Machine Learning Applications for Renewable-Based Energy Systems

Graditi, Giorgio; Buonanno, Amedeo; Caliano, Martina; Somma, Marialaura Di; Valenti, Maria Teresa

doi:10.1007/978-3-031-26496-2_9

Cited by 7 publications

(1 citation statement)

References 120 publications

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“…The support vector machine (SVM) is a type of guided learning that tries to find the most statistically significant hyperplane that splits a dataset into more than one class. DBM is an unsupervised learning method [10] that creates a deep generative model of the data using stochastic binary units. The hybrid algorithm takes the best parts of both methods and uses a majority vote to combine the results.…”

Section: Methodsmentioning

confidence: 99%

The Role of AI in Mitigating Climate Change: Predictive Modelling for Renewable Energy Deployment

Alharbe,

Alluhaibi

2023

IJACSA

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This study looks at how AI algorithms like Random Forest, Support Vector Machines (SVM), and Deep Boltzmann Machine (DBM) can be used for predictive modeling to make it easier to use renewable energy sources while reducing the negative effects of climate change. Predictive models based on Artificial Intelligence show possible ways to get the most out of green energy sources, which could lead to fewer carbon emissions. The results of the preliminary studies show that these AI systems can make accurate predictions about how green energy will be made because they are good at making predictions and generalizing. This feature makes it possible to use resources effectively, which improves the reliability of the grid and encourages more people to use green energy sources. Ultimately, employing these AI programs will serve as powerful tools in combating climate change and fostering a more sustainable and eco-friendly environment.

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Section: Methodsmentioning

confidence: 99%