Machine Learning-Based Energy Consumption Monitoring And Forecasting System For Sustainable Environment

Abstract

The proposed energy consumption monitoring and forecasting system aims to enhance energy monitoring by seamlessly integrating electricity meters with RS485 to Ethernet TCP/IP converters, enabling efficient real-time data collection and analysis. It entails generating graphical depictions of electricity meter use, drawing insightful conclusions from data analysis to offer useful information on energy usage trends and monitoring the state of meters to guarantee optimal performance. The endeavor to present gathered data, graphs, and insights for simple interpretation includes the creation of an intuitive dashboard. Various real-time power consumption parameters through building a robust connection infrastructure are gathered for monitoring and analysis. Upon data collection, it is processed and resampled to form machine learning models to predict time series. The primary aim is to accurately predict energy consumption by employing historic consumption patterns, which would make better decision-making possible and effective resource allocation. By enabling more precise forecasting and monitoring, the system promotes energy conservation and reduces unnecessary power usage, thereby contributing to lower greenhouse gas emissions. This research supports sustainable energy management practices that help mitigate environmental impact and foster a greener future.