Predicting Energy Usage Using Historical Data and Linear Models

This paper presents a method to predict energy usage, based on weather conditions and occupancy, using a multiple linear regression model (MLR) in research office buildings. In this study, linear regression models of four research office sites in different regions of New Zealand were selected to show the capability of simple models to reduce margins of error in energy auditing projects. The final linear regression models developed were based on monthly outside temperatures and numbers of full time employees (FTEs). Comparing actual and predicted energy usage showed that the models can predict energy usage within acceptable errors. The results also showed that each building should be investigated as an individual unit.

[1]  Fabio Bisegna,et al.  Climate models for the assessment of office buildings energy performance , 2004 .

[2]  Soteris A. Kalogirou,et al.  Modeling and simulation of passive and active solar thermal systems , 2012 .

[3]  Alberto Hernandez Neto,et al.  Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption , 2008 .

[4]  Ashish Pandharipande,et al.  Daylight integrated illumination control of LED systems based on enhanced presence sensing , 2011 .

[5]  Sandhya Samarasinghe,et al.  Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition , 2006 .

[6]  W. Abrahamse.  A review of energy conservation studies. , 2003 .

[7]  R. Rajagopal,et al.  Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior , 2013 .

[8]  Weimin Wang,et al.  Applying multi-objective genetic algorithms in green building design optimization , 2005 .

[9]  Tiberiu Catalina,et al.  Multiple regression model for fast prediction of the heating energy demand , 2013 .

[10]  Joseph Virgone,et al.  Development and validation of regression models to predict monthly heating demand for residential buildings , 2008 .

[11]  Francis Rubinstein,et al.  Daylighting, dimming, and the electricity crisis in California , 2001 .

[12]  Fariborz Haghighat,et al.  Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network , 2010 .

[13]  V. I. Hanby,et al.  Regression models for predicting UK office building energy consumption from heating and cooling demands , 2013 .

[14]  Gwendolyn Brandon,et al.  REDUCING HOUSEHOLD ENERGY CONSUMPTION: A QUALITATIVE AND QUANTITATIVE FIELD STUDY , 1999 .

[15]  Sandhya Samarasinghe,et al.  Neural Networks for Applied Sciences and Engineering , 2006 .

[16]  Joseph C. Lam,et al.  Multiple regression models for energy use in air-conditioned office buildings in different climates , 2010 .

[17]  Mahdi Safa,et al.  Determination and Modelling of Energy Consumption in Wheat Production Using Neural Networks , 2011 .

[18]  Arnaud G. Malan,et al.  HVAC control strategies to enhance comfort and minimise energy usage , 2001 .

[19]  Peng Gao,et al.  A hybrid decision support system for sustainable office building renovation and energy performance improvement , 2010 .

[20]  Fehmi Görkem Üçtuğ,et al.  A linear programming approach to household energy conservation: Efficient allocation of budget , 2012 .

[21]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[22]  Dave Cavalcanti,et al.  Closed Loop Integrated Lighting and Daylighting Control for Low Energy Buildings , 2010 .

[23]  V. I. Hanby,et al.  UK office buildings archetypal model as methodological approach in development of regression models for predicting building energy consumption from heating and cooling demands , 2013 .

[24]  L. Suganthi,et al.  Energy models for demand forecasting—A review , 2012 .

[25]  R. Alvarez.  Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach , 2009 .

[26]  Salman Azhar,et al.  A Case Study of Building Performance Analyses Using Building Information Modeling , 2010 .

[27]  Steven W. Su,et al.  HVAC Integrated Control for Energy Saving and Comfort Enhancement , 2011 .