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...
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| Fformat: | Online |
| Iaith: | Saesneg |
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MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Pynciau: | |
| Mynediad Ar-lein: | 29155 |
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Dim Tagiau, Byddwch y cyntaf i dagio'r cofnod hwn!
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| _version_ | 1869519750190596096 |
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| author | Wei-Chiang Hong (Ed.) |
| author_browse | Wei-Chiang Hong (Ed.) |
| author_facet | Wei-Chiang Hong (Ed.) |
| author_sort | Wei-Chiang Hong (Ed.) |
| collection | Directory of Open Access Books |
| description | 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. 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. |
| format | Online |
| id | doab-20.500.12854ir-51077 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-510772024-04-14T10:28:03Z Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting Wei-Chiang Hong (Ed.) QA75.5-76.95 TA1-2040 hybrid models energy forecasting empirical mode decomposition evolutionary algorithms wavelet transform quantum computing mechanism support vector regression / support vector machines chaotic mapping mechanism extreme learning machine fuzzy time series kernel methods spiking neural networks thema EDItEUR::U Computing and Information Technology::UY Computer science 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. 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. 2021-02-11T17:06:20Z 2021-02-11T17:06:20Z 2018-10-22 10:01:53 2018 book 29155 9783038972921 9783038972938 https://directory.doabooks.org/handle/20.500.12854/51077 eng image/jpeg Attribution-NonCommercial-NoDerivatives 4.0 International https://play.google.com/books/publish/a/14935057684283403269#details/ISBN:9783038972921 https://www.mdpi.com/books/pdfview/book/840 https://www.mdpi.com/books/pdfview/book/840 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03897-293-8 10.3390/books978-3-03897-293-8 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783038972921 9783038972938 186 open access |
| spellingShingle | QA75.5-76.95 TA1-2040 hybrid models energy forecasting empirical mode decomposition evolutionary algorithms wavelet transform quantum computing mechanism support vector regression / support vector machines chaotic mapping mechanism extreme learning machine fuzzy time series kernel methods spiking neural networks thema EDItEUR::U Computing and Information Technology::UY Computer science Wei-Chiang Hong (Ed.) Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting |
| title | Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting |
| title_full | Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting |
| title_fullStr | Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting |
| title_full_unstemmed | Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting |
| title_short | Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting |
| title_sort | kernel methods and hybrid evolutionary algorithms in energy forecasting |
| topic | QA75.5-76.95 TA1-2040 hybrid models energy forecasting empirical mode decomposition evolutionary algorithms wavelet transform quantum computing mechanism support vector regression / support vector machines chaotic mapping mechanism extreme learning machine fuzzy time series kernel methods spiking neural networks thema EDItEUR::U Computing and Information Technology::UY Computer science |
| topic_facet | QA75.5-76.95 TA1-2040 hybrid models energy forecasting empirical mode decomposition evolutionary algorithms wavelet transform quantum computing mechanism support vector regression / support vector machines chaotic mapping mechanism extreme learning machine fuzzy time series kernel methods spiking neural networks thema EDItEUR::U Computing and Information Technology::UY Computer science |
| url | 29155 |
| work_keys_str_mv | AT weichianghonged kernelmethodsandhybridevolutionaryalgorithmsinenergyforecasting |