Advanced Automated Machine Learning Framework for Photovoltaic Power Output Prediction Using Environmental Parameters and SHAP Interpretability

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 103838 - 103838

Published: Jan. 1, 2025

Language: Английский

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Enhancing Solar Energy Conversion Efficiency: Thermophysical Property Predicting of MXene/Graphene Hybrid Nanofluids via Bayesian-Optimized Artificial Neural Networks

Results in Engineering, Journal Year: 2024, Volume and Issue: 24, P. 102858 - 102858

Published: Sept. 7, 2024

Language: Английский

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Enhancing wind power prediction with self-attentive variational autoencoders: A comparative study

Results in Engineering, Journal Year: 2024, Volume and Issue: 23, P. 102504 - 102504

Published: July 14, 2024

Accurate wind power prediction is critical for efficient grid management and the integration of renewable energy sources into grid. This study presents an effective deep-learning approach that improves short-term forecasting accuracy. The method incorporates a Variational Autoencoder (VAE) with self-attention mechanism applied in both encoder decoder. empowers model to leverage VAE's strengths time-series modeling nonlinear approximation while focusing on most relevant features within data. effectiveness this evaluated through comprehensive comparison eight established deep learning methods, including Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Bidirectional LSTMs (BiLSTMs), Convolutional (ConvLSTMs), Gated Units (GRUs), Stacked Autoencoders (SAEs), Restricted Boltzmann Machines (RBMs), vanilla VAEs. Real-world data from five turbines France Turkey used evaluation. Five statistical metrics are employed quantitatively assess performance each method. results indicate SA-VAE consistently outperformed other models, achieving highest average R2 value 0.992, demonstrating its superior predictive capability compared existing techniques.

Language: Английский

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BROWN BEAR OPTIMIZED RANDOM FOREST MODEL FOR SHORT TERM SOLAR POWER FORECASTING

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104583 - 104583

Published: March 1, 2025

Language: Английский

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Experimental investigation of aerodynamic behavior of wood chips in fluidized bed reactors as a sustainable biomass fuel

Results in Engineering, Journal Year: 2024, Volume and Issue: 23, P. 102533 - 102533

Published: July 14, 2024

Sustainable energy solutions are necessary in the current manufacturing advancements, where a need is being pressed upon biomaterial-based processes. This study examines aerodynamics of wood chip biomass fluidized-bed reactors, an essential aspect sustainable fuel technologies. Through experimental investigations, methodology determined minimum fluidization rates for particles four distinct sizes and compared these with theoretical prediction-based calculations. A novel laboratory setup featuring Differential Pressure Feedback Exhaust gas recirculation (DPFE) sensor system was developed to measure processes continuously advance enhancements precision reliability findings. Key results include successful adaptation Ergun equation chips, herewith accommodating observed deviations pressure drops within specific ranges. adaptation, along real-time data tracking air phase changes using multifunction measuring device, revealed critical insights into turbulence patterns particle movement. These findings consistent models underscore potential optimize use reactors. The study's also contribute significantly field renewable as they offer validated methodological approach practical modifications existing models.

Language: Английский

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Dynamic Water Valuation for Enhanced Economic Dispatch in Sri Lankan Hydrothermal Power System

Published: Jan. 1, 2024

Language: Английский

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Interpretable Hybrid Artificial Intelligence Model for Predicting Daily Hydropower Generation of Cascade Hydropower Reservoirs

Published: Jan. 1, 2024

Language: Английский

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