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Over the last few years, the term intelligent energy management, also called smart energy management, has emerged as a growing idea in the power systems literature. This is due to the rapid increase in energy consumption in today's applications, ranging from industrial to commercial (Nižetić et al., 2020).
Reinforcement Learning (RL): In our study, we have employed a reinforcement learning model tailored to optimize energy management within a microgrid system. This model leverages historical data on electricity imports, solar energy production, and storage behaviors to make real-time decisions aimed at enhancing energy efficiency and sustainability.
The novel cognitive IoT-enabled framework known as IntDEM that supports effective energy management in smart grid systems, is presented in this study. Typically, a smart electrical system distributes electricity based on usage tracking.
Smart home and smart grid energy management systems offer opportunities and technologies to meet high energy needs. Energy management is designed for the smart home of the future, with one-third of electricity demand generated by the household sector.
Using AI algorithms, several energy management and prediction frameworks have been established in the previous; in the more recent research efforts on smart energy prediction, deep learning techniques have become more prevalent.
For intelligent energy management, a Gradient Boosting Machine (GBM) is chosen as the primary machine learning model to analyze consumption patterns, forecast future demand, and recommend energy-saving strategies.
Abstract: Although intelligent load forecasting is essential for optimal energy management (EM) in smart cities, there is a lack of current research exploring EM in well …
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Intelligent Energy Management Systems (IEMS) are a necessary tool to reduce energy overconsumption in households, commercial, educational and industrial buildings and …
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After an extensive study of the literature, we concluded that local energy systems are still trying to find an AI-based intelligent model that accurately predicts energy …
An intelligent model for efficient load forecasting and sustainable energy management in sustainable microgrids. Research; Open access; Published: 30 July 2024; …
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