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
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Proceedings ArticleDOI
Chaos Prediction of Power Systems by Using Deep Learning
Yingdong Lu,Du Qu Wei +1 more
TL;DR: Experimental results illustrated that a trained DLSTM network can predict the chaotic behavior of power systems by using the time series data of a single state variable and theDLSTM-s network proposed in this paper can achieve competitive prediction performance compared with other baseline methods.
Journal ArticleDOI
Role of Machine Learning Algorithms for Wind Power Generation Prediction in Renewable Energy Management
TL;DR: In this article , a real-life problem in the renewable energy sector by accurately estimating the amount of power generation production per hour by applying machine learning techniques using historical wind power energy production data.
Proceedings ArticleDOI
Electrical Fault Detection Based On Infrared Temperature Measurement Technology
TL;DR: In this article , the distribution of high temperature of transmission line is detected online by infrared thermal image, and the security status is judged through the online measurement technology of the transmission line infrared thermal images.
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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.