Evaluation of Indoor Energy-Saving Optimization Design of Green Buildings Based on the Intelligent GANN-BIM Model

Abstract

The present research showcases the Indoor Energy-Saving Optimization Design of green buildings. This integrated approach synergizes a building’s Indoor Energy-Saving process based on the intelligent GANN-BIM model. The GANN-BIM model is driven by genetic algorithms (GAs), which include artificial neural networks (ANNs) along with building information models (BIMs). Building information modeling (BIM) is a technology that involves the modeling and management of digital representation of all forms of structural buildings. These intelligent models can be exchanged and extracted in the form of files and are mainly used for the designing and decision-making of a building. The BIM model is empowered as an intelligent technology by incorporating artificial neural networks (ANNs) and genetic algorithms (GAs). The main objective of the research is Indoor Energy-Saving by implementing an optimized design of green buildings. Green buildings can benefit from the GANN-BIM model’s ability to handle complex and conflicting design requirements while using less computational power during the evaluation of the proposed approach. There are a variety of new green building technologies being developed, but they all share a common goal: to reduce human health and environmental impact by maximizing energy, water, and other resource efficiencies; protecting occupant health; reducing waste; and decreasing pollution. The empirical results of GANN-BIM proved that the proposed model outperforms well in enhancing energy evaluation.