Minimalist Deep Learning for Solar Power Forecasting: Transformer-Based Prediction Using Key Meteorological Features

Solar power forecasting is important for energy management and grid stability, yet many deep learning studies use a large set of meteorological and time-based variables because of the belief that more inputs improve model performance. In practice, a large feature set can introduce redundancy, increa...

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Autori principali:	Duncan Kibet, Min Seop So, Jong-Ho Shin
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	MDPI AG 2025-12-01
Serie:	Energies
Soggetti:	solar power prediction time-series forecasting feature selection deep learning transformer model Mean Absolute Error (MAE)
Accesso online:	https://www.mdpi.com/1996-1073/18/24/6395
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