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Hybrid deep learning models for multivariate forecasting of global horizontal irradiation

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Abstract

Increasing photovoltaic (PV) instalments could affect the stability of the electrical grid as the PV produces weather-dependent electricity. However, prediction of the power output of the PV panels or incoming radiation could help to tackle this problem. It has been concluded within the European Actions “Weather Intelligence for Renewable Energies” framework that more research is needed on short-term energy forecasting using different models, locations and data for a complete overview of all possible scenarios around the world representing all possible meteorological conditions. On the other hand, for the Mediterranean region, there is a need for studies that cover a larger spectrum of forecasting algorithms. This study focuses on forecasting short-term GHI for Kalkanli, Northern Cyprus, while aiming to contribute to ongoing research on developing prediction models by testing different hybrid forecasting algorithms. Three different hybrid models are proposed using convolutional neural network (CNN), long short-term memory (LSTM) and support vector regression (SVR), and the proposed hybrid models are compared with the performance of stand-alone models, i.e. CNN, LSTM and SVR, for the short-term GHI estimation. We present our results with several evaluation metrics and statistical analysis. This is the first time such a study conducted for GHI prediction.

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References

  1. Ghimire S, Deo RC, Raj N, Mi J (2019) Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms. Appl Energy 253:113541. https://doi.org/10.1016/j.apenergy.2019.113541

    Article  Google Scholar 

  2. Demirtas M, Yesilbudak M, Sagiroglu S (2012) Colak I (2012) Prediction of solar radiation using meteorological data. Int Conf Renew Energy Res Appl ICRERA 2012:1–4. https://doi.org/10.1109/ICRERA.2012.6477329

    Article  Google Scholar 

  3. Voyant C, Notton G, Kalogirou S et al (2017) Machine learning methods for solar radiation forecasting: a review. Renew Energy 105:569–582. https://doi.org/10.1016/j.renene.2016.12.095

    Article  Google Scholar 

  4. Salcedo-Sanz S, Casanova-Mateo C, Pastor-Sánchez A, Sánchez-Girón M (2014) Daily global solar radiation prediction based on a hybrid coral reefs optimization - extreme learning machine approach. Sol Energy 105:91–98. https://doi.org/10.1016/j.solener.2014.04.009

    Article  Google Scholar 

  5. Alzahrani A, Shamsi P, Dagli C, Ferdowsi M (2017) Solar irradiance forecasting using deep neural networks. In: Procedia computer science. Elsevier B.V., pp 304–313

  6. Elliston B, MacGill I (2010) The potential role of forecasting for integrating solar generation into the Australian National Electricity Market. Sol 2010, Aust Sol Energy Soc, pp 1–11

  7. Mazorra-Aguiar L, Díaz F (2018) Solar radiation forecasting with statistical models. In: Green energy and technology. Springer, pp 171–200

  8. Von Appen J, Braun M, Stetz T et al (2013) Time in the sun: The challenge of high PV penetration in the German electric grid. IEEE Power Energy Mag 11:55–64. https://doi.org/10.1109/MPE.2012.2234407

    Article  Google Scholar 

  9. Ferrari S, Lazzaroni M, Piuri V, et al (2012) Illuminance prediction through extreme learning machines. In: 2012 IEEE work environ energy, Struct Monit Syst EESMS 2012 - Proc 97–103. https://doi.org/10.1109/EESMS.2012.6348407

  10. Fouilloy A, Voyant C, Notton G et al (2018) Solar irradiation prediction with machine learning: forecasting models selection method depending on weather variability. Energy 165:620–629. https://doi.org/10.1016/j.energy.2018.09.116

    Article  Google Scholar 

  11. Qing X, Niu Y (2018) Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM. Energy 148:461–468. https://doi.org/10.1016/j.energy.2018.01.177

    Article  Google Scholar 

  12. Guariso G, Nunnari G, Sangiorgio M (2020) Multi-step solar irradiance forecasting and domain adaptation of deep neural networks. Energies 13:3987. https://doi.org/10.3390/en13153987

    Article  Google Scholar 

  13. Voyant C, Muselli M, Paoli C, Nivet ML (2013) Hybrid methodology for hourly global radiation forecasting in Mediterranean area. Renew Energy 53:1–11. https://doi.org/10.1016/j.renene.2012.10.049

    Article  Google Scholar 

  14. Fan J, Wang X, Wu L et al (2018) Comparison of support vector machine and extreme gradient boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: a case study in China. Energy Convers Manag 164:102–111. https://doi.org/10.1016/j.enconman.2018.02.087

    Article  Google Scholar 

  15. Lazzaroni M, Ferrari S, Piuri V et al (2015) Models for solar radiation prediction based on different measurement sites. Meas J Int Meas Confed 63:346–363. https://doi.org/10.1016/j.measurement.2014.11.037

    Article  Google Scholar 

  16. Marquez R, Coimbra CFM (2011) Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS database. Sol Energy 85:746–756. https://doi.org/10.1016/j.solener.2011.01.007

    Article  Google Scholar 

  17. Yadav AK, Chandel SS (2014) Solar radiation prediction using Artificial Neural Network techniques: a review. Renew Sustain Energy Rev 33:772–781

    Article  Google Scholar 

  18. Kong W, Dong ZY, Jia Y et al (2019) Short-term residential load forecasting based on lstm recurrent neural network. IEEE Trans Smart Grid 10:841–851. https://doi.org/10.1109/TSG.2017.2753802

    Article  Google Scholar 

  19. Reikard G (2009) Predicting solar radiation at high resolutions: a comparison of time series forecasts. Sol Energy 83:342–349

    Article  Google Scholar 

  20. Pedro HTC, Coimbra CFM (2015) Nearest-neighbor methodology for prediction of intra-hour global horizontal and direct normal irradiances. Renew Energy 80:770–782. https://doi.org/10.1016/j.renene.2015.02.061

    Article  Google Scholar 

  21. Sözen A, Arcaklioglu E, Özalp M (2004) Estimation of solar potential in Turkey by artificial neural networks using meteorological and geographical data. Energy Convers Manag 45:3033–3052. https://doi.org/10.1016/j.enconman.2003.12.020

    Article  Google Scholar 

  22. Krömer P, Musilek P, Pelikan E et al (2014) Support Vector Regression of multiple predictive models of downward short-wave radiation. Proc Int Jt Conf Neural Netw. https://doi.org/10.1109/IJCNN.2014.6889812

    Article  Google Scholar 

  23. Lima FJL, Martins FR, Pereira EB et al (2016) Forecast for surface solar irradiance at the Brazilian Northeastern region using NWP model and artificial neural networks. Renew Energy 87:807–818. https://doi.org/10.1016/j.renene.2015.11.005

    Article  Google Scholar 

  24. Cai S, Xie Y, Wu Q, Xiang Z (2020) Robust MPC-based microgrid scheduling for resilience enhancement of distribution system. Int J Electr Power Energy Syst 121:106068. https://doi.org/10.1016/J.IJEPES.2020.106068

    Article  Google Scholar 

  25. Podestá GP, Núñez L, Villanueva CA, Skansi MA (2004) Estimating daily solar radiation in the Argentine Pampas. Agric For Meteorol 123:41–53. https://doi.org/10.1016/j.agrformet.2003.11.002

    Article  Google Scholar 

  26. Aggarwal SK, Saini LM (2014) Solar energy prediction using linear and non-linear regularization models: a study on AMS (American Meteorological Society) 2013–14 Solar Energy Prediction Contest. Energy 78:247–256

    Article  Google Scholar 

  27. Vakitbilir N, Hilal A, Direkoğlu C (2021) Prediction of daily solar irradiation using CNN and LSTM networks. Springer, Cham, pp 230–238

    Google Scholar 

  28. Zang H, Liu L, Sun L et al (2020) Short-term global horizontal irradiance forecasting based on a hybrid CNN-LSTM model with spatiotemporal correlations. Renew Energy 160:26–41. https://doi.org/10.1016/j.renene.2020.05.150

    Article  Google Scholar 

  29. Wozniak M, Silka J, Wieczorek M, Alrashoud M (2021) Recurrent neural network model for IoT and networking malware threat detection. IEEE Trans Ind Inform 17:5583–5594. https://doi.org/10.1109/TII.2020.3021689

    Article  Google Scholar 

  30. Woźniak M, Siłka J, Wieczorek M (2021) Deep neural network correlation learning mechanism for CT brain tumor detection. Neural Comput Appl 2021:1–16. https://doi.org/10.1007/S00521-021-05841-X

    Article  Google Scholar 

  31. Zhong H, Wang J, Jia H et al (2019) Vector field-based support vector regression for building energy consumption prediction. Appl Energy 242:403–414. https://doi.org/10.1016/J.APENERGY.2019.03.078

    Article  Google Scholar 

  32. Zhang F, Deb C, Lee SE et al (2016) Time series forecasting for building energy consumption using weighted Support Vector Regression with differential evolution optimization technique. Energy Build 126:94–103. https://doi.org/10.1016/J.ENBUILD.2016.05.028

    Article  Google Scholar 

  33. Niu XX, Suen CY (2012) A novel hybrid CNN–SVM classifier for recognizing handwritten digits. Pattern Recognit 45:1318–1325. https://doi.org/10.1016/J.PATCOG.2011.09.021

    Article  Google Scholar 

  34. Tao QQ, Zhan S, Li XH, Kurihara T (2016) Robust face detection using local CNN and SVM based on kernel combination. Neurocomputing 211:98–105. https://doi.org/10.1016/J.NEUCOM.2015.10.139

    Article  Google Scholar 

  35. Khosravi A, Koury RNN, Machado L, Pabon JJG (2018) Prediction of hourly solar radiation in Abu Musa Island using machine learning algorithms. J Clean Prod 176:63–75. https://doi.org/10.1016/j.jclepro.2017.12.065

    Article  Google Scholar 

  36. Belaid S, Mellit A (2016) Prediction of daily and mean monthly global solar radiation using support vector machine in an arid climate. Energy Convers Manag 118:105–118. https://doi.org/10.1016/j.enconman.2016.03.082

    Article  Google Scholar 

  37. Ilkan M, Erdil E, Egelioglu F (2005) Renewable energy resources as an alternative to modify the load curve in Northern Cyprus. Energy 30:555–572. https://doi.org/10.1016/j.energy.2004.04.059

    Article  Google Scholar 

  38. Zang H, Cheng L, Ding T et al (2018) Hybrid method for short-term photovoltaic power forecasting based on deep convolutional neural network. IET Gener Transm Distrib 12:4557–4567. https://doi.org/10.1049/iet-gtd.2018.5847

    Article  Google Scholar 

  39. Wang F, Zhang Z, Liu C et al (2019) Generative adversarial networks and convolutional neural networks based weather classification model for day ahead short-term photovoltaic power forecasting. Energy Convers Manag 181:443–462. https://doi.org/10.1016/j.enconman.2018.11.074

    Article  Google Scholar 

  40. Gu J, Wang Z, Kuen J et al (2018) Recent advances in convolutional neural networks. Pattern Recognit 77:354–377. https://doi.org/10.1016/j.patcog.2017.10.013

    Article  Google Scholar 

  41. Burkov A (2019) The hundred-page machine learning book

  42. Conv1D layer. https://keras.io/api/layers/convolution_layers/convolution1d/. Accessed 14 Feb 2021

  43. Hochreiter S, Schmidhuber J (1997) Long Short-Term Memory. Neural Comput 9:1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  44. Gers FA, Schmidhuber J, Cummins F (1999) Learning to forget: continual prediction with LSTM. In: IEEE conference publication. IEEE, pp 850–855

  45. Awad M, Khanna R (2015) Support vector regression. Effic Learn Mach. https://doi.org/10.1007/978-1-4302-5990-9_4

    Article  Google Scholar 

  46. Müller KR, Smoła AJ, Rätsch G, et al (1997) Predicting time series with support vector machines. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). Springer, pp 999–1004

  47. Wauters M, Vanhoucke M (2014) Support Vector Machine Regression for project control forecasting. Autom Constr 47:92–106. https://doi.org/10.1016/j.autcon.2014.07.014

    Article  Google Scholar 

  48. Deo RC, Wen X, Qi F (2016) A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset. Appl Energy 168:568–593. https://doi.org/10.1016/j.apenergy.2016.01.130

    Article  Google Scholar 

  49. Kleynhans T, Montanaro M, Gerace A, Kanan C (2017) Predicting top-of-atmosphere thermal radiance using MERRA-2 atmospheric data with deep learning. Remote Sens 9:1133. https://doi.org/10.3390/rs9111133

    Article  Google Scholar 

  50. Mohammadi K, Shamshirband S, Anisi MH et al (2015) Support vector regression based prediction of global solar radiation on a horizontal surface. Energy Convers Manag 91:433–441. https://doi.org/10.1016/j.enconman.2014.12.015

    Article  Google Scholar 

  51. Gensler A, Henze J, Sick B, Raabe N (2017) Deep Learning for solar power forecasting - An approach using AutoEncoder and LSTM Neural Networks. In: 2016 IEEE international conference on systems, man, and cybernetics, SMC 2016 - conference proceedings. Institute of electrical and electronics engineers Inc., pp 2858–2865

  52. Inman RH, Pedro HTC, Coimbra CFM (2013) Solar forecasting methods for renewable energy integration. Prog Energy Combust Sci 39:535–576

    Article  Google Scholar 

  53. Goodwin P, Lawton R (1999) On the asymmetry of the symmetric MAPE. Int J Forecast 15:405–408. https://doi.org/10.1016/S0169-2070(99)00007-2

    Article  Google Scholar 

  54. Makridakis S (1993) Accuracy measures: theoretical and practical concerns. Int J Forecast 9:527–529. https://doi.org/10.1016/0169-2070(93)90079-3

    Article  Google Scholar 

  55. Tofallis C (2015) A better measure of relative prediction accuracy for model selection and model estimation. J Oper Res Soc 66:1352–1362. https://doi.org/10.1057/jors.2014.103

    Article  Google Scholar 

  56. Russell S, Norvig P (2009) Artificial intelligence: a modern approach, 3rd edn. Prentice Hall Press, One Lake Street Upper Saddle River

    MATH  Google Scholar 

  57. Sanner MF (1999) Python: a programming language for software integration and development. J Mol Graph Model 17:57–61

    Google Scholar 

  58. Ketkar N, Ketkar N (2017) Introduction to Keras. In: Deep learning with python. A Press, pp 97–111

  59. Abadi M, Barham P, Chen J, et al (2016) TensorFlow: A system for large-scale machine learning. In: Proceedings of 12th USENIX symposium on operating systems design implementation, OSDI 2016, pp 265–283

  60. Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: Machine learning in Python. J Mach Learn Res 12:2825–2830

    MathSciNet  MATH  Google Scholar 

  61. Wozniak M, Wieczorek M, Silka J, Polap D (2021) Body pose prediction based on motion sensor data and recurrent neural network. IEEE Trans Ind Inform 17:2101–2111. https://doi.org/10.1109/TII.2020.3015934

    Article  Google Scholar 

  62. Lehmann EL, Romano JP (2005) Testing statistical hypotheses, 3rd edn. Springer, New York

    MATH  Google Scholar 

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Authors and Affiliations

  1. Sustainable Environment and Energy Systems, Middle East Technical University, Northern Cyprus Campus, 99738 Kalkanlı, Güzelyurt, Mersin 10, Turkey

    Nuray Vakitbilir & Adnan Hilal

  2. Electrical and Electronics Engineering, Center for Sustainability, Middle East Technical University, Northern Cyprus Campus, 99738 Kalkanlı, Güzelyurt, Mersin 10, Turkey

    Cem Direkoğlu

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  1. Nuray Vakitbilir
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  2. Adnan Hilal
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Correspondence to Cem Direkoğlu.

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Vakitbilir, N., Hilal, A. & Direkoğlu, C. Hybrid deep learning models for multivariate forecasting of global horizontal irradiation. Neural Comput & Applic 34, 8005–8026 (2022). https://doi.org/10.1007/s00521-022-06907-0

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