Application of Long Short-Term Memory Networks and SHAP Evaluation in the Solar Radiation Forecast

This paper proposes a hybrid forecasting framework that combines Long Short-Term Memory (LSTM) networks with Shapley Additive Explanations (SHAPs) to quickly and accurately predict solar radiation. Historical meteorological data from the Central Weather Administration (CWA) in Taiwan, spanning 2018–...

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Autori principali:	Ming-Tang Tsai, I-Cheng Lo
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	MDPI AG 2025-11-01
Serie:	Energies
Soggetti:	solar irradiance forecasting Long Short-Term Memory (LSTM) SHAP values feature selection
Accesso online:	https://www.mdpi.com/1996-1073/18/23/6099
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