Buchumschlag

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...

Ausführliche Beschreibung

Gespeichert in:
1. Verfasser: Hong, Wei-Chiang
Format: Online
Sprache:Englisch
Veröffentlicht: MDPI - Multidisciplinary Digital Publishing Institute 2021
Schlagworte:
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
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