INVESTIGATING THE ROLE OF DATA ANALYTICS IN MONITORING AND MANAGING ENERGY CONSUMPTION IN SMART HOMES, AIMING TO ENHANCE EFFICIENCY AND REDUCE COSTS

Smart home technology is progressing rapidly due to the need for better energy management and resulting new potentials for controlling energy. While smart homes use different connected IoT devices, energy meters, and sensors to gather a massive amount of data that is used to gain insight into energy consumption. This data is governed by data analytics so as to come up with actionable information that the homeowners can put to use in tracking their usage and, in turn, altering it. Energy management in smart homes is particularly critical for individual homes and on a global scale given the rising costs of energy and the fast-rising need for energy sustainability in the world today. The data used in this study to determine the feasibility of data analytics in managing energy systems in smart homes is gathered using both quantitative and qualitative research studies. The main data for this study is collected by Internet of Things (IoT) connected smart devices and energy monitoring sensors that are randomly selected smart homes depending on energy consumption, peak consumption, and cost. Machine learning techniques are used to determine trends, forecast future consumption, and come up with improvements. By combining these quantitative and qualitative views, this work offers a deeper understanding of the use of data in enhancing the efficiency of energy usage in smart homes. Big data as a tool is useful for increasing efficiency in smart homes and, therefore, cutting costs by providing regular real-time monitoring, accurate forecasting of needed energy, and individual energy management techniques. The findings of this research support the significance of data analysis to reveal energy conservation potentials, promote pro-environment utilization, and reduce energy bills. The current work enriches the literature on sustainable energy management with a focus on the significance of further advancements of smart home systems and data science solutions towards a more sustainable energy future.