Forecasting solar power generation using evolutionary mating algorithm-deep neural networks

Sulaiman, Mohd Herwan; Mustaffa, Zuriani

doi:10.1016/j.egyai.2024.100371

Energy and AI

2024

DOI: 10.1016/j.egyai.2024.100371

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Forecasting solar power generation using evolutionary mating algorithm-deep neural networks

Mohd Herwan Sulaiman,

Zuriani Mustaffa

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2024

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Cited by 3 publications

References 67 publications

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Modeling and optimization of renewable hydrogen systems: A systematic methodological review and machine learning integration

Mukelabai,

Barbour,

Blanchard

2024

Energy and AI

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Modeling and optimization of renewable hydrogen systems: A systematic methodological review and machine learning integration

Mukelabai,

Barbour,

Blanchard

2024

Energy and AI

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Recent Advances and Future Challenges of Solar Power Generation Forecasting

Jannah,

Gunawan,

Yusoff

et al. 2024

IEEE Access

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Real-Time Solar Power Generation Scheduling for Maintenance and Suboptimally Performing Equipment Using Demand Response Unified with Model Predictive Control

Li,

Fesseha,

Chen

et al. 2024

Energies

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This paper proposes a novel approach that unifies a demand response (DR) with a master plan of the model predictive control method focusing on scheduling maintenance and replacement for suboptimal equipment in real-time solar power plants. By leveraging DR mechanisms and MPC algorithms, our proposed framework starts with understanding the correlation between solar module temperature, surrounding temperature, and irradiation—essential for predicting and optimizing the performance of solar energy installations. It extends to evaluate the DC to AC conversion ratio, which is an indicator of the efficiency of the inverters. This integration enables proactive decisions for repair, maintenance, or replacement of equipment. Through exploratory data analysis using Python, we establish the efficiency and benefits of our anticipated approach in identifying the relationship between the factors that affect solar power generation.

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Forecasting solar power generation using evolutionary mating algorithm-deep neural networks

Cited by 3 publications

References 67 publications

Modeling and optimization of renewable hydrogen systems: A systematic methodological review and machine learning integration

Modeling and optimization of renewable hydrogen systems: A systematic methodological review and machine learning integration

Recent Advances and Future Challenges of Solar Power Generation Forecasting

Real-Time Solar Power Generation Scheduling for Maintenance and Suboptimally Performing Equipment Using Demand Response Unified with Model Predictive Control

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