|
|
|
|
| LEADER |
04999nma a2201009 u 4500 |
| 001 |
EB001981115 |
| 003 |
EBX01000000000000001144017 |
| 005 |
00000000000000.0 |
| 007 |
cr||||||||||||||||||||| |
| 008 |
210512 ||| eng |
| 020 |
|
|
|a 9783039283651
|
| 020 |
|
|
|a books978-3-03928-365-1
|
| 020 |
|
|
|a 9783039283644
|
| 100 |
1 |
|
|a Hong, Wei-Chiang
|
| 245 |
0 |
0 |
|a Intelligent Optimization Modelling in Energy Forecasting
|h Elektronische Ressource
|
| 260 |
|
|
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2020
|
| 300 |
|
|
|a 1 electronic resource (262 p.)
|
| 653 |
|
|
|a sparse Bayesian learning (SBL)
|
| 653 |
|
|
|a active investment
|
| 653 |
|
|
|a substation project cost forecasting model
|
| 653 |
|
|
|a electrical power load
|
| 653 |
|
|
|a kernel learning
|
| 653 |
|
|
|a state transition algorithm
|
| 653 |
|
|
|a crude oil prices
|
| 653 |
|
|
|a energy forecasting
|
| 653 |
|
|
|a institutional investors
|
| 653 |
|
|
|a multi-objective grey wolf optimizer
|
| 653 |
|
|
|a Markov-switching
|
| 653 |
|
|
|a forecasting
|
| 653 |
|
|
|a artificial intelligence techniques
|
| 653 |
|
|
|a data inconsistency rate
|
| 653 |
|
|
|a short-term load forecasting
|
| 653 |
|
|
|a feature selection
|
| 653 |
|
|
|a LEM2
|
| 653 |
|
|
|a diversification
|
| 653 |
|
|
|a empirical mode decomposition (EMD)
|
| 653 |
|
|
|a crude oil price forecasting
|
| 653 |
|
|
|a combination forecasting
|
| 653 |
|
|
|a asset management
|
| 653 |
|
|
|a Gaussian processes regression
|
| 653 |
|
|
|a complementary ensemble empirical mode decomposition (CEEMD)
|
| 653 |
|
|
|a General Regression Neural Network
|
| 653 |
|
|
|a portfolio management
|
| 653 |
|
|
|a interpolation
|
| 653 |
|
|
|a Markov-switching GARCH
|
| 653 |
|
|
|a Ensemble Empirical Mode Decomposition
|
| 653 |
|
|
|a weighted k-nearest neighbor (W-K-NN) algorithm
|
| 653 |
|
|
|a fuzzy time series
|
| 653 |
|
|
|a Brain Storm Optimization
|
| 653 |
|
|
|a renewable energy consumption
|
| 653 |
|
|
|a particle swarm optimization (PSO) algorithm
|
| 653 |
|
|
|a five-year project
|
| 653 |
|
|
|a metamodel
|
| 653 |
|
|
|a deep convolutional neural network
|
| 653 |
|
|
|a support vector regression (SVR)
|
| 653 |
|
|
|a kernel ridge regression
|
| 653 |
|
|
|a Computer science / bicssc
|
| 653 |
|
|
|a ensemble
|
| 653 |
|
|
|a intrinsic mode function (IMF)
|
| 653 |
|
|
|a regression
|
| 653 |
|
|
|a individual
|
| 653 |
|
|
|a energy price hedging
|
| 653 |
|
|
|a long short-term memory
|
| 653 |
|
|
|a commodities
|
| 653 |
|
|
|a improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN)
|
| 653 |
|
|
|a comparative analysis
|
| 653 |
|
|
|a condition-based maintenance
|
| 653 |
|
|
|a wind speed
|
| 653 |
|
|
|a multi-step wind speed prediction
|
| 653 |
|
|
|a time series forecasting
|
| 653 |
|
|
|a Long Short Term Memory
|
| 653 |
|
|
|a energy futures
|
| 653 |
|
|
|a short term load forecasting
|
| 653 |
|
|
|a differential evolution (DE)
|
| 653 |
|
|
|a modified fruit fly optimization algorithm
|
| 653 |
|
|
|a hybrid model
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b DOAB
|a Directory of Open Access Books
|
| 500 |
|
|
|a Creative Commons (cc), https://creativecommons.org/licenses/by-nc-nd/4.0/
|
| 028 |
5 |
0 |
|a 10.3390/books978-3-03928-365-1
|
| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/50434
|z DOAB: description of the publication
|
| 856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/2147
|7 0
|x Verlag
|3 Volltext
|
| 082 |
0 |
|
|a 000
|
| 082 |
0 |
|
|a 576
|
| 082 |
0 |
|
|a 333
|
| 082 |
0 |
|
|a 700
|
| 520 |
|
|
|a 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.
|