Machine-learning-integrated load scheduling for reduced peak power demand

Minyoung Sung, Younghoo Ko

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


Load scheduling over cyclic electrical devices can reduce the peak power demand. In this paper, we propose a machine-learning-integrated load control (MILC) scheme for improved performance and reliability. By dynamic capacity adjustment and interactive load heuristic, MILC tries to reduce the power deviation while keeping the temperature violation ratio and switching counts within an acceptable range. A prototype of the proposed scheme has been implemented and, through experiments using load traces from a real home, we evaluate the performance of MILC. The results show that MILC reduces the peak demand from 4993 W to 4236 W and successfully decreases the power deviation by 12.1% on average.

Original languageEnglish
Article number7150570
Pages (from-to)167-174
Number of pages8
JournalIEEE Transactions on Consumer Electronics
Issue number2
StatePublished - May 2015


  • Dynamic capacity adjustment
  • Electric load scheduling
  • Machine learning
  • Peak power reduction


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