Journal ArticleDOI
Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm
TLDR
This study combined support vector machine and improved dragonfly algorithm to forecast short-term wind power for a hybrid prediction model and has shown better prediction performance compared with the other models such as back propagation neural network and Gaussian process regression.About:
This article is published in Journal of Cleaner Production.The article was published on 2020-01-01. It has received 231 citations till now. The article focuses on the topics: Wind power forecasting & Wind power.read more
Citations
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Journal ArticleDOI
Mean–variance portfolio optimization using machine learning-based stock price prediction
TL;DR: A novel portfolio construction approach is developed using a hybrid model based on machine learning for stock prediction and mean–variance (MV) model for portfolio selection that is superior to traditional ways and benchmarks in terms of returns and risks.
Journal ArticleDOI
Short-term wind power forecasting using the hybrid model of improved variational mode decomposition and Correntropy Long Short -term memory neural network
TL;DR: A robust short-term wind power hybrid forecasting model based on Long Short-term Memory neural network with Correntropy combining an improved variational mode decomposition (IVMD) and Sample Entropy (SE) is proposed, and results show that proposed method is more effective than other traditional methods.
Journal ArticleDOI
Dragonfly algorithm: a comprehensive review and applications
TL;DR: A comprehensive review of Dragonfly algorithm and its new variants classified into modified and hybrid versions and describes the main diverse applications of DA in several fields and areas such as machine learning, neural network, image processing, robotics, and engineering.
Journal ArticleDOI
Photovoltaic power forecasting based on a support vector machine with improved ant colony optimization
TL;DR: The forecasting accuracies for peak power and nighttime are significantly improved, thereby improving the model’s full-time grid-connected generation abilities.
Journal ArticleDOI
A Survey of Machine Learning Models in Renewable Energy Predictions
TL;DR: This survey attempts to provide a review and analysis of machine-learning models in renewable-energy predictions and depicts procedures, including data pre-processing techniques, parameter selection algorithms, and prediction performance measurements, used in machine- learning models for renewable- energy predictions.
References
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LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Journal ArticleDOI
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
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The Whale Optimization Algorithm
Seyedali Mirjalili,Andrew Lewis +1 more
TL;DR: Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods.
Journal ArticleDOI
The Ant Lion Optimizer
TL;DR: The results of the test functions prove that the proposed ALO algorithm is able to provide very competitive results in terms of improved exploration, local optima avoidance, exploitation, and convergence, showing that this algorithm has merits in solving constrained problems with diverse search spaces.