The Improved Forecasting Model for Short-Term Traffic Flow Based on Phase Space Reconstruction

2009 ◽  
Author(s):  
Hui Zhuo ◽  
Limin Jia ◽  
Guoqiang Cai ◽  
Dongmei Liu
Keyword(s):  
Phase Space ◽  
Traffic Flow ◽  
Phase Space Reconstruction ◽  
Forecasting Model ◽  
Short Term

Short-Term Wind Speed Forecasting by Using Chaotic Theory and SVM

2013 ◽  
Vol 300-301 ◽  
pp. 842-847 ◽  
Author(s):  
Cai Hong Zhu ◽  
Ling Ling Li ◽  
Jun Hao Li ◽  
Jian Sen Gao
Keyword(s):  
Phase Space ◽  
Wind Speed ◽  
Wind Farm ◽  
Phase Space Reconstruction ◽  
Percentage Error ◽  
Support Vector ◽  
Forecasting Model ◽  
Particle Swarm Algorithm ◽  
Short Term ◽  
Wind Speed Forecast

The wind speed forecast is the basis of the wind power forecast. The wind speed has the characteristics of random non-smooth so obviously that its precise forecast is extremely difficult. Therefore, a forecasting method based on the theory of chaotic phase-space reconstruction and SVM was put forward in this paper and a forecasting model of Chaotic Support Vector Machine was built. In order to improve the precision and generalization ability, the key parameters in the phase space reconstruction and the key parameters of SVM were carried out joint optimization by using particle swarm algorithm in the paper. Then the optimal parameters were brought into the forecasting model to forecast short-term wind speed. The above method was applied to wind speed forecast of a wind farm in Inner Mongolia, China. In the experiments of computer simulation, the absolute percentage error of forecasting results was only 12.51%, which showed this method was effective for short-term wind speed forecast.

scholarly journals Short-Term Traffic Flow Forecasting Model Based on GA-TCN

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Rongji Zhang ◽  
Feng Sun ◽  
Ziwen Song ◽  
Xiaolin Wang ◽  
Yingcui Du ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Convolutional Neural Network ◽  
Traffic Flow ◽  
Absolute Error ◽  
Forecasting Model ◽  
Average Absolute Error ◽  
Short Term ◽  
Traffic Flow Forecasting ◽  
Fitness Value

Traffic flow forecasting is the key to an intelligent transportation system (ITS). Currently, the short-term traffic flow forecasting methods based on deep learning need to be further improved in terms of accuracy and computational efficiency. Therefore, a short-term traffic flow forecasting model GA-TCN based on genetic algorithm (GA) optimized time convolutional neural network (TCN) is proposed in this paper. The prediction error was considered as the fitness value and the genetic algorithm was used to optimize the filters, kernel size, batch size, and dilations hyperparameters of the temporal convolutional neural network to determine the optimal fitness prediction model. Finally, the model was tested using the public dataset PEMS. The results showed that the average absolute error of the proposed GA-TCN decreased by 34.09%, 22.42%, and 26.33% compared with LSTM, GRU, and TCN in working days, while the average absolute error of the GA-TCN decreased by 24.42%, 2.33%, and 3.92% in weekend days, respectively. The results indicate that the model proposed in this paper has a better adaptability and higher prediction accuracy in short-term traffic flow forecasting compared with the existing models. The proposed model can provide important support for the formulation of a dynamic traffic control scheme.

scholarly journals An Ultrashort-Term Net Load Forecasting Model Based on Phase Space Reconstruction and Deep Neural Network

2019 ◽  
Vol 9 (7) ◽  
pp. 1487 ◽  
Author(s):  
Fei Mei ◽  
Qingliang Wu ◽  
Tian Shi ◽  
Jixiang Lu ◽  
Yi Pan ◽  
...  
Keyword(s):  
Neural Network ◽  
Phase Space ◽  
Deep Neural Network ◽  
Time Series Data ◽  
Load Forecasting ◽  
Phase Space Reconstruction ◽  
Series Data ◽  
Forecasting Model ◽  
Model Based ◽  
Net Load

Recently, a large number of distributed photovoltaic (PV) power generations have been connected to the power grid, which resulted in an increased fluctuation of the net load. Therefore, load forecasting has become more difficult. Considering the characteristics of the net load, an ultrashort-term forecasting model based on phase space reconstruction and deep neural network (DNN) is proposed, which can be divided into two steps. First, the phase space reconstruction of the net load time series data is performed using the C-C method. Second, the reconstructed data is fitted by the DNN to obtain the predicted value of the net load. The performance of this model is verified using real data. The accuracy is high in forecasting the net load under high PV penetration rate and different weather conditions.

Sign in / Sign up

Export Citation Format

Share Document