The utilization of energy is on the rise in current trends due to increasing consumptions by households. Smart buildings, on the other hand, aim to optimize energy, and hence, the aim of the study is to forecast the cost of energy consumption in smart buildings by effectively addressing the minimal energy consumption. However, smart buildings are restricted, with limited power access and capacity associated with Heating, Ventilation and Air Conditioning (HVAC) units. It further suffers from low communication capability due to device limitations. In this paper, a balanced deep learning architecture is used to offer solutions to address these constraints. The deep learning algorithm considers three constraints, such as a multi-objective optimization problem and a fitness function, to resolve the price management problem and high-level energy consumption in HVAC systems. The study analyzes and optimizes the consumption of power in smart buildings by the HVAC systems in terms of power loss, price management and reactive power. Experiments are conducted over various scenarios to check the integrity of the system over various smart buildings and in high-rise buildings. The results are compared in terms of various HVAC devices on various metrics and communication protocols, where the proposed system is considered more effective than other methods. The results of the Li-Fi communication protocols show improved results compared to the other communication protocols.

Molecular dynamics simulations were performed on copper-tantalum FGMs to investigate the mechanical and thermophysical under uniaxial tensile deformation. Molecular dynamics simulations were carried out to examine the influence of tantalum content, temperature, and strain rate on mechanical response, plastic deformation mechanisms, atomic mobility, and energy dissipation. The study focuses on analyzing the properties of one layer of pure copper and another layer of copper with tantalum content varying from 0% to 10%. The applied strain rate ranged from 10⁸ s⁻¹ to 10¹¹ s⁻¹, and the temperature varied from 200 K to 1000 K in intervals of 200 K. This study offers valuable insights into the high-temperature plastic deformation and thermodynamic behavior of Cu-Ta FGMs. At elevated temperatures, the plastic deformation mechanism accelerates atomic diffusion and enhances thermal energy transport, thereby minimizing dislocation density and flow stress. This study offers fundamental insights into the high-temperature deformation and heat-transfer characteristics of Cu–Ta FGMs, supporting their potential applications in biomedical and aerospace thermal systems. Increasing the Ta content promotes plastic activity within grains and at grain boundaries at low temperatures, while at higher temperatures the plastic and thermal deformation mechanisms stay like those before Ta addition. The novelty of this study lies in the combined analysis of mechanical response, thermodynamic parameters, and atomic mobility in Cu-Ta FGMs under extreme thermo-mechanical loading, offering new insights into high-temperature plasticity and energy transport mechanisms. This integrated understanding has not been reported previously and directly supports the development of Cu-Ta FGMs for advanced biomedical and aerospace thermal applications. These findings contribute to an improved understanding of the coupled thermo-mechanical and thermodynamic behavior of Cu-Ta FGMs at the nanoscale and inform the design of novel materials with tailored mechanical and heat-transfer properties.