  Due to the unavailability of solar irradiance data for many potential sites
of Nepal, the paper proposes predicting solar irradiance based on alternative
meteorological parameters. The study focuses on five distinct regions in Nepal
and utilizes a dataset spanning almost ten years, obtained from CERES SYN1deg
and MERRA-2. Machine learning models such as Random Forest, XGBoost, K-Nearest
Neighbors, and deep learning models like LSTM and ANN-MLP are employed and
evaluated for their performance. The results indicate high accuracy in
predicting solar irradiance, with R-squared(R2) scores close to unity for both
train and test datasets. The impact of parameter integration on model
performance is analyzed, revealing the significance of various parameters in
enhancing predictive accuracy. Each model demonstrates strong performance
across all parameters, consistently achieving MAE values below 6, RMSE values
under 10, MBE within |2|, and nearly unity R2 values. Upon removal of various
solar parameters such as "Solar_Irradiance_Clear_Sky", "UVA", etc. from the
datasets, the model's performance is significantly affected. This exclusion
leads to considerable increases in MAE, reaching up to 82, RMSE up to 135, and
MBE up to |7|. Among the models, KNN displays the weakest performance, with an
R2 of 0.7582546. Conversely, ANN exhibits the strongest performance, boasting
an R2 value of 0.9245877. Hence, the study concludes that Artificial Neural
Network (ANN) performs exceptionally well, showcasing its versatility even
under sparse data parameter conditions.
