Journal ArticleDOI
A robust combination approach for short-term wind speed forecasting and analysis – Combination of the ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vector Machine) and LSSVM (Least Square SVM) forecasts using a GPR (Gaussian Process Regression) model
Jianzhou Wang,Jianming Hu +1 more
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This article is published in Energy.The article was published on 2015-12-15. It has received 212 citations till now. The article focuses on the topics: Wind speed & Autoregressive integrated moving average.read more
Citations
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Application of support vector machine models for forecasting solar and wind energy resources: A review
TL;DR: In this article, a hybrid support vector machine (SVM) model was proposed to forecast both solar and wind energy resources for most of the locations in the United States, where the authors highlighted main problems, opportunities and future work in this research area.
Journal ArticleDOI
Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization
TL;DR: The proposed EnsemLSTM is applied on two case studies data collected from a wind farm in Inner Mongolia, China, to perform ten-minute ahead utmost short term wind speed forecasting and one-hour ahead short term Wind speed forecasting, and Statistical tests of experimental results compared with other popular prediction models demonstrated the proposal can achieve a better forecasting performance.
Journal ArticleDOI
Machine learning prediction of mechanical properties of concrete: Critical review
TL;DR: Examination of several Machine Learning models for forecasting the mechanical properties of concrete, including artificial neural networks, support vector machine, decision trees, and evolutionary algorithms are examined.
Journal ArticleDOI
Spatio-Temporal Graph Deep Neural Network for Short-Term Wind Speed Forecasting
Mahdi Khodayar,Jianhui Wang +1 more
TL;DR: Simulation results show the advantages of capturing deep spatial and temporal interval features in the proposed framework compared to the state-of-the-art deep learning models as well as shallow architectures in the recent literature.
Journal ArticleDOI
Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm
TL;DR: 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.
References
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Journal ArticleDOI
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
Norden E. Huang,Zheng Shen,Steven R. Long,Man-Li C. Wu,Hsing H. Shih,Quanan Zheng,Nai-Chyuan Yen,C. C. Tung,Henry H. Liu +8 more
TL;DR: In this paper, a new method for analysing nonlinear and nonstationary data has been developed, which is the key part of the method is the empirical mode decomposition method with which any complicated data set can be decoded.
Journal ArticleDOI
Extreme Learning Machine for Regression and Multiclass Classification
TL;DR: ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly and in theory, ELM can approximate any target continuous function and classify any disjoint regions.
Book
Analysis of Financial Time Series
TL;DR: The author explains how the Markov Chain Monte Carlo Methods with Applications and Principal Component Analysis and Factor Models changed the way that conventional Monte Carlo methods were applied to time series analysis.
Journal ArticleDOI
Empirical Wavelet Transform
TL;DR: This paper presents a new approach to build adaptive wavelets, the main idea is to extract the different modes of a signal by designing an appropriate wavelet filter bank, which leads to a new wavelet transform, called the empirical wavelets transform.
Journal ArticleDOI
Day-ahead wind speed forecasting using f-ARIMA models
TL;DR: In this article, the authors examined the use of fractional-ARIMA or f-ARAMA models to model, and forecast wind speeds on the day-ahead and two-day-ahead (48 h) horizons.
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Current status and future advances for wind speed and power forecasting
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