Prediction of elevator traffic flow based on SVM and phase space reconstruction

To make elevator group control system better follow the change of elevator traffic flow （ETF） in order to adjust the control strategy, the prediction method of support vector machine （SVM） in combination with phase space reconstruction has been proposed for ETF. Firstly, the phase space reconstruction for elevator traffic flow time series （ETFTS） is processed. Secondly, the small data set method is applied to calculate the largest Lyapunov exponent to judge the chaotic property of ETF. Then prediction model of ETFTS based on SVM is founded. Finally, the method is applied to predict the time series for the incoming and outgoing passenger flow respectively using ETF data collected in some building. Meanwhile, it is compared with RBF neural network model. Simulation results show that the trend of factual traffic flow is better followed by predictive traffic flow. SVM algorithm has much better prediction performance. The fitting and prediction of ETF with better effect are realized.