Predictive Intelligence Technique for Short-Term Load Forecasting in Sustainable Energy Grids

Abstract

Short-term load forecasting remains pivotal in managing sustainable energy grids, with accuracy directly influencing operational decisions. Conventional forecasting methodologies often falter in adapting to the dynamic complexities inherent in modern energy systems. This paper introduces a predictive intelligence technique rooted in machine learning aimed at enhancing short-term load forecasting accuracy within sustainable energy grids. Leveraging historical data, weather patterns, grid operations, and consumer behavior insights, our study develops a robust predictive model. The model's adaptability to evolving patterns and real-time data integration offers a promising solution to the limitations of existing forecasting methods. Through a comparative analysis and validation against established benchmarks, the proposed technique showcases superior performance, demonstrating its potential for more efficient resource allocation and improved grid management. This research contributes to advancing sustainable energy practices by offering a reliable and adaptive solution for short-term load forecasting, fostering more resilient and responsive energy grid operations.