Prediction of Solar Energy Yield Based on Artificial Intelligence Techniques for the Ha’il Region, Saudi Arabia

Lioua Kolsi (), Sameer Al-Dahidi, Souad Kamel, Walid Aich, Sahbi Boubaker and Nidhal Ben Khedher
Additional contact information
Lioua Kolsi: Mechanical Engineering Department, College of Engineering, University of Ha’il, Ha’il 81451, Saudi Arabia
Sameer Al-Dahidi: Department of Mechanical and Maintenance Engineering, School of Applied Technical Sciences, German Jordanian University, Amman 11180, Jordan
Souad Kamel: Department of Computer & Network Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia
Walid Aich: Mechanical Engineering Department, College of Engineering, University of Ha’il, Ha’il 81451, Saudi Arabia
Sahbi Boubaker: Department of Computer & Network Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia
Nidhal Ben Khedher: Mechanical Engineering Department, College of Engineering, University of Ha’il, Ha’il 81451, Saudi Arabia

Sustainability, 2022, vol. 15, issue 1, 1-15

Abstract: In order to satisfy increasing energy demand and mitigate global warming worldwide, the implementation of photovoltaic (PV) clean energy installations needs to become common practice. However, solar energy is known to be dependent on several random factors, including climatic and geographic conditions. Prior to promoting PV systems, an assessment study of the potential of the considered location in terms of power yield should be conducted carefully. Manual assessment tools are unable to handle high amounts of data. In order to overcome this difficulty, this study aims to investigate various artificial intelligence (AI) models—with respect to various intuitive prediction benchmark models from the literature—for predicting solar energy yield in the Ha’il region of Saudi Arabia. Based on the daily data, seven seasonal models, namely, naïve (N), simple average (SA), simple moving average (SMA), nonlinear auto-regressive (NAR), support vector machine (SVM), Gaussian process regression (GPR) and neural network (NN), were investigated and compared based on the root mean square error ( RMSE ) and mean absolute percentage error ( MAPE ) performance metrics. The obtained results showed that all the models provided good forecasts over three years (2019, 2020, and 2021), with the naïve and simple moving average models showing small superiority. The results of this study can be used by decision-makers and solar energy specialists to analyze the power yield of solar systems and estimate the payback and efficiency of PV projects.

Keywords: photovoltaic solar energy; prediction; artificial intelligence; performance; comparative study (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/1/774/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/1/774/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2022:i:1:p:774-:d:1021856

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().