원문정보
초록
영어
In urban management, it is important to precisely forecast the short-term demand for necessary resources, including water, electric power, and gas. Although a variety of prediction models have been proposed in literature, the underlying defects and limitations confine the effectiveness and forecasting precision of these models. In this paper, the short-term prediction problem is modeled as a non-linear multivariate regression problem, which is solved by support vector regression (SVR). The parameters in SVR are optimized by artificial fish-swarm algorithm (AFSA). The proposed prediction model (termed SVR-AFSA) overcomes the defects of existing prediction models, thus promoting forecasting precision. In order to verify the effectiveness and prediction precision of SVR-AFSA, this paper conducts experiments on a real dataset of two-month hourly water consumption. It also compares SVR-AFSA with two commonly adopted models, i.e., traditional BP neural network, and SVR optimized by grid method (SVR-grid). The experiments results show that SVR-AFSA outperforms these two models in prediction precision in terms of mean squared error (MSE) and mean absolute percentage error (MAPE).
목차
1. Introduction
2. Related Work
2.1. Prediction and Regression
2.2. Support Vector Machine (SVM)
2.3. Swarm Intelligence (SI) and Artificial Fish-Swarm Algorithm (AFSA)
3. A Prediction Model Based on SVR Optimized by AFSA
3.1. A Prediction Model for Forecasting Short-Term Urban Warter Consumption
3.2. ε-SVR
3.3. Parameter Optimization by AFSA
4. Experiments and Analyses
4.1. Dataset Description
4.2. Parameters Setting
4.3. Experimental Results and Analyses
5. Conclusion and Future Work
Acknowledgments
References