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A two-phase data-driven approach for detection of solar PV and EV infrastructure in smart grid

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  • Ahir, Rajesh K.
  • Biyawala, Viral
  • Sikotra, Shivam

Abstract

With the rapid transition towards more flexible, interactive, and intelligent power systems by higher penetration of renewables, electric vehicles (EV), and smart meter data analytics, it is essential for electric utilities to identify such infrastructure in customer premises for efficient grid operation. The study introduces a comprehensive two-phase approach for discerning residences equipped with solar Photovoltaic (PV) installations, EV charging infrastructure, or a combination of both. The first phase deals with detection of above mentioned scenarios/changes in the consumption patterns by employing support vector classifier (SVC). The second phase introduces personalized Long Short-Term Memory (LSTM) and novel dynamic threshold calculation technique for identifying the date on which the change occurred. The Error Profile (EP) for each customer is generated to exhibit the identification of the scenarios on the specific date. The proposed approach yields promising results with an overall F1-score of more than 90 % for various scenarios.

Suggested Citation

  • Ahir, Rajesh K. & Biyawala, Viral & Sikotra, Shivam, 2025. "A two-phase data-driven approach for detection of solar PV and EV infrastructure in smart grid," Applied Energy, Elsevier, vol. 391(C).
    • Handle: RePEc:eee:appene:v:391:y:2025:i:c:s0306261925006427
    DOI: 10.1016/j.apenergy.2025.125912
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