The method of short-term load forecasting based on the RBF neural network

2005 ◽  
Author(s):  
Yan Lu ◽  
Xin Lin ◽  
Weifu Qi
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting

An Improved RBF Neural Network for Short Term Load Forecasting

2014 ◽  
Vol 1008-1009 ◽  
pp. 709-713 ◽  
Author(s):  
Chuang Li ◽  
Zhi Qiang Liang ◽  
Min You Chen
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Discriminant Function ◽  
Neural Network Model ◽  
Rbf Neural Network ◽  
Load Forecasting ◽  
Static Model ◽  
Short Term ◽  
Dynamic Neural Network ◽  
Short Term Load Forecasting

Neural network is widely used in the load forecasting area,but the traditional methods of load forecasting usually base on static model,which cannot change as time goes on. And the accuracy is worse and worse. To solve the problem, a dynamic neural network model for load forecasting is proposed .By way of introduce Error discriminant function, to control the error of load forecasting and dynamically modify the predicting model. Through the contrast of the short-term load forecasting result based on static neural network model and dynamic neural network model proposed, the error of load forecasting is decrease effectively.

RBF Neural Network and ANFIS-Based Short-Term Load Forecasting Approach in Real-Time Price Environment

2008 ◽  
Vol 23 (3) ◽  
pp. 853-858 ◽  
Author(s):  
Zhang Yun ◽  
Zhou Quan ◽  
Sun Caixin ◽  
Lei Shaolan ◽  
Liu Yuming ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Rbf Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Time Price

scholarly journals Research on Ultra-Short-Term Load Forecasting of Distribution Network Based on Fuzzy Clustering and RBF Neural Network

2020 ◽  
Vol 213 ◽  
pp. 03002
Author(s):  
Guozhen Ma ◽  
Po Hu ◽  
Yunjia Wang ◽  
Yongli Wang ◽  
Chengcong Cai ◽  
...  
Keyword(s):  
Neural Network ◽  
Fuzzy Clustering ◽  
Rbf Neural Network ◽  
Distribution Network ◽  
Load Forecasting ◽  
Current Load ◽  
Short Term ◽  
Forecast Period ◽  
Short Term Load Forecasting ◽  
Load Characteristics

In order to solve the diversification of the load characteristics of the distribution network due to the difference in the electric structure and the electricity consumption habits of users, the calculation accuracy of the forecast model is difficult to meet the actual demand. In this paper, through in-depth study of the characteristics of ultra-short-term load, an ultra-short-term load forecasting model based on fuzzy clustering and RBF neural network (FCM-RBF) is constructed. The model not only considers the historical load characteristics of locally similar days, but also considers the current load characteristics of the forecast days. The load on a locally similar day can well reflect the overall trend of the predicted load; the current load on the forecast day can well reflect the changing law of real-time data during the forecast period and some random factors in the forecast period. Finally, a power grid load in a certain area of southwestern China is selected as an example to verify the effectiveness and accuracy of the proposed method.

Short Term Load Forecasting Based on Improved RBF Neural Network

2013 ◽  
Vol 860-863 ◽  
pp. 2610-2613
Author(s):  
Hong Zhang ◽  
Zhi Guo Lei ◽  
Jian Guo ◽  
Zhao Yu Pian
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Load Forecasting ◽  
Weather Conditions ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Short Term Load Forecasting ◽  
Radial Basis

An improved radial basis function neural network is proposed that preprocessing is the key to improving the precision of short-term load forecasting. This paper presents a new model which is based on classical RBF neural network, combine the GA-optimized SVM radial basis function and RBF neural network. According to the date of the type, temperature, weather conditions and other factors ,The Application of combined GA-optimized SVM radial basis function is used to extract useful data to improve the load forecasting accuracy of RBF neural network. Spring load data of California were applied for simulation. The simulation indicates that the new method is feasible and the forecasting precision is greatly improved.

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