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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2010.12
- 수록면
- 311 - 318 (8page)
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In the daily multi-reservoir operating problem, monthly storage targets can be used as principal operational guidelines. In this study, we tested the use of a simple back-propagation Artificial Neural Network (ANN) model to derive monthly storage guideline for daily Coordinated Multi-reservoir Operating Model (CoMOM) of the Han-River basin. This approach is based on the belief that the optimum solution of the daily CoMOM has a good performance, and the ANN model trained with the results of daily CoMOM would produce effective monthly operating guidelines. The optimum results of daily CoMOM is used as the training set for the back-propagation ANN model, which is designed to derive monthly reservoir storage targets in the basin. For the input patterns of the ANN model, we adopted the ratios of initial storage of each dam to the storage of Paldang dam, ratios of monthly expected inflow of each dam to the total inflow of the whole basin, ratios of monthly demand at each dam to the total demand of the whole basin, ratio of total storage of the whole basin to the active storage of Paldang dam, and the ratio of total inflow of the whole basin to the active storage of the whole basin. And the output pattern of ANN model is the optimal final storages that are generated by the daily CoMOM. Then, we analyzed the performance of the ANN model by using a real-time simulation procedure for the multi-reservoir system of the Han-river basin, assuming that historical inflows from October 1st, 2004 to June 30th, 2007 (except July, August, September) were occurred. The simulation results showed that by utilizing the monthly storage target provided by the ANN model, we could reduce the spillages, increase hydropower generation, and secure more water at the end of the planning horizon compared to the historical records.
목차
1. 서론
2. 신경망 기법
3. 역전파 인공 신경망 모형을 활용한 저수지군 월간 운영 기준 도출
4. 모형수행 결과
5. 결론 및 향후 연구과제
참고문헌
참고문헌 (23)
참고문헌 신청
1. [학술지(정기간행물)] - Bhaskar,N.R. / 1980 / Derivation of Monthly Reservoir Release Policies / Water Resources Research 16 (6) : 987 ~ 993
2. [학술지(정기간행물)] - Chandramouli,V. / 2001 / Multireservoir Modeling with Dynamic Programming and Neural Networks / Journal of Water Resources Planning and Management 127 (2) : 89 ~ 98
3. [학술지(정기간행물)] - Clark,E.J / 1950 / New York Control Curves / Journal of American Water Works Association 42 (9) : 823 ~ 827
4. [학술지(정기간행물)] - Govindaraju,R.S / 2000 / Artificial Neural Networks in Hydrology, Ⅰ: Preliminary Concepts / Journal of Hydrologic Engineering 5 (2) : 115 ~ 123
5. [학술지(정기간행물)] - Govindaraju,R.S / 2000 / Artificial Neural Networks in Hydrology, Ⅱ : Hydrologic Applications / Journal of Hydrologic Engineering 5 (2) : 124 ~ 137
2. [학술지(정기간행물)] - Chandramouli,V. / 2001 / Multireservoir Modeling with Dynamic Programming and Neural Networks / Journal of Water Resources Planning and Management 127 (2) : 89 ~ 98
3. [학술지(정기간행물)] - Clark,E.J / 1950 / New York Control Curves / Journal of American Water Works Association 42 (9) : 823 ~ 827
4. [학술지(정기간행물)] - Govindaraju,R.S / 2000 / Artificial Neural Networks in Hydrology, Ⅰ: Preliminary Concepts / Journal of Hydrologic Engineering 5 (2) : 115 ~ 123
5. [학술지(정기간행물)] - Govindaraju,R.S / 2000 / Artificial Neural Networks in Hydrology, Ⅱ : Hydrologic Applications / Journal of Hydrologic Engineering 5 (2) : 124 ~ 137
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UCI(KEPA) : I410-ECN-0101-2013-530-002596045