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Power Prediction of Regional Photovoltaic Power Stations Based on Meteorological Encryption and Spatio-Temporal Graph Networks

Energies, ISSN: 1996-1073, Vol: 17, Issue: 14
2024
  • 1
    Citations
  • 0
    Usage
  • 1
    Captures
  • 1
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    1
  • Captures
    1
  • Mentions
    1
    • News Mentions
      1
      • News
        1

Most Recent News

Research on Information and Data Encoding and Encryption Described by Researchers at Xinjiang University (Power Prediction of Regional Photovoltaic Power Stations Based on Meteorological Encryption and Spatio-Temporal Graph Networks)

2024 AUG 08 (NewsRx) -- By a News Reporter-Staff News Editor at Information Technology Daily -- Investigators publish new report on information and data encoding

Article Description

Distributed photovoltaic (PV) power stations generally lack historical meteorological data, which is one of the main reasons for their insufficient power prediction accuracy. To address this issue, this paper proposes a power prediction method for regional distributed PV power stations based on meteorological encryption and spatio-temporal graph networks. First, inverse distance weighted meteorological encryption technology is used to achieve the comprehensive coverage of key meteorological resources based on the geographical locations of PV power stations and the meteorological resources of weather stations. Next, the historical power correlations between PV power stations are analyzed, and highly correlated stations are connected to construct a topological graph structure. Then, an improved spatio-temporal graph network model is established based on this graph to deeply mine the spatio-temporal characteristics of regional PV power stations. Furthermore, a dual-layer attention mechanism is added to further learn the feature attributes of nodes and enhance the spatio-temporal features extracted by the spatio-temporal graph network, ultimately achieving power prediction for regional PV power stations. The simulation results indicate that the proposed model demonstrates excellent prediction accuracy, robustness, extensive generalization capability, and broad applicability.

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