Abstract
Environment-friendly and renewable energy resources are the need of each developed and undeveloped country. Solar energy is one of them, thus accurate forecasting of it can be useful for electricity supply companies. This research focuses on analyzing the daily global solar radiation (GSR) data of Najran province located in Saudi Arabia and proposed a model for the prediction of global horizontal irradiance (GHI). The weather data is collected from Najran University. After inspecting the data, I we found the dependent and independent variables for calculating the GHI. A dataset model has been trained by creating tensor of variables belonging to air, wind, peak wind, relative humidity, and barometric pressure. Furthermore, six machine learning algorithms convolutional neural networks (CNN), K-nearest neighbors (KNN), support vector machines (SVM), logistic regression (LR), random forest classifier (RFC), and support vector classifier (SVC) techniques are used on dataset model to predict the GHI. The evaluation metrics determination coefficients (R2), root mean square error (RMSE), relative root mean square error (rRMSE), mean bias error (MBE), mean absolute bias error (MABE), mean absolute percentage error (MAPE), and T-statistic (t-stat) are used for the result verification of proposed models. Finally, the current work reports that all methods examined in this work may be utilized to accurately predict GHI; however, the SVC technique is the most suitable method amongst all techniques by claiming the precise results using the evaluation metrics.
Highlights
Energy is the essential source of humanoid existence in the world
The current paper uses six different machine learning algorithms to forecast the predictability of daily solar radiation falling onto the horizontal surface of Najran province, located in Saudi Arabia
0.99 contingent on the location and methodology used. To put it another way, all algorithms perform well in terms of R2 when it comes to predicting daily global solar radiation
Summary
Energy is the essential source of humanoid existence in the world. It has a large impact on daily routine work. ANN machine learning technique to predict the global solar radiations at thirteen different study, the author used the ANN machine learning technique to predict the global solar stations For this process, the maximum and minimum temperature conditions were used to train the algorithm for the prediction of solar radiations [16]. Selective features are passed to six machine learning algorithms K-NN, CNN, SVM, SVC, RFC, and LR These algorithms predict the GHI value, and for measuring their performance, seven statistical matrices known as determination coefficients (R2 ), root mean square error (RMSE), relative root mean square error (rRMSE), mean bias error (MBE), mean absolute bias error (MABE), mean absolute percentage error (MAPE), and T-statistic (t-stat) are used.
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