Renewable Energy Potential Estimation Using Climatic-Weather-Forecasting Machine Learning Algorithms

Chika Maduabuchi (), Chinedu Nsude, Chibuoke Eneh, Emmanuel Eke, Kingsley Okoli, Emmanuel Okpara, Christian Idogho, Bryan Waya and Catur Harsito
Additional contact information
Chika Maduabuchi: Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Chinedu Nsude: Artificial Intelligence Laboratory, University of Nigeria Nsukka, Nsukka 410001, Nigeria
Chibuoke Eneh: Artificial Intelligence Laboratory, University of Nigeria Nsukka, Nsukka 410001, Nigeria
Emmanuel Eke: Artificial Intelligence Laboratory, University of Nigeria Nsukka, Nsukka 410001, Nigeria
Kingsley Okoli: Artificial Intelligence Laboratory, University of Nigeria Nsukka, Nsukka 410001, Nigeria
Emmanuel Okpara: Artificial Intelligence Laboratory, University of Nigeria Nsukka, Nsukka 410001, Nigeria
Christian Idogho: Artificial Intelligence Laboratory, University of Nigeria Nsukka, Nsukka 410001, Nigeria
Bryan Waya: Artificial Intelligence Laboratory, University of Nigeria Nsukka, Nsukka 410001, Nigeria
Catur Harsito: Artificial Intelligence Laboratory, University of Nigeria Nsukka, Nsukka 410001, Nigeria

Energies, 2023, vol. 16, issue 4, 1-20

Abstract: The major challenge facing renewable energy systems in Nigeria is the lack of appropriate, affordable, and available meteorological stations that can accurately provide present and future trends in weather data and solar PV performance. It is crucial to find a solution to this because information on present and future solar PV performance is important to renewable energy investors so that they can assess the potential of renewable energy systems in various locations across the country. Although Nigerian weather provides favorable weather conditions for clean power generation, there is little penetration of renewable energy systems in the region, since over 95% of the power is fossil-fuel-generated. This is because there has been no detailed report showing the potential of clean power generation systems due to the dysfunctional meteorological stations in the country. This paper sought to fill this knowledge gap by providing a machine-learning-inspired forecasting of environmental weather parameters that can be used by manufacturing companies in evaluating the profitability of siting renewable energy systems in the region. Crucial weather parameters such as daily air temperature, relative humidity, atmospheric pressure, wind speed, and rainfall were obtained from NASA for a period of 19 years (viz. 2004–2022), resulting in the collection of 6664 high-resolution data points. These data were used to build diverse regressive neural networks with varying hyperparameters to find the best network arrangement. In summary, a low mean-squared error of 7 × 10 −3 and high regression correlations of 96% were obtained during the training.

Keywords: weather parameter forecasting; artificial neural networks; renewable energy potential; hyperparameter tuning; forecasting models (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/4/1603/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/4/1603/ (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:jeners:v:16:y:2023:i:4:p:1603-:d:1058749

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

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