Short-Term Load Forecasting by Artificial Intelligent Technologies
In last few decades, short-term load forecasting (STLF) has been one of the most important research issues for achieving higher efficiency and reliability in power system operation, to facilitate the minimization of its operation cost by providing accurate input to day-ahead scheduling, contingency...
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| 格式: | Online |
| 語言: | 英语 |
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MDPI - Multidisciplinary Digital Publishing Institute
2021
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| 在線閱讀: | 32122 |
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| _version_ | 1869529236758331392 |
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| author | Wei-Chiang Hong (Ed.) Guo-Feng Fan (Ed.) Ming-Wei Li (Ed.) |
| author_browse | Guo-Feng Fan (Ed.) Ming-Wei Li (Ed.) Wei-Chiang Hong (Ed.) |
| author_facet | Wei-Chiang Hong (Ed.) Guo-Feng Fan (Ed.) Ming-Wei Li (Ed.) |
| author_sort | Wei-Chiang Hong (Ed.) |
| collection | Directory of Open Access Books |
| description | In last few decades, short-term load forecasting (STLF) has been one of the most important research issues for achieving higher efficiency and reliability in power system operation, to facilitate the minimization of its operation cost by providing accurate input to day-ahead scheduling, contingency analysis, load flow analysis, planning, and maintenance of power systems. There are lots of forecasting models proposed for STLF, including traditional statistical models (such as ARIMA, SARIMA, ARMAX, multi-variate regression, Kalman filter, exponential smoothing, and so on) and artificial-intelligence-based models (such as artificial neural networks (ANNs), knowledge-based expert systems, fuzzy theory and fuzzy inference systems, evolutionary computation models, support vector regression, and so on). Recently, due to the great development of evolutionary algorithms (EA) and novel computing concepts (e.g., quantum computing concepts, chaotic mapping functions, and cloud mapping process, and so on), many advanced hybrids with those artificial-intelligence-based models are also proposed to achieve satisfactory forecasting accuracy levels. In addition, combining some superior mechanisms with an existing model could empower that model to solve problems it could not deal with before; for example, the seasonal mechanism from the ARIMA model is a good component to be combined with any forecasting models to help them to deal with seasonal problems. |
| format | Online |
| id | doab-20.500.12854ir-59327 |
| 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-593272024-04-14T10:28:06Z Short-Term Load Forecasting by Artificial Intelligent Technologies Wei-Chiang Hong (Ed.) Guo-Feng Fan (Ed.) Ming-Wei Li (Ed.) QA75.5-76.95 TK7885-7895 meta-heuristic algorithms artificial neural networks (ANNs) knowledge-based expert systems statistical forecasting models evolutionary algorithms short term load forecasting novel intelligent technologies support vector regression/support vector machines seasonal mechanism thema EDItEUR::U Computing and Information Technology::UY Computer science In last few decades, short-term load forecasting (STLF) has been one of the most important research issues for achieving higher efficiency and reliability in power system operation, to facilitate the minimization of its operation cost by providing accurate input to day-ahead scheduling, contingency analysis, load flow analysis, planning, and maintenance of power systems. There are lots of forecasting models proposed for STLF, including traditional statistical models (such as ARIMA, SARIMA, ARMAX, multi-variate regression, Kalman filter, exponential smoothing, and so on) and artificial-intelligence-based models (such as artificial neural networks (ANNs), knowledge-based expert systems, fuzzy theory and fuzzy inference systems, evolutionary computation models, support vector regression, and so on). Recently, due to the great development of evolutionary algorithms (EA) and novel computing concepts (e.g., quantum computing concepts, chaotic mapping functions, and cloud mapping process, and so on), many advanced hybrids with those artificial-intelligence-based models are also proposed to achieve satisfactory forecasting accuracy levels. In addition, combining some superior mechanisms with an existing model could empower that model to solve problems it could not deal with before; for example, the seasonal mechanism from the ARIMA model is a good component to be combined with any forecasting models to help them to deal with seasonal problems. 2021-02-12T03:31:06Z 2021-02-12T03:31:06Z 2019-01-29 10:55:39 2019 book 32122 9783038975830 9783038975823 https://directory.doabooks.org/handle/20.500.12854/59327 eng image/jpeg Attribution-NonCommercial-NoDerivatives 4.0 International https://www.mdpi.com/books/pdfview/book/1116 https://play.google.com/books/publish/a/14935057684283403269#details/ISBN:9783038975823 https://www.mdpi.com/books/pdfview/book/1116 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03897-583-0 10.3390/books978-3-03897-583-0 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783038975830 9783038975823 444 open access |
| spellingShingle | QA75.5-76.95 TK7885-7895 meta-heuristic algorithms artificial neural networks (ANNs) knowledge-based expert systems statistical forecasting models evolutionary algorithms short term load forecasting novel intelligent technologies support vector regression/support vector machines seasonal mechanism thema EDItEUR::U Computing and Information Technology::UY Computer science Wei-Chiang Hong (Ed.) Guo-Feng Fan (Ed.) Ming-Wei Li (Ed.) Short-Term Load Forecasting by Artificial Intelligent Technologies |
| title | Short-Term Load Forecasting by Artificial Intelligent Technologies |
| title_full | Short-Term Load Forecasting by Artificial Intelligent Technologies |
| title_fullStr | Short-Term Load Forecasting by Artificial Intelligent Technologies |
| title_full_unstemmed | Short-Term Load Forecasting by Artificial Intelligent Technologies |
| title_short | Short-Term Load Forecasting by Artificial Intelligent Technologies |
| title_sort | short term load forecasting by artificial intelligent technologies |
| topic | QA75.5-76.95 TK7885-7895 meta-heuristic algorithms artificial neural networks (ANNs) knowledge-based expert systems statistical forecasting models evolutionary algorithms short term load forecasting novel intelligent technologies support vector regression/support vector machines seasonal mechanism thema EDItEUR::U Computing and Information Technology::UY Computer science |
| topic_facet | QA75.5-76.95 TK7885-7895 meta-heuristic algorithms artificial neural networks (ANNs) knowledge-based expert systems statistical forecasting models evolutionary algorithms short term load forecasting novel intelligent technologies support vector regression/support vector machines seasonal mechanism thema EDItEUR::U Computing and Information Technology::UY Computer science |
| url | 32122 |
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