Implementing Machine Learning to Enhance Energy Efficiency in IoT Sensors and Devices

Abstract The use of energy in buildings, which includes both residential and commercial structures, accounts for around forty percent of the total energy usage in the United States, and comparable data are being reported from country to country all over the globe. The residents are provided with a pleasant, secure, and productive atmosphere that is maintained by the large quantity of energy that is utilized. Therefore, it is of the utmost importance that the management of energy consumption in buildings be maximized while simultaneously ensuring that occupants continue to experience adequate levels of comfort, health, and safety. During the process of extracting valuable insights from data and improving a variety of systems, Machine Learning (ML) has been shown to be a very useful technique. For the purpose of improving energy economy in Internet of Things sensors and devices, this study using ML approach which is Random Forest (RF). The findings demonstrate that implementing the suggested model leads to a noteworthy decrease of over 18% in the general power consumption of the smart building system compared to its pre-optimization state. This underscores the efficacy of the adaptive optimization control model for smart building systems in refining the operational parameters of energy-saving systems while ensuring the security of IoT devices. The comprehensive results reveal a total power optimization of 360.42 kWh observed at the sampling time of 9:20.