SMART HOMES, SMARTER ENERGY: UNRAVELING WEATHERDRIVEN CONSUMPTION THROUGH REGRESSION ANALYSIS

The study of weather’s impact on energy consumption has evolved significantly since the early days of modern energy systems, where energy demand was primarily assessed based on seasonal variations and historical consumption trends. Traditionally, basic statistical models were used to establish correlations between weather patterns and energy usage, providing foundational insights into consumption trends. However, with the advent of the digital revolution and advancements in computational capabilities, the integration of weather data into energy analysis has become increasingly sophisticated. Over the past two decades, the rapid evolution of machine learning techniques has transformed this field, introducing advanced predictive modeling approaches such as decision trees, random forests, and neural networks. These methodologies have enabled researchers to develop highly accurate models capable of forecasting energy demand based on intricate weather fluctuations. This project builds upon this historical trajectory, employing state-of-the-art technologies to explore the nuanced relationship between weather conditions and energy consumption within smart homes. The increasing prevalence of smart home technology, characterized by interconnected devices and real-time energy monitoring, presents new opportunities to refine energy consumption models and enhance efficiency. By leveraging machine learning techniques, particularly regression-based approaches, this study aims to uncover predictive relationships between weather variables—such as temperature, humidity, wind speed, and solar radiation— and total energy load within smart home environments. Through the application of decision tree regression and random forest regression algorithms, this research provides a detailed analysis of energy consumption patterns under diverse weather conditions, enabling more accurate forecasting and informed energy management strategies. These machine learning models offer a robust framework for identifying key weather-driven consumption trends, allowing for data-driven decision-making in energy optimization and sustainability efforts. Additionally, understanding these relationships holds significant implications for smart grid integration, demand-side management, and the development of adaptive energy policies. By leveraging predictive analytics, this study contributes to the ongoing evolution of energyefficient technologies, helping to reduce energy waste, lower costs, and promote sustainability in modern residential energy systems. Furthermore, the insights derived from  this research can inform the design of intelligent energy management systems that dynamically adjust power consumption based on real-time weather forecasts, optimizing household energy use while mitigating the impact of extreme weather conditions on electricity demand. In a broader context, the findings can aid policymakers, utility companies, and homeowners in making informed decisions regarding energy planning and sustainability initiatives. By advancing the understanding of weather-driven energy consumption patterns, this study not only builds upon the existing body of research but also paves the way for future innovations in energy efficiency within smart home ecosystems. Ultimately, this research underscores the crucial role of machine learning in the pursuit of sustainable energy solutions, demonstrating how advanced regression techniques can enhance predictive modeling and drive more intelligent, weather-responsive energy management strategies.