Journal of the Korean Institute of Illuminating and Electrical Installation Engineers (조명전기설비학회논문지)

The Korean Institute of IIIuminating and Electrical Installation Engineers (한국조명전기설비학회)
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A Study on Solar Power Generation Efficiency Analysis according to Latitude and Altitude

위도와 해발높이에 따른 태양광발전 효율 분석 연구

  • Received : 2014.08.06
  • Accepted : 2014.09.13
  • Published : 2014.10.31
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Abstract

To solve the problem of conventional fossil energy, utilization of renewable energy is growing rapidly. Solar energy as an energy source is infinite, and a variety of research is being conducted into its utilization. To change solar energy into electrical energy, we need to build a solar power plant. The efficiency of such a plant is strongly influenced by meteorological factors; that is, its efficiency is determined by solar radiation. However, when analyzing observed generation data, it is clear that the generated amount is changed by various factors such as weather, location and plant efficiency. In this paper, we proposed a solar power generation prediction algorithm using geographical factors such as latitude and elevation. Hence, changes in generated amount caused by the installation environment are calculated by curve fitting. Through applying the method to calculate this generation amount, the difference between real generated amount is analyzed.

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References

  1. Doo-Hyun Kim, Jin-O Kim, "The solar power with weather and generator scheduling," KIEE Summer Conference 2008, pp 131, 2008.
  2. Il-Ryong Lee, In-Su Bae, Chang-Ho Jung, Jin-O Kim, Hun Shim, "Photovoltaic Generation System Output Forecasting using Irradiance Probability Distribution Function," KIEE Summer Conference 2004 A, pp 548-550, 2004.
  3. M. Detyniecki, C. Marsala, A. Krishnan, M. Siegel, "Weather-based solar energy prediction," WCCI 2012 IEEE world cong. on computational intelligence, pp 1-7, June, 2012.
  4. Kim Dong-Su, Shin U-Cheol, Yoon Jong-Ho, "Annual energy yield prediction of building added PV system depending on the installation angle and in Korea," KIEAE Conference 2014, Vol 4, No. 1, pp 67-74, 2014. 2.
  5. Ju Jai-Wook, Kim Han-Soo, Oh Se-Jin, Lee Min-Suk, Choi Jeong-Min, "A Study on the Difference of Regional Electricity and Economic Comparative Valuation of the Photovoltaic System ," KIAEBS Annual Spring Confernce 2008, pp 137-140, 2008. 4.
  6. A. Prastawa, R. Dalimi, "New Approach on Renewable Energy Solar Power Prediction in indonesia based on Artificial Neural Network technique: Southern region of Sulawesi island study case," 2013 International Conference on Quality in Research, pp 166-169, 2013. 6.
  7. http://www.pvsyst.com
  8. http://www.retscreen.net/ang/software_and_data.php
  9. X. yang, F. Jiang, H. Liu, "Short-Term Solar Radiation Prediction based on SVM with similar data," Renewable Power Generation Conference 2013, 2nd IET, pp 1-4, 2013, 9.
  10. Wang-Cheol Cha, Joung-Ho Park, Uk-Rae Cho, Jae-Cheol Kim, "A Study on Prediction of Photovoltaic Generation Amount through Utilizing Geography, Terrain, Weather Data", KIIEE Annual Spring Conference 2014, pp 211-212, 2014.5.
  11. Wang-cheol Cha, Uk-rae Cho, Joung-ho Park, Jae-Cheol Kim, "A Study on Prediction of Photovoltaic Generation Efficiency through Utilizing Quantity of Solar Radiation Data", KIEE Summer Conference 2014, pp 1114-1115, 2014.7.
  12. http://www.kredc.net
  13. Korea Meteorological Administration Seoul, Republic of Korea, 1981-2010 Climatological normals of Korea, 2011.
  14. Korea Meteorological Administration Seoul, Republic of Korea, Weather resource analysis report for optimal use of solar energy, 2008. 12. 3.

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