Power system short-term load probability forecasting method, device and system

Abstract

The invention discloses a power system short-term load probability forecasting method, a device and a system. The short-term load probability density forecasting model of Gaussian process quantile regression is established by selecting an optimal input variable set affecting the load. Firstly, the importance score of input variables is given by stochastic forest algorithm, and the influence degreeof each input variable is sorted. Secondly, particle swarm optimization algorithm is used to search the super-parameters of the model to form the optimal Gaussian process quantile regression prediction model, avoiding the adverse effect of artificial experience setting initial parameters on the prediction performance of the model. The invention can avoid the shortcomings of manual experience selection, the load forecasting model established in the optimal input variable set has low error, which further reduces the forecasting error, and overcomes the problems that the common conjugate gradient method is easy to fall into the local optimal solution, the iterative number is difficult to determine, and the optimization performance is greatly affected by the initial value selection, so that the self-searching and group cognitive ability can be brought into full play.

Description

technical field [0001] The invention belongs to the technical field of power system load forecasting, and in particular relates to a method, device and system for short-term load probability forecasting of a power system based on Gaussian process quantile regression. Background technique [0002] Improving the accuracy of power system load forecasting is one of the technical measures to effectively ensure the safe, stable, and economical operation of the power system. Load forecasting at different time scales is of great significance for the arrangement of power production scheduling, equipment maintenance planning, and medium- and long-term power grid planning. The actual system operation has accumulated a large amount of historical load and meteorological data, and fully mining the information contained in these data provides a new way to improve the accuracy of power load forecasting. [0003] In the short-term load forecasting modeling process, the selection of input var...

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Problems solved by technology

[0008] In view of the above problems, the present invention proposes a short-term load probability prediction method for power systems based on Gaussian process quantile regression. Local optimal solution, the number of iterations is difficult to determine, and the optimization performance is greatly affected by the selection of the initial value, you can give full play to your own search and group cognition capabilities

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