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.