A grey‐box model of next‐day building thermal load prediction for energy‐efficient control

Accurate building thermal load prediction is essential to many building energy control strategies. To get reliable prediction of the hourly building load of the next day, air temperature/relative humidity and solar radiation prediction modules are integrated with a grey-box model. The regressive solar radiation module predicts the solar radiation using the forecasted cloud amount, sky condition and extreme temperatures from on-line weather stations, while the forecasted sky condition is used to correct the cloud amount forecast. The temperature/relative humidity prediction module uses a dynamic grey model (GM), which is specialized in the grey system with incomplete information. Both weather prediction modules are integrated into a building thermal load model for the on-line prediction of the building thermal load in the next day. The validation of both weather prediction modules and the on-line building thermal load prediction model are presented. Copyright © 2008 John Wiley & Sons, Ltd.

[1]  P. S. Kulkarni,et al.  A case study of solar photovoltaic power system at Sagardeep Island, India , 2009 .

[2]  J. W. Macarthur,et al.  On-line recursive estimation for load profile prediction , 1989 .

[3]  George H. Hargreaves,et al.  Irrigation Water Requirements for Senegal River Basin , 1985 .

[4]  M. Bollen,et al.  Simulation of global solar radiation based on cloud observations , 2005 .

[5]  James E. Braun,et al.  Reducing energy costs and peak electrical demand through optimal control of building thermal storage , 1990 .

[6]  James E. Braun,et al.  An Inverse Gray-Box Model for Transient Building Load Prediction , 2002 .

[7]  John A. Davies,et al.  Estimating solar radiation from incomplete cloud data , 1988 .

[8]  Roberto Lamberts,et al.  The use of simplified weather data to estimate thermal loads of non-residential buildings , 2004 .

[9]  G. Campbell,et al.  On the relationship between incoming solar radiation and daily maximum and minimum temperature , 1984 .

[10]  Leszek Kuchar,et al.  Estimation of solar radiation for use in crop modelling , 1998 .

[11]  David E. Claridge,et al.  Generalization of the Fourier Series Approach to Model Hourly Energy Use in Commercial Buildings , 1999 .

[12]  J. Deng,et al.  Introduction to Grey system theory , 1989 .

[13]  Mohamed Mohandes,et al.  Support vector machines for short‐term electrical load forecasting , 2002 .

[14]  Philip Haves,et al.  Demand Shifting With Thermal Mass in Large Commercial Buildings: Field Tests, Simulation and Audits , 2005 .

[15]  I. Supit,et al.  A Simple Method to Estimate Global Radiation. , 1998 .

[16]  Shengwei Wang,et al.  Simplified building model for transient thermal performance estimation using GA-based parameter identification , 2006 .

[17]  Drury B. Crawley,et al.  EnergyPlus: Energy simulation program , 2000 .

[18]  C. E. Dorgan,et al.  Hourly thermal load prediction for the next 24 hours by ARIMA, EWMA, LR and an artificial neural network , 1995 .

[19]  Howard W. Barker,et al.  Solar Radiative Transfer Through Clouds Possessing Isotropic Variable Extinction Coefficient , 1992 .

[20]  James E. Braun,et al.  Evaluating the Performance of Building Thermal Mass Control Strategies , 2001 .

[21]  J. N. Sheen.  Incentive pricing and economic profitability of load management program , 2006 .

[22]  S. Running,et al.  An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation , 1999 .

[23]  W. J. Wepfer,et al.  Formulation of a load prediction algorithm for a large commercial building , 1984 .

[24]  Ibrahim Dincer,et al.  Thermal energy storage systems as a key technology in energy conservation , 2002 .

[25]  I. Dincer,et al.  Exergy methods for assessing and comparing thermal storage systems , 2003 .

[26]  D. Claridge,et al.  A Fourier Series Model to Predict Hourly Heating and Cooling Energy Use in Commercial Buildings With Outdoor Temperature as the Only Weather Variable , 1999 .