Journal of the Korean Solar Energy Society (한국태양에너지학회 논문집)

The Korean Solar Energy Society (한국태양에너지학회)
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Development of a Prediction Model of Solar Irradiances Using LSTM for Use in Building Predictive Control

건물 예측 제어용 LSTM 기반 일사 예측 모델

  • Jeon, Byung-Ki (Department of Architectural Engineering, Graduate School, Inha University) ;
  • Lee, Kyung-Ho (Department of Solar Thermal Convergence Lab, Korea Institute of Energy Research) ;
  • Kim, Eui-Jong (Department of Architectural Engineering, Inha University)
  • 전병기 (인하대학교 대학원 건축공학과) ;
  • 이경호 (한국에너지기술연구원) ;
  • 김의종 (인하대학교 건축공학과)
  • Received : 2019.10.14
  • Accepted : 2019.10.28
  • Published : 2019.10.30
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Abstract

The purpose of the work is to develop a simple solar irradiance prediction model using a deep learning method, the LSTM (long term short term memory). Other than existing prediction models, the proposed one uses only the cloudiness among the information forecasted from the national meterological forecast center. The future cloudiness is generally announced with four categories and for three-hour intervals. In this work, a daily irradiance pattern is used as an input vector to the LSTM together with that cloudiness information. The proposed model showed an error of 5% for learning and 30% for prediction. This level of error has lower influence on the load prediction in typical building cases.

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References

  1. Zhuang, J., Chen, Y., and Chen, X. A., New Simplified Modeling Method for Model Predictive Control in a Medium-Sized Commercial Building: A Case Study. Build. Environ, Vol. 127, pp. 1-12, 2018. https://doi.org/10.1016/j.buildenv.2017.10.022
  2. Henze, G. P., Felsmann, C., and Knabe, G., Evaluation of Optimal Control for Active and Passive Building Thermal Storage. International Journal of Thermal Sciences, Vol. 43, pp. 173-183, 2004. https://doi.org/10.1016/j.ijthermalsci.2003.06.001
  3. Ferreira, P. M., Ruano, A. E., Silva, S., and Conceicao, E. Z. E., Neural Networks Based Predictive Control for Thermal Comfort and Energy Savings in Public Buildings. Energy and Buildings, Vol. 55, pp. 238-251, 2012. https://doi.org/10.1016/j.enbuild.2012.08.002
  4. Huang, H., Chen, L., and Hu, E., A New Model Predictive Control Scheme for Energy and Cost Savings in Commercial Buildings: An Airport Terminal Building Case Study. Building and Environment, Vol. 89, pp. 203-216. https://doi.org/10.1016/j.buildenv.2015.01.037
  5. Kusiak, A., Li, M., and Tang, F., Modeling and Optimization of HVAC Energy Consumption. Applied Energy, Vol. 87, No.10, pp. 3092-3102, 2010. https://doi.org/10.1016/j.apenergy.2010.04.008
  6. Nguyen, T. T., Yoo, H. J., and Kim, H. M., Analyzing the Impacts of System Parameters on MPC-based Frequency Control for a Stand-alone Microgrid. Energies, Vol. 10, No. 4, p. 417, 2017. https://doi.org/10.3390/en10040417
  7. Afram, A. and Janabi-Sharifi, F., Theory and Applications of HVAC Control Systems-A Review of Model Predictive Control (MPC). Building and Environment, Vol. 72, pp. 343-355. https://doi.org/10.1016/j.buildenv.2013.11.016
  8. Khanmirza, E., Esmaeilzadeh, A., and Markazi, A. H. D., Predictive Control of a Building Hybrid Heating System for Energy Cost Reduction. Applied Soft Computing, Vol. 46, pp. 407-423, 2016. https://doi.org/10.1016/j.asoc.2016.05.005
  9. Jeon, B. K., Kim, E. J., Shin, Y., and Lee, K. H., Learning-Based Predictive Building Energy Model Using Weather Forecasts for Optimal Control of Domestic Energy Systems. Sustainability, Vol. 11, No. 1, pp. 1-16, 2018. https://doi.org/10.1089/sus.2018.29124.upfront
  10. Premalatha, N. and Valan Arasu, A., Prediction of Solar Radiation for Solar Systems by Using ANN Models with Different Back Propagation Algorithms. Journal of Applied Research and Technology, Vol. 14, No. 3, pp. 206-214, 2016. https://doi.org/10.1016/j.jart.2016.05.001
  11. Black, J. N., The Distribution of Solar Radiation Over the Earth's Surface. Archiv fur Meteorologie, Geophysik und Bioklimatologie, Serie B, Vol. 7, No. 2, pp. 165-189, 1956. https://doi.org/10.1007/BF02243320
  12. Samimi, J., Estimation of Height-dependent Solar Irradiation and Application to the Solar Climate of Iran. Solar Energy, Vol. 52, No. 5, pp. 401-409, 1994. https://doi.org/10.1016/0038-092X(94)90117-K
  13. Paltridge, G. W. and Proctor, D., Monthly Mean Solar Radiation Statistics for Australia. Solar Energy, Vol. 18, No. 3, pp. 235-243, 1976. https://doi.org/10.1016/0038-092X(76)90022-0
  14. Daneshyar, M., Solar Radiation Statistics for Iran. Sol. Energy;(United States), Vol. 21, No. 4, 1978.
  15. Premalatha, N. and Valan Arasu, A., Prediction of Solar Radiation for Solar Systems by Using ANN Models with Different Back Propagation Algorithms. Journal of Applied Research and Technology, Vol. 14, No. 3, pp. 206-214, 2016. https://doi.org/10.1016/j.jart.2016.05.001
  16. Lago, J., De Ridder, F., and De Schutter, B., Forecasting Spot Electricity Prices: Deep Learning Approaches and Empirical Comparison of Traditional Algorithms, Applied Energy, Vol. 221, pp. 386-405, 2018. https://doi.org/10.1016/j.apenergy.2018.02.069
  17. Jiang, Y., Computation of Monthly Mean Daily Global Solar Radiation in China Using Artificial Neural Networks and Comparison with Other Empirical Models, Energy, Vol. 34, No. 9, pp. 1276-1283, 2009. https://doi.org/10.1016/j.energy.2009.05.009
  18. Ahmad, A., Anderson, T. N., and Lie, T. T., Hourly global solar irradiation forecasting for New Zealand, Solar Energy, Vol. 122, pp. 1398-1408, 2015. https://doi.org/10.1016/j.solener.2015.10.055
  19. Solmaz, O., Kahramanli, H., Kahraman, A., and Ozgoren, M., Prediction of Daily Solar Radiation Using ANNs for Selected Provinces in Turkey. In International Scientific Conference, pp. 450-456, November, 2010.
  20. Qing, X. and Niu, Y., Hourly day-ahead Solar Irradiance Prediction Using Weather Forecasts by LSTM. Energy, Vol. 148, pp. 461-468, 2018. https://doi.org/10.1016/j.energy.2018.01.177
  21. http://www.kma.go.kr/, Korea Official Meteorological Agency.
  22. https://www.metoffice.gov.uk/, UK Official Meteorological Agency.
  23. https://www.weather.gov/, United States Official Meteorological Agency.
  24. https://weather.gc.ca/, Canadian Official Meteorological Agency.
  25. http://www.kses.re.kr/, The Korean Solar Energy Society.
  26. Lee, L. J., A Study on Fundamental and Application of CNN and RNN. Broadcasting and Media Magazine, Vol. 22, No. 1, pp. 87-95, 2017.
  27. Saito, G., Deep Learning from Scratch, Hanbit Media Inc, 2017.
  28. Baccouche, M., Mamalet, F., Wolf, C., Garcia, C., and Baskurt, A., Sequential Deep Learning for Human Action Recognition. In International Workshop on Human Behavior Understanding, Springer, Berlin, Heidelberg, pp. 29-39, November, 2011.
  29. Kinga, D. and Adam, J. B., A Method for Stochastic Optimization. In International Conference on Learning Representations(ICLR), Vol. 5, 2015.
  30. Kong, D. S., Kwak, Y. H., and Huh, J. H., Artificial Neural Network Based Energy Demand Prediction for the Urban District Energy Planning. Journal of the Architectural Institute of Korea, Vol. 26, No. 2, pp. 221-230, 2010.
  31. Documentation, M., The MathWorks Inc, 2005.
  32. Gutierrez-Corea, F. V., Manso-Callejo, M. A., Moreno-Regidor, M. P., and Manrique-Sancho, M. T., "Forecasting Short-term Solar Irradiance Based on Artificial Neural Networks and Data from Neighboring Meteorological Stations." Solar Energy, Vol. 134, pp. 119-131, 2016. https://doi.org/10.1016/j.solener.2016.04.020
  33. Kim, T. H., Yoo, S. Y., Han, K. H., Kang, H. C., and Yoon, H. I., A Study on Solar Radiation Model for Prediction of Solar Insolation. The Korean Society of Mechanical Engineers, pp. 670-675, 2013.

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