupport vector regression / support vector machines*
dc.subject.otherchaotic mapping mechanism*
dc.subject.otherextreme learning machine*
dc.subject.otherfuzzy time series*
dc.subject.otherkernel methods*
dc.subject.otherspiking neural networks*
dc.titleKernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting*
dc.typebook
oapen.identifier.doi10.3390/books978-3-03897-293-8*
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0*
virtual.oapen_relation_isPublishedBy.publisher_nameMDPI - Multidisciplinary Digital Publishing Institute
virtual.oapen_relation_isPublishedBy.publisher_websitewww.mdpi.com/books
oapen.relation.isbn9783038972921*
oapen.relation.isbn9783038972938*
oapen.pages186*
oapen.edition1st*


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

https://creativecommons.org/licenses/by-nc-nd/4.0/
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0/