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IJEETC 2023 Vol.12(2): 134-141
doi: 10.18178/ijeetc.12.2.134-141

Long-Term Solar Irradiance Forecasting Using Multilinear Predictors

Abdulamjeed Sulaiman 1, Farhad E. Mahmood 2, and Sayf A. Majeed 3*
1. Department of optics technique, Alnoor University College, Bartella, Iraq
2. Department of Electrical Engineering, College of Engineering, University of Mosul, Mosul, Iraq
3. Computer Technologies Engineering Department, Al-Hadba University College, Mosul, Iraq

Manuscript received September 6, 2022; revised November 7, 2022; accepted January 6, 2023.

Abstract—As the demand for crude oil is increasing every day, prices and pollution are both increasing in return, which has harmful effects on the environment. Thus, more attempts are being made to develop clean energy to rescue the planet and provide humanity with a cleaner energy source. As the renewable energy sector grows, new issues and challenges have emerged. Instability in electricity production from wind turbines, solar power plants, and dams creates challenges for energy transmission and storage systems. In order to achieve a more reliable and effective energy system, machine learning techniques have been used to forecast energy changes. Predicting the sun's Global Horizontal Irradiance (GHI) is one of the machine learning applications used in that sector. In this paper, many machine learning methods have been utilized, such as linear regression and long-short term memory (LSTM) methods to have long term GHI forecasting. Moreover, the significance of this paper is located in the way of prediction of the GHI irradiance prediction by using different levels of the linear regressors to find the best regressor level that provides the minimum error for the testing set based on cross-validation. Results showed that the regressor method provides a lower error compared with a single vanilla LSTM system for a shorter time computationally.
Index Terms—Cross-validation, linear regression, LSTM, renewable energy, Recurrent Neural Network (RNN), Solar irradiance forecasting

Cite: Abdulamjeed Sulaiman, Farhad E. Mahmood, and Sayf A. Majeed, "Long-Term Solar Irradiance Forecasting Using Multilinear Predictors," International Journal of Electrical and Electronic Engineering & Telecommunications, Vol. 12, No. 2, pp. 134-141, March 2023. Doi: 10.18178/ijeetc.12.2.134-141

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.