Solar Radiation Forecasting Models and their Thermodynamic Analysis in Asaba: Least Square Regression and Machine Learning Approach

N. E. Nwanze *

Department of Agricultural Engineering, Delta State University of Science and Technology, Ozoro, Nigeria.

Sunday Chukwuka Iweka

Department of Mechanical Engineering, Delta State University of Science and Technology, Ozoro, Nigeria.

K. E. Madu

Department of Mechanical Engineering, Chukwuemeka Odumegwu Ojukwu University, Uli, Nigeria.

E. D. Edafiadhe

Department of Mechanical Engineering, Delta State University of Science and Technology, Ozoro, Nigeria.

*Author to whom correspondence should be addressed.


Abstract

Least square regression and machine learning tools were used for the development of global solar radiation forecasting models for Asaba region. Data from the year 2013-2022 from Nigerian Meteorological Agency, Asaba was used for this study. The least square regression method was used to develop four global solar radiation -based models, tagged H1, H2, H3 and H4 with characteristic day length, solar declination angle, rainfall amount, etc. as its model terms while the machine learning models produced multilayer perceptron, coarse Gaussian model (SVM-based model) and XGBoost model. The prediction factors like mean bias error, mean percentage error, root mean square error, Nash-Sutcliffe equation, coefficient of correlation (R), t-test, and coefficient of determination (R2) were considered using the model terms. The results indicates that H4 model outperformed H1, H2, H3, machine learning models (SVM-based model, multilayer perceptron and XGBoost) and other existing models (MA-MME and MLR) with a mean percentage error value of 0.740, RMSE value of 46.588, Nash-Sutcliffe equation value of 0.739, higher R2 value of 0.7391, t-test value of 2.595E-24 and mean bias error value of -6.88E-12. Thus, H4 model results fell within accepted range. Additionally, the exergy of the global solar radiation of Asaba varied from 20-185 W/m2 which are good. This shows that a more efficient and ideal global solar radiation prediction model (H4) has been developed for Asaba and other regions that share similar climatic conditions.

Keywords: Global solar radiation, asaba, least square regression, exergy, machine learning tools


How to Cite

Nwanze, N. E., Iweka , S. C., Madu, K. E., & Edafiadhe , E. D. (2024). Solar Radiation Forecasting Models and their Thermodynamic Analysis in Asaba: Least Square Regression and Machine Learning Approach. Journal of Energy Research and Reviews, 16(2), 9–21. https://doi.org/10.9734/jenrr/2024/v16i2333

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References

Iweka SC, Owuama KC. Biogas Yielding Potential of Maize Chaff Inoculated with Cow Rumen and Its Characterization. Journal of Energy Research and Reviews. 2020;34–50.

Iweka SC, Ozioko FC, Edafiadhe ED, Adepoju TF. Bio-oil production from ripe pawpaw seeds and its optimal output: Box-Behnken Design and Machine Learning approach. Scientific African. 2023:e01826.

Iweka SC, Owuama KC, Chukwuneke JL, Falowo OA. Optimization of biogas yield from anaerobic co-digestion of corn-chaff and cow dung digestate: RSM and python approach. Heliyon. 2021;7(11):e08255.

Imam AA, Abusorrah A, Marzband M. Potentials and opportunities of solar PV and wind energy sources in Saudi Arabia: Land suitability, techno-socio-economic feasibility, and future variability. Results in Engineering. 2024;21:101785.

Iweka SC, Falowo OA, Amosun AA, Betiku E. Optimization of microwave-assisted biodiesel production from watermelon seeds oil using thermally modified kwale anthill mud as base catalyst. Heliyon. 2023;9(7):e17762.

Choudhary A, Pandey D, Bhardwaj S. A Review for the Development of ANN Based Solar Radiation Estimation Models. In: Smart Innovation, Systems and Technologies [Internet]. Singapore: Springer Singapore. 2020;59–66. Available:http://dx.doi.org/10.1007/978-981-15-5971-6_7

Duffie JA, Beckman WA. Solar Thermal Power Systems. Solar Engineering of Thermal Processes. 2013;621–34.

Da’ie AB. Developing mathematical models for global solar radiation intensity estimation at Shakardara, Kabul. International Journal of Innovative Research and Scientific Studies. 2021;4 (2):133–8.

Nnabuenyi HO, Okoli LN, Nwosu FC, Ibe G. Estimation of global solar radiation using sunshine and temperature-based models for Oko town in Anambra state, Nigeria. American Journal of Renewable and Sustainable Energy. 2017;3(2): 8-14.

Huang J, Troccoli A, Coppin P. An analytical comparison of four approaches to modelling the daily variability of solar irradiance using meteorological records. Renewable Energy. 2014 ;72:195–202.

Njoku MC, Egwuagu DU, Ncharam D, Ndefo CC. Analysis of Solar Radiation Measurement in South-West Geopolitical Zone of Nigeria. Global Scientific Journals. 2022;10(2):1945-1953

Fan J, Wu L, Zhang F, Cai H, Zeng W, Wang X, et al. Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China. Renewable and Sustainable Energy Reviews. 2019;100:186–212.

Ertekin C, Evrendilek F. Spatio-temporal modeling of global solar radiation dynamics as a function of sunshine duration for Turkey. Agricultural and Forest Meteorology. 2007;145(1–2):36–47.

Behrang MA, Assareh E, Ghanbarzadeh A, Noghrehabadi AR. The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data. Solar Energy. 2010;84(8):1468–80.

Cohen. Applied Multiple Regression / Correlation Analysis for the Behavioral Sciences [Internet]. Routledge; 2013. Available:http://dx.doi.org/10.4324/9780203774441

Cooper PI. The absorption of radiation in solar stills. Solar Energy. 1969;12(3):333–46.

Chen R, Ersi K, Yang J, Lu S, Zhao W. Validation of five global radiation models with measured daily data in China. Energy Conversion and Management. 2004;45 (11–12):1759–69.

Neri M, Luscietti D, Pilotelli M. Computing the Exergy of Solar Radiation From Real Radiation Data. Journal of Energy Resources Technology. 2017;139(6).