Abstract:

The history of studying weather's impact on energy consumption dates back to the early days of modern energy systems. Historically, energy demand was primarily analyzed based on seasonal variations and historical consumption data. With the digital revolution, the integration of weather data into energy analysis began to gain prominence. Early studies used basic statistical models to correlate weather patterns with energy usage. However, the emergence of machine learning techniques in the last two decades has revolutionized this field. The utilization of decision trees, random forests, and neural networks has enabled researchers to create highly accurate predictive models. This project builds upon this historical evolution, leveraging cutting-edge technologies to delve deeper into the relationship between weather patterns and energy consumption in the context of smart homes, contributing to the ongoing evolution of energy-efficient technologies and practices. Thus, this research aims to investigate the intricate relationship between weather patterns and energy consumption in smart homes through a regression analysis. Leveraging machine learning techniques, the study explores predictive models to comprehend how weather variables impact the total energy load in these environments. The analysis involves the use of decision tree and random forest regression algorithms, providing valuable insights into energy consumption patterns under varying weather conditions.

Description:

Smart energy meters, Internet of Things, weather patterns, electric consumption, machine learning, predictive analytics