열화율 기반 태양광 발전량 예측 머신러닝 모델 개발
Development of Machine-Learning Model for Predicting Solar-Power Generation Based on Deterioration Rate
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초록
정부의 ‘제11차 전력수급기본계획 실무안’에 따르면 2038년까지 신재생에너지 발전량 230.8TWh, 발전비중 32.9% 를 제시하였고, 이를 위한 태양광 및 풍력 등 재생에너지 발전 비중은 상승세를 이어 나갈 것으로 예상된다. 하지만, 재생에너지에 의해 생산된 전력은 전력 생산의 변동성이 크기 때문에 발전량을 정확하게 예측할 필요가 있다. 본논문에서는 경기도 성남에 위치한 2.4kW 급 실증단지의 `13년도 데이터를 학습하여 ’14∼’16년도 8월 발전량을 6가지 머신러닝 알고리즘을 활용하여 데이터를 분석하였고, 모델 적합성 평가를 위한 RMSE 및 MBE를 활용하여 실측값과예측값의 차이를 활용해 열화율 기반 발전량 예측 모델에 상관관계를 분석하였다. 정확도가 높은 SVR 모델에 대하여예측 분포도를 비교 분석 하였으며, 예측 결과는 3%이내 임을 확인하였다.
Abstract
According to the government’s "11th Basic Plan for Electricity Supply and Demand," 230.8 TWh of renewable-energy generation and 32.9% of power generation are proposed by 2038, and the proportion of renewable-energy generation, such as solar and wind power, is expected to increase continuously. However, the power generated by renewable-energy sources fluctuates significantly; thus, the power-generation volume must be predicted accurately. In this study, we investigate 2013 data from a 2.4kW demonstration complex located in Seongnam, Gyeonggi-do; analyze the power generation from August 2014 to August 2016 using six machine-learning algorithms; and analyze the correlation indicated by a power-generation prediction model based on the degradation rate, where the difference between actual and predicted values is considered. The prediction distribution is compared and analyzed for a highly accurate model, and the prediction result is confirmed to be within 3%.
참고문헌 (8)
- 11th Basic Electricity Supply and Demand Plan, "Ministry of Trade, Industry and Energy", Working draft of the Korean, 1~8 (2024)
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