Hybrid Advanced Techniques for Forecasting in Energy Sector
Accurate forecasting performance in the energy sector is a primary factor in the modern restructured power market, accomplished by any novel advanced hybrid techniques. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| التنسيق: | Online |
| اللغة: | الإنجليزية |
| منشور في: |
MDPI - Multidisciplinary Digital Publishing Institute
2021
|
| الموضوعات: | |
| الوصول للمادة أونلاين: | 29149 |
| الوسوم: |
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1869515802246381568 |
|---|---|
| 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 | Accurate forecasting performance in the energy sector is a primary factor in the modern restructured power market, accomplished by any novel advanced hybrid techniques. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated by factors such as seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. To comprehensively address this issue, it is insufficient to concentrate only on simply hybridizing evolutionary algorithms with each other, or on hybridizing evolutionary algorithms with chaotic mapping, quantum computing, recurrent and seasonal mechanisms, and fuzzy inference theory in order to determine suitable parameters for an existing model. It is necessary to also consider hybridizing or combining two or more existing models (e.g., neuro-fuzzy model, BPNN-fuzzy model, seasonal support vector regression–chaotic quantum particle swarm optimization (SSVR-CQPSO), etc.). These advanced novel hybrid techniques can provide more satisfactory energy forecasting performances. This book aimed to attract researchers with an interest in the research areas described above. Specifically, we were interested in contributions towards recent developments, i.e., hybridizing or combining any advanced techniques in energy forecasting, with the superior capabilities over the traditional forecasting approaches, with the ability to overcome some embedded drawbacks, and with the very superiority to achieve significant improved forecasting accuracy. |
| format | Online |
| id | doab-20.500.12854ir-49698 |
| 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-496982024-04-14T10:28:02Z Hybrid Advanced Techniques for Forecasting in Energy Sector Wei-Chiang Hong (Ed.) QA75.5-76.95 TA1-2040 hybrid models autoregressive moving average with exogenous variable (ARMAX) energy forecasting fuzzy group quantile forecasting evolutionary algorithms quantum computing mechanism cluster validity support vector regression / support vector machines artificial neural networks principal component analysis bayesian inference thema EDItEUR::U Computing and Information Technology::UY Computer science Accurate forecasting performance in the energy sector is a primary factor in the modern restructured power market, accomplished by any novel advanced hybrid techniques. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated by factors such as seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. To comprehensively address this issue, it is insufficient to concentrate only on simply hybridizing evolutionary algorithms with each other, or on hybridizing evolutionary algorithms with chaotic mapping, quantum computing, recurrent and seasonal mechanisms, and fuzzy inference theory in order to determine suitable parameters for an existing model. It is necessary to also consider hybridizing or combining two or more existing models (e.g., neuro-fuzzy model, BPNN-fuzzy model, seasonal support vector regression–chaotic quantum particle swarm optimization (SSVR-CQPSO), etc.). These advanced novel hybrid techniques can provide more satisfactory energy forecasting performances. This book aimed to attract researchers with an interest in the research areas described above. Specifically, we were interested in contributions towards recent developments, i.e., hybridizing or combining any advanced techniques in energy forecasting, with the superior capabilities over the traditional forecasting approaches, with the ability to overcome some embedded drawbacks, and with the very superiority to achieve significant improved forecasting accuracy. 2021-02-11T15:40:30Z 2021-02-11T15:40:30Z 2018-10-19 10:39:42 2018 book 29149 9783038972914 9783038972907 https://directory.doabooks.org/handle/20.500.12854/49698 eng image/jpeg Attribution-NonCommercial-NoDerivatives 4.0 International https://play.google.com/books/publish/a/14935057684283403269#details/ISBN:9783038972907 https://www.mdpi.com/books/pdfview/book/841 https://www.mdpi.com/books/pdfview/book/841 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03897-291-4 10.3390/books978-3-03897-291-4 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783038972914 9783038972907 250 open access |
| spellingShingle | QA75.5-76.95 TA1-2040 hybrid models autoregressive moving average with exogenous variable (ARMAX) energy forecasting fuzzy group quantile forecasting evolutionary algorithms quantum computing mechanism cluster validity support vector regression / support vector machines artificial neural networks principal component analysis bayesian inference thema EDItEUR::U Computing and Information Technology::UY Computer science Wei-Chiang Hong (Ed.) Hybrid Advanced Techniques for Forecasting in Energy Sector |
| title | Hybrid Advanced Techniques for Forecasting in Energy Sector |
| title_full | Hybrid Advanced Techniques for Forecasting in Energy Sector |
| title_fullStr | Hybrid Advanced Techniques for Forecasting in Energy Sector |
| title_full_unstemmed | Hybrid Advanced Techniques for Forecasting in Energy Sector |
| title_short | Hybrid Advanced Techniques for Forecasting in Energy Sector |
| title_sort | hybrid advanced techniques for forecasting in energy sector |
| topic | QA75.5-76.95 TA1-2040 hybrid models autoregressive moving average with exogenous variable (ARMAX) energy forecasting fuzzy group quantile forecasting evolutionary algorithms quantum computing mechanism cluster validity support vector regression / support vector machines artificial neural networks principal component analysis bayesian inference thema EDItEUR::U Computing and Information Technology::UY Computer science |
| topic_facet | QA75.5-76.95 TA1-2040 hybrid models autoregressive moving average with exogenous variable (ARMAX) energy forecasting fuzzy group quantile forecasting evolutionary algorithms quantum computing mechanism cluster validity support vector regression / support vector machines artificial neural networks principal component analysis bayesian inference thema EDItEUR::U Computing and Information Technology::UY Computer science |
| url | 29149 |
| work_keys_str_mv | AT weichianghonged hybridadvancedtechniquesforforecastinginenergysector |