A novel competitive swarm optimized RBF neural network model for short-term solar power generation forecasting

2020 ◽  
Vol 397 ◽  
pp. 415-421 ◽  
Author(s):  
Zhile Yang ◽  
Monjur Mourshed ◽  
Kailong Liu ◽  
Xinzhi Xu ◽  
Shengzhong Feng
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Network Model ◽  
Neural Network Model ◽  
Solar Power ◽  
Rbf Neural Network ◽  
Short Term ◽  
Solar Power Generation

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.

Comparison of Three Short Term Photovoltaic System Power Generation Forecasting Methods

2013 ◽  
Vol 479-480 ◽  
pp. 585-589 ◽  
Author(s):  
Wen Yeau Chang
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Solar Power ◽  
Short Term ◽  
Pv System ◽  
Percent Error ◽  
Solar Power Generation ◽  
Forecasting Methods ◽  
Network Method ◽  
Absolute Percent Error

An accurate forecasting method for solar power generation of the photovoltaic (PV) system is urgent needed under the relevant issues associated with the high penetration of solar power in the electricity system. This paper presents a comparison of three forecasting approaches on short term solar power generation of PV system. Three forecasting methods, namely, persistence method, back propagation neural network method, and radial basis function (RBF) neural network method, are investigated. To demonstrate the performance of three methods, the methods are tested on the practical information of solar power generation of a PV system. The performance is evaluated based on two indexes, namely, maximum absolute percent error and mean absolute percent error.

An improved self-organizing incremental neural network model for short-term time-series load prediction

2021 ◽  
Vol 292 ◽  
pp. 116912
Author(s):  
Rong Wang Ng ◽  
Kasim Mumtaj Begam ◽  
Rajprasad Kumar Rajkumar ◽  
Yee Wan Wong ◽  
Lee Wai Chong
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Model ◽  
Neural Network Model ◽  
Load Prediction ◽  
Short Term ◽  
Self Organizing

Assessment of the Underground Water Contaminated by the Leachate of Waste Dump of Open Pit Coal Mine Based on RBF Neural Network

2012 ◽  
Vol 599 ◽  
pp. 272-277 ◽  
Author(s):  
Zhi Bin Liu ◽  
Xiao Wei Yang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Coal Mine ◽  
Comprehensive Evaluation ◽  
Rbf Neural Network ◽  
Training Sample ◽  
Underground Water ◽  
Open Pit ◽  
Waste Dump

This paper used RBF artificial neural network to evaluate the underground water contaminated by the leachate of waste dump of open pit coal mine of Xinqiu in Fuxin. Firstly, with the advantages of neural network method in dealing with nonlinear problem, the RBF neural network model was built. Then, the normalized standard matrix was taken as training sample and the MATLAB software was used to train the training sample. Finally, the monitoring data were taken as test samples and were inputted in the RBF neural network model to evaluate the groundwater quality of study area. At the same time, the concept of degree of membership was adopted in the result making it more objective and accurate. The result shows that the ground water of this mining is seriously polluted, class of its pollution is Ⅳ-Ⅴ.The method with strong classification function and reliable evaluation results is simple and effective, and can be widely applied in all kinds of water resources comprehensive evaluation.

A Novel Quantum Neural Network Model with Variable Selection for Short Term Load Forecasting

2010 ◽  
Vol 20-23 ◽  
pp. 612-617 ◽  
Author(s):  
Wei Sun ◽  
Yu Jun He ◽  
Ming Meng
Keyword(s):  
Neural Network ◽  
Variable Selection ◽  
Network Model ◽  
Neural Network Model ◽  
Load Forecasting ◽  
Short Term ◽  
Bp Network ◽  
Quantum Neural Network ◽  
Short Term Load Forecasting ◽  
Selection For

The paper presents a novel quantum neural network (QNN) model with variable selection for short term load forecasting. In the proposed QNN model, first, the combiniation of maximum conditonal entropy theory and principal component analysis method is used to select main influential factors with maximum correlation degree to power load index, thus getting effective input variables set. Then the quantum neural network forecating model is constructed. The proposed QNN forecastig model is tested for certain province load data. The experiments and the performance with QNN neural network model are given, and the results showed the method could provide a satisfactory improvement of the forecasting accuracy compared with traditional BP network model.

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