计及行为特征的市场化用户电量数据拟合方法

Data fitting method for electricity consumption of power market users considering behavioral characteristics

针对市场化用户用电行为复杂多变、电量数据的规律难以精确表征的问题,提出考虑行为特征的市场化用户电量数据拟合方法。采用K-means聚类算法对用户用电行为进行分类,明确各类用户的典型特征;构建基于正交多项式的电量数据拟合神经网络模型,其中神经网络权值系数采用梯度下降算法进行训练,正交多项式分别采用Chebyshev多项式、Hermite多项式、Legendre多项式及Laguerre多项式进行对比分析;采用山东省济南市用户电量数据进行仿真分析,对不同类别的用户分别采用基于4种不同正交多项式的实现方法进行电量数据拟合和评价指标计算,总结具有不同行为特征的用户最适宜的拟合方法。仿真结果表明,同类用户在不同的实现方法下电量数据拟合效果差异明显,基于Hermite多项式及Laguerre多项式的神经网络模型拟合精度相对较高,但不同类别用户电量数据拟合精度最高的多项式模型有所不同。根据用电行为类型选择相应正交多项式构成神经网络拟合模型,是实现用户电量数据精确拟合的有效途径。

To address the problem that the electricity consumption behavior of market-based users was complex and variable,and the laws of electricity data were difficult to be accurately characterized,a market-based user electricity data fitting method considering behavioral characteristics was proposed.The K-means clustering algorithm was used to classify the electricity consumption behavior of customers and clarify the typical characteristics of each type of customers;the neural network model based on orthogonal polynomials was constructed,in which the neural network weight coefficients were trained by gradient descent algorithm and the orthogonal polynomials were Chebyshev polynomials,Hermite polynomials,Legendre polynomials and Laguerre polynomials.The simulation analysis was carried out using the electricity data of users in Jinan,Shandong Province,and four different orthogonal polynomials were used to fit the electricity data and calculate the evaluation indexes for different categories of users,so as to summarize the most suitable fitting methods for users with different behavioral characteristics.The simulation results showed that the power data fitting effect differed significantly among different implementation methods for similar users,and the fitting accuracy of the neural network models based on Hermite polynomials and Laguerre polynomials was relatively high,but the polynomial models with the highest power data fitting accuracy for different categories of users were different.Selecting the corresponding orthogonal polynomials to form a neural network fitting model according to the type of electricity consumption behavior was an effective way to achieve accurate fitting of user electricity data.