Journal ArticleDOI
Wind power prediction using deep neural network based meta regression and transfer learning
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TLDR
The effectiveness of the proposed, DNN-MRT technique is expressed by comparing statistical performance measures in terms of root mean squared error (RMSE), mean absolute error (MAE), and standard deviation error (SDE) with other existing techniques.About:
This article is published in Applied Soft Computing.The article was published on 2017-09-01. It has received 285 citations till now. The article focuses on the topics: Deep belief network & Ensemble learning.read more
Citations
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A Survey on Deep Learning Role in Distribution Automation System: A New Collaborative Learning-to-Learning (L2L) Concept
TL;DR: A new smart technique called Learning-to-learning (L2L) based DL is proposed that can enhance and improve the efficiency, reliability, and security of DAS.
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Rolling Bearing Fault Diagnosis Based on Domain Adaptation and Preferred Feature Selection under Variable Working Conditions
TL;DR: An improved domain adaptation method, transfer component analysis with preserving local manifold structure (TCAPLMS) and preferred feature selection by fault sensitivity and feature correlation (PSFFC) is embedded into this framework for selecting features which are more beneficial to fault pattern recognition and reduce the redundancy of feature set.
Journal ArticleDOI
Estimating the Vigilance of High-Speed Rail Drivers Using a Stacking Ensemble Learning Method
TL;DR: A two-layer stacking ensemble learning model to predict HSR drivers’ reaction time to sudden stimuli based on electroencephalogram (EEG) signals and the results show that the mean absolute error, root mean square error, and goodness of fit of the estimated reaction time when using the proposed model were better than the corresponding results when using any of the regression models individually or comparing to six other popular methods.
Journal ArticleDOI
Forecasting customers' response to incentives during peak periods: A transfer learning approach
Dissertation
Prévision de production de parcs éoliens par systèmes multi-agents auto-adaptatifs
TL;DR: Adaptive Multi-Agent Systems (AMAS) as mentioned in this paper propose to resoudre des problemes complexes par auto-organisation for lesquels aucune solution algorithmique n'est connue.
References
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Journal ArticleDOI
A review on the forecasting of wind speed and generated power
TL;DR: A bibliographical survey on the general background of research and developments in the fields of wind speed and wind power forecasting and further direction for additional research and application is proposed.
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Current methods and advances in forecasting of wind power generation
TL;DR: A review of the current methods and advances in wind power forecasting and prediction can be found in this article, where numerical wind power prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed.
Journal ArticleDOI
A review on the young history of the wind power short-term prediction
TL;DR: This paper makes a brief review on 30 years of history of the wind power short-term prediction, since the first ideas and sketches to the actual state of the art on models and tools, giving emphasis to the most significant proposals and developments.
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Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification
TL;DR: An effective and reliable deep learning method known as stacked denoising autoencoder (SDA), which is shown to be suitable for certain health state identifications for signals containing ambient noise and working condition fluctuations, is investigated.
Journal ArticleDOI
A sparse auto-encoder-based deep neural network approach for induction motor faults classification
TL;DR: Compared with traditional neural network, the SAE-based DNN can achieve superior performance for feature learning and classification in the field of induction motor fault diagnosis.