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. It excels in deciphering intricate consumption patterns, adapting to external influences, and refining real-time predictions. Leveraging a comprehensive dataset spanning electricity consumption and weather-related attributes, meticulously curated by the Company of Electrolysia, the model showcases unparalleled predictive accuracy. Its superiority over existing techniques is evident in navigating nonlinear temporal dependencies and optimizing data integration. The model’s adaptability, precision, and strategic insights redefine energy consumption management. This innovative model holds significant implications for energy consumption forecasting, promising societal and environmental benefits by enabling optimized energy production. The temporal hierarchical embeddings encode multiple temporal scales, capturing short-term fluctuations and long-term trends. Feature fusion seamlessly integrates historical weather data, allowing dynamic adaptation to changing weather conditions. The adaptive attention mechanism dynamically allocates weights, enhancing the model’s accuracy by focusing on influential factors. The online learning component facilitates real-time adjustments, ensuring responsiveness to evolving trends. The dataset used comprises a comprehensive amalgamation of electricity consumption and weather-related data, its meticulous curation ensures the model’s robustness and precision. In essence, this research redefines energy consumption management, heralding an era of innovation and efficiency within electric power systems, while paving the way for further advancements and applications in optimized energy production and management.