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Open AccessArticle

A New Hybrid Approach to Forecast Wind Power for Large Scale Wind Turbine Data Using Deep Learning with TensorFlow Framework and Principal Component Analysis

1
School of Electrical Engineering, Sichuan University, Chengdu 610065, China
2
Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus, Sahiwal 57000, Pakistan
*
Author to whom correspondence should be addressed.
Energies 2019, 12(12), 2229; https://doi.org/10.3390/en12122229
Received: 13 April 2019 / Revised: 30 May 2019 / Accepted: 6 June 2019 / Published: 12 June 2019
(This article belongs to the Special Issue Solar and Wind Power and Energy Forecasting)
Wind power forecasting plays a vital role in renewable energy production. Accurately forecasting wind energy is a significant challenge due to the uncertain and complex behavior of wind signals. For this purpose, accurate prediction methods are required. This paper presents a new hybrid approach of principal component analysis (PCA) and deep learning to uncover the hidden patterns from wind data and to forecast accurate wind power. PCA is applied to wind data to extract the hidden features from wind data and to identify meaningful information. It is also used to remove high correlation among the values. Further, an optimized deep learning algorithm with a TensorFlow framework is used to accurately forecast wind power from significant features. Finally, the deep learning algorithm is fine-tuned with learning error rate, optimizer function, dropout layer, activation and loss function. The algorithm uses a neural network and intelligent algorithm to predict the wind signals. The proposed idea is applied to three different datasets (hourly, monthly, yearly) gathered from the National Renewable Energy Laboratory (NREL) transforming energy database. The forecasting results show that the proposed research can accurately predict wind power using a span ranging from hours to years. A comparison is made with popular state of the art algorithms and it is demonstrated that the proposed research yields better predictions results. View Full-Text
Keywords: wind power forecast; feature extraction and selection; TensorFlow deep learning; principal component analysis; windfarm wind power forecast; feature extraction and selection; TensorFlow deep learning; principal component analysis; windfarm
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MDPI and ACS Style

Khan, M.; Liu, T.; Ullah, F. A New Hybrid Approach to Forecast Wind Power for Large Scale Wind Turbine Data Using Deep Learning with TensorFlow Framework and Principal Component Analysis. Energies 2019, 12, 2229.

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