A comparative study on ensemble soft-computing methods for geothermal power production potential forecasting
Copyright policy )A comparative study on ensemble soft-computing methods for geothermal power production potential forecasting
Many developed countries are increasingly interested in renewable energy sources (RESs) as a result of environmental changes and the depletion of fossil fuels in recent years. Since geothermal energy can be used as both a source of electricity and heat, it occupies an important spot among renewable energy sources. In this study, soft-computing ensemble models (SCEMs) based on supervised deep neural network (SDNN) models supported by the forward stepwise regression (FSR) method are used in estimating the power generation from geothermal resources. Outputs of the FSR process led SDNN phase. Adaptive Moment Estimation (ADAM) and Nesterov-accelerated Adaptive Moment Estimation (NADAM) methods were used to optimize SDNN models. For the daily power generation, the best performance has been shown by the model of SDNN optimized using ADAM optimizer with a coefficient of determination (R2) of 0.9807 and root mean square error (RMSE) of 0.0466, respectively.
- Osmaniye Korkut Ata University Turkey
- İskenderun Technical University Turkey
- Gaziantep University Turkey
- Osmaniye Korkut Ata University Turkey
- Gaziantep University Turkey
Soft computing, Engineering & Materials Science - Thermodynamics - Organic Rankine Cycle, Energy & Fuels, Fossil fuels, Neural network models, Renewable energy source, Geothermal fields, Mean square error, Soft computing methods, Geothermal energy, Supervised deep neural network, Forward stepwise regressions, Moment estimation, Adaptive moment estimation, Nesterov-accelerated adaptive moment estimation, Comparatives studies, Deep neural networks, Thermodynamics, Neural network model, Power generation
Soft computing, Engineering & Materials Science - Thermodynamics - Organic Rankine Cycle, Energy & Fuels, Fossil fuels, Neural network models, Renewable energy source, Geothermal fields, Mean square error, Soft computing methods, Geothermal energy, Supervised deep neural network, Forward stepwise regressions, Moment estimation, Adaptive moment estimation, Nesterov-accelerated adaptive moment estimation, Comparatives studies, Deep neural networks, Thermodynamics, Neural network model, Power generation
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