Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting
More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, c...
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| Главный автор: | |
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| Формат: | Online |
| Язык: | английский |
| Опубликовано: |
MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Предметы: | |
| Online-ссылка: | 29152 |
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Нет меток, Требуется 1-ая метка записи!
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| _version_ | 1869530932588838912 |
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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 | More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers. This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy. |
| format | Online |
| id | doab-20.500.12854ir-49697 |
| 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-496972024-04-14T10:28:06Z Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting Wei-Chiang Hong (Ed.) QA75.5-76.95 TK7885-7895 hybrid models chaotic mapping mechanism recurrence plot theory energy forecasting empirical mode decomposition evolutionary algorithms quantum computing mechanism general regression neural network optimization methodologies support vector regression/support vector machines thema EDItEUR::U Computing and Information Technology::UY Computer science More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers. This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy. 2021-02-11T15:40:26Z 2021-02-11T15:40:26Z 2018-10-19 11:45:03 2018 book 29152 9783038972877 9783038972860 https://directory.doabooks.org/handle/20.500.12854/49697 eng image/jpeg Attribution-NonCommercial-NoDerivatives 4.0 International https://www.mdpi.com/books/pdfview/book/839 https://play.google.com/books/publish/a/14935057684283403269#details/ISBN:9783038972860 https://www.mdpi.com/books/pdfview/book/839 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03897-287-7 10.3390/books978-3-03897-287-7 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783038972877 9783038972860 250 open access |
| spellingShingle | QA75.5-76.95 TK7885-7895 hybrid models chaotic mapping mechanism recurrence plot theory energy forecasting empirical mode decomposition evolutionary algorithms quantum computing mechanism general regression neural network optimization methodologies support vector regression/support vector machines thema EDItEUR::U Computing and Information Technology::UY Computer science Wei-Chiang Hong (Ed.) Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting |
| title | Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting |
| title_full | Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting |
| title_fullStr | Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting |
| title_full_unstemmed | Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting |
| title_short | Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting |
| title_sort | hybrid advanced optimization methods with evolutionary computation techniques in energy forecasting |
| topic | QA75.5-76.95 TK7885-7895 hybrid models chaotic mapping mechanism recurrence plot theory energy forecasting empirical mode decomposition evolutionary algorithms quantum computing mechanism general regression neural network optimization methodologies support vector regression/support vector machines thema EDItEUR::U Computing and Information Technology::UY Computer science |
| topic_facet | QA75.5-76.95 TK7885-7895 hybrid models chaotic mapping mechanism recurrence plot theory energy forecasting empirical mode decomposition evolutionary algorithms quantum computing mechanism general regression neural network optimization methodologies support vector regression/support vector machines thema EDItEUR::U Computing and Information Technology::UY Computer science |
| url | 29152 |
| work_keys_str_mv | AT weichianghonged hybridadvancedoptimizationmethodswithevolutionarycomputationtechniquesinenergyforecasting |