... consumption prediction model for the turbine system has been developed using various machine learning algorithms [ 42 ]. XGBoost and LSBoost were modeled independently, and CNN, BiGRU, GRU, LSTM, and BiLSTM were modeled intuitively. The results of the energy consumption prediction of each model are shown...

... neural network alternative energy sources electricity generation technologies energy resources machine learning renewable energy sources References [1] Singh , S. , and Singh , S. , 2024 , “ Advancements and Challenges in Integrating Renewable Energy Sources...

... the multistage mechanism. Apart from empirical-based research, machine learning can be an alternative methodology to minimize the allocation of resources, and the comprehensive set of data can be easily tracked without any quantitative analysis. Ozveren [ 21...

... algorithm fuel combustion internal combustion engines machine learning In recent years, in order to cope with energy security and environmental pollution problems, countries have begun to develop new energy power technologies, such as the popularization of electrified vehicles [ 1 ]. However...

... prediction of fuel dispersion in leak scenarios. To this extent, we propose a novel machine learning approach to model gas leaks. The model is trained on a dataset of numerical simulations accounting for several hydrogen/methane concentrations in the fuel, different storage to ambient pressure ratios...