scholarly journals A Forecasting Method Based on Online Self-Correcting Single Model RBF Neural Network

2012 ◽  
Vol 29 ◽  
pp. 2516-2520 ◽  
Author(s):  
Yanzhi Wang ◽  
Guixiong Liu
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Single Model ◽  
Forecasting Method

scholarly journals An RBF Neural Network Combined with OLS Algorithm and Genetic Algorithm for Short-Term Wind Power Forecasting

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Wen-Yeau Chang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Power Generation ◽  
Wind Power ◽  
Rbf Neural Network ◽  
Optimal Number ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Forecasting Method ◽  
Power Forecasting

An accurate forecasting method for power generation of the wind energy conversion system (WECS) is urgently needed under the relevant issues associated with the high penetration of wind power in the electricity system. This paper proposes a hybrid method that combines orthogonal least squares (OLS) algorithm and genetic algorithm (GA) to construct the radial basis function (RBF) neural network for short-term wind power forecasting. The RBF neural network is composed of three-layer structures, which contain the input, hidden, and output layers. The OLS algorithm is used to determine the optimal number of nodes in a hidden layer of RBF neural network. With an appropriate RBF neural network structure, the GA is then used to tune the parameters in the network, including the centers and widths of RBF and the connection weights in second stage. To demonstrate the effectiveness of the proposed method, the method is tested on the practical information of wind power generation of a WECS installed in Taichung coast of Taiwan. Comparisons of forecasting performance are made to the persistence method and back propagation neural network. The good agreements between the realistic values and forecasting values are obtained; the test results show the proposed forecasting method is accurate and reliable.

A multivariate forecasting method for short-term load using chaotic features and RBF neural network

2010 ◽  
Vol 21 (3) ◽  
pp. 1376-1391 ◽  
Author(s):  
Yuming Liu ◽  
Shaolan Lei ◽  
Caixin Sun ◽  
Quan Zhou ◽  
Haijun Ren
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Short Term ◽  
Forecasting Method ◽  
Multivariate Forecasting

Power Generation Forecasting of Solar Photovoltaic System Using Radial Basis Function Neural Network

2013 ◽  
Vol 368-370 ◽  
pp. 1262-1265 ◽  
Author(s):  
Wen Yeau Chang
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Photovoltaic System ◽  
Solar Photovoltaic ◽  
Pv System ◽  
Forecasting Method ◽  
Radial Basis

An accurate forecasting method for power generation of the solar photovoltaic (PV) system can help the power systems operator to reduce the risk of unreliability of electricity supply. This paper proposed a radial basis function (RBF) neural network method to forecast the power generation of PV system. To demonstrate the effectiveness of the proposed method, the method is tested on the practical information of power generation of a PV system. The good agreements between the realistic values and forecasting values are obtained; the numerical results show that the proposed forecasting method is accurate and reliable.

scholarly journals Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2538 ◽  
Author(s):  
Li ◽  
Wang ◽  
Zhang ◽  
Xin ◽  
Liu
Keyword(s):  
Neural Network ◽  
Solar Power ◽  
Time Series Data ◽  
Short Term Memory ◽  
Rbf Neural Network ◽  
Back Propagation ◽  
Series Data ◽  
Support Vector ◽  
Short Term ◽  
Forecasting Method

The intermittency of solar energy resources has brought a big challenge for the optimization and planning of a future smart grid. To reduce the intermittency, an accurate prediction of photovoltaic (PV) power generation is very important. Therefore, this paper proposes a new forecasting method based on the recurrent neural network (RNN). At first, the entire solar power time series data is divided into inter-day data and intra-day data. Then, we apply RNN to discover the nonlinear features and invariant structures exhibited in the adjacent days and intra-day data. After that, a new point prediction model is proposed, only by taking the previous PV power data as input without weather information. The forecasting horizons are set from 15 to 90 minutes. The proposed forecasting method is tested by using real solar power in Flanders, Belgium. The classical persistence method (Persistence), back propagation neural network (BPNN), radial basis function (RBF) neural network and support vector machine (SVM), and long short-term memory (LSTM) networks are adopted as benchmarks. Extensive results show that the proposed forecasting method exhibits a good forecasting quality on very short-term forecasting, which demonstrates the feasibility and effectiveness of the proposed forecasting model.

Research on City Gas Load Forecasting Method

2013 ◽  
Vol 405-408 ◽  
pp. 2986-2989
Author(s):  
Ting Ting Wang ◽  
Hong Yan Tan
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Load Forecasting ◽  
Original Data ◽  
Uncertain Demand ◽  
Fuzzy Method ◽  
Factors Influencing ◽  
Forecasting Method ◽  
City Gas

The paper mainly studied the gas forecasting in gas-consuming rush hour and long-term load forecasting.The paper analyzed the factors influencing the volume of gas forecasting in rush hour and forecast the volume of gas in rush hour using fuzzy method and RBF neural network. In long term city gas load forecasting, there are some characters such as longer time and uncertain demand increasing. A method is proposed to weakening the original data by using buffer operator before use GM(1,1)model. It indicated that the result acquired by these methods was satisfied with the requirement of engineering, and it was helpful to dispatchers.

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