Mathematical Optimization of Energy Utilization in Smart Meter Analytics Using Liquid Neural Networks

IntelliMeter, a novel framework integrating Liquid Neural Networks (LNN) with Wireless IoT technology and advanced Machine Learning techniques, is introduced for smart meter data analytics. Comparative evaluations against traditional methods like Linear Regression with Seasonal Decomposition (LRSD) and AutoRegressive Integrated Moving Average (ARIMA) reveal superior performance across energy demand forecasting accuracy, anomaly detection, and grid optimization efficiency. The LNN model within IntelliMeter achieves a notable 25% reduction in energy wastage compared to conventional methods. Additionally, it demonstrates a precision rate of 96.97% in anomaly detection, surpassing LRSD and ARIMA. Furthermore, the LNN model exhibits enhanced grid optimization efficiency, highlighting its potential to revolutionize energy management practices through intelligent resource allocation and operational optimization. These findings underscore the transformative impact of LNN-based approaches in enabling more efficient, reliable, and sustainable energy management solutions. IntelliMeter represents a significant advancement in smart meter data analytics, offering utilities actionable insights for optimizing energy distribution, minimizing operational costs, and enhancing grid stability.