SpringerLink
Log in

Optimized Machine Learning-Based Forecasting Model for Solar Power Generation by Using Crow Search Algorithm and Seagull Optimization Algorithm

  • Research Article-Electrical Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

Forecasting Solar Power is an important aspect for power trading companies. It helps in energy bidding, planning, and control. The challenge in forecasting is to predict nonlinear data, which can be fulfilled by the computation technique and machine learning model. ML models have high accuracy for time-series forecasting, but their accuracy is poor for nonlinear forecasting. To enhance the ML model for nonlinear prediction, an optimization algorithm is used for training. This paper presents how the computation technique is incorporated into the machine learning model and compared it with the conventional model. CSA-ANN and SOA-ANN models are developed and forecast solar power for a-day-ahead, three-day-ahead, and a week-ahead solar power generation by considering time, irradiation, and temperature as input parameters for the model. The models are compared with ANN, DE-ANN, and PSO-ANN since these models are widely used. Upon comparison, ANN gives the best result for short-term prediction but is unable to predict midterm and long-term predictions, whereas this problem is overcome by SOA-ANN, which is done by changing its training algorithm, and its performance is measured via statistical parameters such as MAE, MSE, MAPE, and R2. The percentage improvement of SOA-ANN is obtained with these statistical parameters as 6.54%, 16.05%, 1.67%, and 3.61%. Hence, SOA-ANN gives best result as compared to other models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Thailand)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Abbreviations

ANN:

Artificial neural network

ARIMA:

Auto-regressive integrated moving average

ARMA:

Autoregressive moving average

ARMAX:

Autoregressive integrated moving average with exogenous inputs

BP:

Backpropagation

CSA:

Crow search algorithm

CSA-ANN:

Crow search algorithm-based artificial neural network

CSPTCL:

Chhattisgarh State power transmission company limited

DE:

Differntial evolution

DE-ANN:

Differential evolution-based artificial neural network

ESN:

Echo state network

GA:

Genetic algorithm

MAE:

Mean absolute error

MAPE:

Mean absolute percentage error

ML:

Machine learning

MSE:

Mean square error

OANN:

Optimized artificial neural network

PSO:

Particle swarm optimization

PSO-ANN:

Particle swarm optimization-based artificial neural network

R2 :

Co-relation of determination

RNN:

Recurrent neural network

SOA:

Seagull optimization algorithm

SOA-ANN:

Seagull optimization algorithm-based artificial neural network

SPG:

Solar power generation

SPGF:

Solar power generation forecasting

SVM:

Space vector machine

References

  1. Das, U.K., et al.: Forecasting of photovoltaic power generation and model optimization: A review. Renew. Sustain. Energy Rev. 81, 912–928 (2017). https://doi.org/10.1016/j.rser.2017.08.017

    Article  Google Scholar 

  2. Strzalka, A.; Alam, N.; Duminil, E.; Coors, V.; Eicker, U.: Large scale integration of photovoltaics in cities. Appl. Energy 93, 413–421 (2012). https://doi.org/10.1016/j.apenergy.2011.12.033

    Article  Google Scholar 

  3. Woyte, A., et al.: “Voltage fluctuations on distribution level introduced by photovoltaic systems. IEEE J. Mag. 21(1), 202–209 (2017)

    Google Scholar 

  4. Sahu, R.K.; Shaw, B.; Nayak, J.R.; Shashikant,: Short/medium term solar power forecasting of Chhattisgarh state of India using modified TLBO optimized ELM. Eng. Sci. Technol. an Int. J. 24(5), 1180–1200 (2021). https://doi.org/10.1016/j.jestch.2021.02.016

    Article  Google Scholar 

  5. Ji, W.; Chee, K.C.: Prediction of hourly solar radiation using a novel hybrid model of ARMA and TDNN. Sol. Energy 85(5), 808–817 (2011). https://doi.org/10.1016/j.solener.2011.01.013

    Article  Google Scholar 

  6. Pieri, E.; Kyprianou, A.; Phinikarides, A.; Makrides, G.; Georghiou, G.E.: Forecasting degradation rates of different photovoltaic systems using robust principal component analysis and ARIMA. IET Renew. Power Gener. 11(10), 1245–1252 (2017). https://doi.org/10.1049/iet-rpg.2017.0090

    Article  Google Scholar 

  7. Li, Y.; Su, Y.; Shu, L.: An ARMAX model for forecasting the power output of a grid-connected photovoltaic system. Renew. Energy 66, 78–89 (2014). https://doi.org/10.1016/j.renene.2013.11.067

    Article  Google Scholar 

  8. Prema, V.; Uma Rao, K.: “Development of statistical time series models for solar power prediction.” Renew Energy 83, 100–109 (2015). https://doi.org/10.1016/j.renene.2015.03.038

    Article  Google Scholar 

  9. Decencière, E., et al.: TeleOphta: Machine learning and image processing methods for teleophthalmology. Irbm 34(2), 196–203 (2013). https://doi.org/10.1016/j.irbm.2013.01.010

    Article  Google Scholar 

  10. Sun, Y.; Babu, P.; Palomar, D.P.: Majorization-minimization algorithms in signal processing, communications, and machine learning. IEEE Trans. Signal Process. 65(3), 794–816 (2017). https://doi.org/10.1109/TSP.2016.2601299

    Article  MathSciNet  MATH  Google Scholar 

  11. Dash, R.; Dash, P.K.: A hybrid stock trading framework integrating technical analysis with machine learning techniques. J. Financ. Data Sci. 2(1), 42–57 (2016). https://doi.org/10.1016/j.jfds.2016.03.002

    Article  MathSciNet  Google Scholar 

  12. Mathioulakis, E.; Panaras, G.; Belessiotis, V.: Artificial neural networks for the performance prediction of heat pump hot water heaters. Int. J. Sustain. Energ. 37(2), 173–192 (2018). https://doi.org/10.1080/14786451.2016.1218495

    Article  Google Scholar 

  13. Mubiru, J.; Banda, E.J.K.B.: Estimation of monthly average daily global solar irradiation using artificial neural networks. Sol. Energy 82(2), 181–187 (2008). https://doi.org/10.1016/j.solener.2007.06.003

    Article  Google Scholar 

  14. Chen, J.L.; Li, G.S.: Evaluation of support vector machine for estimation of solar radiation from measured meteorological variables. Theor. Appl. Climatol. 115(3–4), 627–638 (2014). https://doi.org/10.1007/s00704-013-0924-y

    Article  Google Scholar 

  15. Chen, J.L.; Bin Liu, H.; Wu, W.; **e, D.T.: Estimation of monthly solar radiation from measured temperatures using support vector machines: A case study. Renew. Energy 36(1), 413–420 (2011). https://doi.org/10.1016/j.renene.2010.06.024

    Article  Google Scholar 

  16. Jang, H.S.; Bae, K.Y.; Park, H.; Sung, D.K.: Solar power prediction based on satellite images and support vector machine. IEEE Trans. Sustain. Energy 7(3), 1255–1263 (2016). https://doi.org/10.1109/TSTE.2016.2535466

    Article  Google Scholar 

  17. Zeng, J.; Qiao, W.: Short-term solar power prediction using a support vector machine. Renew. Energy 52, 118–127 (2013). https://doi.org/10.1016/j.renene.2012.10.009

    Article  Google Scholar 

  18. AlKandari, M.; Ahmad, I.: Solar power generation forecasting using ensemble approach based on deep learning and statistical methods. Appl. Comput. Inform. (2019). https://doi.org/10.1016/j.aci.2019.11.002

    Article  Google Scholar 

  19. Mishra, M.; Byomakesha Dash, P.; Nayak, J.; Naik, B.; Kumar Swain, S.: Deep learning and wavelet transform integrated approach for short-term solar PV power prediction. Meas. J. Int. Meas. Confed. 166, 108250 (2020). https://doi.org/10.1016/j.measurement.2020.108250

    Article  Google Scholar 

  20. Mohammed, A.A.; Aung, Z.: Ensemble learning approach for probabilistic forecasting of solar power generation. Energies (2016). https://doi.org/10.3390/en9121017

    Article  Google Scholar 

  21. Long, H.; Zhang, C.; Geng, R.; Wu, Z.; Gu, W.: A combination interval prediction model based on biased convex cost function and auto-encoder in solar power prediction. IEEE Trans. Sustain. Energy 12(3), 1561–1570 (2021). https://doi.org/10.1109/TSTE.2021.3054125

    Article  Google Scholar 

  22. Melin, P.; Mancilla, A.; Lopez, M.; Mendoza, O.: A hybrid modular neural network architecture with fuzzy Sugeno integration for time series forecasting. Appl. Soft Comput. J. 7(4), 1217–1226 (2007). https://doi.org/10.1016/j.asoc.2006.01.009

    Article  Google Scholar 

  23. Van Ooyen, A.; Nienhuis, B.: Improving the convergence of the back-propagation algorithm. Neural Netw. 5(3), 465–471 (1992). https://doi.org/10.1016/0893-6080(92)90008-7

    Article  Google Scholar 

  24. Sherstinsky, A.: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Phys. D Nonlinear Phenom. 404, 132306 (2020). https://doi.org/10.1016/j.physd.2019.132306

    Article  MathSciNet  MATH  Google Scholar 

  25. Rodan, A.; Tiňo, P.: Minimum complexity echo state network. IEEE Trans. Neural Netw. 22(1), 131–144 (2011). https://doi.org/10.1109/TNN.2010.2089641

    Article  Google Scholar 

  26. Behera, S.; Sahoo, S.; Pati, B.B.: A review on optimization algorithms and application to wind energy integration to grid. Renew. Sustain. Energy Rev. 48, 214–227 (2015). https://doi.org/10.1016/j.rser.2015.03.066

    Article  Google Scholar 

  27. Abderazek, H.; Yildiz, A. R.; and Mirjalili, S. “Comparison of recent optimization algorithms for design optimization of a cam-follower mechanism,” Knowledge-Based Syst., vol. 191, p. 105237, 2020, doi: https://doi.org/10.1016/j.knosys.2019.105237.

  28. Dhiman, G.; Kumar, V.: Knowledge-based systems seagull optimization algorithm : Theory and its applications for large-scale industrial engineering problems. Knowl. Based Syst. 165, 169–196 (2019). https://doi.org/10.1016/j.knosys.2018.11.024

    Article  Google Scholar 

  29. Dhiman, G., et al.: 2021 “MOSOA: A new multi-objective seagull optimization algorithm.” Expert Syst. Appl. 167, 114150 (2020). https://doi.org/10.1016/j.eswa.2020.114150

    Article  Google Scholar 

  30. McCulloch, W.S.; Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5(4), 115–133 (1943). https://doi.org/10.1007/BF02478259

    Article  MathSciNet  MATH  Google Scholar 

  31. Verma, S.; Thampi, G.T.; Rao, M.: ANN based method for improving gold price forecasting accuracy through modified gradient descent methods. IAES Int. J. Artif. Intell. 9(1), 46–57 (2020). https://doi.org/10.11591/ijai.v9.i1.pp46-57

    Article  Google Scholar 

  32. Singh, B.K.; Verma, K.; Thoke, A.S.: Adaptive gradient descent backpropagation for classification of breast tumors in ultrasound imaging. Procedia Comput. Sci. 46(2014), 1601–1609 (2015). https://doi.org/10.1016/j.procs.2015.02.091

    Article  Google Scholar 

  33. Vijayalakshmi, K.; Vijayakumar, K.; Nandhakumar, K.: Prediction of virtual energy storage capacity of the air-conditioner using a stochastic gradient descent based artificial neural network. Electr. Power Syst. Res. 208, 107879 (2022). https://doi.org/10.1016/j.epsr.2022.107879

    Article  Google Scholar 

  34. Chattopadhyay, S.; Chattopadhyay, G.: Conjugate gradient descent learned ANN for Indian summer monsoon rainfall and efficiency assessment through Shannon-Fano coding. J. Atmos. Solar Terr. Phys. 179, 202–205 (2018). https://doi.org/10.1016/J.JASTP.2018.07.015

    Article  Google Scholar 

  35. Santosh Kumar, G.; Rajasekhar, K.: Performance analysis of Levenberg-Marquardt and steepest descent algorithms based ANN to predict compressive strength of SIFCON using manufactured sand. Eng. Sci. Technol., Int. J. 20(4), 1396–1405 (2017). https://doi.org/10.1016/j.jestch.2017.07.005

    Article  Google Scholar 

  36. Saputra, W.; Tulus, T.; Zarlis, M.; Sembiring, R.W.; Hartama, D.: Analysis resilient algorithm on artificial neural network backpropagation. J. Phys.: Conf. Series (2017). https://doi.org/10.1088/1742-6596/930/1/012035

    Article  Google Scholar 

  37. Behera, S.P.H.S.: Time series forecasting using differential evolution-based ANN modelling scheme. Arab. J. Sci. Eng. 45(12), 11129–11146 (2020). https://doi.org/10.1007/s13369-020-05004-5

    Article  Google Scholar 

  38. Hu, Y.; Chen, L.: A nonlinear hybrid wind speed forecasting model using LSTM network, hysteretic ELM and differential evolution algorithm. Energy Convers. Manage. 173, 123–142 (2018). https://doi.org/10.1016/j.enconman.2018.07.070

    Article  Google Scholar 

  39. Asadnia, M.; Chua, L. H. C.; Qin, X. S.; Asce, A. M.; & Talei, A. (2014). Improved particle swarm optimization – based artificial neural network for rainfall-runoff modeling. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000927.

  40. Adhikari, R.; & Agrawal, R. K. (n.d.). Effectiveness of PSO based neural network for seasonal time series forecasting. pp.231–244.

  41. Riaz, S.; Khan, A.; Riaz, S.; & Khan, A. (2007). Short Term load forecasting using particle swarm optimization based ANN approach

  42. Patra, S. K. (2008). Short term load forecasting using neural network trained with genetic algorithm & particle swarm optimization Sanjib Mishra. pp. 606–611. https://doi.org/10.1109/ICETET.2008.94

  43. Tonnizam, E.; Roohollah, M.; Faradonbeh, S.; Jahed, D.: An optimized ANN model based on genetic algorithm for predicting rip** production. Neural Comput. Appl. 28(s1), 393–406 (2017). https://doi.org/10.1007/s00521-016-2359-8

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Institute of Technology Raipur, Raipur, Chhattisgarh, India

    Shashikant Kaushaley & Binod Shaw

  2. Vignan’s Institute of Information Technology, Andhra Pradesh, India

    Jyoti Ranjan Nayak

Authors
  1. Shashikant Kaushaley
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Binod Shaw
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jyoti Ranjan Nayak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shashikant Kaushaley.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kaushaley, S., Shaw, B. & Nayak, J.R. Optimized Machine Learning-Based Forecasting Model for Solar Power Generation by Using Crow Search Algorithm and Seagull Optimization Algorithm. Arab J Sci Eng 48, 14823–14836 (2023). https://doi.org/10.1007/s13369-023-07822-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-023-07822-9

Keywords

Search

Navigation