Skip to main content

Advertisement

SpringerLink
Log in

Short Term Solar Power and Temperature Forecast Using Recurrent Neural Networks

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Solar energy is one of the world's clean and renewable source of energy and it is an alternative power with the ability to serve a greater proportion of rising demand needs. The operation and maintenance of solar energy have a significant impact on PV integrated distribution grids. Hence, the short-term forecasting of solar power is an important task for the effective management of grid-connected PV. In recent developments, most of the electric appliances (air conditioners, geysers, clothes dryers, electric blankets, etc.) usage mainly depends on the weather temperature. Therefore, temperature variations are considered to have a significant impact on the use of electrical appliances. Rapid solar integration and advanced temperature-dependent electrical appliances have drawn attention to the prediction of solar power and temperature in advance for efficient grid operation. Therefore, this paper proposes a Long Short Term Memory (LSTM) based forecast model for accurate forecasting. The suitable network structure for accurate forecasting of solar power and temperature is obtained by doing statistical analysis on the various network models. The statistical analysis gives the two-layer LSTM structure (i.e. layer 1 with 10 nodes and layer 2 with 20 nodes) is the suitable architecture for accurate forecasting of solar and temperature data. The proposed LSTM structure gives 0.2478 Mean Absolute Percentage Error (MAPE) and 6.7207 Root Mean Square Error (RMSE) for solar data, while for temperature data, it gives 0.014 MAPE and 1.0423 RMSE. The proposed network model showed an improvement in the forecast accuracy over the traditional network models available in the literature.

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Raghuwanshi SS, Arya R (2019) Renewable energy potential in India and future agenda of research. Int J Sustain Eng 12:291–302

    Article  Google Scholar 

  2. Aghbashlo M et al (2020) A new systematic decision support framework based on solar extended exergy accounting performance to prioritize photovoltaic sites. J Clean Prod 256:120356

    Article  Google Scholar 

  3. Huang J et al (2018) Assessing model performance of daily solar irradiance forecasts over Australia. Sol Energy 176:615–626

    Article  Google Scholar 

  4. Sahin AZ, Rehman S, Al-Sulaiman F (2017) Global solar radiation and energy yield estimation from photovoltaic power plants for small loads. Int J Green Energy 14:490–498

    Article  Google Scholar 

  5. Chamsa-ard, Wisut (2019) Synthesis, characterisation and thermo-physical properties of highly stable graphene oxide-based aqueous nanofluids for low-temperature direct absorption solar collectors and solar still desalination. Diss. Murdoch University

  6. Rad MAV et al (2020) A comprehensive study of techno-economic and environmental features of different solar tracking systems for residential photovoltaic installations. Renew Sustain Energy Rev 129

  7. Wang, HW et al. (2020) Evaluation and prediction of transportation resilience under extreme weather events: a diffusion graph convolutional approach. Transp Res part C Emerg Technol 115:102619

  8. Huynh ANL et al (2020) Near real-time global solar radiation forecasting at multiple time-step horizons using the long short-term memory network. Energies 13:3517

    Article  Google Scholar 

  9. Perdigão J et al (2020) Assessment of direct normal irradiance forecasts based on IFS/ECMWF data and observations in the south of portugal. Forecasting 2:130–150

    Article  Google Scholar 

  10. Arulmurugan R, Anandakumar H (2018) Early detection of lung cancer using wavelet feature descriptor and feed forward back propagation neural networks classifier. In: Computational vision and bio inspired computing :103–110.

  11. Peng L, Zhu Q, Lv SX et al (2020) Effective long short-term memory with fruit fly optimization algorithm for time series forecasting. Soft Comput 24:15059–15079

    Article  Google Scholar 

  12. Takilalte S Harrouni, Mora J (2019) Forecasting global solar irradiance for various resolutions using time series models—case study: Algeria. Energy Sources Part A Recovery Utilization Environ Effects. https://doi.org/10.1080/15567036.2019.1649756

    Article  Google Scholar 

  13. M. Ma and Z. Mao (2021) Deep-convolution-based LSTM network for remaining useful life prediction. In: IEEE Transactions on Industrial Informatics 17:1658–1667.

  14. Li B, Zhang J, He Y, Wang Y (2017) Short-term load-forecasting method based on wavelet decomposition with second-order gray neural network model combined with ADF test. IEEE Access 5:16324–16331

    Article  Google Scholar 

  15. Du S et al (2020) Multivariate time series forecasting via attention-based encoder–decoder framework. Neurocomputing 388:269–279

    Article  Google Scholar 

  16. Chan KY, Dillon TS, Chang E (2013) An intelligent particle swarm optimization for short-term traffic flow forecasting using on-road sensor systems. IEEE Trans Ind Electron 60(10):4714–4725

    Article  Google Scholar 

  17. Liu Y, Sun Y, Infield D, Zhao Y, Han S, Yan J (2017) A hybrid forecasting method for wind power ramp based on orthogonal test and support vector machine (OT-SVM). IEEE Trans Sustain Energy 8(2):451–457

    Article  Google Scholar 

  18. Kumar S, Karmakar A, Nath SK (2021) Construction of hot deformation processing maps for 9Cr-1Mo steel through conventional and ANN approach. Mater Today Commun 26:101903

    Article  Google Scholar 

  19. Benvenuto, Domenico, et al. (2020) Application of the ARIMA model on the COVID-2019 epidemic dataset Data in brief 29.

  20. Yang Li, Shami A (2020) On hyperparameter optimization of machine learning algorithms: theory and practice. Neurocomputing 415:295–316

    Article  Google Scholar 

  21. Olaofe ZO (2014) A 5-day wind speed & power forecasts using a layer recurrent neural network (LRNN). Sustain Energy Technol Assess 6:1–24

    Google Scholar 

  22. Gorai AK, Mitra G (2017) A comparative study of the feed forward back propagation (FFBP) and layer recurrent (LR) neural network model for forecasting ground level ozone concentration. Air Qual Atmos Health 10(2):213–223

    Article  Google Scholar 

  23. Sabah M et al (2021) Hybrid machine learning algorithms to enhance lost-circulation prediction and management in the Marun oil field. J Petrol Sci Eng 198:108125

    Article  Google Scholar 

  24. Díaz-Vico D, Torres-Barrán A, Omari A, Dorronsoro JR (2017) Deep neural networks for wind and solar energy prediction. Neural Process Lett 46(3):829–844

    Article  Google Scholar 

  25. Kisi O, Alizamir M, Trajkovic S, Shiri J, Kim S (2020) Solar radiation estimation in Mediterranean climate by weather variables using a novel Bayesian model averaging and machine learning methods. Neural Process Lett 52(3):2297–2318

    Article  Google Scholar 

  26. Saoud LS, Rahmoune F, Tourtchine V, Baddari K (2017) Fully complex valued wavelet network for forecasting the global solar irradiation. Neural Process Lett 45(2):475–505

    Article  Google Scholar 

  27. Hu J et al (2020) Time Series Prediction Method based on variant LSTM recurrent neural network. Neural Process Lett 52:1485–1500

    Article  Google Scholar 

  28. Miebs G et al (2020) Efficient strategies of static features incorporation into the recurrent neural network. Neural Process Lett 51:2301–2316

    Article  Google Scholar 

  29. Sarkar A (2021) Deep learning guided double hidden layer neural synchronization through mutual learning. Neural Process Lett 53:1355–1384

    Article  Google Scholar 

  30. Bi M et al (2020) Bi-directional LSTM model with symptoms-frequency position attention for question answering system in medical domain. Neural Process Lett 51:1185–1199

    Article  Google Scholar 

  31. Gundu V, Simon SP (2021) PSO–LSTM for short term forecast of heterogeneous time series electricity price signals. J Ambient Intell Humaniz Comput 12:2375–2385

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, National Institute of Technology, Tiruchirappalli, India

    Venkateswarlu Gundu & Sishaj P. Simon

Authors
  1. Venkateswarlu Gundu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sishaj P. Simon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Venkateswarlu Gundu.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gundu, V., Simon, S.P. Short Term Solar Power and Temperature Forecast Using Recurrent Neural Networks. Neural Process Lett 53, 4407–4418 (2021). https://doi.org/10.1007/s11063-021-10606-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-021-10606-7

Keywords

Search

Navigation