Journal ArticleDOI
Random vector functional link network for short-term electricity load demand forecasting
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TLDR
The RVFL network overall outperforms the non-ensemble methods, namely the persistence method, seasonal autoregressive integrated moving average (sARIMA), artificial neural network (ANN).About:
This article is published in Information Sciences.The article was published on 2016-11-01. It has received 233 citations till now. The article focuses on the topics: Demand forecasting & Autoregressive integrated moving average.read more
Citations
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Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting
TL;DR: An ensemble deep learning method has been proposed for load demand forecasting that composes of Empirical Mode Decomposition and Deep Belief Network and results demonstrated attractiveness of the proposed method compared with nine forecasting methods.
Journal ArticleDOI
A survey of randomized algorithms for training neural networks
TL;DR: A comprehensive survey of the earliest work and recent advances on network training is presented as well as some suggestions for future research.
Journal ArticleDOI
Short term load forecasting based on feature extraction and improved general regression neural network model
TL;DR: A hybrid model which combines empirical mode decomposition (EMD), minimal redundancy maximal relevance (mRMR), general regression neural network (GRNN) with fruit fly optimization algorithm (FOA), namely EMD- mRMR-FOA-GRNN is proposed.
Journal ArticleDOI
Ultra-Short-Term Industrial Power Demand Forecasting Using LSTM Based Hybrid Ensemble Learning
TL;DR: Experimental results show that the proposed LSTM network based hybrid ensemble learning forecasting model outperforms several state-of-the-art time series forecasting models, and obtains higher accuracy and robustness to forecast peak demand.
Journal ArticleDOI
Short-term wind speed and wind power prediction using hybrid empirical mode decomposition and kernel ridge regression
TL;DR: A fast reduced version of the EMD-KRR is presented in the paper to reduce the computational overhead substantially using randomly selected support vectors from the data set while resulting in a reasonably accurate forecast.
References
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TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
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Multilayer feedforward networks are universal approximators
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
Journal ArticleDOI
Multilayer feedforward networks are universal approximators
HornikK.,StinchcombeM.,WhiteH. +2 more
Proceedings Article
Support Vector Regression Machines
TL;DR: This work compares support vector regression (SVR) with a committee regression technique (bagging) based on regression trees and ridge regression done in feature space and expects that SVR will have advantages in high dimensionality space because SVR optimization does not depend on the dimensionality of the input space.