Optimization of energy consumption in smart homes using firefly algorithm and deep neural networks

Electronic gadget advancements have increased the demand for IoT-based smart homes as the number of connected devices grows rapidly. The most prevalent connected electronic devices are smart environments in houses, grids, structures, and metropolises. Smart grid technology advancements have enabled smart structures to cover every nanosecond of energy use. The problem with smart, intelligent operations is that they use a lot more energy than traditional ones. Because of the growing growth of smart cities and houses, there is an increasing demand for efficient resource management. Energy is a valuable resource with a high unit cost. Consequently, authors are endeavoring to decrease energy usage, specifically in smart urban areas, while simultaneously ensuring a consistent terrain. The objective of this study is to enhance energy efficiency in intelligent buildings for both homes and businesses. For the comfort indicator ("thermal, visual, and air quality"), three parameters are used: temperature, illumination, and CO2. A hybrid rule-based Deep Neural Network (DNN) and Fire Fly (FF) algorithm are used to read the sensor parameters and to operate the comfort indication, as well as optimize energy consumption, respectively. The anticipated user attributes contributed to the system's enhanced performance in terms of the ease of use of the smart system and its energy usage. When compared to traditional approaches in expressions of Multi View with 98.23%, convolutional neural network (CNN) with 99.17%, and traffic automatic vehicle (AV) with 98.14%, the activities of the contributed approach are negligibly commanding

Tangent Hyperbolic Fluid Flow under Condition of Divergent Channel in the Presence of Porous Medium with Suction/Blowing and Heat Source: Emergence of the Boundary Layer

A boundary layer’s appearance in a diverging permeable channel for a non-Newtonian hyperbolic tangent fluid with heat transfer in the availability of a heat source and suction or injection is investigated. By controlling backflow, nonlinearly associated ODEs are derived from flow-regulating PDEs, and the restrictions under which the formation of a boundary layer for tangent hyperbolic fluid emerges are investigated. It is obtained that mass suction is an expression of the Hartmann number, porosity parameter, and power law index parameter, and when it surpasses a specific quantity, flow within a boundary layer is conceivable. “Bvp4c,” a MATLAB solver, is used to obtain numerical solutions of flow problem, and for validation of results obtained via Bvp4c, a comparison is made with the methodology of the Runge–Kutta fourth order. As the Weissenberg number enhances, flow in a boundary layer decreases. Furthermore, radiation and heat source parameters have a significant influence on the overall temperature pattern, and as the findings, the thermal boundary layer enhances