Short-term Wind Power Prediction Method Based on UAV Patrol and Deep Confidence Network

Authors

  • Zhang Yiming State Grid Gansu Electric Power Company, Gansu Lanzhou, China
  • Cheng Li State Grid Gansu Electric Power Company, Gansu Lanzhou, China

DOI:

https://doi.org/10.13052/dgaej2156-3306.3761

Keywords:

Short-term, wind power, generation forecasting, deep belief network

Abstract

At present, wind power has become the most promising energy supply. However, the intermittent and fluctuating wind power also poses a huge challenge to accurately adjust the electrical load. In order to find a method capable of forecasting wind power generation in a short period of time, we propose a short-term wind power generation forecasting method based on an optimized deep belief network approach. Based on GEFCom2012 competition dataset, by continuously tuning the parameters of the deep belief network for 15 sets of experiments, we obtained three optimal laboratory combinations: Experiment 4, Experiment 10, and Experiment 12. The results show that the R-squared values of Experiment 4, Experiment 10 and Experiment 12 are the highest, which are 0.955, 0.93 and 0.98, respectively. The average R-squared value of these three tuned experiments is 0.2342 higher than the average of the other 12 experiments. At the same time, it is concluded that when the learning frequency is low, the linear function can learn the most obvious features more directly; When the learning frequency is high, the nonlinear function can learn the internal latent features more directly.

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Author Biographies

Zhang Yiming, State Grid Gansu Electric Power Company, Gansu Lanzhou, China

Yiming Zhang, graduated from Northeast Electric Power University in 2012 with a bachelor’s degree in transmission engineering and a bachelor’s degree in power system automation. In 2020, he obtained a master’s degree in electrical engineering from North China Electric Power University. Zhang Yiming has been working in State Grid Gansu Electric Power Company since 2012. As an electrical engineer and professional, he has extensive experience in the operation and maintenance of transmission lines and drone inspection management. He was responsible for the autonomous inspection business of drones and the construction of digital management and control platform, and the cultivation and optimization of artificial intelligence image recognition technology of the State Grid Gansu Electric Power Company. In addition, he also presided over the large-scale application of drones in the inspection and maintenance of overhead transmission lines of Gansu Electric Power Company.

Cheng Li, State Grid Gansu Electric Power Company, Gansu Lanzhou, China

Cheng Li, she graduated from Lanzhou Jiaotong University in 2014 with a bachelor’s degree in electrical engineering and automation. Cheng Li has been working at State Grid Gansu Electric Power Company since 2014. He was responsible for the low-carbon energy service station project, researched on energy storage, photovoltaics, and optical fiber lighting, and was awarded by the State Grid Corporation. As an electrical engineer and professional, rich experience in power grid planning, line loss management, power marketing management.

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Published

2022-07-27

How to Cite

Yiming, Z. ., & Li, C. . (2022). Short-term Wind Power Prediction Method Based on UAV Patrol and Deep Confidence Network. Distributed Generation &Amp; Alternative Energy Journal, 37(06), 1739–1754. https://doi.org/10.13052/dgaej2156-3306.3761

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Section

Articles