| 基于主成分分析和遗传优化BP神经网络的光伏输出功率短期预测 |
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| 引用本文： | 许童羽,马艺铭,曹英丽,唐 瑞,陈俊杰.基于主成分分析和遗传优化BP神经网络的光伏输出功率短期预测[J].电力系统保护与控制,2016,44(22):90-95. |
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| 作者姓名： | 许童羽 马艺铭 曹英丽 唐 瑞 陈俊杰 |
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| 作者单位： | 沈阳农业大学信息与电气工程学院，辽宁 沈阳 110161,沈阳农业大学信息与电气工程学院，辽宁 沈阳 110161,沈阳农业大学信息与电气工程学院，辽宁 沈阳 110161,沈阳农业大学信息与电气工程学院，辽宁 沈阳 110161,沈阳农业大学信息与电气工程学院，辽宁 沈阳 110161 |
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| 基金项目： | 辽宁省自然科学基金项目(2013020141) |
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| 摘 要： | 针对光伏系统输出功率的波动性和间歇性特点，提出一种基于主成分分析(PCA)和遗传算法(GA)优化的BP神经网络功率短期预测方法。通过历史功率数据和实时气象因素对输出功率进行直接预测，利用主成分分析法将多个原始变量降维成少数彼此独立的变量，作为神经网络的输入。同时利用遗传算法的全局搜索特性在解空间中定位一个较好的空间，优化BP的初始权值阈值，克服了传统BP神经网络易陷入局部极小点、学习收敛速度慢的问题。通过建立不同预测模型进行对比，验证了所提算法和模型的有效性。
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| 关 键 词： | 主成分分析 遗传算法 功率预测 BP神经网络 光伏系统 |
| 收稿时间： | 2016/1/13 0:00:00 |
| 修稿时间： | 4/5/2016 12:00:00 AM |
Short term forecasting of photovoltaic output power based on principal component analysis and genetic optimization of BP neural network |
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| XU Tongyu,MA Yiming,CAO Yingli,TANG Rui and CHEN Junjie.Short term forecasting of photovoltaic output power based on principal component analysis and genetic optimization of BP neural network[J].Power System Protection and Control,2016,44(22):90-95. |
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| Authors: | XU Tongyu MA Yiming CAO Yingli TANG Rui and CHEN Junjie |
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| Affiliation: | College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110161, China,College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110161, China,College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110161, China,College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110161, China and College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110161, China |
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| Abstract: | In view of the fluctuation and the intermittence of the output power of the photovoltaic system, based on principal component analysis (PCA) and genetic algorithm (GA) optimization, a short term forecasting method of BP neural network power is proposed. Direct forecasting of output power is done by historical power data and real time meteorological factors. It uses principal component analysis to reduce the dimension of multiple original variables into a few independent variables, so that it can optimize the initial weights of back-propagation''s threshold and overcome the traditional BP neural network easy to fall into local minimum point, and the problems of slow convergence speed. The results of the comparison for different forecast models validate the effectiveness of the algorithm and proposed model. This work is supported by Natural Science Foundation of Liaoning Province (No. 2013020141). |
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| Keywords: | principal component analysis genetic algorithm power forecasting BP neural network photovoltaic system |
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