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Analysis of deceptive data attacks with adversarial machine learning for solar photovoltaic power generation forecasting

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Abstract

The solar photovoltaics (PV) energy resources have become more important with their significant contribution to the current power grid among renewable energy resources. However, the integration of the solar PV causes reliability issues in the power grid due to its high dependence on the weather condition. The predictability and stability of forecasting are critical for fully utilizing solar power. This study presents an Artificial Neural Network (ANN)-based solar PV power generation forecasting using a public dataset to form a basis experimental testbed to demonstrate analysis and impact of deceptive data attacks with adversarial machine learning. In addition, it evaluates the algorithms’ performance using the Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Average Error (MAE) metrics for two main cases, i.e., with and without adversarial machine learning attacks. The results show that the ANN-based models are vulnerable to adversarial attacks.

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References

  1. US. Department of Energy FE (2002) Using distributed energy resources-a how-to guide for federal energy managers. Cogener Compet Power J 17(4):37–68

    Google Scholar 

  2. U.S. Solar Market and 15 States See Best Quarter Ever for Residential Solar, https://www.seia.org/news/us-solar-market-and-15-states-see-best-quarter-ever-residential-solar. Retrieved: December 2021

  3. Solar Market Insight Report 2019 Q4, http://www.seia.org/research-resources/solar-market-insight-report-2019-q4, Retrieved: September 2021

  4. Solar Industry Research Data, https://www.seia.org/solar-industry-research-data, Retrieved: September 2021

  5. Ahmed Razin et al (2020) A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization. Renew Sustain Energy Rev 124:109792

    Article  Google Scholar 

  6. Bayindir R, Colak I, Fulli G, Demirtas K (2016) Smart grid technologies and applications. Renew Sustain Energy Rev 66:499–516

    Article  Google Scholar 

  7. Kuzlu M, Sarp S, Pipattanasomporn M, Cali U (2020) Realizing the potential of blockchain technology in smart grid applications. In: 2020 IEEE power and energy society innovative smart grid technologies conference (ISGT), pp 1–5. IEEE

  8. Cali U, Kuzlu M, Pipattanasomporn M, Kempf J, Bai L (2021) Applications of artificial intelligence in the energy domain. In: Digitalization of power markets and systems using energy informatics. Springer, Cham. https://doi.org/10.1007/978-3-030-83301-5_7

  9. Onen A (2021) Role of artificial intelligence in smart grids. Electr Eng, pp 1–1

  10. Sarp S, Kuzlu M, Cali U, Elma O, Guler O (2021) An interpretable solar photovoltaic power generation forecasting approach using an explainable artificial intelligence tool In: IEEE power and energy society innovative smart grid technologies conference (ISGT), pp 1–5, https://doi.org/10.1109/ISGT49243.2021.9372263

  11. Wang Huaizhi et al (2020) Taxonomy research of artificial intelligence for deterministic solar power forecasting. Energy Convers Manage 214:112909

    Article  Google Scholar 

  12. Radian B (2013) Artificial intelligence techniques for solar energy and photovoltaic applications. Handbook of Research on Solar Energy Systems and Technologies. IGI Global, pp 376–436

  13. Mandal Paras et al (2012) Forecasting power output of solar photovoltaic system using wavelet transform and artificial intelligence techniques. Proc Comput Sci 12:332–337

    Article  Google Scholar 

  14. Kuo Ping-Huan, Huang Chiou-Jye (2018) A green energy application in energy management systems by an artificial intelligence-based solar radiation forecasting model. Energies 11(4):819

    Article  Google Scholar 

  15. Dong N, Chang JF, Wu AG, Gao ZK (2020) A novel convolutional neural network framework based solar irradiance prediction method. Int J Electr Power Energy Syst 114:105411

    Article  Google Scholar 

  16. Zhang J, Chi Y, Xiao L (2018) Solar power generation forecast based on LSTM, In: 2018 IEEE 9th international conference on software engineering and service science (ICSESS), Beijing, China, pp 869–872

  17. Isaksson E, Karpe Conde M (2018) Solar power forecasting with machine learning techniques

  18. Mellit Adel et al (2020) Advanced methods for photovoltaic output power forecasting: a review. Appl Sci 10(2):487

    Article  Google Scholar 

  19. Kou J, et al (2013) Photovoltaic power forecasting based on artificial neural network and meteorological data, In: 2013 IEEE international conference of IEEE region 10 (TENCON 2013), Xi’an, pp 1–4

  20. Liu D, Sun K (2019) Random forest solar power forecast based on classification optimization. Energy 187:115940

    Article  Google Scholar 

  21. Alfadda A, Adhikari R, Kuzlu M, Rahman S (2017) Hour-ahead solar PV power forecasting using SVR based approach. In: 2017 IEEE power and energy society innovative smart grid technologies conference (ISGT), Washington, DC, pp 1–5

  22. Sharifzadeh Mahdi, Sikinioti-Lock Alexandra, Shah Nilay (2019) Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression. Renew Sustain Energy Rev 108:513–538

    Article  Google Scholar 

  23. Yuan X, He P, Zhu Q, Li X (2019) Adversarial examples: attacks and defenses for deep learning. IEEE Trans Neural Netw Learn Syst 30(9):2805–2824

    Article  MathSciNet  Google Scholar 

  24. Santana EJ, Silva RP, Zarpelão BB, Junior SB (2020) Photovoltaic generation forecast: model training and adversarial attack aspects. In: Brazilian conference on intelligent systems 2020 Oct 20, pp 634–649. Springer, Cham

  25. Santana EJ, Silva RP, Zarpelão BB, Barbon Junior S (2021) Detecting and mitigating adversarial examples in regression tasks: a photovoltaic power generation forecasting case study. Information 12(10):394

    Article  Google Scholar 

  26. Luo J, Hong T, Fang S (2018) Benchmarking robustness of load forecasting models under data integrity attacks. Int J Forecast 34(1):89–104

    Article  Google Scholar 

  27. Chen Y, Tan Y, Zhang B (2019, June) Exploiting vulnerabilities of load forecasting through adversarial attacks. In: Proceedings of the tenth ACM international conference on future energy systems, pp 1–11

  28. Tang N, Mao S, Nelms RM (2021) Adversarial attacks to solar power forecast. In: Proceedings of the IEEE GLOBECOM

  29. Sarp S, Kuzlu M, Cali U, Elma O, Guler O (2021) Analysis of false data injection impact on AI based solar photovoltaic power generation forecasting. arXiv preprint arXiv:2110.09948

  30. Tuna OF, Catak FO, Eskil T (2021) Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples. arXiv preprint arXiv:2102.04150

  31. Unsal DB, Ustun TS, Hussain SM, Onen A (2021) Enhancing cybersecurity in smart grids: false data injection and its mitigation. Energies 14(9):2657

  32. Aladag M, Catak FO, Gul E (2019) Preventing data poisoning attacks by using generative models, In: International informatics and software engineering conference (UBMYK), pp 1–5, https://doi.org/10.1109/UBMYK48245.2019.8965459

  33. Kurakin A, Goodfellow I, Bengio S (2016) Adversarial machine learning at scale. arXiv preprint arXiv:1611.01236

  34. Rosenberg I, Shabtai A, Elovici Y, Rokach L (2021) Adversarial machine learning attacks and defense methods in the cyber security domain. ACM Comput Surv (CSUR) 54(5):1–36

    Article  Google Scholar 

  35. Catak FO, Kuzlu M, Catak E, Cali U, Unal D (2022) Security concerns on machine learning solutions for 6G networks in mmWave beam prediction. Phys Commun, p 101626

  36. Cali U, Kuzlu M, Pipattanasomporn M, Kempf J, Bai L (2021) Foundations of big data, machine learning, and artificial intelligence and explainable artificial intelligence. In: Digitalization of power markets and systems using energy informatics. Springer, Cham. https://doi.org/10.1007/978-3-030-83301-5_6

  37. Hong Tao, Pinson Pierre, Fan Shu, Zareipour Hamidreza, Troccoli Alberto, Hyndman Rob J (2016) Probabilistic energy forecasting: global energy forecasting competition 2014 and beyond. Int J Forecast 32(3):896–913

    Article  Google Scholar 

  38. Goodfellow IJ, Shlens J, Szegedy C (2014) Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572

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Acknowledgements

This work was supported in part by the Commonwealth Cyber Initiative, an investment in the advancement of cyber R &D, innovation, and workforce development in Virginia. For more information about CCI, visit cyberinitiative.org.

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Author notes

  1. Murat Kuzlu, Ferhat Ozgur Catak, Umit Cali, Yanxiao Zhao, Onur Elma and Ozgur Guler have contributed equally to this work.

Authors and Affiliations

  1. Engineering Technology, Old Dominion University, Norfolk, VA, USA

    Murat Kuzlu

  2. Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA, USA

    Salih Sarp & Yanxiao Zhao

  3. Electrical Engineering and Computer Science, University of Stavanger, Stavanger, Norway

    Ferhat Ozgur Catak

  4. Department of Electric Power Engineering, Norwegian University of Science and Technology, Trondheim, Norway

    Umit Cali

  5. Electronics Engineering, Canakkale Onsekiz Mart University, Canakkale, Turkey

    Onur Elma

  6. R &D, eKare Inc, Merrifield, VA, USA

    Ozgur Guler

Authors
  1. Murat Kuzlu
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  2. Salih Sarp
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  3. Ferhat Ozgur Catak
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  4. Umit Cali
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  5. Yanxiao Zhao
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  6. Onur Elma
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  7. Ozgur Guler
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Corresponding author

Correspondence to Salih Sarp.

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Kuzlu, M., Sarp, S., Catak, F.O. et al. Analysis of deceptive data attacks with adversarial machine learning for solar photovoltaic power generation forecasting. Electr Eng 106, 1815–1823 (2024). https://doi.org/10.1007/s00202-022-01601-9

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  • DOI: https://doi.org/10.1007/s00202-022-01601-9

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