Sustainable Optimization for Innovative Practices of Smart Buildings with the AI Based Deep Learning Techniques

Abstract

A major driver in the rethinking of sustainable engineering and management practices is artificial intelligence (AI), especially in the use of cutting-edge deep learning techniques. The primary focus of this study is to assess the efficacy of Long Short-Term Memory (LSTM) networks as an example of the substantial role that artificial intelligence (AI) plays. When it comes to improving operational performance, optimizing resource allocation, and reducing environmental implications, the Long Short-Term Memory (LSTM) model—a complex sort of recurrent neural network—is crucial. An interesting case study illustrating the use of LSTM algorithms to optimize smart building energy usage in real time is included in this research. By utilizing LSTM for comprehensive pattern analysis and making real-time adjustments, AI exhibits impressive efficiency gains in reducing energy waste. Within the broader context of sustainable engineering, this study demonstrates the effectiveness and efficiency of Long Short-Term Memory (LSTM), therefore contributing significantly to the development of a resilient and ecologically conscious future.