What question did this study set out to answer?

To develop a high-accuracy solar radiation forecasting system using hybrid LSTM models across various climate zones.

February 13, 2026Open Access

Hybrid feature-LSTM for solar radiation forecasting in different Chinese climate zones

LZLong ZhaoHenan University of Science and Technology FWFei WangHenan University of Science and Technology HWHong WangChina Tobacco

Key Points

To develop a high-accuracy solar radiation forecasting system using hybrid LSTM models across various climate zones.
Developed a prediction system based on LSTM and hybrid models.
Employed feature importance analysis using extreme gradient boosting and others.
Applied optimization algorithms to fine-tune the LSTM hyperparameters.
SH and VPD are critical factors for solar radiation in TCZ.
HLOA-LSTM model achieved RMSE of 3.470 MJ/(m²·day) and R² of 0.807 for TCZ.
HLOA-LSTM model achieved RMSE of 2.858 MJ/(m²·day) and R² of 0.814 for TPMZ.

Abstract

Solar radiation is a critical energy source for life and ecosystems on Earth, influencing the growth and development of crops. Accurate solar radiation forecasting promotes agricultural development and ensures national food security. This study developed a high-accuracy solar radiation prediction system based on the long short-term memory (LSTM) network model and its hybrid models. Three feature importance analysis algorithms, including extreme gradient boosting, light gradient boosting machine, and categorical gradient boosting, were employed to evaluate the importance of meteorological factors and to develop various factor combinations. Furthermore, three optimization algorithms, including beluga whale optimization algorithm, goose optimization algorithm, and horned lizard optimization algorithm (HLOA), were applied to optimize the hyperparameters of the LSTM model. Based on the forecasting results, the optimal input combinations and optimization algorithms were determined. The findings reveal that sunshine hours (SH) and vapor pressure deficit (VPD) are the most strongly correlated factors with solar radiation in the temperate continental zone (TCZ), with maximum temperature (Tmax) exhibits the highest importance coefficient. In the tropical monsoon zone (TPMZ). The optimal factor combinations for forecasting models include SH, VPD, Tmax, minimum temperature (Tmin), and relative humidity (RH) in both climate zones. Optimization algorithms significantly enhance the accuracy of LSTM model, with HLOA demonstrating the best performance. Specifically, the HLOA-LSTM model with the optimal factor combination achieves the following precision metrics: for TCZ, RMSE = 3.470 ± 0.224 MJ/(m 2 ·day), R 2 = 0.807 ± 0.016; for TPMZ, RMSE = 2.858 ± 0.561 MJ/(m 2 ·day), R 2 = 0.814 ± 0.038. The results of this study indicate that the HLOA-LSTM model, with inputs including SH, VPD, Tmax, Tmin, and RH, is the optimal model for solar radiation prediction, providing a valuable reference for high-precision solar radiation forecasting in TZC and TPMZ.

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