DOI QR코드

DOI QR Code

Explainable Photovoltaic Power Forecasting Scheme Using BiLSTM

BiLSTM 기반의 설명 가능한 태양광 발전량 예측 기법

  • 박성우 (고려대학교 전기전자공학부) ;
  • 정승민 (고려대학교 전기전자공학부) ;
  • 문재욱 (고려대학교 전기전자공학부) ;
  • 황인준 (고려대학교 전기전자공학부)
  • Received : 2021.12.30
  • Accepted : 2022.01.17
  • Published : 2022.08.31

Abstract

Recently, the resource depletion and climate change problem caused by the massive usage of fossil fuels for electric power generation has become a critical issue worldwide. According to this issue, interest in renewable energy resources that can replace fossil fuels is increasing. Especially, photovoltaic power has gaining much attention because there is no risk of resource exhaustion compared to other energy resources and there are low restrictions on installation of photovoltaic system. In order to use the power generated by the photovoltaic system efficiently, a more accurate photovoltaic power forecasting model is required. So far, even though many machine learning and deep learning-based photovoltaic power forecasting models have been proposed, they showed limited success in terms of interpretability. Deep learning-based forecasting models have the disadvantage of being difficult to explain how the forecasting results are derived. To solve this problem, many studies are being conducted on explainable artificial intelligence technique. The reliability of the model can be secured if it is possible to interpret how the model derives the results. Also, the model can be improved to increase the forecasting accuracy based on the analysis results. Therefore, in this paper, we propose an explainable photovoltaic power forecasting scheme based on BiLSTM (Bidirectional Long Short-Term Memory) and SHAP (SHapley Additive exPlanations).

최근 화석연료의 무분별한 사용으로 인한 자원고갈 문제 및 기후변화 문제 등이 심각해짐에 따라 화석연료를 대체할 수 있는 신재생에너지에 대한 관심이 증가하고 있다. 특히 신재생에너지 중 태양광 에너지는 다른 신재생에너지원에 비해 고갈될 염려가 적고, 공간적인 제약이 크지 않아 전국적으로 수요가 증가하고 있다. 태양광 발전 시스템에서 생산된 전력을 효율적으로 사용하기 위해서는 보다 정확한 태양광 발전량 예측 모델이 필요하다. 이를 위하여 다양한 기계학습 및 심층학습 기반의 태양광 발전량 예측 모델이 제안되었지만, 심층학습 기반의 예측 모델은 모델 내부에서 일어나는 의사결정 과정을 해석하기가 어렵다는 단점을 보유하고 있다. 이러한 문제를 해결하기 위하여 설명 가능한 인공지능 기술이 많은 주목을 받고 있다. 설명 가능한 인공지능 기술을 통하여 예측 모델의 결과 도출 과정을 해석할 수 있다면 모델의 신뢰성을 확보할 수 있을 뿐만 아니라 해석된 도출 결과를 바탕으로 모델을 개선하여 성능 향상을 기대할 수도 있다. 이에 본 논문에서는 BiLSTM(Bidirectional Long Short-Term Memory)을 사용하여 모델을 구성하고, 모델에서 어떻게 예측값이 도출되었는지를 SHAP(SHapley Additive exPlanations)을 통하여 설명하는 설명 가능한 태양광 발전량 예측 기법을 제안한다.

Keywords

Acknowledgement

본 연구는 2019년도 정부(과학기술정보통신부)의 재원으로 한국연구재단-에너지클라우드기술개발사업(No. 2019M3F2A1073184)의 지원을 받아 수행된 연구임.

References

  1. A. A. A. Abuelnuor, K. M. Saqr, S. A. A. Mohieldein, K. A. Dafallah, M. M. Abdullah, and Y. A. M. Nogoud, "Exergy analysis of Garri '2' 180 MW combined cycle power plant," Renewable and Sustainable Energy Reviews, Vol.79, pp.960-969, 2017. https://doi.org/10.1016/j.rser.2017.05.077
  2. M. Taylor, P. Ralon, H. Anuta, and S. Al-Zoghoul, "IRENA Renewable Power Generation Costs in 2019," International Renewable Energy Agency, Abu Dhabi, UAE, 2020.
  3. K. E. Lonngren and E. W. Bai, "On the global warming problem due to carbon dioxide," Energy Policy, Vol.36, No.4, pp.1567-1568, 2008. https://doi.org/10.1016/j.enpol.2007.12.019
  4. S. Shafiee and E. Topal, "When will fossil fuel reserves be diminished?," Energy Policy, Vol.37, No.1, pp.181-189, 2009. https://doi.org/10.1016/j.enpol.2008.08.016
  5. A. Karmaker, M. M. Rahman, M. A. Hossain, and M. R. Ahmed, "Exploration and corrective measures of greenhouse gas emission from fossil fuel power stations for Bangladesh," Journal of Cleaner Production, Vol.244, 2020.
  6. S. N. Seo, "Beyond the Paris agreement: Climate change policy negotiations and future directions," Regional Science Policy & Practice, Vol.9, No.2, pp.121-140, 2017. https://doi.org/10.1111/rsp3.12090
  7. H. Chitsaz, H. Shaker, H. Zareipour, D. Wood, and N. Amjady, "Short-term electricity load forecasting of building in microgrids," Energy and Buildings, Vol.99, pp.50-60, 2015. https://doi.org/10.1016/j.enbuild.2015.04.011
  8. M. Morandin, S. Bolognani, and A. Faggion, "Active Torque Damping for an ICE-Based Domestic CHP System with an SPM Machine Drive," IEEE Transactions on Industry Applications, Vol.51, No.4, pp.3137-3146, 2015. https://doi.org/10.1109/TIA.2015.2399617
  9. A. Khosravi, R. N. N. Koury, L. Machado, and J. J. G. Pabon, "Prediction of hourly solar radiation in abu musa island using machine learning algorithms," Journal of Cleaner Production, Vol.176, pp.63-75, 2018. https://doi.org/10.1016/j.jclepro.2017.12.065
  10. M. Lee, W. Lee, and J. Jung, "24-Hour photovoltaic generation forecasting using combined very-short-term and shortterm multivariate time series model," In 2017 IEEE Power & Energy Society General Meeting, pp.1-5, 2017.
  11. B. Zhang, et al., "A multiple time series-based recurrent neural network for short-term load forecasting," Soft Computing, Vol.22, No.12, pp.4099-4112, 2018. https://doi.org/10.1007/s00500-017-2624-5
  12. J. H. Jeong and Y. T. Chae, "Improvement for forecasting of photovoltaic power output using real time weather data based on machine learning," Journal of Korean Society of Living Environmental System, Vol.25, pp.119-125, 2018. https://doi.org/10.21086/ksles.2018.02.25.1.119
  13. D. Gunning, "Explainable artificial intelligence (XAI)," Defense Advanced Research Projects Agency (DARPA), 2017.
  14. D. H. Park, et al., "Multimodal explanations: Justifying decisions and pointing to the evidence," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.8779-8788, 2018.
  15. L. Massidda and M. Marrocu, "Use of multilinear adaptive regression splines and numerical weather prediction to forecast the power output of a PV plant in Borkum," Solar Energy, Vol.146, pp.141-149, 2017. https://doi.org/10.1016/j.solener.2017.02.007
  16. L. M. Halabi, S. Mekhilef, and M. Hossain, "Performance evaluation of hybrid adaptive neuro-fuzzy inference system models for predicting monthly global solar radiation," Applied Energy, Vol.213, pp.247-261, 2018. https://doi.org/10.1016/j.apenergy.2018.01.035
  17. F. A. Kraemer, D. Ammar, A. E. Braten, N. Tamkittikhun, and D. Palma, "Solar energy prediction for constrained IoT nodes based on public weather forecasts," In Proceedings of the Seventh International Conference on the Internet of Things, pp.1-8, 2017.
  18. F. Almonacid, C. Rus, P. Perez-Higueras, and L. Hontoria, "Calculation of the energy provided by a PV generator. Comparative study: Conventional methods vs. artificial neural networks," Energy, Vol.36, No.1, pp.375-384, 2011. https://doi.org/10.1016/j.energy.2010.10.028
  19. K. Wang, X. Qi, and H. Liu, "Photovoltaic power forecasting based LSTM-Convolutional Network," Energy, Vol.189, pp.116225, 2019.
  20. G. de Freitas Viscondi and S. N. Alves-Souza, "A systematic literature review on big data for solar photovoltaic electricity generation forecasting," Sustainable Energy Technologies and Assessments, Vol.31, pp.54-63, 2019. https://doi.org/10.1016/j.seta.2018.11.008
  21. R. Meenal and A. I. Selvakumar, "Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters," Renewable Energy, Vol.121, pp.324-343, 2018. https://doi.org/10.1016/j.renene.2017.12.005
  22. S. Park, J. Moon, S. Jung, S. Rho, S. W. Baik, and E. Hwang, "A two-stage industrial load forecasting scheme for dayahead combined cooling, heating and power scheduling," Energies, Vol.13, No.2, pp.443-465, 2020. https://doi.org/10.3390/en13020443
  23. S. Bhanja and A. Das, "Impact of data normalization on deep neural network for time series forecasting," arXiv Preprint arXiv:1812.05519, 2018.
  24. Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, Vol.521, No.7553, pp.436-444, 2015. https://doi.org/10.1038/nature14539
  25. H. Sak, A. W. Senior, and F. Beaufays, "Long short-term memory recurrent neural network architectures for large scale acoustic modeling," 2014.
  26. M. Schuster and K. K. Paliwal, "Bidirectional recurrent neural networks," IEEE Transactions on Signal Processing, Vol.45, No.11, pp.2673-2681, 1997. https://doi.org/10.1109/78.650093
  27. S. M. Lundberg and S. I. Lee, "A Unified approach to interpreting model predictions," Advances in Neural Information Processing Systems, Vol.30, pp.4765-4774, 2017