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An Efficient Simultaneous Thermal and Electrical State Estimation for Preventive Maintenance of PV-ESS in Low-Temperature Environments: A Case Study of LSTM/Transformer

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2026, 31(3), pp.1~8
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : January 8, 2026
  • Accepted : March 6, 2026
  • Published : March 31, 2026

Yong-Je Ko 1 Ho-Young Kwak 2

1(재)제주테크노파크
2제주대학교

Accredited

ABSTRACT

This study proposes a time-series model that simultaneously predicts the battery cell average temperature (cTavg) and state of charge (SOC) based on high-frequency (5-second) operational logs from a single residential PV-ESS system operated in a low-temperature environment during winter (2024-12-29 ~ 2025-02-13, total 812,859 records). It compares LSTM encoder-decoder and Transformer-based architectures and suggests a method for early detection of low-SOC/low-temperature events by linking the multi-output predictions to a preventive maintenance logic. The model's performance is evaluated against low-SOC days and low-temperature periods observed in actual data, and the operational benefits provided by the prediction-based alerts are quantitatively discussed.

Citation status

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