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September 18, 2025BuildingsOpen Access

A Hybrid Prediction Model for Wind–Solar Power Generation with Hyperparameter Optimization and Application in Building Heating Systems

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Observational analysis reveals accurate predictions of wind and solar power generation in heating systems, indicating enhanced energy efficiency.

The proposed hybrid prediction model achieves an R2 value exceeding 0.995, demonstrating high accuracy in forecasting energy generation.
Experimental results indicate minimal mean absolute error (MAE) and root mean square error (RMSE), confirming the model's reliability in predictions.
The framework's strong adaptability allows it to maintain an R2 above 0.99 even with different benchmark models, showcasing its versatility.
Implementing this model in building heating systems helps reduce temperature fluctuations, enhance supply stability, and improve energy efficiency.

Dai et al. (2025) studied this question.

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Observational analysis reveals accurate predictions of wind and solar power generation in heating systems, indicating enhanced energy efficiency.