The continuous increase in energy consumption has brought worldwide attention to its significant environmental effect, which is triggered by the increase in greenhouse gas emissions, global warming, and rapid climate change. As such, more energy efficient buildings are required to minimize the energy consumption of heating and cooling. The present study introduces a set of machine learning-based models to predict the heating and cooling loads in buildings. This includes back-propagation artificial neural network, generalized regression neural network, radial basis neural network, radial kernel support vector machines and ANOVA kernel support vector machines. The comparisons were conducted as per mean absolute percentage error (MAPE), mean absolute error (MAE) and root-mean squared error (RMSE). Finally, the significance of the capacities of the machine learning models are evaluated using two-tailed student’s t-tests. Results demonstrate that the radial basis function network outperformed the aforementioned machine learning models.

The construction industry is acknowledged as one of the major primary energy consumers and contributors to global environmental emissions. To this end, proper selection of building materials is imperative to maintain the sustainability of a built environment. This research study proposed a building information modeling-based framework for lifecycle assessment of building materials in Saudi Arabia. Both Autodesk Revit and Autodesk Quantity Takeoff were adopted to define building materials, components, and their quantities. In addition, Athena Impact Estimator and Ecotect® Analysis software were leveraged to conduct thorough environmental impact assessment and energy simulation of building materials. The conducted lifecycle impact assessment tackled project phases of site preparation, construction, use, and demolition. The environmental dimensions of energy consumption, global warming, air emissions, land emissions, and water emissions were also explored. The capabilities of the developed model were tested using a case study of a college building in Saudi Arabia. The findings from this study can assist in the selection of environmentally friendly building materials that can be employed in Saudi Arabia’s construction market.

Energy-saving design is becoming a trending topic and top-priority over the past decades due to high energy costs, limited available resources and growing urban development. Buildings are alluded to as the major contributors of energy consumption and environmental emissions across the globe. This calls for the development of precise forecasting models of energy consumption and carbon emissions. Hence, this research paper harnesses the implementation of several contemporary metaheuristics to accurately project heating and cooling energy (HEN and CEN) in residential buildings. In this respect, black widow optimization, dandelion optimization, dingo optimization, sparrow search, and red-tailed hawk optimization are among the studied metaheuristics in this research study. The prediction accuracies of the developed models are assessed stepping on the measures of i) relative absolute error (RAE), ii) mean absolute error (MAE), iii) mean absolute percentage error (MAPE), iv) root mean squared error (RMSE) and v) Nash-Sutcliffe efficiency (NSE). It is shown that the developed red-tailed hawk optimization-based model succeeded in accomplishing the most precise results of HEN and CEN. In this context, it predicted HEN with RAE (0.201), MAE (1.838), MAPE (7.626%), RMSE (2.826), and NSE (0.921). Besides, it anticipated CEN with RAE (0.234), MAE (2.009), MAPE (7.519%), RMSE (3.246), and NSE (0.883). It can be argued that this research study could benefit architects and designers in creating more energy-efficient buildings at an early stage.