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

Abstract

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).

Wind Power Probability Density Prediction Based on Quantile Regression Model of Dilated Causal Convolutional Neural Network

“…In addition, it can prevent and eliminate wind power ramp and other wind power events posing a significant threat to the power grid [1][2] With a statistical learning model, stroke power prediction is divided into point (deterministic) prediction and interval (uncertain) prediction. Currently, point prediction methods mainly include support vector machines and neural networks [3][4][5][6][7] . However, a deterministic prediction cannot quantitatively describe the uncertainty of wind power.…”