Solar Power Prediction with an Hour-based Ensemble Machine Learning Method


In recent years, the share of solar power in total energy production has gained a rapid increase. Therefore, prediction of solar power production has become increasingly important for energy regulations. In this study we proposed an ensemble method which gives competitive prediction performance for solar power production. This method firstly decomposes the nonlinear power production data into components with a multi-scale decomposition technique such as Empirical Mode Decomposition (EMD). These components are then enriched with the explanatory exogenous feature set. Finally, each component is separately modeled by nonlinear machine learning methods and their results are aggregated as final prediction. We use two different training approaches such as Hour-based and Day-based, for predicting the power production at each hour in a day. Experimental results show that our ensemble method with Hour-based approach outperform the examined machine learning methods.


Solar power, Time series forecasting, Machine learning, Ensemble methods, Empirical mode decomposition.

DOI: 10.17350/HJSE19030000169

Full Text: page_white_acrobat.png


Download data is not yet available.


1. Inman RH, Hugo TC, Carlos FM. Solar forecasting methods
for renewable energy integration. Volume 39, Issue
6, December 2013, Pages 535-576, doi:10.1016/j.

2. Enerji Atlasi, 2019, Gunes Enerji Santralleri, retrieved
September 12, 2019, from:

3. GUYAD, 2019, retrieved October 22, 2019, from: http://,360/teias-10-yill ik-kapasiteplanlamasini-

4. Heinemann D, Lorenz E, Girodo M. Forecasting of solar
radiation in: solar energy resource management for
electricity generation from local level to global scale. Nova
Sciences Pulishers; 2006.

5. Perez R, Beauharnois M, Lorenz E, Pelland S, Schlemmer J.
Evaluation of Numerical Weather Prediction Solar Irradiance
Forecasts in the US Proc. ASES Annual Conference. Raleigh,
NC, USA-1721, May; 2011.

6. Marquez R, Carlos FM. Intra-hour dni forecast based on
cloud tracking image analysis.Solar Energy,91, 327-336,
May 2013,

7. Bosh J, Zheng Y, Kleissl J. Deriving cloud velocity from a area
of solar radiation measurements. Solar Energy, 87, 196-
203, Jan. 2013,

8. Zhang P. Time Series Forecasting Using a Hybrid ARIMA
and NeuralNetwork Model. Neurocomputing 50, 159-
175, Neurocomputing, 50, 159–175, 2002, doi:10.1016/

9. Buyuksahin UC, Ertekin S. Improving forecasting accuracy
of time series data using a new ARIMA-ANN hybrid method
and empirical mode decomposition, Neurocomputing 361,
151-163, 2019,

10. Khashei M, Bijari M. A Novel Hybridization of Artificial Neural
Networksand ARIMA Models for Time Series Forecasting.
Appl. Soft Comput., 11, 2664–2675, 2011, doi:10.1016/j.

11. Buyuksahin UC, Ertekin S. A feature-based hybrid ARIMAANN
model for univariate time series forecasting. Journal
of the Faculty of Engineering and Architecture of Gazi
University 35:1 (2020) 467-478

12. Huang J, Korolkiewicz M, Agrawal M, Boland J. Forecasting
solar radiation on an hourly time scale using a coupled
autoregressive and dynamical system (cards) model. Solar
Energy; 87, 136-149, Jan. 2013,

13. Glasbey CA, Allcroft DJ. A spatiotemporal auto-regressive
moving average model for solar radiation. Appl Stat, 57,
343-355, 2007.

14. Mellit, A. Artificial Intelligence Technique for Modelling and
Forecasting of Solar Radiation Data: A Review. Int. J. Artif.
Intell. Soft Comput., 1, 52–76, 2008.

15. Martin L, Zarzalejo LF, Polo J, Navarro A, Marchante, R, and
Cony M. Prediction of Global Solar Irradiance Based on Time
Series Analysis: Application to Solar Thermal Power Plants
Energy Production. Solar Energy, 84, 1772-1781, Oct.

16. Mohamed A, Chowdhury C. Solar Power Forecasting Using
Artificial Neural Networks. In 2015 North American Power
Symposium (NAPS), 1–5, 2015.

17. Hamid E, Himdi K. Artificial Neural Network for Forecasting
One Day Ahead of Global Solar Irradiance. SSRN Scholarly
Paper. Rochester, NY: Social Science Research Network,
May 29, 2018.

18. Daniel O, Kubby J. Feature Selection and ANN Solar Power
Prediction. Research Article. Journal of Renewable Energy,

19. Xu X, Qi Y, Hua Z. Forecasting demand of commodities
after natural Disasters. Expert Systems with Applications,
Volume 37, Issue 6, June 2010, Pages 4313-4317, doi.

20. Yu W, Mu-Chen C. Forecasting the short-term metro
passenger flow with empirical mode decomposition and
neural networks, Transportation Research Part C, Volume
21, Issue 1, April 2012, Pages 148-162, https://doi.

21. Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen
NC, Tung CC, Liu HH. The empirical mode decomposition
and the Hilbert spectrum for nonlinear and non-stationary
time series analysis. Proceedings of the Royal Society of
London A: Mathematical, Physical and Engineering Sciences
454 (1998) 903-995. doi:10.1098/rspa.1998.0193.

22. Barnhart BL, Eichinger WE. Empirical mode decomposition
applied to solar irradiance, global temperature,sun spot
number and co2 concentration data. J Atmos Solar Terr
Phys 2011; 73:1771.

23. Majumder I, Behera MK, Nayak N. Solar Power Forecasting
Using a Hybrid EMD-ELM Method. International Conference
on Circuit, Power and Computing Technologies (ICCPCT),
20-21 April 2017, doi:10.1109/ICCPCT.2017.8074179.

24. Monjoly S, Andre M, Calif R, Soubdhan T. Hourly forecasting
of global solar radiation based on multiscale decomposition
methods: A hybrid approach. Energy, Volume 119, 15
January 2017, Pages 288-298,

25. Calif R, Schimtt FG, Huang Y, Soubdhan T. Intermittency
study of high frequancy global solar raiation sequences
under a tropical climate. Solar Energy, 98, 349-365, 2013.

26. Huang NE, Wu ML, Qu W, Long SR, Shen SP. Applications
of Hilbert Huang transform to non-stationary nancial time
series analysis. Applied Stochastic Models in Business and
Industry, 19(3), (2003), 245-268.

27. Angela Z, Faltermeier R, Keck I, Tomé A, Puntonet C,
Lang E. Empirical Mode Decomposition - an Introduction.
Proceedings of the International Joint Conference on Neural
Networks, 1–8, 2010.

28. Kutlu, C, Li J, Su Y, Wang Y, Pei G, Riffat S. Annual
Performance Simulation of a Solar Cogeneration Plant with
Sensible Heat Storage to Provide Electricity Demand for a
Small Community: A Transient Model. Hittite Journal of
Science & Engineering, 6(1), (2010), 75-81.

29. Epias, 2018, Gerçek Zamanlı Üretim, retrieved October 26,
2018, from:
How to Cite
Ertekin, S. (2020). Solar Power Prediction with an Hour-based Ensemble Machine Learning Method. Hittite Journal of Science & Engineering, 7(1), 35-40. Retrieved from