Research on Forecasting Method of Urban Water Demand Based on Fuzzy Theory

Liu, Hongbo; Deng, Tegang; Zhang, Hongwei

doi:10.1109/fskd.2009.639

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…Although many prediction models have been proposed in literature, including multivariate regression [2], artificial neural network [2], [3], [4], Fuzzy model [5], and grey model [6]. Usually, these models are faced with problems including low prediction The original SVM algorithm was invented by Vladimir N. Vapnik and the current standard incarnation (soft margin SVM) was proposed by Corinna Cortes and Vapnik in 1993 and published in 1995 [9].…”

Section: Prediction and Regressionmentioning

confidence: 99%

“…x (4) : The actual hourly water consumption of the i-th hour in the last day. x (5) : The actual hourly water consumption of the (i-2)-th hour in the forecasting weekday. x (6) : The actual hourly water consumption of the (i-1)-th hour in the forecasting weekday.…”

Section: A Prediction Model For Forecasting Short-term Urban Warter Cmentioning

confidence: 99%

“…In recent years, many prediction models, including multivariate regression [2], artificial neural network [2], [3], [4], Fuzzy model [5], and grey model [6], are proposed in literature. The application fields of these models differ, depending on the forecast variable, its periodicity, and the forecast horizon [1].…”

Section: Introductionmentioning

confidence: 99%

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A Short-Term Prediction Model Based on Support Vector Regression Optimized by Artificial Fish-Swarm Algorithm

Wang¹,

Chen²,

Liu³

et al. 2015

IJCA

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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 shortterm 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).

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