Smart air condition load forecasting based on thermal dynamic model and finite memory estimation for peak-energy distribution

Hyun Duck Choi, Soon Woo Lee, Dong Sung Pae, Sung Hyun You, Myo Taeg Lim

Research output: Contribution to journalArticle

1 Citation (Scopus)


In this paper, we propose a new load forecasting method for smart air conditioning (A/C) based on the modified thermodynamics of indoor temperature and the unbiased finite memory estimator (UFME). Based on modified first-order thermodynamics, the dynamic behavior of indoor temperature can be described by the time-domain state-space model, and an accurate estimate of indoor temperature can be achieved by the proposed UFME. In addition, a reliable A/C load forecast can be obtained using the proposed method. Our study involves the experimental validation of the proposed A/C load forecasting method and communication construction between DR server and HEMS in a test bed. Through experimental data sets, the effectiveness of the proposed estimation method is validated.

Original languageEnglish
Pages (from-to)559-567
Number of pages9
JournalJournal of Electrical Engineering and Technology
Issue number2
Publication statusPublished - 2018 Mar 1



  • Air condition(A/C)
  • Demand response(DR)
  • Home energy management system(HEMS)
  • Thermodynamic model
  • Unbiased finite memory estimation (UFME)

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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