PREDICTIVE MODELING OF SMART HOME ENERGY CONSUMPTION BASED ON WEATHER PATTERNS AND IOT SENSOR DATA

Abstract

Smart homes, equipped with advanced sensors and automation systems, offer significant potential for optimizing energy consumption and enhancing user comfort. In this study, we investigate the intricate relationship between weather patterns and energy consumption in smart homes. The dataset comprises 4.5 months of detailed information, including temperature and humidity data collected through ZigBee wireless sensor networks, as well as energy consumption records obtained from m-bus energy meters. Additionally, weather data from the nearby Chievres Airport in Belgium has been integrated with the experimental data, allowing us to analyze how external environmental factors impact energy usage within the home. In conventional energy management systems, energy consumption patterns are often based on historical data and predefined schedules, lacking the adaptability required to respond to changing weather conditions. This rigidity can lead to inefficient energy use, increased costs, and decreased environmental sustainability. Furthermore, these systems tend to overlook the dynamic nature of energy demand, which can be influenced by varying weather conditions. Therefore, there is a need for a more sophisticated approach that leverages advanced analytics to harness the relationship between weather patterns and energy consumption for improved efficiency. Our proposed system addresses the limitations of conventional energy management systems by utilizing regression analysis techniques to model the relationship between weather parameters and energy consumption in smart homes. Through the integration of the extensive dataset, we aim to develop predictive models that can forecast energy demand-based weather conditions. This approach will enable smart homes to dynamically adjust heating, cooling, and other energy-intensive systems in response to weather changes, ultimately optimizing energy usage and reducing costs. By exploring this novel machine learning methodology, we seek to contribute to the development of more energy-efficient and environmentally friendly smart home solutions, paving the way for a sustainable future in residential energy management.