Research on sustainability evaluation of green building engineering based on artificial intelligence and energy consumption

Abstract

A green building is a structure that avoids or eliminates negative environmental impacts and generates environmental benefits through its design, construction, or functioning. The use of ecologically friendly building materials increases the quality of life. The overuse of electronic equipment hinders the achievement of the overall green aim, even if smart buildings are a beneficial stimulus for sustainability. Demand-side management and green building energy consumption prediction are connected and depend on accurate estimates of how much energy a facility will need. While several approaches have been offered for predicting energy use, each method has advantages and disadvantages, and there is always room for improvement. This paper suggests the Artificial Intelligence-based Energy Management Model (AI-EMM) in green building. Adaptable to human choices, it can act intelligently to increase user comfort, safety, and energy efficiency. One of the key components of the AI-EMM model is a universal infrared communication system and subsystems for smart user identification and monitoring of the internal and exterior surroundings. Long Short-Term Memory (LSTM) models are used to enhance energy consumption. A green building’s energy usage data is analysed using the suggested approach. For a better interior climate, studies examining the relationship between Heating, Ventilation, and Air Conditioning (HVAC) system should focus on airside design optimization. According to the findings, economic gains and environmentally sustainable building coexist harmoniously. A green or sustainable building is one whose structure and characteristics preserve or enhance the local environment. The experimental outcome of the AI-EMM achieved a high-performance ratio of 94.3%, less energy consumption ratio of 15.7%, accuracy ratio of 97.4%, energy management level of 95.7%, and prediction ratio of 97.1%.