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周帆,郑常宝,胡存刚,等. 基于高斯混合模型的光伏发电功率概率区间预测[J]. 科学技术与工程, 2021, 21(24): 10284-10290.
Zhou Fan,Zheng Changbao,Hu Cungang,et al.Research on prediction of photovoltaic power generation probability interval based on Gaussian mixture model[J].Science Technology and Engineering,2021,21(24):10284-10290.
基于高斯混合模型的光伏发电功率概率区间预测
Research on prediction of photovoltaic power generation probability interval based on Gaussian mixture model
投稿时间:2020-12-16  修订日期:2021-05-26
DOI:
中文关键词:  光伏发电  高斯混合分布  误差分布  区间预测  天气划分
英文关键词:photovoltaic power generation  gaussian mixture model  error distribution  interval prediction  weather division
基金项目:国家重点研发计划(No.2016YFB0900400);安徽省高校自然科学基金项目(KJ2020A0038)
           
作者单位
周帆 安徽大学电气工程与自动化学院
郑常宝 安徽大学电气工程与自动化学院
胡存刚 安徽大学电气工程与自动化学院
芮涛 教育部电能质量工程研究中心
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中文摘要:
      随着世界经济的绿色发展,大力发展可再生能源逐渐成为共识。可再生能源中太阳能的开发利用已成为当前能源转型中的重要领域,并在很多科技发达国家得到了较广泛的应用。高精度的光伏发电功率预测对电力系统的优化调度、安全运行十分重要。由于光照强度和能见度等会影响太阳能发电量的随机性,文中提出一种基于高斯混合模型的光伏发电功率概率区间预测方法,通过利用K-means算法将光伏发电历史数据按天气进行划分,以划分后的预测误差为统计样本,采用高斯混合模型进行拟合并使用期望最大化算法估计模型参数,通过计算指定置信水平下的置信区间进行光伏发电功率概率区间预测。仿真结果表明所提方法在进行光伏发电功率区间预测时的性能评价指标均优于典型单一分布模型,证明了所提方法的准确性和适用性。
英文摘要:
      With the green development of the world economy, it has gradually become a consensus to develop renewable energy. The development and utilization of solar energy in renewable energy has become an important field in the current energy transition, and has been widely used in many technologically developed countries. High-precision photovoltaic power generation prediction is very important to the optimal dispatch and safe operation of the power system. Because the randomness of solar power generation is affected by light intensity and visibility, a method for predicting the probability interval of photovoltaic power generation based on the Gaussian mixture model was proposed in this paper. The K-means algorithm was used to divide historical photovoltaic power generation data by weather, then a Gaussian mixture model was utilized to fit the divided prediction errors, and an expectation maximization algorithm was applied to estimate model parameters. Calculating the confidence interval under the given confidence level was used to obtain the probability interval prediction results of photovoltaic power generation. The results show that the performance evaluation indexes of the proposed method are better than the typical single distribution model when forecasting the photovoltaic power interval, which proves the accuracy and applicability of the proposed method.
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