Intelligent Optimization Modelling in Energy Forecasting - Hong, Wei-Chiang
English[eng]
Keywords: 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