최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기한국방재안전학회논문집 = Journal of Korean Society of Disaster and Security, v.14 no.2, 2021년, pp.35 - 42
이병현 (국립강원대학교 방재전문대학원) , 정세진 (강원종합기술연구원) , 김병식 (국립강원대학교 방재전문대학원)
In order to produce electric vehicle demand forecasting information, which is an important element of the plan to expand charging facilities for electric vehicles, a model for predicting electric vehicle demand was proposed using Exponential Smoothing. In order to establish input data for the model,...
Kim, C.-H. (2013). Prediction of Short-Term Electricity Load Using Multi-Seasonal Exponential Smoothing Method. Ulsan: Korea Energy Economics Institute.
Kim, D.-H. (2019). Short-Term Load Forecasting Based on LSTM and CNN. Konkuk University Graduate School. Electric Engineering Department. Master's Dissertation.
Kim, K.-H., Park, R.-J., Cho, S.-W., and Song, K.-B. (2017). 24-Hour Load Forecasting Algorithm Using Artificial Neural Network in Summer Weekdays. Journal of the Korean Institute of Illuminating and Electrical Installation Engineers. 31(12): 113-119.
Ko, C. N. and Lee, C. M. (2016). Short-term Load Forecasting Using SVR (Support Vector Regression)-based Radial Basis Function Neural Network with Dual Extended Kalman Filter. ELSEVIER Energy. 49: 413-422.
Ramanarayanan, T. S., Williams, J. R., Dugas, W. A., Hauck, L. M., and McFarland, A. M. S. (1997). Using APEX to Identify Alternative Practices for Animal Waste Management. ASAE International Meeting. Paper 97-2209. pp. 1-7.
Yang, Y., Che, J., Li, Y., Zhao, Y., and Zhu, S. (2016). An Incremental Electric Load Forecasting Model based on Support Vector Regression. ELSEVIER Energy. 113: 796-808.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
오픈액세스 학술지에 출판된 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.