Optimizing solar photovoltaic power forecasting via multi-architecture machine learning framework with multiple hyperparameter optimization techniques

Muhammad Faizan Tahir

doi:10.1016/j.esr.2025.101859

Optimizing solar photovoltaic power forecasting via multi-architecture machine learning framework with multiple hyperparameter optimization techniques

2025-08-15

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Muhammad Faizan Tahir *

Mohamed Shawky El Moursi

DOI：10.1016/j.esr.2025.101859

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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

Optimizing solar photovoltaic power forecasting via multi-architecture machine learning framework with multiple hyperparameter optimization techniques

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