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Smart Air Condition Load Forecasting based on Thermal Dynamic Model and Finite Memory Estimation for Peak-energy Distribution

  • Choi, Hyun Duck ;
  • Lee, Soon Woo ;
  • Pae, Dong Sung ;
  • You, Sung Hyun ;
  • Lim, Myo Taeg
  • Received : 2016.12.02
  • Accepted : 2017.09.26
  • Published : 2018.03.01

Abstract

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.

Keywords

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

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Acknowledgement

Supported by : KEIT, Korea Institute of Energy Technology Evaluation and Planning