This article proposed machine learning-based short-term PV power generation forecasting techniques by using XGBoost, SARIMA, and long short-term memory network (LSTM) algorithms.
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The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management.
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In order to address the impact of the uncertainty and intermittency of a photovoltaic power generation system on the smooth operation of the power system, a microgrid scheduling model
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To this end, we researched the literature and identified the commonly used Machine Learning-based prediction models and compared them computationally using power generation and
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This advanced forecasting approach allows us to better understand and model the complex relationships between weather variables and power generation in microgrids.
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Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources.
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In this research, the study delves into predictive analysis of volatile load demand and the detection of anomalies in power generation within microgrid infrastructures, aiming to enhance the
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This research explored the use of machine learning to forecast renewable energy generation and improve the operation of microgrids, which are small-scale power grids.
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This work developed a secure AI-driven predictable and resilient power generation system for efficient microgrid energy use and management.
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Simulations in optimizing microgrid operations, with ML techniques contribute to more effective analysis and planning in the electrical sector. The study highlights the significance of research in this area to
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