Korean Journal of Construction Engineering and Management (한국건설관리학회논문집)

Korea Institute of Construction Engineering and Management (한국건설관리학회)
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Multiple Linear Regression Analysis of PV Power Forecasting for Evaluation and Selection of Suitable PV Sites

태양광 발전소 건설부지 평가 및 선정을 위한 선형회귀분석 기반 태양광 발전량 추정 모델

  • Heo, Jae (Department of Civil and Environmental Engineering, Hanyang University) ;
  • Park, Bumsoo (Department of Civil and Environmental Engineering, Hanyang University) ;
  • Kim, Byungil (Department of Civil Engineering, Andong Nataional University) ;
  • Han, SangUk (Department of Civil and Environmental Engineering, Hanyang University)
  • Received : 2019.10.30
  • Accepted : 2019.11.12
  • Published : 2019.11.30
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Abstract

The estimation of available solar energy at particular locations is critical to find and assess suitable locations of PV sites. The amount of PV power generation is however affected by various geographical factors (e.g., weather), which may make it difficult to identify the complex relationship between affecting factors and power outputs and to apply findings from one study to another in different locations. This study thus undertakes a regression analysis using data collected from 172 PV plants spatially distributed in Korea to identify critical weather conditions and estimate the potential power generation of PV systems. Such data also include solar radiation, precipitation, fine dust, humidity, temperature, cloud amount, sunshine duration, and wind speed. The estimated PV power generation is then compared to the actual PV power generation to evaluate prediction performance. As a result, the proposed model achieves a MAPE of 11.696(%) and an R-squred of 0.979. It is also found that the variables, excluding humidity, are all statistically significant in predicting the efficiency of PV power generation. According, this study may facilitate the understanding of what weather conditions can be considered and the estimation of PV power generation for evaluating and determining suitable locations of PV facilities.

최근 태양광의 발전 효율성과 경제성이 높은 발전소 부지를 확보하기 위해 특정 지역을 대상으로 태양광 발전량을 정확히 예측하기 위한 연구들이 수행되었다. 하지만 국내의 경우 기존 발전량 데이터가 부족함에 따라 정확한 발전량 추정에 문제가 발생할 수 있으며, 우리나라 기준으로 어떠한 기상조건을 나타내는 변수가 태양광발전에 어느 정도의 영향을 미치는지에 대한 연구가 부족한 실정이다. 따라서 본 연구는 지형 효과를 충분히 고려하여 제작된 태양복사에너지 지도와 미세먼지와 같은 기상조건을 추가하여 태양광 발전량 추정 회귀모델을 제시하고, 추정된 발전량과 실제 발전량을 비교 분석하였다. 그 결과, 습도를 제외한 태양복사에너지, 온도, 풍속, 운량, 강수량, 일조시간, 미세먼지가 발전 효율에 통계적으로 유의미한 영향을 미치는 것으로 나타났으며, 회귀 분석모델을 통해 추정된 발전량과 실제 발전량을 비교 분석하여 RMSE는 48.261(h), nRMSE는 1.592(%), MAPE는 11.696(%), 그리고 는 0.979이 도출되었다. 이러한 결과는 국내 태양광 발전 부지를 평가함에 있어서 고려해야 하는 중요한 기상 조건 등 태양광 발전량 추정 모델을 설계하는데 활용할 수 있으며, 이를 바탕으로 태양광 발전소 건설 부지를 선정함에 있어 중요한 지표인 발전량을 정확히 추정하는데 기여할 것으로 사료된다.

Keywords

References

  1. Aly, A., Jensen, S.S., and Pedersen, A.B. (2017). "Solar power potential of Tanzania: Identifying CSP and PV hot spots through a GIS multicriteria decision making analysis." Renew Energy, 113, pp. 159-75. https://doi.org/10.1016/j.renene.2017.05.077
  2. Al Garni, H.Z., and Awasthi, A. (2017). "Solar PV power plant site selection using a GIS-AHP based approach with application in Saudi Arabia." Applied Energy, 206, pp. 1225-1240. https://doi.org/10.1016/j.apenergy.2017.10.024
  3. Cha, W.C., Park, J.H., Cho, U.R., and Kim, J.C. (2014). "Design of generation efficiency fuzzy prediction model using solar power element data." The Transaction of The Korean Institute of Electrical Engineers, 63(10), pp. 1423-1427. https://doi.org/10.5370/KIEE.2014.63.10.1423
  4. Charabi, Y., and Gastli, A. (2011). "PV site suitability analysis using GIS-based spatial fuzzy multi-criteria evaluation." Renew Energy, 36(9), pp. 2554-2561. https://doi.org/10.1016/j.renene.2010.10.037
  5. Darwish, Z.A., Kazem, H.A., Sopian, K., AI-Goul, M.A., and Alawadhi, H. (2015). "Effect of dust pollution type on photovoltaic performance." Renewable and Sustainable Energy Reviews, 41, pp. 735-744. https://doi.org/10.1016/j.rser.2014.08.068
  6. Han, J.M., Choi, S., Kim, S., and Jung, Y. (2018). "A study on the contamination of photovoltaic cells by fine dust in the air." Trans. of Korean Hydrogen and New Energy Society, 29(3), pp. 292-298. https://doi.org/10.7316/KHNES.2018.29.3.292
  7. Huld, T., and Amillo, A. (2015). "Estimating PV module performance over large geographical regions: The role of irradiance, air temperature, wind speed and solar spectrum." Energies, 8(6), pp. 5159-5181. https://doi.org/10.3390/en8065159
  8. International Finance Corporation (IFC) (2019). Utility-Scale Solar Photovoltaic Power Plants: A Project Developer's Guide. [accessed 30 March 2019].
  9. Jang, T.M,, and Kim, G.N. (2018). "A study on the Economic of photovoltaic power generation in the military free space." Bulletin of the Korea Photovoltaic Society, 4(2), pp. 62-70.
  10. Jung, J., Han, S., and Kim, B. (2019). "Digital numerical map-oriented estimation of solar energy potential for site selection of photovoltaic solar panels on national highway slopes." Applied Energy, 242, pp. 57-68. https://doi.org/10.1016/j.apenergy.2019.03.101
  11. Kim, J., Kim, T., Lee, J., and Ham, K.S. (2019). "Predicting 24-hours ahead photovoltaic power output using forecast information." In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, pp. 1462-1464.
  12. Lee, K., and Kim, W.J. (2016). "Forecasting of 24hours ahead photovoltaic power output using support vector regression." J. Korean Inst. Inf. Technol, 14, pp. 175-183.
  13. Mellit, A., Benghanem, M., and Kalogirou, S.A. (2006) "An adaptive wavelet-network model for forecasting daily total solar-radiation." Applied Energy, 83(7), pp. 705-722. https://doi.org/10.1016/j.apenergy.2005.06.003
  14. Noh, Y., Lim, Y., Yoo, W., and Kim, J. (2013). "A study on the estimating power generation for a PV solar plant using weather forecast." Society of Korea Industrial and Systems Engineering, pp. 161-166.
  15. Sahin, M., Kaya, Y., and Uyar, M. (2013). "Comparison of ANN and MLR models for estimating solar radiation in Turkey using NOAA/AVHRR data." Advances in Space Research, 51(5), pp. 891-904. https://doi.org/10.1016/j.asr.2012.10.010
  16. Song, J., Lee, S., and Jeong. Y. (2014). "Analysis of prediction model for solar power generation." Journal of Digital Convergence, 12(3), pp. 243-248. https://doi.org/10.14400/JDC.2014.12.3.243
  17. Yang, I.S., and An, H.S. (2018). "Optimal location analysis in terms of efficiency for solar energy facilities." The Journal of the Korea Contents Association, 18(7), pp. 656-664. https://doi.org/10.5392/JKCA.2018.18.07.656
  18. Zaharim, A., Razali, A.M., Gim, T.P., and Sopian, K. (2009). "Time series analysis of solar radiation data in the tropics." European Journal of Scientific Research, 25(4), pp. 672-678.