A cooling change-point model of community-aggregate electrical load

Abstract Estimates of daily electrical cooling load for a city of 800,000 are developed based on the relationship between weather variables and daily-average electricity consumption over 1 year. The relationship is found to be nearly linear above a threshold temperature. Temperature and humidity were found to be the largest, at 59%, and second largest, at 21%, contributors to electrical cooling load. Direct normal irradiation intercepted by a vertical cylinder, DNI sin θ, was found to be a useful explanatory variable when modeling aggregates of buildings without a known or dominant orientation. The best study case model used DNI sin θ and diffuse horizontal irradiation (DHI) as distinct explanatory variables with annual electrical cooling load contributions of 9% and 11% respectively. Although the seasonal variation in electrical cooling load is large – on peak summer days more than 1.5 times the winter base load – the combined direct and diffuse solar contribution is essentially flat through the year, a condition at odds with the common assumption that solar cooling always provides a good match between supply and demand. The final model gives an electrical cooling load estimate for Abu Dhabi Island that corresponds to 40% of the total annual electrical load and 61% on the peak day.

[1]  Kenneth J. White,et al.  THE DURBIN-WATSON TEST FOR SERIAL CORRELATION WITH EXTREME SAMPLE SIZES OR MANY REGRESSORS' , 1977 .

[2]  Stig Hammarsten,et al.  A critical appraisal of energy-signature models , 1987 .

[3]  R. C. Sonderegger.  Dynamic models of house heating based on equivalent thermal parameters , 1978 .

[4]  F. W. Yu,et al.  Energy signatures for assessing the energy performance of chillers , 2005 .

[5]  E. Georgopoulou,et al.  Models for mid-term electricity demand forecasting incorporating weather influences , 2006 .

[6]  Joseph C. Lam,et al.  An analysis of climatic influences on chiller plant electricity consumption , 2009 .

[7]  David E. Claridge,et al.  A Four-Parameter Change-Point Model for Predicting Energy Consumption in Commercial Buildings , 1992 .

[8]  David E. Claridge,et al.  Bias in Predicting Annual Energy Use in Commercial Buildings with Regression Models Developed from Short Data Sets , 1994 .

[9]  Andrej Pázman,et al.  Nonlinear Regression , 2019, Handbook of Regression Analysis With Applications in R.

[10]  W. Kessling,et al.  Innovative Systems for Solar Air Conditioning of Buildings , 2004 .

[11]  Jeffrey M. Gordon,et al.  Cool thermodynamics : The engineering and physics of predictive, diagnostic and optimization methods for cooling systems , 2000 .

[12]  Peter Lamp,et al.  European research on solar-assisted air conditioning , 1998 .

[13]  T. Amemiya.  Non-linear regression models , 1983 .

[14]  Ahmed Z. Al-Garni,et al.  Model for Electric Energy Consumption in Eastern Saudi Arabia , 1997 .

[15]  Daniel O. Stram,et al.  The applicability of PRISM to electric heating and cooling , 1986 .

[16]  Harrison M. Wadsworth.  Handbook of Statistical Methods for Engineers and Scientists , 1990 .

[17]  Abdallah Al-Shehri,et al.  A simple forecasting model for industrial electric energy consumption , 2000 .

[18]  W. M. Warwick,et al.  End-Use Load and Consumer Assessment Program: motivation and overview , 1993 .

[19]  S. Probert,et al.  Energy-saving options , 1988 .

[20]  S. Iniyan,et al.  A review of energy models , 2006 .

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

[22]  H. Lee Willis,et al.  Comparison Tests of Fourteen Distribution Load Forecasting Methods , 1984, IEEE Power Engineering Review.

[23]  H. C. S. Thom,et al.  NORMAL DEGREE DAYS BELOW ANY BASE , 1954 .

[24]  F. Flouquet,et al.  Local weather correlations and bias in building parameter estimates from energy-signature models , 1992 .

[25]  David E. Claridge,et al.  Ambient-temperature regression analysis for estimating retrofit savings in commercial buildings , 1998 .

[26]  F. Joutz,et al.  Seasonality and Weather Effects on Electricity Loads: Modeling and Forecasting , 2001 .

[27]  David E. Claridge,et al.  An Energy Delivery Efficiency Index to Evaluate Simultaneous Heating and Cooling Effects in Large Commercial Buildings , 1994 .

[28]  Tony N.T. Lam,et al.  Artificial neural networks for energy analysis of office buildings with daylighting , 2010 .

[29]  C. Ghiaus.  Experimental estimation of building energy performance by robust regression , 2006 .

[30]  Mourad Badri,et al.  EDSSF: a decision support system (DSS) for electricity peak-load forecasting , 1997 .

[31]  Hassan Radhi,et al.  Evaluating the potential impact of global warming on the UAE residential buildings – A contribution to reduce the CO2 emissions , 2009 .

[32]  Radu Zmeureanu.  Assessment of the energy savings due to the building retrofit , 1990 .

[33]  J. Kelly Kissock,et al.  Measuring industrial energy savings , 2008 .

[34]  Michael T. Manry,et al.  Comparison of very short-term load forecasting techniques , 1996 .

[35]  David E. Claridge,et al.  Using synthetic data to evaluate multiple regression and principal component analyses for statistical modeling of daily building energy consumption , 1994 .

[36]  F. E. Grubbs.  Procedures for Detecting Outlying Observations in Samples , 1969 .

[37]  T. A. Reddy,et al.  Uncertainty in baseline regression modeling and in determination of retrofit savings , 1998 .

[38]  Silja Meyer-Nieberg,et al.  Electric load forecasting methods: Tools for decision making , 2009, Eur. J. Oper. Res..

[39]  Hesham K. Alfares,et al.  Electric load forecasting: Literature survey and classification of methods , 2002, Int. J. Syst. Sci..

[40]  G. J. Levermore.  Performance lines and energy signatures: Analysis with reference to the CIBSE Building Energy Code , 1995 .

[41]  Robert C. Sonderegger,et al.  A Baseline Model for Utility Bill Analysis Using Both Weather and Non-Weather Related Variables 1 , 1998 .

[42]  Jean-Louis Scartezzini,et al.  A simplified correlation method accounting for heating and cooling loads in energy-efficient buildings , 1998 .

[43]  David E. Claridge,et al.  A Change-Point Principal Component Analysis (CP/PCA) Method for Predicting Energy Usage in Commercial Buildings: The PCA Model , 1993 .

[44]  Mahmoud A. Al-Iriani.  Climate-related electricity demand-side management in oil-exporting countries--the case of the United Arab Emirates , 2005 .

[45]  Alfred Heller.  Heat-load modelling for large systems , 2002 .