단기 물 수요예측 시뮬레이터 개발과 예측 알고리즘 성능평가 = Development of Water Demand Forecasting Simulator and Performance Evaluation

다국어 초록 (Multilingual Abstract)

Generally, treated water or raw water is transported into storage reservoirs which are receiving facilities of local governments from multi-regional water supply systems. A water supply control and operation center is operated not only to manage the water facilities more economically and efficiently but also to mitigate the shortage of water resources due to the increase in water consumption.
To achieve the goal, important information such as the flow-rate in the systems, water levels of storage reservoirs or tanks, and pump-operation schedule should be considered based on the resonable water demand forecasting. However, it is difficult to acquire the pattern of water demand used in local government, since the operating information is not shared between multi-regional and local water systems. The pattern of water demand is irregular and unpredictable. Also, additional changes such as an abrupt accident and frequent changes of electric power rates could occur. Consequently, it is not easy to forecast accurate water demands. Therefore, it is necessary to introduce a short-term water demands forecasting and to develop an application of the forecasting models.
In this study, the forecasting simulator for water demand is developed based on mathematical and neural network methods as linear and non-linear models to implement the optimal water demands forecasting. It is shown that MLP(Multi-Layered Perceptron) and ANFIS(Adaptive Neuro-Fuzzy Inference System) can be applied to obtain better forecasting results in multi-regional water supply systems with a large scale and local water supply systems with small or medium scale than conventional methods, respectively

참고문헌 (Reference)

1 최기선, "데이터 마이닝과 칼만필터링에 기반한 단기 물 수요예측 알고리즘" 제어·로봇·시스템학회 15 (15): 1056-1061, 2009

2 Box,G.E.P, "Time Series Analysis" Forecasting and Control 1970

3 Benito Fernandez, "Nonlinear Dynamics System Identification using Artificial Neural Networks" 2 : 133-142, 1990

4 Barto Kosko, "Neural Networks and Fuzzy Systems" Prentice Hall 1992

5 Ramesh Sharda, "Neural Networks As Forecasting Experts: An Empirical Test" 2 : 491-494, 1990

6 S. Chenthur Pandian, "Fuzzy approach for short term load forecasting" 76 : 541-548, 2006

7 H. S. Hwang, "Fuzzy GMDH-type neural network model and its application to forecasting of mobile communication" 50 : 450-457, 2006

8 J. L. Torres, "Forecast of hourly average wind speed with ARMA models in Navarre(Spain)" 79 : 65-77, 2005

9 G.B. Huang, "Extreme learning machine: Theory and applications" 70 : 489-501, 2006

10 Andres S. Weigend, "Back-Propagation, Weight-Elimination and Time Series Prediction" 105-116, 1990

11 J.S. Roger Jang, "ANFIS: Adaptive-Network-Based Fuzzy Inference System" 23 : 665-685, 1993

12 R.E. Kalman, "A New Approach to Linear Filtering and Prediction Problems" 82 : 35-45, 1960