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Intelligent Optimization Modelling in Energy Forecasting
Wei-Chiang Hong
2020
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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.
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Keywords
- active investment
- artificial intelligence techniques
- asset management
- brain storm optimization
- combination forecasting
- Commodities
- comparative analysis
- complementary ensemble empirical mode decomposition (CEEMD)
- Computer science
- Computing & information technology
- condition-based maintenance
- crude oil price forecasting
- crude oil prices
- data inconsistency rate
- deep convolutional neural network
- differential evolution (DE)
- diversification
- electrical power load
- empirical mode decomposition (EMD)
- energy forecasting
- energy futures
- energy price hedging
- ensemble
- Ensemble empirical mode decomposition
- feature selection
- five-year project
- Forecasting
- fuzzy time series
- Gaussian processes regression
- general regression neural network
- hybrid model
- improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN)
- individual
- Institutional investors
- interpolation
- intrinsic mode function (IMF)
- kernel learning
- kernel ridge regression
- LEM2
- long short term memory
- long short-term memory
- Markov-switching
- Markov-switching GARCH
- metamodel
- modified fruit fly optimization algorithm
- multi-objective grey wolf optimizer
- multi-step wind speed prediction
- Particle Swarm Optimization (PSO) algorithm
- Portfolio management
- régression
- renewable energy consumption
- short term load forecasting
- short-term load forecasting
- sparse Bayesian learning (SBL)
- state transition algorithm
- substation project cost forecasting model
- Support Vector Regression (SVR)
- thema EDItEUR::U Computing and Information Technology::UY Computer science
- time series forecasting
- weighted k-nearest neighbor (W-K-NN) algorithm
- wind speed
Links
DOI: 10.3390/books978-3-03928-365-1Editions
