지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.8
- 수록면
- 278 - 290 (13page)
- DOI
- 10.21086/ksles.2024.08.31.4.278
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초록· 키워드
오류제보하기
This study aims to develop a reliable solar radiation prediction model by analyzing the similarity of data to reduce the uncertainty of Machine Learning (ML) based predictions. Variable selection was conducted using the Pearson Correlation Coefficient (PCC), and the similarity of the selected variables was measured through Cosine similarity, Euclidean distance, and Manhattan distance analyses. 8 scenarios were established to analyze hourly errors and Q1, Q2, and Q3 statistics. As a result, it was found that higher data similarity leads to increased prediction accuracy and a reduction in the range of error values. Normalization methods such as QuantileTransformer contributed to improving model performance by being less sensitive to data outliers. Notably, Scenario 8 recorded the lowest average prediction error, maintaining consistency in predictions.
목차
Abstract
1. 서론
2. 인공지능에서의 불확실성
3. 연구 방법
4. 예측 결과 및 시나리오 비교 분석
5. 결론
REFERENCES
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