Traffic Prediction Based on Grey Model Optimized by Buffer Operator and PSO in Communication Network for Electric Power

2013 ◽  
Vol 397-400 ◽  
pp. 1994-1998
Author(s):  
Run Ze Wu ◽  
Ying He ◽  
Liang Rui Tang
Keyword(s):  
Communication Network ◽  
Prediction Model ◽  
Electric Power ◽  
Fitness Function ◽  
Prediction Method ◽  
Grey Model ◽  
Traffic Prediction ◽  
Prediction Result ◽  
Correlation Degree ◽  
Improved Model

To meet the requirements of planning and to improve accuracy and stability of traffic prediction model in the communication network for electric power, a traffic prediction method based on grey model optimized by buffer operator and particle swarm optimization (PSO) is proposed in this paper. Variable weights buffer operators are implemented for preprocessing traffic data to enhance the adaptability of gray prediction model. Taking the maximum grey correlation degree between prediction series and true series as objective function, based on the search ability of PSO, the fitness function is founded, which can determine the optimal parameters of gray model. Applying the improved model to traffic prediction in communication network for electric power, a new prediction result is drawn. The prediction result shows that the improved model has higher prediction accuracy compared with the traditional GM (1, N) model.

Prediction Based on PSO-SVM in Power Communication Network Traffic

2014 ◽  
Vol 602-605 ◽  
pp. 2889-2892
Author(s):  
Zhen Dong Zhao ◽  
Rui Ju Xiao ◽  
Meng Meng Pei ◽  
Yi Zhou
Keyword(s):  
Communication Network ◽  
Electric Power ◽  
Network Traffic ◽  
Prediction Method ◽  
Parameters Optimization ◽  
Traffic Prediction ◽  
Model Parameters ◽  
Economy Running ◽  
Important Basis

Power communication network traffic prediction is important basis of safely assigning and economically running. The forecasting precision will directly affect the reliability, economy running and supplying power quality of power system. Paper first expounds the electric power communication network traffic prediction research present situation, summarized the characteristics of the forecast and the influencing factors, summarizes the commonly used method, is put forward to the return of the electric power communication network traffic based on libsvm prediction method, and the PSO (particle swarm optimization) algorithm is adopted to model parameters optimization, with the test set error as the decision, based on the optimization of model parameters, choice, makes the prediction precision is improved.

Traffic Prediction Method for GEO Satellites Combining ARIMA Model and Grey Model

2019 ◽  
Vol 25 (1) ◽  
pp. 65-69 ◽  
Author(s):  
Jian Zhou ◽  
Qidong Yang ◽  
Xiaofei Zhang ◽  
Chong Han ◽  
Lijuan Sun
Keyword(s):  
Arima Model ◽  
Prediction Method ◽  
Grey Model ◽  
Traffic Prediction ◽  
Geo Satellites

Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Input Variables ◽  
Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

scholarly journals Tensor Decomposition for Spatial—Temporal Traffic Flow Prediction with Sparse Data

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6046
Author(s):  
Funing Yang ◽  
Guoliang Liu ◽  
Liping Huang ◽  
Cheng Siong Chin
Keyword(s):  
Traffic Flow ◽  
Prediction Method ◽  
Sparse Data ◽  
Time Of Day ◽  
Traffic Prediction ◽  
Traffic Surveillance ◽  
Data Sparsity ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Sparsity Problem

Urban transport traffic surveillance is of great importance for public traffic control and personal travel path planning. Effective and efficient traffic flow prediction is helpful to optimize these real applications. The main challenge of traffic flow prediction is the data sparsity problem, meaning that traffic flow on some roads or of certain periods cannot be monitored. This paper presents a transport traffic prediction method that leverages the spatial and temporal correlation of transportation traffic to tackle this problem. We first propose to model the traffic flow using a fourth-order tensor, which incorporates the location, the time of day, the day of the week, and the week of the month. Based on the constructed traffic flow tensor, we either propose a model to estimate the correlation in each dimension of the tensor. Furthermore, we utilize the gradient descent strategy to design a traffic flow prediction algorithm that is capable of tackling the data sparsity problem from the spatial and temporal perspectives of the traffic pattern. To validate the proposed traffic prediction method, case studies using real-work datasets are constructed, and the results demonstrate that the prediction accuracy of our proposed method outperforms the baselines. The accuracy decreases the least with the percentage of missing data increasing, including the situation of data being missing on neighboring roads in one or continuous multi-days. This certifies that the proposed prediction method can be utilized for sparse data-based transportation traffic surveillance.

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