Operational Energy and Carbon Cost Assessment Model for Family Houses in Saudi Arabia

Abstract

In Saudi Arabia, housing projects account for almost half of the total electricity consumed by the construction industry because of the large number of housing projects compared to other types of buildings. This paper proposes a quantitative approach using a multiple linear regression assessment model to predict the energy cost and environmental cost of housing building in Saudi Arabia. It was developed to assist house owners in Saudi Arabia in estimating the monthly energy cost and associated operational carbon cost according to several predictor parameters. Based on related literature, these parameters were reviewed and discussed by experts from the Ministry of Housing. They included building location, wall type, number of occupants, window type, envelope insulation, building age, building area, number of air-conditioning units and their systems, and lighting system. The model development process included five main stages: collecting the energy and carbon cost data from completed operating housing units, categorizing the collected data based on parameters, diagnosing the quality of gathered data and filtering outlier data if any, building and generating a model, and lastly, testing and validating the model. More than 77 datasets were collected across the country during different times of the year. The findings of this study reveal that the relationship between the number of users and the building area with the energy cost is significant and that the number of users is more correlated to the energy cost than the age of the building or the number of central air conditioners installed. Moreover, the results show that the developed model has the ability to predict energy and carbon costs with high accuracy. The developed model serves as a decision support tool for householders and decision makers in the Ministry of Housing to control the predicted parameters. This would be beneficial for the housing unit owners for allocating constrained budgets.