A Communication Framework for Smart Grid Load Forecasting Driven by Data from IoT
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Abstract
Modern smart grids have transformed energy management by include the Internet of Things (IoT), therefore enabling real-time communication and data-informed decision-making. Important for improving energy efficiency and preserving grid stability is a smart communication system presented in this work intended to improve load forecasting for smart meters. Using IoT technology to gather data and sophisticated predictive analytics to enhance load forecasting across several time periods—short-term (one hour to one week), medium-term (spanning one week to one month), and long-term (extending from one month to several years)—the proposed system. By means of accurate forecasts spanning several years, utility companies may maximize their resources, lower running expenses, and improve grid dependability. This research investigates how big data analytics and machine learning techniques might help to create adaptable, real-time strategies and enhance forecasting accuracy. This strategy helps the smart grid to minimize energy waste, better balance supply and demand, and assist projects for sustainable energy.
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