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Chen, Y., Luh, P.B., Guan, C., Zhao, Y., Michel, L.D., Coolbeth, M.A., Friedland, P.B. and Rourke, S.J. (2010) Short- Term Load Forecasting: Similar Day-Based Wavelet Neural Networks. IEEE Transactions on Power Systems, 25, 322- 330. http://dx.doi.org/10.1109/TPWRS.2009.2030426

has been cited by the following article:

• TITLE: AC Scheduling Based on Thermodynamics of Indoor for On-Campus Small Data

  AUTHORS: Hyun Duck Choi, Soon Woo Rhee, Choon Ki Ahn, Myo Taeg Lim

  KEYWORDS: Home Energy Management System (HEMS), Least-Square, Convex Optimization, Demand Response (DR), Air-Conditioning (AC)

  JOURNAL NAME: Journal of Power and Energy Engineering, Vol.3 No.4, April 14, 2015

  ABSTRACT: This paper proposes a new day-ahead control scheme of an air conditioning (AC) based on thermodynamic model of indoor-temperature. The thermodynamic model of indoor-temperature can be achieved by modified first-order thermal dynamic equation. For the practical verification of proposed model, we implemented the home energy management system (HEMS) in the laboratory and used real experiment data sets. The proposed model can be represented by a state-space model of indoor-temperature and its parameters are obtained by least square algorithm. Through the proposed thermodynamic model, indoor-temperature can be predicted closely, and a behavior pattern of AC can also be achieved. This research involves the experimental verification of the proposed approach and communication architecture between the aggregator and a system user in a laboratory environment.