Intelligent Optimization Modelling in Energy Forecasting
Accurate energy forecasting is important to facilitate the decision-making process in order to achieve higher efficiency and reliability in power system operation and security, economic energy use, contingency scheduling, the planning and maintenance of energy supply systems, and so on. In recent de...
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| Hlavní autor: | |
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| Médium: | Online |
| Jazyk: | angličtina |
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MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Témata: | |
| On-line přístup: | 44869 |
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Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
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| _version_ | 1869517790903271424 |
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| author | Hong, Wei-Chiang |
| author_browse | Hong, Wei-Chiang |
| author_facet | Hong, Wei-Chiang |
| author_sort | Hong, Wei-Chiang |
| collection | Directory of Open Access Books |
| description | Accurate energy forecasting is important to facilitate the decision-making process in order to achieve higher efficiency and reliability in power system operation and security, economic energy use, contingency scheduling, the planning and maintenance of energy supply systems, and so on. In recent decades, many energy forecasting models have been continuously proposed to improve forecasting accuracy, including traditional statistical models (e.g., ARIMA, SARIMA, ARMAX, multi-variate regression, exponential smoothing models, Kalman filtering, Bayesian estimation models, etc.) and artificial intelligence models (e.g., artificial neural networks (ANNs), knowledge-based expert systems, evolutionary computation models, support vector regression, etc.). Recently, due to the great development of optimization modeling methods (e.g., quadratic programming method, differential empirical mode method, evolutionary algorithms, meta-heuristic algorithms, etc.) and intelligent computing mechanisms (e.g., quantum computing, chaotic mapping, cloud mapping, seasonal mechanism, etc.), many novel hybrid models or models combined with the above-mentioned intelligent-optimization-based models have also been proposed to achieve satisfactory forecasting accuracy levels. It is important to explore the tendency and development of intelligent-optimization-based modeling methodologies and to enrich their practical performances, particularly for marine renewable energy forecasting. |
| format | Online |
| id | doab-20.500.12854ir-50434 |
| 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-504342024-04-14T10:28:02Z Intelligent Optimization Modelling in Energy Forecasting Hong, Wei-Chiang QA75.5-76.95 T58.5-58.64 Ensemble Empirical Mode Decomposition Brain Storm Optimization asset management institutional investors state transition algorithm kernel ridge regression energy price hedging multi-objective grey wolf optimizer five-year project complementary ensemble empirical mode decomposition (CEEMD) active investment portfolio management Long Short Term Memory time series forecasting LEM2 improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) feature selection Markov-switching GARCH condition-based maintenance substation project cost forecasting model Gaussian processes regression deep convolutional neural network individual wind speed empirical mode decomposition (EMD) crude oil prices artificial intelligence techniques intrinsic mode function (IMF) multi-step wind speed prediction support vector regression (SVR) short term load forecasting energy futures General Regression Neural Network metamodel sparse Bayesian learning (SBL) commodities ensemble comparative analysis crude oil price forecasting electrical power load differential evolution (DE) fuzzy time series kernel learning short-term load forecasting data inconsistency rate renewable energy consumption long short-term memory energy forecasting modified fruit fly optimization algorithm forecasting combination forecasting Markov-switching weighted k-nearest neighbor (W-K-NN) algorithm hybrid model interpolation particle swarm optimization (PSO) algorithm regression diversification thema EDItEUR::U Computing and Information Technology::UY Computer science Accurate energy forecasting is important to facilitate the decision-making process in order to achieve higher efficiency and reliability in power system operation and security, economic energy use, contingency scheduling, the planning and maintenance of energy supply systems, and so on. In recent decades, many energy forecasting models have been continuously proposed to improve forecasting accuracy, including traditional statistical models (e.g., ARIMA, SARIMA, ARMAX, multi-variate regression, exponential smoothing models, Kalman filtering, Bayesian estimation models, etc.) and artificial intelligence models (e.g., artificial neural networks (ANNs), knowledge-based expert systems, evolutionary computation models, support vector regression, etc.). Recently, due to the great development of optimization modeling methods (e.g., quadratic programming method, differential empirical mode method, evolutionary algorithms, meta-heuristic algorithms, etc.) and intelligent computing mechanisms (e.g., quantum computing, chaotic mapping, cloud mapping, seasonal mechanism, etc.), many novel hybrid models or models combined with the above-mentioned intelligent-optimization-based models have also been proposed to achieve satisfactory forecasting accuracy levels. It is important to explore the tendency and development of intelligent-optimization-based modeling methodologies and to enrich their practical performances, particularly for marine renewable energy forecasting. 2021-02-11T16:26:51Z 2021-02-11T16:26:51Z 2020-04-07 23:07:09 2020 book 44869 9783039283651 9783039283644 https://directory.doabooks.org/handle/20.500.12854/50434 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/2147 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03928-365-1 10.3390/books978-3-03928-365-1 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783039283651 9783039283644 262 open access |
| spellingShingle | QA75.5-76.95 T58.5-58.64 Ensemble Empirical Mode Decomposition Brain Storm Optimization asset management institutional investors state transition algorithm kernel ridge regression energy price hedging multi-objective grey wolf optimizer five-year project complementary ensemble empirical mode decomposition (CEEMD) active investment portfolio management Long Short Term Memory time series forecasting LEM2 improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) feature selection Markov-switching GARCH condition-based maintenance substation project cost forecasting model Gaussian processes regression deep convolutional neural network individual wind speed empirical mode decomposition (EMD) crude oil prices artificial intelligence techniques intrinsic mode function (IMF) multi-step wind speed prediction support vector regression (SVR) short term load forecasting energy futures General Regression Neural Network metamodel sparse Bayesian learning (SBL) commodities ensemble comparative analysis crude oil price forecasting electrical power load differential evolution (DE) fuzzy time series kernel learning short-term load forecasting data inconsistency rate renewable energy consumption long short-term memory energy forecasting modified fruit fly optimization algorithm forecasting combination forecasting Markov-switching weighted k-nearest neighbor (W-K-NN) algorithm hybrid model interpolation particle swarm optimization (PSO) algorithm regression diversification thema EDItEUR::U Computing and Information Technology::UY Computer science Hong, Wei-Chiang Intelligent Optimization Modelling in Energy Forecasting |
| title | Intelligent Optimization Modelling in Energy Forecasting |
| title_full | Intelligent Optimization Modelling in Energy Forecasting |
| title_fullStr | Intelligent Optimization Modelling in Energy Forecasting |
| title_full_unstemmed | Intelligent Optimization Modelling in Energy Forecasting |
| title_short | Intelligent Optimization Modelling in Energy Forecasting |
| title_sort | intelligent optimization modelling in energy forecasting |
| topic | QA75.5-76.95 T58.5-58.64 Ensemble Empirical Mode Decomposition Brain Storm Optimization asset management institutional investors state transition algorithm kernel ridge regression energy price hedging multi-objective grey wolf optimizer five-year project complementary ensemble empirical mode decomposition (CEEMD) active investment portfolio management Long Short Term Memory time series forecasting LEM2 improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) feature selection Markov-switching GARCH condition-based maintenance substation project cost forecasting model Gaussian processes regression deep convolutional neural network individual wind speed empirical mode decomposition (EMD) crude oil prices artificial intelligence techniques intrinsic mode function (IMF) multi-step wind speed prediction support vector regression (SVR) short term load forecasting energy futures General Regression Neural Network metamodel sparse Bayesian learning (SBL) commodities ensemble comparative analysis crude oil price forecasting electrical power load differential evolution (DE) fuzzy time series kernel learning short-term load forecasting data inconsistency rate renewable energy consumption long short-term memory energy forecasting modified fruit fly optimization algorithm forecasting combination forecasting Markov-switching weighted k-nearest neighbor (W-K-NN) algorithm hybrid model interpolation particle swarm optimization (PSO) algorithm regression diversification thema EDItEUR::U Computing and Information Technology::UY Computer science |
| topic_facet | QA75.5-76.95 T58.5-58.64 Ensemble Empirical Mode Decomposition Brain Storm Optimization asset management institutional investors state transition algorithm kernel ridge regression energy price hedging multi-objective grey wolf optimizer five-year project complementary ensemble empirical mode decomposition (CEEMD) active investment portfolio management Long Short Term Memory time series forecasting LEM2 improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) feature selection Markov-switching GARCH condition-based maintenance substation project cost forecasting model Gaussian processes regression deep convolutional neural network individual wind speed empirical mode decomposition (EMD) crude oil prices artificial intelligence techniques intrinsic mode function (IMF) multi-step wind speed prediction support vector regression (SVR) short term load forecasting energy futures General Regression Neural Network metamodel sparse Bayesian learning (SBL) commodities ensemble comparative analysis crude oil price forecasting electrical power load differential evolution (DE) fuzzy time series kernel learning short-term load forecasting data inconsistency rate renewable energy consumption long short-term memory energy forecasting modified fruit fly optimization algorithm forecasting combination forecasting Markov-switching weighted k-nearest neighbor (W-K-NN) algorithm hybrid model interpolation particle swarm optimization (PSO) algorithm regression diversification thema EDItEUR::U Computing and Information Technology::UY Computer science |
| url | 44869 |
| work_keys_str_mv | AT hongweichiang intelligentoptimizationmodellinginenergyforecasting |