Building stock energy modeling: Feasibility study on selection of important input parameters using stepwise regression

Abstract

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.

Investigating the Relative Importance of Spatial and Environmental Factors on Energy Consumption in the Residential Sectors Using GIS (Case Study: Kerman)

“…The study area was the city of Kerman, located in the southeast of Iran, with an area of around 240 km 2 . According to the 2016 census, the population of this city is 738,724 people.…”

“…These factors can be categorized into three categories. The first category, named building-related factors, includes factors such as age and area (e.g., [2]). The second category includes occupant-related factors.…”

An insight on B2B Firms in the Age of Digitalization and Paperless Processes

“…Four multiple linear regression models were calculated by using the stepwise methods to complement hypothesis analysis (see Tables 3 and 4), aiming to identify the predictors of the contribution of dematerialization for global image, ESS spending on paper, the overall importance of dematerialization, and perceived profitability increase. In this analysis, stepwise regression was used as the standard technique; this technique is used by Arababadi et al [88] and Noryani et al [89]. The selection of the variables is based on their level of significance in addition to the advantages of a quick method of automatic selection of the best model that allows the information on the variables to be removed and added, thus, being very useful for analysing the quality of variables predictors.…”