| 基于气象相似性的光伏电站输出功率估计 |
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| 引用本文： | 纪德洋,金锋,冬雷,郝颖. 基于气象相似性的光伏电站输出功率估计[J]. 太阳能学报, 2022, 43(5): 173-179. DOI: 10.19912/j.0254-0096.tynxb.2020-0769 |
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| 作者姓名： | 纪德洋 金锋 冬雷 郝颖 |
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| 作者单位： | 1.北京理工大学自动化学院,北京 100081; 2.中山北京理工大学研究院,中山 528437; 3.北京信息科技大学自动化学院,北京 100192 |
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| 基金项目： | 中山市2020年社会公益与基础研究项目（200813093628997） |
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| 摘 要： | 为了得到光伏电站的输出功率,提出一种基于气象相似性的输出功率估计方法。该方法通过对大量历史数据的分析,利用光伏电站输出功率与气象数据密切相关的特点,基于皮尔逊相关系数在历史数据中计算得到与待估计输出功率相似的气象时刻,再根据相似气象时刻的输出功率估计待估计时刻的输出功率。利用甘肃省某光伏电站采集的包含9个气象要素的历史数据,经过数据预处理之后挑选出214 d不含有异常数据的数据集作为数据样本,并随机选出14 d不同天气类型的数据作为测试样本,其余作为训练样本。通过对测试样本数据进行验证,所提出的估计方法平均绝对误差仅为0.46 MW,相对误差仅为15.74%,具有较高的计算准确度,且适用的气象条件更广泛。
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| 关 键 词： | 光伏电站 光伏出力 数据分析 气象相似性 皮尔逊相关系数 |
| 收稿时间： | 2020-08-06 |
ESTIMATION OF OUTPUT POWER OF PHOTOVOLTAIC POWER STATION BASED ON METEOROLOGICAL SIMILARITY |
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| Ji Deyang,Jin Feng,Dong Lei,Hao Ying. ESTIMATION OF OUTPUT POWER OF PHOTOVOLTAIC POWER STATION BASED ON METEOROLOGICAL SIMILARITY[J]. Acta Energiae Solaris Sinica, 2022, 43(5): 173-179. DOI: 10.19912/j.0254-0096.tynxb.2020-0769 |
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| Authors: | Ji Deyang Jin Feng Dong Lei Hao Ying |
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| Affiliation: | 1. School of Automation, Beijing Institute of Technology, Beijing 100081, China; 2. Zhongshan Research Institute, Beijing Institute of Technology, Zhongshan 528437, China; 3. School of Automation, Beijing Information Science and Technology University, Beijing 100192, China |
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| Abstract: | In order to obtain the output power of photovoltaic power plants, an output power estimation method based on meteorological similarity is proposed. This method analyzes a large number of historical data, using the characteristics of photovoltaic power plant output power and meteorological data are closely related, based on the Pearson correlation coefficient to calculate the similar meteorological time to the estimated output power in the historical data. Finally, according to the similar meteorological time’s output power estimates the output power at the time to be estimated. The used historical data contain 9 meteorological elements from a photovoltaic power station in Gansu Province. After data preprocessing, a 214-day data set containing no abnormal data are selected as the data sample, in which 14-day data of different weather types are randomly selected as the test samples, and the rest are used as training samples. Case study indicate that the average absolute error and relative error of the proposed estimation method are only 0.46 MW and 15.74%, respectively. It has high calculation accuracy and is applicable to a wider range of meteorological conditions. |
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| Keywords: | photovoltaic power plant electric power generation data analysis meteorological similarty Pearson correlation coefficient |
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