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.
초록· 키워드
오류제보하기
Load forecasting is essential to the economical and the stable power system operations. In general, the forecasting days can be classified into weekdays, weekends, special days and special light-load periods in short-term load forecast. Special light-load periods are the consecutive holidays such as Lunar New Years holidays, Korean Thanksgiving holidays and summer special light-load period. For the weekdays and the weekends forecast, the conventional methods based on the statistics are mainly used and show excellent results for the most part. The forecast algorithms for special days yield good results also but its forecast error is relatively high than the results of the weekdays and the weekends forecast methods. For summer special light-load period, none of the previous studies have been performed ever before so if the conventional methods are applied to this period, forecasting errors of the conventional methods are considerably high. Therefore, short-term load forecast for summer special light-load period have mainly relied on the experience of power system operation experts. In this study, the trends of load profiles during summer special light-load period are classified into three patterns and new forecast algorithms for each pattern are suggested. The proposed method was tested with the last ten years’ summer special light-load periods. The simulation results show the excellent average forecast error near 2%.
목차
Abstract
1. 서론
2. 본론
3. 사례 연구
4. 결론
감사의 글
References
참고문헌 (9)
참고문헌 신청
1. [학술지(정기간행물)] - Kyung-Bin Song / 2003 / Load Forecasting for Holidays Using a Fuzzy Least Squares Linear Regression Algorithm / The Transaction of the KIEE 52 (4) : 233 ~ 237
2. [학술지(정기간행물)] - Kwang-Ho Kim / 2000 / Short-Term Load Forecasting for Special Days in Anomalous Load Conditions Using Neural Networks and Fuzzy Inference Method / IEEE Transaction on Power Systems 15 (2) : 559 ~ 565
3. [학술대회논문] - Saeid Nahi / 2006 / Load Forecasting on Special Days & Holidays in Power Distribution Substation Using Neural & Fuzzy Networks / IEEE International Conference on Computational Intelligence for Modeling Control and Automation : 118 ~
4. [학술대회논문] - I. Aquino / 2007 / Daily Load Forecasting Using Quick Propagation Neural Network with a Special Holiday Encoding / IJCNN : 1935 ~ 1940
5. [학술대회논문] - Yuanzhe Cai / 2011 / Short-term load forecasting for city holidays based on genetic support vector machines / ICECE 2011 : 3144 ~ 3147
2. [학술지(정기간행물)] - Kwang-Ho Kim / 2000 / Short-Term Load Forecasting for Special Days in Anomalous Load Conditions Using Neural Networks and Fuzzy Inference Method / IEEE Transaction on Power Systems 15 (2) : 559 ~ 565
3. [학술대회논문] - Saeid Nahi / 2006 / Load Forecasting on Special Days & Holidays in Power Distribution Substation Using Neural & Fuzzy Networks / IEEE International Conference on Computational Intelligence for Modeling Control and Automation : 118 ~
4. [학술대회논문] - I. Aquino / 2007 / Daily Load Forecasting Using Quick Propagation Neural Network with a Special Holiday Encoding / IJCNN : 1935 ~ 1940
5. [학술대회논문] - Yuanzhe Cai / 2011 / Short-term load forecasting for city holidays based on genetic support vector machines / ICECE 2011 : 3144 ~ 3147
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UCI(KEPA) : I410-ECN-0101-2014-500-003121162