STUDY ON REAL-TIME LOAD PREDICTION METHODS FOR HAVAC SYSTEM CONTROL

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  • KAWASHIMA Minoru
    Institute of Technology, Shimizu Corporation
  • ITO Naoaki
    Dept. of Architecture, Faculty of Engineering, Tokyo Metropolitan University

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  • 空調システム運転制御を対象とした実時間負荷予測手法に関する研究
  • クウチョウ システム ウンテン セイギョ オ タイショウ ト シタ ジツジカン

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Abstract

First, importance of real-time load prediction and several kinds of optimization on HVAC system control are discussed. Then, six prediction models, ARIMA (Autoregressed Integrated Moving Average), EWMA (Exponential Weighted Moving Average), RLR (Recursive Linear Regression), ANN (Artificial Neural Network), KALMAN (Kalman filter) and FNN (Fuzzy Neural Network), are examined to compare their accuracy under situations where the all models use the same 3-month-long calculated load data and weather data in cooling and heating season. The results shows that the ANN model has the best prediction accuracy. It is confirmed that the ANN is a potential prediction model for practical utilization in HVAC system control.

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