Linear regression models for prediction of annual heating and cooling demand in representative Australian residential dwellings

Linear regression models for prediction of annual heating and cooling demand in representative Australian residential dwellings

Authors:

Navid Asadzadeh Aghdaei, Georgios Kokogiannakis, Dan Daly and Timothy McCarthy

Organisation of Presenter:

University of Wollongong

Abstract:

This paper presents the development methodology of linear regression models that were developed for the prediction of annual thermal loads in representative residential buildings across three major climates in New South Wales, Australia, and the assessment of the impact of building envelope upgrades. A differential sensitivity analysis was undertaken for sixteen building envelope parameters, with six parameters being identified as significant. These six parameters were then explored using EnergyPlus simulation, and a number of linear regression models developed from the simulation outputs. Random values for design parameters were generated, and the results of EnergyPlus simulations using these parameters were used to verify the outputs of the regression models. The differences between regression-predicted and EnergyPlus-simulated annual thermal energy requirements were of order 10%-15%. The coefficient of determination (R 2) was over 0.90, indicating a good agreement between simulation and the regression models, and suggesting that the annual heating and cooling energy requirements can be forecasted with an acceptable accuracy using the regression models. It is envisaged that the regression models developed can be used as a quick alternative to building simulation for residential buildings of the area and the climate covered by our study, and can serve to rapidly estimate the likely energy savings/penalty during the retrofitting design stage when different building schemes and design concepts are being considered.