Effect of a resampling method on the effectiveness of multi-layer neural network models in PV power forecasting

Authors

  • Abderrahman Bensalem Applied Automation and Industrial Diagnostics Laboratory LAADI Faculty of Science and Technology, University of Djelfa, Djelfa, Algeria
  • Toual Belgacem
  • Abdellah Kouzou
  • Zakaria Belboul

DOI:

https://doi.org/10.4108/ew.3705

Keywords:

Photovoltaic, MNN, Forecasting, Resampling Method

Abstract

The primary aim of this study was to explore the impact of employing the K-fold Cross Validation resampling method in contrast to the hold-out set validation approach on the efficacy of forecasting models utilizing Multi-layer Neural Networks (MNN) for predicting photovoltaic (PV) output power. Real data sourced from southern Algeria was utilized for this purpose. The performance of various configurations of MNN models, with differing learning rate values, was evaluated using the coefficient of variation of Root Mean Square Error (CV(RMSE)). The findings consistently demonstrate that models developed using K-fold Cross Validation exhibited superior performance across most scenarios. These results underscore the potential advantages of leveraging such resampling techniques in terms of both generalization and robustness of forecasting models.

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References

J. R. Agüero and S. J. Steffel, “Integration challenges of photovoltaic distributed generation on power distribution systems,” IEEE Power Energy Soc. Gen. Meet., pp. 1–6, 2011, doi: 10.1109/PES.2011.6039097. DOI: https://doi.org/10.1109/PES.2011.6039097

A. Dolara, F. Grimaccia, S. Leva, M. Mussetta, and E. Ogliari, “A physical hybrid artificial neural network for short term forecasting of PV plant power output,” Energies, vol. 8, no. 2, pp. 1138–1153, 2015, doi: 10.3390/en8021138. DOI: https://doi.org/10.3390/en8021138

W. C. Kuo, C. H. Chen, S. H. Hua, and C. C. Wang, “Assessment of Different Deep Learning Methods of Power Generation Forecasting for Solar PV System,” Appl. Sci., vol. 12, no. 15, 2022, doi: 10.3390/app12157529. DOI: https://doi.org/10.3390/app12157529

P. Gupta and R. Singh, “PV power forecasting based on data-driven models: a review,” Int. J. Sustain. Eng., vol. 14, no. 6, pp. 1733–1755, 2021, doi: 10.1080/19397038.2021.1986590. DOI: https://doi.org/10.1080/19397038.2021.1986590

J. N. Velasco and C. F. Ostia, “Development of a Neural Network Based PV Power Output Prediction Application Using Reduced Features and Tansig Activation Function,” in 2020 6th International Conference on Control, Automation and Robotics (ICCAR), Apr. 2020, pp. 732–735, doi: 10.1109/ICCAR49639.2020.9108101. DOI: https://doi.org/10.1109/ICCAR49639.2020.9108101

L. Alhmoud, A. M. Al-Zoubi, and I. Aljarah, “Solar PV power forecasting at Yarmouk University using machine learning techniques,” Open Eng., vol. 12, no. 1, pp. 1078–1088, 2022, doi: 10.1515/eng-2022-0386. DOI: https://doi.org/10.1515/eng-2022-0386

H. Zhou, Q. Liu, K. Yan, and Y. Du, “Deep Learning Enhanced Solar Energy Forecasting with AI-Driven IoT,” Wirel. Commun. Mob. Comput., vol. 2021, 2021, doi: 10.1155/2021/9249387. DOI: https://doi.org/10.1155/2021/9249387

S. Tajmouati, B. El Wahbi, A. Bedoui, A. Abarda, and M. Dakkoun, “Applying k-nearest neighbors to time series forecasting : two new approaches,” pp. 1–20, 2021, [Online]. Available: http://arxiv.org/abs/2103.14200.

C. Voyant, P. Randimbivololona, M. L. Nivet, C. Paoli, and M. Muselli, “Twenty four hours ahead global irradiation forecasting using multi-layer perceptron,” Meteorol. Appl., vol. 21, no. 3, pp. 644–655, 2014, doi: 10.1002/met.1387. DOI: https://doi.org/10.1002/met.1387

B. K. Bose, “Artificial intelligence applications in renewable energy systems and smart grid – some novel applications,” Power Electron. Renew. Energy Syst. Smart Grid Technol. Appl., pp. 625–675, 2019, doi: 10.1002/9781119515661.ch12. DOI: https://doi.org/10.1002/9781119515661.ch12

A. Elamim, B. Hartiti, A. Barhdadi, A. Haibaoui, A. Lfakir, and P. Thevenin, “Photovoltaic output power forecast using artificial neural networks,” J. Theor. Appl. Inf. Technol., vol. 96, no. 15, pp. 5116–5126, 2018.

A. El Kounni, H. Radoine, H. Mastouri, H. Bahi, and A. Outzourhit, “Solar Power Output Forecasting Using Artificial Neural Network,” Proc. 2021 9th Int. Renew. Sustain. Energy Conf. IRSEC 2021, 2021, doi: 10.1109/IRSEC53969.2021.9741130. DOI: https://doi.org/10.1109/IRSEC53969.2021.9741130

M. K. Thukral, “Solar power output prediction using multilayered feedforward neural network: A case study of Jaipur,” Proc. - 2020 IEEE Int. Symp. Sustain. Energy, Signal Process. Cyber Secur. iSSSC 2020, pp. 2–7, 2020, doi: 10.1109/iSSSC50941.2020.9358821. DOI: https://doi.org/10.1109/iSSSC50941.2020.9358821

F. Sohil, M. U. Sohali, and J. Shabbir, “An introduction to statistical learning with applications in R,” Stat. Theory Relat. Fields, vol. 6, no. 1, pp. 87–87, 2022, doi: 10.1080/24754269.2021.1980261. DOI: https://doi.org/10.1080/24754269.2021.1980261

M. Konstantinou, S. Peratikou, and A. G. Charalambides, “Solar photovoltaic forecasting of power output using lstm networks,” Atmosphere (Basel)., vol. 12, no. 1, pp. 1–17, 2021, doi: 10.3390/atmos12010124. DOI: https://doi.org/10.3390/atmos12010124

[Ammar H, . Elsheikh; Swellam W, . Sharshir; Mohamed , Abd Elaziz; A.E. , Kabee; Wang , Guilan; Zhang , Haiou"Modeling of solar energy systems using artificial neural network: A," Solar Energy,p. 622_639, 2019.. DOI: https://doi.org/10.1016/j.solener.2019.01.037

A. Gabriel and C. De Rocha Vaz, “UNIVERSIDADE DE LISBOA FACULDADE DE CIÊNCIAS DEPARTAMENTO DE ENGENHARIA GEOGRÁFICA, GEOFÍSICA E ENERGIA Photovoltaic Forecasting with Artificial Neural Networks,” 2014.

G. F. Reed, F. Lynn, and B. D. Meade, “Use of Coefficient of Variation in Assessing Variability of Quantitative Assays,” Clin. Vaccine Immunol., vol. 9, no. 6, pp. 1235–1239, Nov. 2002, doi: 10.1128/CDLI.9.6.1235-1239.2002. DOI: https://doi.org/10.1128/CDLI.9.6.1235-1239.2002

J. S. Haberl, D. E. Claridge, and C. Culp, “ASHRAE’s Guideline 14-2002 for Measurement of Energy and Demand Savings: How to Determine what was Really Saved by the Retrofit,” Fifth Int. Conf. Enhanc. Build. Oper., no. January, pp. 1–13, 2005, [Online]. Available: http://repository.tamu.edu/handle/1969.1/5147.

S. Yeon, B. Yu, B. Seo, Y. Yoon, and K. H. Lee, “ANN based automatic slat angle control of venetian blind for minimized total load in an office building,” Sol. Energy, vol. 180, no. January, pp. 133–145, 2019, doi: 10.1016/j.solener.2019.01.027. DOI: https://doi.org/10.1016/j.solener.2019.01.027

M. K. Park, J. M. Lee, W. H. Kang, J. M. Choi, and K. H. Lee, “Predictive model for PV power generation using RNN (LSTM),” J. Mech. Sci. Technol., vol. 35, no. 2, pp. 795–803, 2021, doi: 10.1007/s12206-021-0140-0. DOI: https://doi.org/10.1007/s12206-021-0140-0

F. Lazzeri, Machine learning for time series forecasting with python. 2020. DOI: https://doi.org/10.1002/9781119682394

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Published

18-06-2024

How to Cite

1.
Bensalem A, Belgacem T, Kouzou A, Belboul Z. Effect of a resampling method on the effectiveness of multi-layer neural network models in PV power forecasting . EAI Endorsed Trans Energy Web [Internet]. 2024 Jun. 18 [cited 2024 Jul. 13];11. Available from: https://publications.eai.eu/index.php/ew/article/view/3705