Open AccessBuilding stock energy modeling: Feasibility study on selection of important input parameters using stepwise regression
Author(s) -
Arababadi Reza,
Naganathan Hariharan,
Saffari Pour Mohsen,
Dadvar Atefeh,
Parrish Kristen,
Chong Oswald
Publication year - 2021
Publication title -
energy science and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.638
H-Index - 29
ISSN - 2050-0505
DOI - 10.1002/ese3.847
Subject(s) - stepwise regression , retrofitting , regression analysis , computer science , sensitivity (control systems) , reliability engineering , efficient energy use , predictive modelling , regression , reliability (semiconductor) , stock (firearms) , data mining , engineering , statistics , machine learning , mathematics , mechanical engineering , power (physics) , physics , electrical engineering , structural engineering , quantum mechanics , electronic engineering
Building energy assessment is essential to accomplish the sustainable energy targets of new and present buildings. Retrofitting of the existing buildings by assessing them through energy models is the most prominent method. Studies revealed that there is still blank information about the building stocks, and these affect the valuation of building energy efficiency policies. Literature also recommends that the existing energy models are too complex and unreliable to predict the energy use. Reliability of such energy models would improve through a better alignment of the input parameters and the model assumptions. The authors hypothesized that the reliability of models would be improved through identification of the most relevant energy use parameters for the building stocks in different regions and models. One of the most commonly accepted methods for detecting the most dominant input parameters is sensitivity analysis, though its shortcomings include the need for a large number of data samples and long computing time. In this research, the Energy, Carbon, and Cost Assessment for Buildings Stocks (ECCABS) model is adopted to identify the most important parameters of the presented model. The research team uses the model that has been validated by studies conducted for the UK building stock. Moreover, by assessing the feasibility study with the stepwise regression to identify the significant input parameters have been discussed. Results show that stepwise regression method could produce the same results compared to sensitivity analysis. This paper also indicates that stepwise regression is considerably faster and less computationally intensive compared to common sensitivity analysis methods.