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NTIS 바로가기정보처리학회논문지. KIPS transactions on software and data engineering. 소프트웨어 및 데이터 공학, v.8 no.2, 2019년, pp.67 - 78
문지훈 (고려대학교 전기전자공학과) , 박성우 (고려대학교 전기전자공학과) , 황인준 (고려대학교 전기전자공학과)
Accurate electric load forecasting is very important in the efficient operation of the smart grid. Recently, due to the development of IT technology, many works for constructing accurate forecasting models have been developed based on big data processing using artificial intelligence techniques. The...
핵심어 | 질문 | 논문에서 추출한 답변 |
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MLP의 은닉층 수가 증가하면 우수한 성능을 나타내지만 지나치게 늘리는 것이 적합하지 않는 이유는? | 일반적으로 MLP의 은닉층 수가 증가하면 우수한 성능을 기대할 수 있다. 하지만 학습 과정에서 과적합 문제가 자칫 발생할수 있으므로, 지나치게 은닉층의 수를 늘리는 것은 적합하지 않다. 은닉층의 수는 ANN 또는 SNN(Shallow Neural Network)의 구성인 1층부터 DNN의 구성인 2층에서 4층까지의 모델을 각기 구성하여 각 예측 모델에 대한 예측 정확성을 통해 최적의 MLP 구조를 검출한다. | |
스마트 그리드란? | 최근 스마트 그리드(Smart Grid)는 전 세계적으로 문제가 되는 온실가스 배출 및 에너지 부족 문제 등에 대한 해결책으로 많은 주목을 받고 있다[1-5]. 스마트 그리드는 ICT(Information and Communication Technology)를 기존 전력 그리드에 결합하여 전력 공급 업체와 소비자 간에 실시간 정보를 교환함으로써, 에너지 효율성을 최적화하는 차세대 지능형 전력 그리드이다. 구체적으로, 스마트 그리드의 핵심 구성요소 중 하나인 에너지관리시스템(Energy Management System, EMS) 은 냉방기기 사용량, 조명 사용량 등과 같은 스마트 그리드 시스템 내에서 에너지 소비와 관련된 데이터를 분석하여 수요 측면에서 에너지 절감을 위한 방안을 찾아 에너지를 절약한다 [5]. | |
단기 전력 수요 예측은 무엇으로 인해 정확하게 예측하는 것이 어려운가? | 여기서, 하루 1시간 이하의 예측을 수행하는 VSTLF 또는 STLF은 일일 스마트 그리드 스케줄링을 위해 필수적인 절차이며, 최대수요전력 대응을 포함한 경제적 이득 발생을 기대할 수 있다. 그러나 단기 전력 수요 예측은 건물의 복잡한 전력수요 패턴과 불확실한 외부 요인들로 인한 수요의 잦은 변동으로 인해 정확하게 예측하는 것은 쉽지 않다[2]. 이러한 변동에 영향을 미치는 요인으로 건물의 용도, 시간대, 전기요금 체계 및 특별 행사, 거주자의 일정, 기후 조건(기온, 습도 등), 조명 또는 공조(Heating, Ventilation and Air Conditioning) 시스템과 같은 하위 수준의 시스템 구성 요소들이 있다[11]. |
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