Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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A study on comparing short-term wind power prediction models in Gunsan wind farm

군산풍력발전단지의 풍력발전량 단기예측모형 비교에 관한 연구

  • Received : 2013.04.21
  • Accepted : 2013.05.20
  • Published : 2013.05.31
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Abstract

As the needs for alternative energy and renewable energy increase, there has been a lot of investment in developing wind energy, which does not cause air pollution nor the greenhouse gas effect. Wind energy is an environment friendly energy that is unlimited in its resources and is possible to be produced wherever the wind blows. However, since wind energy heavily relies on wind that has unreliable characteristics, it may be difficult to have efficient energy transmissions. For this reason, an important factor in wind energy forecasting is the estimation of available wind power. In this study, Gunsan wind farm data was used to compare ARMA model to neural network model to analyze for more accurate prediction of wind power generation. As a result, the neural network model was better than the ARMA model in the accuracy of the wind power predictions.

최근 신재생에너지와 대체에너지의 필요성이 증가함에 따라 환경오염과 온실효과를 초래하지 않는 풍력에너지 개발에 많은 연구와 투자가 이루어지고 있다. 풍력에너지는 무공해 에너지이며 자원양이 무한대이고 바람이 부는 곳이라면 어디에서든지 전력생산이 가능하다. 그러나 풍력에너지는 바람에 크게 의존하며 불규칙적인 특성이 있어 효율적인 풍력발전이 어렵다는 단점이 있다. 이러한 이유로 풍력발전에 있어서 정확한 풍력발전량 예측은 매우 중요한 요소이다. 본 연구에서는 이러한 풍력발전량의 효율적인 예측을 위해 군산 풍력단지의 자료를 이용해 시계열모형인 ARMA모형과 데이터 마이닝 기법 중 신경망모형을 사용하여 풍력발전량을 예측하고 비교분석 하였다. 그 결과 신경망모형 적합결과가 ARMA모형 적합결과 보다 더 좋은 예측력을 나타내었다.

Keywords

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