Electricity power is an essential need for the development of technology and industries. It is an essential component of modern human life. Using fossil and other petroleum components for electricity generation harms the environment. Many countries and industries have started using renewable sources like solar systems for electrical power generation. However, the main inconvenience of these systems is that they are unpredictable. The purpose of this work is to develop a machine learning-based method to estimate the generated power of PV solar systems based on environmental data such as sun irradiation, wind speed, and others. Before implementing Machine Learning techniques, the built system goes through a feature selection stage to identify the most influential parts. This step improves system performance by removing unnecessary data. Only three ML approaches were used and compared: CNN (Convolutional Neural Network), SVR (Support Vector Regression), and (RF) Random Forest. The SVR out
MyJournals.org - Science - Molecules, Vol. 28, Pages 3521: Synergy of Small Antiviral Molecules on a Black-Phosphorus Nanocarrier: Machine Learning and Quantum Chemical Simulation Insights (Molecules)
In an article recently published in the journal Energies, researchers presented a comparative study of efficient wind power prediction using machine learning methods.