Machine learning for high-performance solar radiation prediction

Abstract

This research paper focuses on predicting surface solar radiation for 45 glaciers situated in two locations within the northern regions of Pakistan: Khyber Pakhtunkhwa (KPK) and Gilgit. The study investigates the relationship between input parameters, including date, temperature, pressure, precipitation, and aerosol, and solar radiation output parameters. The data used for this analysis is sourced from the CERES dataset, covering the period from 2001 to 2021. The study considers eight glaciers in KPK and thirty-seven glaciers in Gilgit to provide a comprehensive understanding of the surface solar radiation patterns in these regions. Various machine learning algorithms are employed for this prediction, including linear regression (LR), decision tree (DT), random forest (RF), feed-forward neural network (FFNN), and long short-term memory (LSTM). We considered the mean squared error (MSE), mean absolute error (MAE), normal root mean squared (nRMSE), and R-squared (R 2 ) score as the performance metrics to assess the performance of the models. The results for the KPK location indicate that the FFNN algorithm achieved the highest accuracy with an MSE of 598.326, MAE of 18.9685, nRMSE of 0.06973, and R 2 score of 0.916399. Similarly, the FFNN algorithm outperformed other models for the Gilgit location with an MSE of 738.78, MAE of 20.6887, nRMSE of 0.08071, and R 2 score of 0.886703. This work contributes to understanding surface solar radiation patterns in the northern regions of Pakistan, specifically in KPK and Gilgit. This research has practical implications for renewable energy planning, climate change assessments, and glacier monitoring. By accurately predicting surface solar radiation, stakeholders can make informed decisions and develop sustainable strategies for these regions.