The Journal of Society for e-Business Studies (한국전자거래학회지)

Society for e-Business Studies (한국전자거래학회)
권호 표지
DOI QR코드

DOI QR Code

A LSTM Based Method for Photovoltaic Power Prediction in Peak Times Without Future Meteorological Information

미래 기상정보를 사용하지 않는 LSTM 기반의 피크시간 태양광 발전량 예측 기법

  • Lee, Donghun (Department of Industrial and Management Engineering, Incheon National University) ;
  • Kim, Kwanho (Department of Industrial and Management Engineering, Incheon National University)
  • Received : 2019.10.29
  • Accepted : 2019.11.26
  • Published : 2019.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, the importance prediction of photovoltaic power (PV) is considered as an essential function for scheduling adjustments, deciding on storage size, and overall planning for stable operation of PV facility systems. In particular, since most of PV power is generated in peak time, PV power prediction in a peak time is required for the PV system operators that enable to maximize revenue and sustainable electricity quantity. Moreover, Prediction of the PV power output in peak time without meteorological information such as solar radiation, cloudiness, the temperature is considered a challenging problem because it has limitations that the PV power was predicted by using predicted uncertain meteorological information in a wide range of areas in previous studies. Therefore, this paper proposes the LSTM (Long-Short Term Memory) based the PV power prediction model only using the meteorological, seasonal, and the before the obtained PV power before peak time. In this paper, the experiment results based on the proposed model using the real-world data shows the superior performance, which showed a positive impact on improving the PV power in a peak time forecast performance targeted in this study.

최근 태양광 발전량 예측은 태양광 발전량 설비 시스템의 안정적인 작동을 위한 조정 계획, 설비 규격 결정 및 생산 계획 일정을 수립하기 위해 필수적인 요소로 고려된다. 특히, 대부분의 태양광 발전량은 피크시간에 측정되기 때문에, 태양광 시스템 운영자의 이익 최대화와 전력 계통량 안정화를 위해 피크시간의 태양광 발전량 예측은 매우 중요한 요소이다. 또한, 기존 연구들은 광범위한 지역에서 예측된 불확실한 기후 정보들을 이용하여 태양광 발전량을 예측하는 한계점 때문에 일사량, 운량, 온도 등과 기상정보 없이 피크시간의 태양광 발전량을 예측하는 것은 매우 어려운 문제로 고려된다. 따라서 본 논문에서는 피크이전의 기후, 계절 및 관측된 태양광 발전량을 이용하여 미래의 기후 및 계절 정보 없이 피크시간의 태양광 발전량을 예측할 수 있는 LSTM(Long-Shot Term Memory) 기반의 태양광 발전량 예측 기법을 제안한다. 본 연구에서 제안한 모델을 기반으로 실 데이터를 통한 실험 결과, 단기 및 장기적 관점에서 높은 성능을 보였으며, 이는 본 연구에서 목표로 한 피크시간의 태양광 발전량 예측 성능 향상에 긍정적인 영향을 나타내었음을 보여준다.

Keywords

References

  1. Ahn, S., Kang, H., Cho, J., Kim T.-O., and Shin, D., "Forecasting Model Design of Fire Occurrences with ARIMA Models," Journal of the Korean Institute for Gas, Vol. 19, No. 2, pp. 20-28, 2015. https://doi.org/10.7842/kigas.2015.19.2.20
  2. Ashraf, I. and Chandra, A., "Artificial Neural Network Based Models for Forecasting Electricity Generation of Grid Connected Solar PV Power Plant," International Journal of Global Energy Issues, Vol. 21, No. 1-2, pp. 119-130, 2004. https://doi.org/10.1504/IJGEI.2004.004704
  3. Bacher, P., Madsen, H., and Nielsen, H.A., "Online Short-term Solar Power Forecasting," Solar Energy, Vol. 83, No. 10, pp. 1772-1783, 2009. https://doi.org/10.1016/j.solener.2009.05.016
  4. Choi, J., Shin Y., and Lee, I.-W., "DER Energy Management System for Optimal Management of Grid-Connected Microgrids," The Journal of Korean Institue of Communications and Information Sciences, Vol. 42, No. 4, pp. 932-938, 2017. https://doi.org/10.7840/kics.2017.42.4.932
  5. Chan, S. C., Tsui, K. M., Wu, H. C., Hou, Y., Wu, Y. C., and Wu, F. F., "Load/Price Forecasting and Managing Demand Response for Smart Grids," Proceedings of Methodologies and Challenges, Vol. 29, No. 5, pp. 68-85, 2012.
  6. Chattopadhyay, K., Kies, A., Lorenz, E., von Bremen, L., and Heinemann, D., "The Impact of Different PV Module Configurations on Storage and Additional Balancing Needs for A Fully Renewable European Power System," Journal of Renewable Energy, Vol. 113, pp. 176-189, 2017. https://doi.org/10.1016/j.renene.2017.05.069
  7. Chung, W. H., Park, G., Gu, Y. H., Kim, S., Yoo, S. J., and J. Y., "City Gas Pipeline Pressure Prediction Model," The Journal of Society for e-Business Studies, Vol. 23, No. 2, pp. 33-77, 2018. https://doi.org/10.7838/JSEBS.2018.23.2.033
  8. De Giorgi, M. G., Malvoni, M., and Congedo, P. M., "Comparison of Strategies for Multi-step Ahead Photovoltaic Power Forecasting Models Based on Hybrid Group Method of Data Handling Networks and Least Square Support Vector Machine," Energy, Vol. 107, pp. 360-373, 2016. https://doi.org/10.1016/j.energy.2016.04.020
  9. Da Silva Fonseca, Jr, J. G., Oozeki, T., Takashima, T., Koshimizu, G., Uchida, Y., and Ogimoto, K., "Use of Support Vector Regression and Numerically Predicted Cloudiness to Forecast Power Output of A Photovoltaic Power Plant in Kitakyushu, Japan," Proceedings of Photovoltaics: Research and Applications, Vol. 20, No. 7, pp. 874-882, 2012. https://doi.org/10.1002/pip.1152
  10. Donahue, J., Anne Hendricks, L., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., and Darrell, T., "Long-term Recurrent Convolutional Networks for Visual Recognition and Description," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2625-2634, 2015.
  11. Graves, A., "Generating Sequences With Recurrent Neural Networks," arXiv preprint arXiv:1308.0850, 2013.
  12. Greff, K., Srivastava, R. K., Koutnik, J., Steunebrink, B. R., and Schmidhuber, J., "LSTM: A Search Space Odyssey," IEEE Transactions on Neural Networks and Learning Systems, Vol. 28, No. 10, pp. 2222-2232, 2016. https://doi.org/10.1109/TNNLS.2016.2582924
  13. Glorot, X. and Bengio, Y., "Understanding The Difficulty of Training Deep Feedforward Neural Networks," Proceedings of The Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249-256, 2010.
  14. Hoffmann, W., "PV Solar Electricity Industry: Market Growth and Perspective," Solar Energy Materials and Solar Cells, Vol. 90, No. 18-19, pp. 3285-3311, 2006. https://doi.org/10.1016/j.solmat.2005.09.022
  15. Iglesias, G., Kale, D. C., and Liu, Y., "An Examination of Deep Learning for Extreme Climate Pattern Analysis," Proceedings of International Workshop on Climate Informatics, 2015.
  16. Izgi, E., Oztopal, A., Yerli, B., Kaymak, M.K., and Sahin, A. D., "Short-mid-term Solar Power Prediction by Using Artificial Neural Networks," Solar Energy, Vol. 86, No. 2, pp. 725-733, 2012. https://doi.org/10.1016/j.solener.2011.11.013
  17. Jiahao, K., Jun, L., Qifan, L., Wanliang, F., Zhenhuan, C., Linlin, L., and Tieying, G., "Photovoltaic Power Forecasting Based on Artificial Neural Network and Meteorological Data," Proceedings of International Conference of IEEE Regional World Map, pp. 1-4, 2013.
  18. Jurasz, J. and Ciapala, B., "Solar-hydro Hybrid Power Station As A Way to Smooth Power Output and Increase Water Retention," Solar Energy, Vol. 173, pp. 675-690, 2018. https://doi.org/10.1016/j.solener.2018.07.087
  19. Li, Y., Su, Y., and Shu, L., "An ARMAX Model for Forecasting The Power Output of A Grid Connected Photovoltaic System," Renewable Energy, Vol. 66, pp. 78-89, 2014. https://doi.org/10.1016/j.renene.2013.11.067
  20. Lee, H. and Kim, H., "Deep Learning Based Prediction Method of Long-term Photovoltaic Power Generation Using Meteorological and Seasonal Information," The Journal of Society for e-Business Studies, Vol. 24, No. 1, pp. 1-16, 2019.
  21. Maqsood, I., Khan, M. R., and Abraham, A., "An Ensemble of Neural Networks for Weather Forecasting," Neural Computing and Applications, Vol. 13, No. 2, pp. 112-122, 2004. https://doi.org/10.1007/s00521-004-0413-4
  22. Ordonez, F. and Roggen, D., "Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition," Sensors, Vol. 16, No. 1, 2016.
  23. Park, J., Lee, C., and Kim, S., "Survey and Analysis of DR Resources Potential for Each Sectors," Proceedings of The Korean Institute of Electrical Engineers, pp. 180-181, 2012.
  24. Rowlands, I. H., "Solar PV Electricity and Market Characteristics: Two Canadian Case-studies," Renewable Energy, Vol. 30, No. 6, pp. 815-834, 2005. https://doi.org/10.1016/j.renene.2004.08.001
  25. Sulaiman, S. I., Rahman, T. K. A., and Musirin, I., "Partial Evolutionary ANN for Output Prediction of a Grid-Connected Photovoltaic System," International Journal of Computer and Electrical Engineering, Vol. 1, No. 1, pp. 40-45, 2009.
  26. Shaheen, N. I. and Ahmed, O., "Simple Methodology to Predict Local Temperature and Humidity," In ASHRAE Transactions, 1998.
  27. Shi, J., Lee, W. J., Liu, Y., Yang, Y., and Wang, P., "Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines," IEEE Transactions on Industry Applications, Vol. 48, No. 3, pp. 1064-1069, 2012. https://doi.org/10.1109/TIA.2012.2190816
  28. Yona, A., Senjyu, T., Funabashi, T., Mandal, P., and Kim, C. H., "Optimizing Re-planning Operation for Smart House Applying Solar Radiation Forecasting," Applied Sciences, Vol. 4, No. 3, pp. 366-379, 2014. https://doi.org/10.3390/app4030366
  29. Zeng, J. and Qiao, W., "Short-term Solar Power Prediction Using A Support Vector Machine," Renewable Energy, Vol. 52, pp. 118-127, 2013. https://doi.org/10.1016/j.renene.2012.10.009
  30. Zaremba, W., Sutskever, I., and Vinyals, O., "Recurrent Neural Network Regularization," arXiv preprint arXiv:1409.2329, 2014.
  31. Zhou, Y., Wang, C., Wu, J., Wang, J., Cheng, M., and Li, G., "Optimal Scheduling of Aggregated Thermostatically Controlled Loads with Renewable Generation in the Intraday Electricity Market," Applied Energy, Vol. 188, pp. 456-465, 2017. https://doi.org/10.1016/j.apenergy.2016.12.008