Precise predictions of renewable energy sources play a vital role in bringing them into the electric grid. This research
presents one of the most powerful machine learning algorithms to forecast the hourly global solar radiance. This study utilizes artificial
neural networks (ANN) as the machine learning predictor due to its ability to tackle the nonlinear aspects existing in solar data. The type
of the used ANN in this analysis is a multilayer feed-forward back-propagating neural network, denoted (MLFFBPNN). Nevertheless,
choosing the ideal set of input variables, known as features, to train the predictive models created, which are typically user-determined, is
a continuing, primary obstacle in obtaining high predictive efficiency. Therefore, this study's precise purpose is to forecast global
horizontal irradiance by building models of neural networks whose input variables are optimally and systemically chosen by the Boruta
Algorithm, a powerful feature selection method. Prediction models were built based on real-world solar data collected for a site known as
Buraydah in Saudi Arabia. For the creation of the developed forecasting models, thirteen features of solar data are considered, including
month of the year, day of the month, hour of day, air temperature, relative humidity, surface pressure, wind speed at 3 meters, wind
direction, peak wind direction at 3 meters, diffuse horizontal irradiance, direct normal irradiance, azimuth angle, and solar zenith angle.
The performance of the suggested models was assessed using four of the most common measures of error. the results stress the
importance of using feature selection techniques when using computational intelligence models to achieve precise solar radiation
predictions.
Published In:IJCSN Journal Volume 9, Issue 2
Date of Publication : April 2020
Pages : 19-27
Figures :07
Tables : 02
Abdulatif Alresheedi :
Currently is a Master's student at Qassim
University in Computer college, Computer Science department.
Alresheedi received his Bachelor's degree in Computer Science in
2004. From 2004 until now, I joined the public sector as the
manager of Information Technology Management. My research
interests lie in data mining, machine learning algorithms, advance
optimization techniques, and big data.
Mohammed Abdullah Al-Hagery :
received his B.Sc in Computer
Science from the University of Technology in Baghdad Iraq-1994.
He got his MSc in Computer Science from the University of
Science and Technology Yemen-1998. AlHagery finished his Ph.D.
in Computer Science and in Information Technology, (Software
Engineering) from the Faculty of Computer Science and IT,
University of Putra Malaysia (UPM), 2004. He was head of the
Computer Science Department at the College of Science and
Engineering, USTY, Sana'a from 2004 to 2007. From 2007 to this
date, he is a staff member at the Faculty of Computer, Department
of Computer Science, Qassim University in KSA. He published
more than 15 papers in international journals. Dr. Al-Hagery was
appointed head of the Research Centre at the Computer College,
Qassim University, KSA from September 2012 to October 2018.
Global horizontal irradiance, Artificial neural networks, Feature selection, Boruta algorithm, Big data, Machine learning
This paper discusses the use of an advanced
embedded feature selection algorithm and artificial neural
networks to forecast the hourly solar radiation at the site of
Buraydah in Saudi Arabia. Data were obtained from one of
the monitoring stations in Saudi Arabia to analyze the
performance of the established models in prediction. The
five and eight most significant variables among a wide
variety of weather variables that might influence solar
radiation in the future were defined optimally and
systematically using a modern selection technique called
Boruta algorithm. In addition, and mainly for purposes of
comparison, an all-feature model was developed to
determine the advantages of using a selection method of
features.
[1] D. Lew et al., "Sub-Hourly Impacts of High Solar
Penetrations in the Western United States," To be Present.
2nd Annu. Int. Work. Integr. Sol. Power into Power Syst.
Conf. Lisbon, Port., pp. 1-9, 2012.
[2] E. M. Sandhu and T. Thakur, "Issues, Challenges, Causes,
Impacts and Utilization of Renewable Energy Sources -Grid
Integration," J. Eng. Res. Appl., vol. 4, no. 1, pp. 636-643,
2014.
[3] I. J. Perez-Arriage, "Managing large-scale penetration of
intermittent renewables," Anesthesiology, vol. 115, no. 3, p.
A13, 2011.
[4] A. Tuohy et al., "Solar Forecasting: Methods, Challenges,
and Performance," IEEE Power Energy Mag., vol. 13, no. 6,
pp. 50-59, 2015.
[5] S. Shamshirband, M. Danesh, M. Hossain, S. Mekhilef, and
L. Olatomiwa, "Application of extreme learning machine for
short term output power forecasting of three grid-connected
PV systems," J. Clean. Prod., vol. 167, pp. 395-405, 2017.
[6] J. Polo, A. Navarro, M. Cony, L. Martín, R. Marchante, and
L. F. Zarzalejo, "Prediction of global solar irradiance based
on time series analysis: Application to solar thermal power
plants energy production planning," Sol. Energy, vol. 84,
no. 10, pp. 1772-1781, 2010.
[7] A. Alzahrani, J. W. Kimball, and C. Dagli, "Predicting solar
irradiance using time series neural networks," Procedia
Comput. Sci., vol. 36, no. C, pp. 623-628, 2014.
[8] A. Sharma and A. Kakkar, "Forecasting daily global solar
irradiance generation using machine learning," Renew.
Sustain. Energy Rev., vol. 82, no. May 2017, pp. 2254-
2269, 2018.
[9] I. A. Ibrahim and T. Khatib, "A novel hybrid model for
hourly global solar radiation prediction using random forests
technique and firefly algorithm," Energy Convers. Manag.,
vol. 138, pp. 413-425, 2017.
[10] S. Ghimire, R. C. Deo, N. J. Downs, and N. Raj, "Selfadaptive
differential evolutionary extreme learning
machines for long-term solar radiation prediction with
remotely-sensed MODIS satellite and Reanalysis
atmospheric products in solar-rich cities," Remote Sens.
Environ., vol. 212, no. November 2017, pp. 176-198, 2018.
[11] S. Haykin, Neural Networks: A Comprehensive Foundation,
2nd ed. Upper Saddle River, NJ, USA: Prentice Hall PTR,
1998.
[12] R. Kumar, R. K. Aggarwal, and J. D. Sharma, "Comparison
of regression and artificial neural network models for
estimation of global solar radiations," Renew. Sustain.
Energy Rev., vol. 52, pp. 1294-1299, 2015.
[13] W.-Y. Chang, "Short-Term Load Forecasting Using Radial
Basis Function Neural Network," J. Comput. Commun., vol.
03, no. 11, pp. 40-45, 2015.
[14] C. Renno, F. Petito, and A. Gatto, "Artificial neural network
models for predicting the solar radiation as input of a
concentrating photovoltaic system," Energy Convers.
Manag., vol. 106, pp. 999-1012, 2015.
[15] Chiteka, K., Enweremadu, C.C., 2016. Prediction of global
horizontal solar irradiance in Zimbabwe using artificial
neural networks. J. Clean. Prod. 135, 701e711.
[16] F. Rosenblatt, "The Perceptron: A Probabilistic Model for
Information Storage and Organization in the Brain," Psy
chol. Rev., vol. 65, no. 6, pp. 386-408, 1956.
[17] Kursa, Miron B., and Witold R. Rudnicki. "Feature
Selection with the Boruta package." J Stat Softw 36.11
(2010): 1-13.
