基于加性分位数GB算法的智能电能表数据不确定性预测

与传统电力数据相比,智能电能表数据波动性更大,可预测性更低。能源行业需对智能电能表数据进行概率预测,以量化未来电力需求的不确定性,以便对发电和配电进行合理规划。文章提出了一种用梯度提升算法估计智能电能表数据未来分布的加性分位数回归模型。所提方法提出了电能表数据概率预测的分位数回归及分位数修正算法。基于分位数算法给出了综合考虑外部影响因素的加性分位数的GB算法,并研究了该基于梯度提升算法的智能电能表数据概率预测加性分位数模型的关键性能参数选择,从而建立起了高性能的智能电能表数据概率预测模型;通过算例分析证明了该方法在综合和单个用户智能电能表数据概率预测中的准确性和有效性,尤其是在单个用户电能表数据概率预测方面具有远超其他算法的优越性能。

Uncertainty prediction of smart meter data using gradient boosting algorithm of additive quantiles

Compared with the traditional data of electricity consumption, the data of smart meter is more volatile and less predictable. In order to realize the reasonable planning of power generation and distribution, the energy industry needs to quantify the uncertainty of power demand through the probability prediction of smart meter data. In this paper, an additive quantile regression model is proposed to estimate the future distribution of smart meter data by using the gradient boosting(GB) algorithm. Firstly, the method gives the quantile regression and quantile correction algorithm for the probability prediction. Based on the quantile algorithm, the GB algorithm of the additive quantile considering the external factors is given, and the selection method of main parameters in additive quantile model based on the GB algorithm is studied, thus the high-performance probability prediction model is established. Finally, compared with others, the case study shows that the method is more accurate and effective, especially in the probability prediction of single user"s meter data, it has far superior performance than other algorithms.