A Multi-Applications Comprehensive Traffic Prediction model for the electric power data network

2016 ◽  
Author(s):  
Yu Zhou ◽  
Ningzhe Xing ◽  
Yutong Ji ◽  
Wenjing Li ◽  
Shao-Yong Guo
Keyword(s):  
Prediction Model ◽  
Electric Power ◽  
Traffic Prediction ◽  
Data Network

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.

scholarly journals Prediction model for day-ahead solar insolation using meteorological data for smart grid

2019 ◽  
Vol 111 ◽  
pp. 06040
Author(s):  
Min Hee Chung
Keyword(s):  
Power Generation ◽  
Prediction Model ◽  
Electric Power ◽  
Meteorological Data ◽  
Weather Data ◽  
Mean Bias Error ◽  
The Internet ◽  
Bias Error ◽  
Photovoltaic Systems ◽  
Internet Platform

In the overseas market, power generation and energy service companies have been engaged in the business of providing personalized trading services for the production of electric power through the Internet platform. This is, so that the electric power sharing system between individuals is being developed through the Internet platform. The prediction of insolation is essential for the prediction of power generation for photovoltaic systems. In this study, we present a prediction model for insolation from data observed at the Meteorological Administration. We also present basic data for the development of the insolation prediction model through meteorological parameters provided in future weather forecasts. The prediction model presented is for five years of observation of weather data in the Seoul area. The proposed model was trained by using the feed-forward neural networks, taking into account the daily climatic elements. To validate the reliability of the model, the root mean square error (RMSE), mean bias error (MBE), and mean absolute error (MAE) were used for estimation. The results of this study can be used to predict the solar power generation system and to provide basic information for trading generated output by photovoltaic systems.

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