Advanced LSTM-Based Time Series Forecasting for Enhanced Energy Consumption Management in Electric Power Systems

Abstract

In the realm of electric power systems, the optimization of energy consumption emerges as a strategic imperative. This research paper introduces a groundbreaking approach to enhance energy consumption management by proposing an advanced Long Short-Term Memory (LSTM) based forecasting model. This model synthesizes temporal hierarchical embeddings, feature fusion, adaptive attention, and online learning mechanisms to capture intricate consumption patterns, adapt to external influences, emphasize influential factors, and refine predictions in real-time. Leveraging a comprehensive dataset encompassing electricity consumption and weather-related attributes, the proposed model unveils unparalleled predictive prowess. The results showcase the model's exceptional accuracy, navigating nonlinear temporal dependencies and seamlessly integrating weather data. Comparative analysis demonstrates the model's superiority over existing techniques in deciphering consumption trends. Advantages include enhanced adaptability, precision, and strategic insights, while limitations emphasize the need for robust data and computational resources. In conclusion, this research redefines energy consumption management, ushering in an era of innovation, efficiency, and strategic empowerment within electric power systems. The proposed model's transformative impact paves the path for future developments and applications in optimized energy production and management.