基于因子分析和神经网络分位数回归的月度风电功率曲线概率预测
发布时间：2019-03-07 14:23
【摘要】：针对月度风电曲线预测存在的预测变量多且关系复杂、可利用天气信息少以及不确定性强等问题,提出了一种基于因子分析和神经网络分位数回归的月度风电曲线概率预测方法。采用因子分析对日内小时级风电功率序列向量降维,提取出相互独立的风电公共因子作为预测变量,分别建立以日天气特征为输入的神经网络分位数条件概率模型;利用中期天气预报信息,预测未来30日各公共因子的概率分布;最后通过模拟服从预测分布的风电公共因子和各时刻特殊因子,并代入因子模型逐日还原风电预测曲线,生成未来月风电曲线的随机场景。两个实际风电场的预测结果验证了所提风电曲线概率预测方法的准确性、适应性和高效性,为中长期风电功率概率预测提供了一种可行的解决思路。
[Abstract]:There are many forecasting variables and complex relationships in monthly wind power curve prediction, such as low availability of weather information and strong uncertainty, and so on. A probabilistic forecasting method of monthly wind power curve based on factor analysis and neural network quantile regression is proposed. Factor analysis is used to reduce the dimension of wind power series vector, and independent common wind power factors are extracted as prediction variables. The conditional probability models of neural network quantiles are established with daily weather characteristics as input. Using the medium-term weather forecast information, the probability distribution of the common factors in the next 30 days is predicted. Finally, the wind power forecasting curve is reduced day by simulating the common and special wind power factors which obey the forecast distribution, and the random scene of the future monthly wind power curve is generated by using the factor model to restore the wind power forecast curve day by day. The prediction results of two practical wind farms verify the accuracy, adaptability and efficiency of the proposed wind power curve probability prediction method, which provides a feasible solution for the medium-and long-term wind power probability prediction.
【作者单位】： 重庆大学电气工程学院;南方电网科学研究院;
【基金】：国家自然科学基金项目(51177178,51677012) 重庆市科委基础与前沿研究计划项目(cstc2013jcyj A90001)~~
【分类号】：TM614
本文编号：2436193
[Abstract]:There are many forecasting variables and complex relationships in monthly wind power curve prediction, such as low availability of weather information and strong uncertainty, and so on. A probabilistic forecasting method of monthly wind power curve based on factor analysis and neural network quantile regression is proposed. Factor analysis is used to reduce the dimension of wind power series vector, and independent common wind power factors are extracted as prediction variables. The conditional probability models of neural network quantiles are established with daily weather characteristics as input. Using the medium-term weather forecast information, the probability distribution of the common factors in the next 30 days is predicted. Finally, the wind power forecasting curve is reduced day by simulating the common and special wind power factors which obey the forecast distribution, and the random scene of the future monthly wind power curve is generated by using the factor model to restore the wind power forecast curve day by day. The prediction results of two practical wind farms verify the accuracy, adaptability and efficiency of the proposed wind power curve probability prediction method, which provides a feasible solution for the medium-and long-term wind power probability prediction.
【作者单位】： 重庆大学电气工程学院;南方电网科学研究院;
【基金】：国家自然科学基金项目(51177178,51677012) 重庆市科委基础与前沿研究计划项目(cstc2013jcyj A90001)~~
【分类号】：TM614
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