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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* and Myo Taeg Lim*
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.
Keyword Home energy management system(HEMS), Air condition(A/C), Demand response(DR), Unbiased finite memory estimation (UFME), Thermodynamic model
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