Skip to main content

Advertisement

SpringerLink
Log in

A novel solar power prediction model based on stacked BiLSTM deep learning and improved extreme learning machine

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

Photovoltaic (PV) energy is a renewable and permanent source of electricity available all the time. Nowadays, the solar energy integration presents an active research area due to its random and intermittent behavior. In fact, PV power prediction is valuable because it avoids complex mathematical modeling to calculate the output of a PV generator, and solves the problem of weather data uncertainty characterized by the future’s ignorance. To this goal, we present in this paper a novel solar power prediction model based on stacked Bidirectional Long-Short Term Memory (BiLSTM) deep learning model and the Extreme Learning Machine algorithm (ELM). The BiLSTM is used to predict each input weather data that affect directly the output PV power (radiation, temperature, humidity and wind speed). Then, an improved ELM algorithm is proposed and served as the main prediction model that forecast the next PV power generated based on the predicted weather data. Real data is used in this paper to test the effectiveness of the proposed prediction model whose superiority is proved through the test period. The forecasting model better performs compared to other related algorithms designed to the prediction of produced PV power.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

References

  1. Chawla R, Singhal P, Garg AK (2018) Design and modelling of G-ZnO nanocomposite electrode for a-Si:H/µc-Si: H micromorph solar cell. Int J Inf Tecnol 10:265–277. https://doi.org/10.1007/s41870-017-0043-6

    Article  Google Scholar 

  2. Bourakadi DE, Yahyaouy A, Boumhidi J (2021) Improved extreme learning machine with AutoEncoder and particle swarm optimization for short-term wind power prediction. Neural Comput Appl.  https://doi.org/10.1007/s00521-021-06619-x

  3. Bourakadi DE, Yahyaouy A (2018) Boumhidi, multi-agent system based on the extreme learning machine and fuzzy control for intelligent energy management in microgrid. J Intell Syst 29:877–893. https://doi.org/10.1515/jisys-2018-0125

    Article  Google Scholar 

  4. Praynlin E, Jensona JI (2017) Solar radiation forecasting using artificial neural network. In: 2017 Innovations in power and advanced computing technologies (i-PACT), avr. p. 1–7.  https://doi.org/10.1109/IPACT.2017.8244939

  5. Cao J, Lin X (2008) Study of hourly and daily solar irradiation forecast using diagonal recurrent wavelet neural networks. Energy Conversion Manag 49(6):1396–1406. https://doi.org/10.1016/j.enconman.2007.12.030

    Article  Google Scholar 

  6. Suyono H, Santoso H, Hasanah RN, Wibawa U, Musirin I (2018) Prediction of solar radiation intensity using extreme learning machine. Indonesian J Electrical Eng Comput Sci. https://doi.org/10.11591/ijeecs.v12.i2.pp691-698

    Article  Google Scholar 

  7. SOLAR PHOTOVOLTAIC OUTPUT POWER FORECASTING USING BACK PROPAGATION JB (2016) NEURAL NETWORK. ICTACT J Soft Comput 06:1144–1152. https://doi.org/10.21917/ijsc.2016.0159

  8. Bourakadi DEL, Yahyaouy A, Boumhidi J (2019) Multi-agent system based sequential energy management strategy for Micro-Grid using optimal weighted regularized extreme learning machine and decision tree. Intell Decision Technol 13:479–494. https://doi.org/10.3233/IDT-190003

    Article  Google Scholar 

  9. Buwei W, Jianfeng C, Bo W,  Shuanglei F, et al (2018) A solar power prediction using support vector machines based on multi-source data fusion. In: International Conference on Power System Technology (POWERCON) p. 4573–4577.  https://doi.org/10.1109/POWERCON.2018.8601672

  10. Bhattacharya T, Chakraborty AK, Pal K (2014) Effects of ambient temperature and wind speed on performance of monocrystalline solar photovoltaic module in Tripura, India. J Solar Energy.  https://doi.org/10.1155/2014/817078

  11. Annamalai R, Nedunchelian R (2022) Design of optimal bidirectional long short term memory based predictive analysis and severity estimation model for diabetes mellitus. Int J Inf Tecnol.  https://doi.org/10.1007/s41870-022-00933-w

  12. Zheng J et al (2020) Time series prediction for output of multi-region solar power plants. Applied Energy 257

  13. Ozoegwu CG (2019) Artificial neural network forecast of monthly mean daily global solar radiation of selected locations based on time series and month number. J Clean Prod 216:1–13.  https://doi.org/10.1016/j.jclepro.2019.01.096

  14. Sharma N et al (2021) A sequential ensemble model for photovoltaic power forecasting. Comput Electr Eng 96:107484. https://doi.org/10.1016/j.compeleceng.2021.107484

    Article  Google Scholar 

  15. Bourakadi DE, Yahyaouy A, Boumhidi J (2022) Intelligent energy management for micro-grid based on deep learning LSTM prediction model and fuzzy decision-making. Sustain Comput

  16. Meshram S, Anand Kumar M et al (2021) Long short-term memory network for learning sentences similarity using deep contextual embeddings. Int J Inf Tecnol 13(4):1633–1641. https://doi.org/10.1007/s41870-021-00686-y

    Article  Google Scholar 

  17. Chhachhiya D, Sharma A, Gupta M et al (2019) Designing optimal architecture of recurrent neural network (LSTM) with particle swarm optimization technique specifically for educational dataset. Int J Inf Tecnol 11(1):159–163. https://doi.org/10.1007/s41870-017-0078-8

    Article  Google Scholar 

  18. Wang Z, Wang F, Su S (2011) Solar irradiance short-term prediction model based on BP neural network. Energy Proc 12:488–494. https://doi.org/10.1016/j.egypro.2011.10.065

    Article  Google Scholar 

  19. Shadab A, Ahmad S, Said S (2020) Spatial forecasting of solar radiation using ARIMA model. Remote Sens Appl 20

  20. Zhu T, Li Y, Li Z, Guo Y, Ni C et al (2022) Inter-hour forecast of solar radiation based on long short-term memory with attention mechanism and genetic algorithm. Energies. https://doi.org/10.3390/en15031062

Download references

Author information

Authors and Affiliations

  1. LISAC Laboratory, Department of Computer Sciences, Faculty of Sciences Dhar Mehraz, Sidi Mohammed Ben Abdellah University, 30003, Fez, Morocco

    Dounia El Bourakadi, Hiba Ramadan, Ali Yahyaouy & Jaouad Boumhidi

Authors
  1. Dounia El Bourakadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hiba Ramadan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali Yahyaouy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jaouad Boumhidi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dounia El Bourakadi.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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

El Bourakadi, D., Ramadan, H., Yahyaouy, A. et al. A novel solar power prediction model based on stacked BiLSTM deep learning and improved extreme learning machine. Int. j. inf. tecnol. 15, 587–594 (2023). https://doi.org/10.1007/s41870-022-01118-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41870-022-01118-1

Keywords

Search

Navigation