A Time Series Forecasting of Global Horizontal Irradiance on Geographical Data of Najran Saudi Arabia

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.

Prediction Model of Wastewater Pollutant Indicators Based on Combined Normalized Codec

“…compares the predicted and actual values of each model. We can see that the RMSE[48], MAE[49], and MAPE[50] of the CNC model proposed in this paper are reduced by 1.5%, 3.2%, and 0.5%, respectively, and the R[51] indicator is increased by 0.1% compared with the suboptimal Codec model. The comparison results show that the model proposed in this paper has better performance indicators, and the prediction results are closer to the actual situation.…”

“…In this paper, four evaluation indicators are used to evaluate the experimental results: root mean square error (RMSE) [48], mean absolute error (MAE) [49], mean absolute percentage error (MAPE) [50], and Pearson correlation coefficient (R) [51]. All four evaluation indicators can measure the difference between the prediction value given by the model and the actual value and evaluate the model's performance.…”

“…All four evaluation indicators can measure the difference between the prediction value given by the model and the actual value and evaluate the model's performance. The smaller RMSE [48], MAE [49], and MAPE [50] values represent the minor difference between the prediction value given by the model and the actual value. In comparison, the larger R [51] values represent the model's better-fitting ability.…”

Development and Assessment of Feed Forward Back Propagation Neural Network Models to Predict Sunshine Duration

“…In this study, Levenberg Marquardt (LM) was chosen among the several FF training algorithms available. The LM algorithm is extensively used in a variety of disciplines since it is quicker and gives better outcomes than other training techniques [26]. For the purpose of calculating, the annually daily sunshine in this study, six combinations of input parameter or meteorological predictor variables were evaluated for the whole period 2013-2020, as indicated in Table 1, to get their impacts on SD, and for four seasons, one model that incorporated all input parameters was studied.…”

A hybrid deep learning framework for modeling the short term global horizontal irradiance prediction of a solar power plant in India

“…In India, the installation of solar power plants is at its peak due to favorable weather conditions and local governments are extending the benefits to entrepreneurs for the construction of solar plants. Integrating solar energy into electrical grids requires an understanding of the generating capacity of solar systems, which is primarily related to global solar radiation (Alghamdi 2022). In addition, the accurate prediction of the GHI by solar energy providers is useful for generating more revenue.…”