Advanced Hybrid Model''s feature extraction and prediction outperform other models. This study introduces an innovative framework designed to forecast the fluctuating short-term generation
This research employed RFR to forecast demand, energy tariffs, wind, and solar generation in a microgrid. Data from Ontario, Canada, was collected for this purpose.
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.
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.
Addressing this limitation, this study investigates the simultaneous correlation between source and load power in a microgrid and weather features, conducting research on the joint ultra
The core objective of this paper is to optimize the synergetic integration of microgrids and hydrogen refueling systems assisted with AI-driven performance prediction modeling.
The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of
Abstract—In this research, an effort is made to address mi-crogrid systems'' operational challenges, characterized by power oscillations that eventually contribute to grid instability. An integrated strategy
This research delves into a comparative analysis of two machine learning models, specifically the Light Gradient Boosting Machine (LGBM) and K Nearest Neighbors (KNN), with the objective of
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