To achieve rapid and accurate online prediction, we propose a method that combines Principal Component Analysis (PCA) with a multi-strategy improved Squirrel Search Algorithm (SSA)
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Accurate prediction of photovoltaic (PV) power for an ultra-short term can improve the usage of grid-connected PV power. In this study, data preprocessing based on an ultra-short-term
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This study investigated the application of advanced Machine Learning techniques to predict power generation and detect abnormalities in solar Photovoltaic systems.
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This paper introduces a hybrid competitive particle swarm optimization (HCPSO) algorithm-based support vector machine (SVM) model for short-term forecasting of distributed
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Machine-learning techniques are widely used across many disciplines, including electricity generation forecasting. In this study, the Support Vector Machine (SVM) based models, one of the...
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A multi-variable long-term photovoltaic (PV) power production prediction approach based on support vector machine (SVM) is developed in this study with the aim of completely evaluating the...
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In this research, an Integrated Support Vector Machine with K-Nearest Neighbor (ISVM-KNN) model is proposed for prediction of solar power generation. Simulated findings reveal that compared to current
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PV power generation is affected by environmental factors such as solar radiation intensity, temperature and humidity, and PV power generation is characterized b
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To address this challenge, this study aims to develop an optimal ML pipeline for accurate forecasting of power generation in an existing solar power system in Jordan.
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