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Day-Ahead Photovoltaic Power Forecasting Using Empirical Mode Decomposition Based on Similarity-Day Extension Without Information Leakage

  • Research Article-Electrical Engineering
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Abstract

Photovoltaic (PV) power generation prediction is a significant research topic in photovoltaics due to the clean and pollution-free characteristics of solar energy, which have contributed to its popularity worldwide. Photovoltaic data, as a type of time series data, exhibit strong periodicity and volatility. Researchers typically employ time–frequency signal processing methods, like empirical mode decomposition (EMD), to smooth the data during the feature engineering stage. However, improper operations at this stage could result in information leakage. Unfortunately, many existing studies on photovoltaic prediction fail to provide sufficient details on how signal processing methods are used during model training. To address this issue, this paper proposes the similarity-day extension EMD that avoids information leakage. The proposed method is validated through experiments conducted on the PV dataset of the Desert Knowledge Australia Solar Center, using mainstream models such as GRU, LSTM, CNN-LSTM, LSTN-CNN, and Bi-LSTM. The experimental results demonstrate an average improvement of 3.67% in MAE and 5.71% in RMSE when using this method, thus verifying its feasibility and effectiveness. Moreover, the proposed method can be applied to other data processing methods that may suffer from information leakage.

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References

  1. Zang, H.; Cheng, L.; Ding, T.; Cheung, K.W.; Wei, Z.; Sun, G.: Day-ahead photovoltaic power forecasting approach based on deep convolutional neural networks and meta learning. Int. J. Electr. Power Energy Syst. 118, 105790 (2020). https://doi.org/10.1016/j.ijepes.2019.105790

    Article  Google Scholar 

  2. Pietrosemoli, L.; Rodríguez-Monroy, C.: The Venezuelan energy crisis: renewable energies in the transition towards sustainability. Renew. Sustain. Energy Rev. 105, 415–426 (2019). https://doi.org/10.1016/j.rser.2019.02.014

    Article  Google Scholar 

  3. Antonanzas, J.; Osorio, N.; Escobar, R.; Urraca, R.; Martinez-de-Pison, F.J.; Antonanzas-Torres, F.: Review of photovoltaic power forecasting. Sol. Energy 136, 78–111 (2016). https://doi.org/10.1016/j.solener.2016.06.069

    Article  Google Scholar 

  4. Edenhofer, O.; Pichs Madruga, R.; Sokona, Y.: United Nations Environment Programme, World Meteorological Organization, Intergovernmental Panel on Climate Change, Potsdam-Institut für Klimafolgenforschung eds: Renewable Energy Sources and Climate Change Mitigation: Special Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, New York (2012)

  5. Iheanetu, K.J.: Solar photovoltaic power forecasting: a review. Sustainability 14, 17005 (2022). https://doi.org/10.3390/su142417005

    Article  Google Scholar 

  6. Maka, A.O.M.; Alabid, J.M.: Solar energy technology and its roles in sustainable development. Clean Energy. 6, 476–483 (2022). https://doi.org/10.1093/ce/zkac023

    Article  Google Scholar 

  7. Feilat, E.A.; Azzam, S.; Al-Salaymeh, A.: Impact of large PV and wind power plants on voltage and frequency stability of Jordan’s national grid. Sustain. Cities Soc. 36, 257–271 (2018). https://doi.org/10.1016/j.scs.2017.10.035

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. 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 

  10. Pascaris, A.S.; Schelly, C.; Burnham, L.; Pearce, J.M.: Integrating solar energy with agriculture: Industry perspectives on the market, community, and socio-political dimensions of agrivoltaics. Energy Res. Soc. Sci. 75, 102023 (2021). https://doi.org/10.1016/j.erss.2021.102023

    Article  Google Scholar 

  11. Lorenz, E.; Hurka, J.; Heinemann, D.; Beyer, H.G.: Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2, 2–10 (2009). https://doi.org/10.1109/JSTARS.2009.2020300

    Article  Google Scholar 

  12. Dolara, A.; Leva, S.; Manzolini, G.: Comparison of different physical models for PV power output prediction. Sol. Energy 119, 83–99 (2015). https://doi.org/10.1016/j.solener.2015.06.017

    Article  Google Scholar 

  13. Cui, C.; Zou, Y.; Wei, L.; Wang, Y.: Evaluating combination models of solar irradiance on inclined surfaces and forecasting photovoltaic power generation. IET Smart Grid. 2, 123–130 (2019). https://doi.org/10.1049/iet-stg.2018.0110

    Article  Google Scholar 

  14. Phinikarides, A.; Makrides, G.; Kindyni, N.; Kyprianou, A.; Georghiou, G.E.: ARIMA modeling of the performance of different photovoltaic technologies. In: 2013 IEEE 39th Photovoltaic Specialists Conference (PVSC). pp. 0797–0801. IEEE, Tampa, FL, USA (2013). https://doi.org/10.1109/PVSC.2013.6744268

  15. Alsharif, M.; Younes, M.; Kim, J.: Time series ARIMA model for prediction of daily and monthly average global solar radiation: the case study of Seoul, South Korea. Symmetry 11, 240 (2019). https://doi.org/10.3390/sym11020240

    Article  Google Scholar 

  16. Das, U.; Tey, K.; Seyedmahmoudian, M.; Idna Idris, M.; Mekhilef, S.; Horan, B.; Stojcevski, A.: SVR-based model to forecast PV power generation under different weather conditions. Energies 10, 876 (2017). https://doi.org/10.3390/en10070876

    Article  Google Scholar 

  17. Meng, M.; Song, C.: Daily photovoltaic power generation forecasting model based on random forest algorithm for North China in Winter. Sustainability 12, 2247 (2020). https://doi.org/10.3390/su12062247

    Article  Google Scholar 

  18. Wang, F.; Zhen, Z.; Wang, B.; Mi, Z.: Comparative study on KNN and SVM based weather classification models for day ahead short term solar PV power forecasting. Appl. Sci. 8, 28 (2017). https://doi.org/10.3390/app8010028

    Article  Google Scholar 

  19. Fara, L.; Diaconu, A.; Craciunescu, D.; Fara, S.: Forecasting of energy production for photovoltaic systems based on ARIMA and ANN advanced models. Int. J. Photoenergy 2021, 1–19 (2021). https://doi.org/10.1155/2021/6777488

    Article  Google Scholar 

  20. Wang, J.; Li, P.; Ran, R.; Che, Y.; Zhou, Y.: A short-term photovoltaic power prediction model based on the gradient boost decision tree. Appl. Sci. 8, 689 (2018). https://doi.org/10.3390/app8050689

    Article  Google Scholar 

  21. Abdel-Nasser, M.; Mahmoud, K.: Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural Comput. Appl. 31, 2727–2740 (2019). https://doi.org/10.1007/s00521-017-3225-z

    Article  Google Scholar 

  22. Wang, F.; Xuan, Z.; Zhen, Z.; Li, K.; Wang, T.; Shi, M.: A day-ahead PV power forecasting method based on LSTM-RNN model and time correlation modification under partial daily pattern prediction framework. Energy Convers. Manag. 212, 112766 (2020). https://doi.org/10.1016/j.enconman.2020.112766

    Article  Google Scholar 

  23. Agga, A.; Abbou, A.; Labbadi, M.; El Houm, Y.: Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM. ConvLSTM Models. Renew. Energy. 177, 101–112 (2021). https://doi.org/10.1016/j.renene.2021.05.095

    Article  Google Scholar 

  24. Agga, A.; Abbou, A.; Labbadi, M.; Houm, Y.E.; Ou Ali, I.H.: CNN-LSTM: An efficient hybrid deep learning architecture for predicting short-term photovoltaic power production. Electr. Power Syst. Res. 208, 107908 (2022). https://doi.org/10.1016/j.epsr.2022.107908

    Article  Google Scholar 

  25. Jalali, S.M.J.; Ahmadian, S.; Kavousi-Fard, A.; Khosravi, A.; Nahavandi, S.: Automated deep CNN-LSTM architecture design for solar irradiance forecasting. IEEE Trans. Syst. Man Cybern. Syst. 52, 54–65 (2022). https://doi.org/10.1109/TSMC.2021.3093519

    Article  Google Scholar 

  26. Qu, J.; Qian, Z.; Pei, Y.: Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern. Energy 232, 120996 (2021). https://doi.org/10.1016/j.energy.2021.120996

    Article  Google Scholar 

  27. Lim, S.-C.; Huh, J.-H.; Hong, S.-H.; Park, C.-Y.; Kim, J.-C.: Solar power forecasting using CNN-LSTM hybrid model. Energies 15, 8233 (2022). https://doi.org/10.3390/en15218233

    Article  Google Scholar 

  28. Wang*, Y.; Yang, Q.; Xue, H.; Mi, Y.; Tu, Y.: Ultra‐short‐term PV power prediction model based on HP‐OVMD and enhanced emotional neural network. IET Renew. Power Gener. 16, 2233–2247 (2022). https://doi.org/10.1049/rpg2.12514

  29. Hu, L.; Zhen, Z.; Li, K.; Wang, F.: An ultra-short-term PV power prediction model based on path space distance cross-similar clustering and STL decomposition. In: 2019 IEEE Sustainable Power and Energy Conference (iSPEC). pp. 1353–1358. IEEE, Beijing (2019). https://doi.org/10.1109/iSPEC48194.2019.8974906

  30. Huang, N.E.; Shen, Z.; Long, S.R.; Wu, M.C.; Shih, H.H.; Zheng, Q.; Yen, N.-C.; Tung, C.C.; Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. Ser. Math. Phys. Eng. Sci. 454, 903–995 (1998). https://doi.org/10.1098/rspa.1998.0193

    Article  MathSciNet  Google Scholar 

  31. Majumder, I.; Behera, M.K.; Nayak, N.: Solar power forecasting using a hybrid EMD-ELM method. In: 2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT). pp. 1–6. IEEE, Kollam (2017). https://doi.org/10.1109/ICCPCT.2017.8074179

  32. Li, F.-F.; Wang, S.-Y.; Wei, J.-H.: Long term rolling prediction model for solar radiation combining empirical mode decomposition (EMD) and artificial neural network (ANN) techniques. J. Renew. Sustain. Energy. 10, 013704 (2018). https://doi.org/10.1063/1.4999240

    Article  Google Scholar 

  33. Yadav, H.K.; Pal, Y.; Tripathi, M.M.: Short-term PV power forecasting using empirical mode decomposition in integration with back-propagation neural network. J. Inf. Optim. Sci. 41, 25–37 (2020). https://doi.org/10.1080/02522667.2020.1714181

    Article  Google Scholar 

  34. Khelifi, R.; Guermoui, M.; Rabehi, A.; Taallah, A.; Zoukel, A.; Ghoneim, S.S.M.; Bajaj, M.; AboRas, K.M.; Zaitsev, I.: Short-term PV power forecasting using a hybrid TVF-EMD-ELM strategy. Int. Trans. Electr. Energy Syst. 2023, 1–14 (2023). https://doi.org/10.1155/2023/6413716

    Article  Google Scholar 

  35. Bao, Y.; Guo, W.: Photovoltaic Power Prediction Based on EMD-BLS Model. J. Phys.: Confer. Ser. 2427, 012016 (2023). https://doi.org/10.1088/1742-6596/2427/1/012016

    Article  Google Scholar 

  36. Wang, L.; Mao, M.; Xie, J.; Liao, Z.; Zhang, H.; Li, H.: Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model. Energy 262, 125592 (2023). https://doi.org/10.1016/j.energy.2022.125592

    Article  Google Scholar 

  37. Gupta, P.; Singh, R.: Combining a deep learning model with multivariate empirical mode decomposition for hourly global horizontal irradiance forecasting. Renew. Energy 206, 908–927 (2023). https://doi.org/10.1016/j.renene.2023.02.052

    Article  Google Scholar 

  38. Balraj, G.; Victoire, A.A.; Victoire, A.: Variational mode decomposition combined fuzzy-Twin support vector machine model with deep learning for solar photovoltaic power forecasting. PLoS ONE 17, e0273632 (2022). https://doi.org/10.1371/journal.pone.0273632

    Article  Google Scholar 

  39. Li, K.; Shen, R.; Wang, Z.; Yan, B.; Yang, Q.; Zhou, X.: An efficient wind speed prediction method based on a deep neural network without future information leakage. Energy 267, 126589 (2023). https://doi.org/10.1016/j.energy.2022.126589

    Article  Google Scholar 

  40. Zhu, H.; Xu, R.; Deng, H.: A novel STL-based hybrid model for forecasting hog price in China. Comput. Electron. Agric. 198, 107068 (2022). https://doi.org/10.1016/j.compag.2022.107068

    Article  Google Scholar 

  41. Xiong, T.; Bao, Y.; Hu, Z.: Does restraining end effect matter in EMD-based modeling framework for time series prediction? Some experimental evidences. Neurocomputing 123, 174–184 (2014). https://doi.org/10.1016/j.neucom.2013.07.004

    Article  Google Scholar 

  42. Deng, Y.; Wang, W.; Qian, C.; Wang, Z.; Dai, D.: Boundary-processing-technique in EMD method and Hilbert transform. Chin. Sci. Bull. 46, 954–960 (2001). https://doi.org/10.1007/BF02900475

    Article  Google Scholar 

  43. Coughlin, K.T.; Tung, K.K.: 11-Year solar cycle in the stratosphere extracted by the empirical mode decomposition method. Adv. Space Res. 34, 323–329 (2004). https://doi.org/10.1016/j.asr.2003.02.045

    Article  Google Scholar 

  44. Chen, Q.; Huang, N.; Riemenschneider, S.; Xu, Y.: A B-spline approach for empirical mode decompositions. Adv. Comput. Math. 24, 171–195 (2006). https://doi.org/10.1007/s10444-004-7614-3

    Article  MathSciNet  Google Scholar 

  45. Shao, C.; Wang, J.; Fan, J.; Yang, M.; Wang, Z.: A self-adaptive method dealing with the end issue of EMD. Acta Electron. Sin. 290(10), 1944–1948 (2007) (In Chinese)

    Google Scholar 

  46. Yu, Y.; Si, X.; Hu, C.; Zhang, J.: A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput. 31, 1235–1270 (2019). https://doi.org/10.1162/neco_a_01199

    Article  MathSciNet  Google Scholar 

  47. Kim, K.G.: Book review: deep learning. Healthc. Inform. Res. 22, 351 (2016). https://doi.org/10.4258/hir.2016.22.4.351

    Article  Google Scholar 

  48. Hochreiter, S.; Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  49. Cho, K.; van Merrienboer, B.; Gulcehre, C.; Bahdanau, D.; Bougares, F.; Schwenk, H.; Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation (2014). https://doi.org/10.48550/ARXIV.1406.1078

  50. Gu, J.; Wang, Z.; Kuen, J.; Ma, L.; Shahroudy, A.; Shuai, B.; Liu, T.; Wang, X.; Wang, G.; Cai, J.; Chen, T.: Recent advances in convolutional neural networks. Pattern Recogn. 77, 354–377 (2018). https://doi.org/10.1016/j.patcog.2017.10.013

    Article  Google Scholar 

  51. Wang, T.; Yang, S.: Research on EMD algorithm and its Application in signal denoising. Ph.D. Dissertation, Dept. Communication and Information Systems, Harbin Engineering University. Harbin, China (2010) (in Chinese)

  52. Pan, M.; Li, C.; Gao, R.; Huang, Y.; You, H.; Gu, T.; Qin, F.: Photovoltaic power forecasting based on a support vector machine with improved ant colony optimization. J. Clean. Prod. 277, 123948 (2020). https://doi.org/10.1016/j.jclepro.2020.123948

    Article  Google Scholar 

  53. DKASC, Alice Springs. https://dkasolarcentre.com.au/download?location=alice-springs

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Funding

This work was supported by the Graduate Student Innovation Program of Chongqing University of Technology (Grant No. gzlcx20233345).

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Authors and Affiliations

  1. School of Science, Chongqing University of Technology, Chongqing, 400054, China

    Gen Li, Tian Tian, Fuchong Hao, Zifan Yuan, Rong Tang & Xueqin Liu

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  1. Gen Li
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  2. Tian Tian
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  3. Fuchong Hao
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  4. Zifan Yuan
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  5. Rong Tang
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Contributions

Gen Li contributed to conceptualization, methodology, software, formal analysis, and writing—original draft. Tian Tian and Fuchong Hao performed visualization and investigation. Zifan Yuan performed writing—reviewing and editing. Rong Tang contributed to software and validation. Xueqin Liu contributed to project administration, funding acquisition, and supervision.

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Correspondence to Xueqin Liu.

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Li, G., Tian, T., Hao, F. et al. Day-Ahead Photovoltaic Power Forecasting Using Empirical Mode Decomposition Based on Similarity-Day Extension Without Information Leakage. Arab J Sci Eng 49, 6941–6957 (2024). https://doi.org/10.1007/s13369-023-08534-w

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