Reprint
Intelligent Optimization Modelling in Energy Forecasting
Edited by
March 2020
262 pages
- ISBN978-3-03928-364-4 (Paperback)
- ISBN978-3-03928-365-1 (PDF)
This book is a reprint of the Special Issue Intelligent Optimization Modelling in Energy Forecasting that was published in
Chemistry & Materials Science
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary
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
- Paperback
License
© 2020 by the authors; CC BY-NC-ND license
Keywords
short-term load forecasting; weighted k-nearest neighbor (W-K-NN) algorithm; comparative analysis; empirical mode decomposition (EMD); particle swarm optimization (PSO) algorithm; intrinsic mode function (IMF); support vector regression (SVR); short term load forecasting; crude oil price forecasting; time series forecasting; hybrid model; complementary ensemble empirical mode decomposition (CEEMD); sparse Bayesian learning (SBL); multi-step wind speed prediction; Ensemble Empirical Mode Decomposition; Long Short Term Memory; General Regression Neural Network; Brain Storm Optimization; substation project cost forecasting model; feature selection; data inconsistency rate; modified fruit fly optimization algorithm; deep convolutional neural network; multi-objective grey wolf optimizer; long short-term memory; fuzzy time series; LEM2; combination forecasting; wind speed; electrical power load; crude oil prices; time series forecasting; improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN); kernel learning; kernel ridge regression; differential evolution (DE); artificial intelligence techniques; energy forecasting; condition-based maintenance; asset management; renewable energy consumption; Gaussian processes regression; state transition algorithm; five-year project; forecasting; Markov-switching; Markov-switching GARCH; energy futures; commodities; portfolio management; active investment; diversification; institutional investors; energy price hedging; metamodel; ensemble; individual; regression; interpolation