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Optimizing solar photovoltaic power forecasting via multi-architecture machine learning framework with multiple hyperparameter optimization techniques
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DOI：10.1016/j.esr.2025.101859.png)
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Abstract
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• Various machine learning configurations and hyperparameter optimization are evaluated for PV power forecasting. • Wide ANN outperforms all machine learning models and ANN configurations. • Four hyperparameter optimization techniques evaluated to optimize best-performing model. • Tree-structured Parzen estimator shows superior optimization of ANN parameters. • The analysis of trade-offs between model complexity, accuracy and computational efficiency is provided.
Keywords:
Photovoltaic power forecasting
Machine learning
Artificial neural network
Hyperparameter optimization
Tree-structured parzen estimator
Grey wolf optimizer
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