대한안전경영과학회지 (Journal of the Korea Safety Management & Science)

  • 제19권4호
  • /
  • Pages.157-167
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  • 2017
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  • 1229-6783(pISSN)
  • /
  • 2288-1484(eISSN)
대한안전경영과학회 (Korea Safety Management & Science)
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미세먼지와 기상정보 기반의 AHP 분석을 통하여 태양광 발전소 최적입지선정에 대한 사례연구

A Case Study for Analyzing the Optimal Location for A Solar Power Plant via AHP Analysis with Fine Dust and Weather Information

  • 이건주 (인하대학교 산업경영공학과) ;
  • 이기현 (인하대학교 산업경영공학과) ;
  • 강성우 (인하대학교 산업경영공학과)
  • Lee, Geon-ju (Department of Industrial and Management Engineering, INHA University) ;
  • Lee, Gi-Hyun (Department of Industrial and Management Engineering, INHA University) ;
  • Kang, Sung-Woo (Department of Industrial and Management Engineering, INHA University)
  • 투고 : 2015.04.20
  • 심사 : 2015.06.11
  • 발행 : 2017.12.31
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초록

Solar energy has been known as a successful alternative energy source, however it requires a large area to build power generation facilities compared to other energy sources such as nuclear power. Weather factors such as rainy weather or night time impact on solar power generation because of lack of insolation and sunshine. In addition, solar power generation is vulnerable to external elements such as changes in temperature and fine dust. There are four seasons in the Republic of Korea hereby variations of temperature, insolation and sunshine are broad. Currently factors that cause find dust are continuously flowing in to Korea from abroad. In order to build a solar power plant, a large area is required for a limited domestic land hereby selecting the optimal location for the plant that maximizes the efficiency of power generation is necessary. Therefore, this research analyze the optimal site for solar power generation plant by implementing analytic hierarchy process based on weather factors such as fine dust. In order to extract weather factors that impact on solar power generation, this work conducts a case study which includes a correlation analysis between weather information and power generation.

키워드

참고문헌

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피인용 문헌

  1. 미세먼지의 영향을 고려한 머신러닝 기반 태양광 발전량 예측 vol.28, pp.4, 2017, https://doi.org/10.15266/kerea.2019.28.4.467