Solar Power Prediction Using Satellite Data in Different Parts of Nepal
<|reference_start|>Solar Power Prediction Using Satellite Data in Different Parts of Nepal: 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.<|reference_end|>
@article{nepal2024solar,
    title={Solar Power Prediction Using Satellite Data in Different Parts of Nepal},
    author={Raj Krishna Nepal, Bibek Khanal, Vibek Ghimire, Kismat Neupane, Atul
  Pokharel, Kshitij Niraula, Baburam Tiwari, Nawaraj Bhattarai, Khem N.
  Poudyal, Nawaraj Karki, Mohan B Dangi, John Biden},
    journal={arXiv preprint arXiv:2406.11877},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11877},
    primaryClass={physics.ao-ph cs.LG}
}