灰色动态BP神经网络在光伏短期出力预测中的应用

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (2047 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract In order to avoid the shortcomings of traditional BP neural network in predicting the short-term output power of photovoltaic generation, a prediction model based on a grey dynamic BP neural network was proposed and realized in this paper. The grey system theory was adopted to improve the factor set of traditional BP neural network, meanwhile the number of nodes of hidden layer in the BP neural network was adjusted dynamically. At the end the proposed model was used to predict the short-term output power in a real photovoltaic power station. The results show that the predicted results agree well with the measured results, which verifies the validity of the model.

Key words： BP neural network grey model photovoltaic generation short-term output power prediction

Published: 09 December 2015

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Lou Baolei

Cite this article:

Lou Baolei. Short-term Output Power Prediction of Photovoltaic Generation based on a Grey Dynamic BP Neural Network[J]. Electrical Engineering, 2015, 16(12): 47-51.

URL:

https://dqjs.cesmedia.cn/EN/Y2015/V16/I12/47

[1] 陈树勇, 宋书芳, 李兰欣, 等. 智能电网技术综述[J]. 电网技术, 2009, 33(8): 1-7.
[2] Li Yz, Niu Jc. Forecast of power Generation for grid-connected photovoltaic system based on Markov chain[C]//Proceedings of International Conference on Asia-Pacific Power and Energy Engineering. Wuhan, China, 2009: 1-4.
[3] Diagne M, David M, Lauret P, et al. Review of solar irradiance forecasting methods and a proposition for small-scale insular grids[J]. Renewable and Sustain- able Energy Reviews, 2013, 27: 65-76.
[4] Cao Jc, Cao Sh. Study of forecasting solar irradiance using neural networks with preprocessing sample data by wavelet analysis[J]. Energy, 2006, 31(15): 3435- 3445.
[5] 陈都平. 光伏功率预测与预测模型[J]. 内蒙古电业, 2011, 19-20.
[6] 胡学浩. 智能电网——未来电网的发展态势[J]. 电网技术, 2009, 33(14): 1-5.
[7] 廖芳芳, 肖建. 基于BP神经网络PID参数自整定的研究[J]. 系统仿真学报, 2005, 17(7): 1711-1713.
[8] 高洪深, 陶有德. BP神经网络模型的改进[J]. 系统工程理论与实践, 1996, 16(1): 67-71.
[9] 雷成华, 刘刚, 李钦豪. BP神经网络模型用于单芯电缆导体温度的动态计算[J]. 高电压技术, 2011, 37(1): 184-189.
[10] 王蕾, 张九根, 李腾, 等. 基于改进BP神经网络的中央空调冷负荷预测研究[J]. 计算机测量与控制, 2014, 22(6): 1690-1692.
[11] 岳建平. 灰色动态神经网络模型及其应用[J]. 水利学报, 2003, 7(7): 120-123.
[12] 盛建龙, 熊绵国. 基于灰色-BP神经网络组合模型的边坡稳定性预报方法[J]. 有色金属: 矿山部分, 2012, 64(4): 71-73.
[13] Picault D, Raison B, Bacha S, et al. Forecasting photovoltaic array power production subject to mismatch losses[J]. Solar Energy, 2010, 84(7): 1301-1309.
[14] Shi J, Lee WJ, Liu Yq, et al. Forecasting power output of photovoltaic systems based on weather classification and support vector machines[J]. IEEE Transactions on Industry Applications, 2012, 48(3): 1064-1069.
[15] Lin Yonghuang, Lee PC, Chang Tapeng. Adaptive and high-precision grey forecasting model[J]. Expert Systems with Applications, 2009, 36(6): 9658-9662.
[16] Zhu Xl. Application of composite grey BP neural network forecasting model to motor vehicle fatality risk[C]//International Conference on Computer Modeling and Simulation, 2010: 236-240.
[17] Han YZ, Ruan Z, Han JG. On fuzzily-processed- BP-network-based dual-mode control with realization on on-line reconfigurable FPGA[C]//Proceedings of the 2005 International Conference on Neural Networks and Brain, VOLS 1-3, 2005: 841-846.
[18] 王守相, 张娜. 基于灰色神经网络组合模型的光伏短期出力预测[J]. 电力系统自动化, 2012, 36(19): 37-41.
[19] Xue JG, Uchida S, Rand BP, et al. Asymmetric tandem organic photovoltaic cells with hybrid planar-mixed molecular heterojunctions[J]. Applied Physics Letters, 2004, 85(23): 5757-5759.
[20] 王飞, 余世杰, 苏建徽, 等. 太阳能光伏并网发电系统的研究[J]. 电工技术学报, 2005, 20(5): 72-74, 91.
[21] 李蓓, 李兴源. 分布式发电及其对配电网的影响[J].
国际电力, 2005, 9(3): 45-49.
[22] 周念成, 闫立伟, 王强钢. 光伏发电在微电网中接入及动态特性研究[J]. 电力系统保护与控制, 2010, 38(14): 119-127.
[23] 王淳, 高元海. 基于概率统计的含间歇性分布式发电的配电网无功优化[J]. 电网技术, 2014, 38(4): 32-36.