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A Design of Electricity Generating Station Power Prediction Unit with Low Power Consumption Based on Support Vector Regression

  • Bing Liu
  • Qifan Tong
  • Lei FengEmail author
  • Ping Fu
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 157)

Abstract

During the process of electricity generating station operation, its output power will be affected by environmental factors, so there will be a large fluctuation. If we can monitor the environmental data and the output power of the electricity generating station in real time, we can make an accurate and effective estimation of the operation status of the electricity generating station. To meet this demand, we designed an electricity generating station power prediction unit based on support vector regression algorithm. The power consumption of the unit is very low, and by using machine learning, the characteristics and rules of each index can be learned from the environmental data collected by sensors. By processing and analyzing the newly collected data, the real-time operation status of the electricity generating station can be monitored.

Keywords

Output power Real-time monitor Machine learning Support vector regression Low power consumption 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Harbin Institute of TechnologyHarbinChina

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