引用本文:罗建春,晁勤,罗洪,等.基于LVQ-GA-BP神经网络光伏电站出力短期预测[J].电力系统保护与控制,2014,42(13):89-94.
LUO Jian-chun,CHAO Qin,LUO Hong,et al.PV short-term output forecasting based on LVQ-GA-BP neural network[J].Power System Protection and Control,2014,42(13):89-94
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基于LVQ-GA-BP神经网络光伏电站出力短期预测
罗建春1, 晁勤2, 罗洪1, 冉鸿1, 杨杰1, 罗庆2, 阿里努尔.阿木提2
1.国网重庆武隆县供电有限责任公司，重庆 408500;2.新疆大学电气工程学院，新疆 乌鲁木齐 830008
摘要:
为了实现对大规模并网型光伏电站调度，分析影响光伏出力的气象相关因素，以光照强度和温度作为输入量，分季节建立了一种基于LVQ-GA-BP神经网络预测系统。通过LVQ（Learning Vector Quantization）神经网络对样本进行分类，将分类后的样本训练，得出基于BP神经网络光伏电站出力预测系统，从而提高光伏预测精度。采用GA算法替代传统的学习算法优化BP神经网络的权值和阀值，提高预测网络的训练速度。将建立的LVQ-GA-BP预测系统与传统系统进行了比较和分析，结果表明：该方法的建立，不仅提高了光伏出力的预测精度，而且还提高了BP神经网络的训练速度，具有潜在的工程应用价值。
关键词:  光伏出力预测  LVQ-GA-BP预测模型  气象因素  神经网络
DOI：
分类号:
基金项目:国家自然科学基金资助项目（51267020）；教育部2012年高等学校博士学科点专项科研基金博导类联合资助课题资助项目（20126501110003）；新疆科技支疆资助项目（201091204）
PV short-term output forecasting based on LVQ-GA-BP neural network
LUO Jian-chun1, CHAO Qin2, LUO Hong1, RAN Hong1, YANG Jie1, LUO Qing2, ALINUER Amuti2
1.Chongqing Wulong Power Supply Company, Chongqing 408500, China;2.School of Electrical Engineering, Xinjiang University, Urumqi 830008, Chin
Abstract:
In order to schedule the large-scale grid-connected PV generation, the weather-related factors of PV power output are analyzed. PV short-term power output forecasting system is proposed in accordance with the four seasons based on LVQ-GA-BP neural network, whose input parameters are light intensity and temperature. The samples about PV output and weather-related factors are classified by learning vector quantization (LVQ) neural network. Then, the classified samples are trained to get the PV short-term output forecasting system based on GA-BP neural network in purpose of increasing forecasting accuracy. Secondly, we propose GA algorithm is an alternative to traditional learning algorithm to optimize BP neural network weights and thresholds, improving forecasting network training speed. At last, the LVQ-GA-BP forecasting system and the traditional forecasting system are compared and analyzed. The results show that the proposed forecasting system not only improve the PV output forecasting accuracy, but also raise the BP neural network training speed, which has potential value in engineering applications.
Key words:  PV output forecasting  LVQ-GA-BP forecasting model  meteorological factor  neural network
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