Predicting Internal Energy Consumption of a Wind Turbine Using Semi-Supervised Deep Learning

Shih Sheng Hsu, Chun Cheng Lin*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Most previous works on wind power generation focused on the impact of the external environment on the efficiency of energy generation, but ignored the energy consumption of internal parts of the wind turbine. Reducing internal energy consumption can not only improve the generation efficiency, but also reduce the maintenance cost of wind turbines. Therefore, this study uses deep learning to predict the energy consumption inside the wind turbine by installing dozens of sensors inside it, and finds the parts that have greater impact on energy consumption to reduce energy consumption and improve generating efficiency. Since most data on wind turbines is collected by humans currently, it is inevitable that the data will have missing or wrong. Due to the large number of parts inside the wind turbine, the collected data belongs to multi-dimension data. In order to use these data effectively, this study proposes a semi-supervised deep learning method which can correct the data to solve this problem. After all the data are corrected and the model is completely trained, this study uses the MCC method to judge the predicting results of the model. The results show that when the label data accounts for 15-20% of the total data the trained model has the best predictive ability. Therefore, this study suggests that when establishing a prediction model of internal energy consumption of wind turbine in the future, the label data should account for 15-20% of the total data. In this way, not only can train a model with considerable accuracy, but also the most economical ways to determine the amount of revision data.

Original languageEnglish
Title of host publicationProceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages223-228
Number of pages6
ISBN (Electronic)9780738142623
DOIs
StatePublished - Dec 2020
Event1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020 - Taipei, Taiwan
Duration: 3 Dec 20205 Dec 2020

Publication series

NameProceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020

Conference

Conference1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020
Country/TerritoryTaiwan
CityTaipei
Period3/12/205/12/20

Keywords

  • deep learning
  • energy forecasting
  • Renewable energy
  • wind power generation

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