Earticle

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

Abstract
 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

키워드

저자

• GuiPing Wang [ College of Computer Science, Chongqing University, Chongqing, China, College of Software Engineering, Chongqing University, Chongqing, China ]
• ShuYu Chen [ College of Computer Science, Chongqing University, Chongqing, China, College of Software Engineering, Chongqing University, Chongqing, China ]
• Jun Liu [ College of Computer Science, Chongqing University, Chongqing, China, College of Software Engineering, Chongqing University, Chongqing, China ]
• TianShu Wu [ College of Computer Science, Chongqing University, Chongqing, China, College of Software Engineering, Chongqing University, Chongqing, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605