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Prediction of Time-Series Discharge Characteristics of Primary Batteries for IoT Device Using Machine Learning

EasyChair Preprint 13477

4 pagesDate: May 30, 2024

Abstract

Efforts are made to construct a prediction model for the discharge characteristics of IoT device batteries using time-series prediction models based on Transformers. With the aim of calculating SOC (State of Charge) through recursive prediction, conditions and processes conducive to more accurate recursive prediction were investigated. Currently, recursive prediction with iTransformer tend to be good score. It was also suggested that adding noise during recursive prediction may enable more stable long-term predictions.

Keyphrases: IoT device, Non-stationary Transformers, PatchTST, SoC, iTransformer, primary battery, transformer

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@booklet{EasyChair:13477,
  author    = {Shuhei Matsushita and Masahiro Fukui and Kiyotsugu Takaba},
  title     = {Prediction of Time-Series Discharge Characteristics of Primary Batteries for IoT Device Using Machine Learning},
  howpublished = {EasyChair Preprint 13477},
  year      = {EasyChair, 2024}}
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