THE APPLICATIONS OF THE GREEN BUILDING DESIGN CONCEPT IN MEDICAL BUILDING DESIGN

Abstract

Green construction is widely recognized as a significant technique used by governments throughout the globe to combat environmental change and energy shortages. Despite the availability of several modern green building technologies, designers often struggle with the selection and use of green building technologies in varied contexts. The healthcare industry has a significant duty and potential to decrease greenhouse gas emissions to minimize the broad health effects of climate change. Furthermore, there has been a rise in green building design (GBD) for most new construction; nevertheless, the implications of GBD on the usage phase and occupant health have not been well researched. Hence, in this paper, we apply a machine learning algorithm for green building design in a medical building. Initially, the atmospheric and physical assets data is collected and preprocessed using the normalization method. The features are extracted using Linear discrimination analysis (LDA). The energy usage is predicted using the Multi-linear Random Forest Regression (MRFR) algorithm. The predicted energy efficiency is optimized using Boolean Ant Colony Optimization (BACO). From the predicted values, the green building can be designed. The performance of the proposed system is evaluated and compared with traditional approaches to prove the efficacy of the system