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Vol. 159, Issue 11, November 2013, pp. 138-142

 

Bullet

 

RBF Neural Network Combined with Knowledge Mining Based on Environment Simulation Applied
for Photovoltaic Generation Forecasting
 
* Dongxiao Niu, Ling Ji, Xiaomin Xu, Peng Wang

School of Economics and Management, North China Electric Power University, Beijing, 102206, China

* Tel.: +86-10-5196-3828, fax: +86-10-8079-8531

1 E-mail: niudx617@gmial.com

 

Received: 28 August 2013   /Accepted: 25 October 2013   /Published: 30 November 2013

Digital Sensors and Sensor Sysstems

 

Abstract: Photovoltaic generation forecasting is one of the main tasks of the planning and operation in power system. Especially with the development of mico-grid, relative study on renewable energy generation gain more and more concerns. In this paper, a short-term forecasting model combining knowledge and intelligent algorithm is developed for photovoltaic array generation. Self-organizing map (SOM) is proposed to extract the relative knowledge, and to choose the most similar history situation and efficient data for wind power forecasting with numerical weather prediction (NWP). The historical data is classified into several groups, though which we could find the similar days and excavate the hidden rules. According to the data reprocessing, the selected samples will improve the forecast accuracy radial basis function network (RBF) trained by the class of the forecasting day is adopted to forecast the photovoltaic output accordingly. A case study is conducted to verify the effectiveness and the accuracy. Compared with the conventional BP neural network, the forecasting results demonstrate the method proposed in this paper can gain better forecasting performance with higher accuracy.

 

Keywords: Knowledge mining, Self-organizing map, Radial basis function network, Photovoltaic generation, Forecast.

 

 

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