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Proceedings Paper

A distributed big data storage and data mining framework for solar-generated electricity quantity forecasting
Author(s): Jianzong Wang; Yanjun Chen; Rui Hua; Peng Wang; Jia Fu
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Paper Abstract

Photovoltaic is a method of generating electrical power by converting solar radiation into direct current electricity using semiconductors that exhibit the photovoltaic effect. Photovoltaic power generation employs solar panels composed of a number of solar cells containing a photovoltaic material. Due to the growing demand for renewable energy sources, the manufacturing of solar cells and photovoltaic arrays has advanced considerably in recent years. Solar photovoltaics are growing rapidly, albeit from a small base, to a total global capacity of 40,000 MW at the end of 2010. More than 100 countries use solar photovoltaics. Driven by advances in technology and increases in manufacturing scale and sophistication, the cost of photovoltaic has declined steadily since the first solar cells were manufactured. Net metering and financial incentives, such as preferential feed-in tariffs for solar-generated electricity; have supported solar photovoltaics installations in many countries. However, the power that generated by solar photovoltaics is affected by the weather and other natural factors dramatically. To predict the photovoltaic energy accurately is of importance for the entire power intelligent dispatch in order to reduce the energy dissipation and maintain the security of power grid. In this paper, we have proposed a big data system--the Solar Photovoltaic Power Forecasting System, called SPPFS to calculate and predict the power according the real-time conditions. In this system, we utilized the distributed mixed database to speed up the rate of collecting, storing and analysis the meteorological data. In order to improve the accuracy of power prediction, the given neural network algorithm has been imported into SPPFS.By adopting abundant experiments, we shows that the framework can provide higher forecast accuracy-error rate less than 15% and obtain low latency of computing by deploying the mixed distributed database architecture for solar-generated electricity.

Paper Details

Date Published: 22 February 2012
PDF: 7 pages
Proc. SPIE 8333, Photonics and Optoelectronics Meetings (POEM) 2011: Optoelectronic Devices and Integration, 83330S (22 February 2012); doi: 10.1117/12.919640
Show Author Affiliations
Jianzong Wang, Huazhong Univ. of Science and Technology (China)
Wuhan National Lab. for Optoelectronics, (China)
Yanjun Chen, Huazhong Univ. of Science and Technology (China)
Rui Hua, Huazhong Univ. of Science and Technology (China)
Peng Wang, Huazhong Univ. of Science and Technology (China)
Wuhan National Lab. for Optoelectronics, (China)
Jia Fu, Meteorological Service Ctr. of Hubei Province (China)


Published in SPIE Proceedings Vol. 8333:
Photonics and Optoelectronics Meetings (POEM) 2011: Optoelectronic Devices and Integration
Erich Kasper; Jinzhong Yu; Xun Li; Xinliang Zhang; Jinsong Xia; Junhao Chu; Zhijiang Dong; Bin Hu; Yan Shen, Editor(s)

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