Smart Energy Consumption Control in Commercial Buildings Using Machine Learning and IOT

Abstract

In recent years, the use of artificial intelligence (AI) techniques such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), in combination with the Internet of Things (IoT), has gained significant attention for optimizing energy consumption in commercial buildings. With the increasing demand for energy and the rising costs of energy, there is a pressing need for efficient methods for energy management in commercial buildings. Smart energy consumption control systems that utilize machine learning algorithms and IoT devices can provide real-time data on energy usage and automate energy usage decisions in commercial buildings. In this paper, we investigate the potential of ANN and SVM-based smart energy consumption control systems in commercial buildings. We aim to analyze the impact of using these algorithms on energy consumption patterns in commercial buildings and evaluate the efficiency and effectiveness of these systems in reducing energy consumption and costs while maintaining the desired level of comfort for the occupants. Our study will focus on comparing the performance of ANN and SVM-based algorithms in terms of energy consumption reduction and cost savings. The results of this study can provide valuable insights into the application of ANN and SVM-based smart energy consumption control systems in commercial buildings and contribute to the development of more sustainable and energy-efficient buildings.

Advancements in Friction Stir Welding: A Comprehensive Review of Process Variables and Emerging Developments

“…The usage of three types of pin tools in various forms, including cylinder, square, and taper square [16].Using various weld rates and distance adjustments between the sheets, aluminium alloy (2A70) was joined. When the weld speed was maintained at roughly 185 mm/min, the tensile strength increased up to 400Mpa [17,25].The use of an ultrasonic type shoulder as a tool for creating connections led to the development of optimum WS of 60 mm/min, RS of 915 rpm and ultrasonic power around 1500 W parameters that increased UTS by around 158MPa [18,24].In D. Maneiah used three input variables to determine the optimal TS. The results showed that the maximum elongation 10 percent and TS -190Mpa were achieved at a tool's rotating speed of 1400 rev / min, a 0 degree tilt angle, and a feed rate of 110 mm/min [19].Al6061/SiC/Alumina was created by using FSW, and it demonstrated excellent hardness when especially in comparison to base metal 6061.…”

“…This reduction is achieved by increasing the rotating speed while simultaneously decreasing the welding speed [21].the relationship between shoulder and pin diameters to achieve superior mechanical characteristics. The shoulder diameter of a pin strongly impacts the heat generated during the FSW process [22][23][24].The joint with shoulder diameter 15mm), rotating speed (900 rpm), and welding speed (100mm/min) provided the higher tensile strength when compared to other joints, according to Rajkumer et al's investigation on the impact of tool shoulder diameters on tensile strength of AA1100 joints [25]. Y. Zhang et al [26]investigated the relationship between microstructure, welding conditions, and mechanical properties.…”