Forecasting Future Electricity Consumption in IoT-Linked Meters Using Machine Learning: The Power Prophecy Approach

The conventional method of manually reading energy meters have been associated with inefficiencies and error-prone processes. To address these challenges, there’s been a growing interest in leveraging advanced technologies to revolutionize metering systems. One such innovation involves the integration of Machine Learning models with existing energy meters to create smart meters capable of predictive analysis. In this envisioned model, Machine Learning algorithms are utilized to predict electricity usage for the forthcoming month based on historical data collected from the smart electric meter. By analyzing patterns and trends in the preceding month’s readings, the model can generate accurate forecasts, empowering users with insights into their future electricity consumption. These predictions are then displayed through a user-friendly interface, providing real-time access to usage data and projected costs. Moreover, the execution of automatic meter activation and deactivation, alongside customizable threshold settings for alerts, add a layer of efficiency and convenience. By proactively notifying users of potential excessive power consumption, individuals are empowered to actively monitor and manage their electricity usage, ultimately leading to savings in both energy expenditure and expenses. This project not only augments the accuracy and efficiency of metering processes but also contributes to broader sustainability efforts by encouraging environmentally conscious practices. Equipping consumers with the tools and insights needed To make enlightened choices regarding their energy usage, the integration of machine learning into smart metering systems represent a significant step towards creating a more sustainable future