基于LASSO分位数回归的中期电力负荷概率密度预测方法

中期电力负荷预测过程中往往会受到多种外界因素(诸如温度、节假日、风力大小等)的不确定性干扰,并且影响中期电力负荷预测的因素复杂多变、规律各异,难以精准地进行预测.在大数据环境下,如何在种类繁多、数量庞大的影响因素中快速获取有价值信息成为了电力负荷预测问题的关键所在.提出的基于LASSO分位数回归概率密度预测方法,首先从影响电力负荷预测的多种外界因素中挑选出重要的影响因子,建立LASSO分位数回归模型.然后,使用triangular核函数,将LASSO分位数回归与核密度估计方法相结合,进行中期电力负荷概率密度预测.以中国东部某副省级市的历史负荷和外界影响因素(包括温度、节假日及风力大小)为算例,运用LASSO分位数回归方法进行中期电力负荷概率密度预测,得到的平均绝对误差在中位数和众数上分别为3.53%和3.69%,优于未考虑外界因素和考虑外界因素未进行变量选择的情况.为了进一步验证该方法的优越性,将其与非线性分位数回归和基于三角核的分位数回归神经网络概率密度预测方法进行对比分析,说明该方法能较好解决电力负荷预测中的高维数据问题,从而获得比较准确的电力负荷预测结果.

Medium-term power load probability density forecasting method based on LASSO quantile regression

The medium-term power load forecasting is often disturbed by a variety of external factors (such as temperature, holidays and wind power) and uncertainties. Also, the factors affecting the medium-term power load forecasting are complex and changeable, and it is difficult to predict accurately. In the big data environment, how to obtain valuable information quickly in a variety of large number of influence factors has become the key to the power load forecasting problems. A method of density forecasting based on LASSO quantile regression was proposed in this paper. First, the important influence factors were selected from the various external factors affecting the power load forecasting, and the LASSO quantile regression model was established. Then, by using the triangular kernel function, LASSO quantile regression was combined with the method of kernel density estimation for the medium-term power load probability density forecasting. Taking the historical load and external influence factors (including temperature, holidays and wind power) of a sub-provincial city in eastern China as an example, the probability density prediction of medium-term power load was carried out. The average absolute error obtained was respectively 3.53% and 3.69% in the median and the mode, which was better than the results without considering the external factors and without variable selection. In order to further verify the superiority of the method, the method was compared with the nonlinear quantile regression (NLQR) and the quantile regression neural network based on triangle kernel (QRNNT) probability density forecasting methods. The results illustrate that this method can better solve the high-dimensional data problem in power load forecasting, and obtain more accurate results of power load forecasting.