Wind Turbine Power Prediction

By Elijah Reber1, Guang Lin2, Nickolas D Winovich2

1. Penn State University 2. Purdue University

This tool uses a trained neural network algorithm to predict the energy output and failure of a wind turbine using sensor data

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Version 1.0 - published on 30 Jul 2018

doi:10.4231/D3QJ78131 cite this

This tool is closed source.

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Usage

World usage

Location of all "Wind Turbine Power Prediction" Users Since Its Posting

Cumulative Simulation Users

34

1 2 4 5 7 9 9 9 10 10 11 11 12 12 12 12 13 16 17 18 19 20 21 22 23 23 23 23 23 24 24 24 24 24 25 25 25 26 26 27 27 27 27 28 29 30 30 30 31 32 33 34 34 34 34

Users By Organization Type
Type Users
Unidentified 26 (76.47%)
Educational - University 8 (23.53%)
Users by Country of Residence
Country Users
us UNITED STATES 5 (62.5%)
cn CHINA 1 (12.5%)
bd BANGLADESH 1 (12.5%)
in INDIA 1 (12.5%)

Simulation Runs

101

5 13 19 21 28 34 35 35 38 38 39 39 41 41 41 41 43 51 53 60 63 65 70 72 74 74 74 74 74 76 76 76 76 76 78 78 78 80 80 81 81 81 81 82 84 87 87 87 89 91 94 101 101 101 101
Overview
Average Total
Wall Clock Time 4.66 hours 10.88 days
CPU time 13.38 seconds 12.49 minutes
Interaction Time 20.37 minutes 19.01 hours