| 基于GA-GNNM的极地光伏发电功率预测方法 |
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| 引用本文： | 杨帆,申亚,李东东,李杰,王哲超,林顺富.基于GA-GNNM的极地光伏发电功率预测方法[J].太阳能学报,2022,43(4):167-174. |
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| 作者姓名： | 杨帆 申亚 李东东 李杰 王哲超 林顺富 |
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| 作者单位： | 1.上海电力大学电气工程学院,上海 200090; 2.中国极地研究中心,上海 200090 |
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| 基金项目： | 上海市“科技创新行动计划”（19DZ1207604）；;国家自然科学基金（51977127）； |
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| 摘 要： | 为了更加准确有效地对极地光伏发电功率做出预测,提出一种基于GA-GNNM的极地光伏发电功率预测方法.首先对采集到的气候因素数据以及光伏发电数据中缺失、异常部分进行清洗归一化处理;通过最大相关最小冗余算法(MRMR)选择最佳的气候特征组合,构建多维气候特征数据集;并将其输入到K均值聚类算法中完成不同季节天气类型聚类划分,...
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| 关 键 词： | 光伏发电 K均值算法 特征选择 预测分析 灰色神经融合模型 |
| 收稿时间： | 2020-08-05 |
POLAR PHOTOVOLTAIC POWER FORECASTING METHOD BASED ON GA-GNNM |
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| Yang Fan,Shen Ya,Li Dongdong,Li Jie,Wang Zhechao,Lin Shunfu.POLAR PHOTOVOLTAIC POWER FORECASTING METHOD BASED ON GA-GNNM[J].Acta Energiae Solaris Sinica,2022,43(4):167-174. |
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| Authors: | Yang Fan Shen Ya Li Dongdong Li Jie Wang Zhechao Lin Shunfu |
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| Affiliation: | 1. School of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China; 2. Polar Research Institute of China, Shanghai 200090, China |
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| Abstract: | Photovoltaic power forecasting is one of the basics for optimal configuration and coordinated operation of the microgrid. However, due to the extreme conditions of the two seasons in the polar region： polar day and night, and more stormy snow, occasional snow fog weather etc., the polar photovoltaic power is greatly affected, with instability, uncertainty and non-linear characteristics. In order to forecasting the power of polar photovoltaic more accurately and effectively, a polar photovoltaic power forecasting method based on GA-GNNM is proposed. First, cleaning and normalizing abnormal and missing data, useing the maximum correlation minimum redundancy algorithm to select the best feature set, the K-means clustering algorithm is used to cluster the weather types of different seasons, and the relative distance is used to quantify the power with high similarity to the predicted day under different weather types. Construct a gray neural hybrid model （GNNM）, map the differential equations of the gray model into the neural network model, and use genetic optimization algorithm （GA） to optimize the model parameters to avoid local optimization and improve the accuracy of the polar energy prediction algorithm. Finally, the climate and photovoltaic power data of Enksburg Island in Antarctica are used as examples for verification. The results of the example analysis lay a theoretical foundation for the establishment of the fifth new station in the polar region. |
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| Keywords: | photovoltaic power generation K-means algorithms feature selection predictive analysis grey neural network modal |
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