An integration method of multiple prediction results of power load probability density

Abstract

The invention relates to an integration method of multiple prediction results of power load probability density, and belongs to the technical field of power system analysis. According to the method, aplurality of probability density or quantile probability prediction models are obtained through training of a plurality of three-class regression models set by different hyper-parameters, and the output of the quantile prediction models is converted into a probability density model conforming to Gaussian distribution through Gaussian distribution assumption of loads and a least square method. A probability density prediction integration method is adopted, a probability density prediction optimal integration model is constructed based on the trained probability density prediction model and result, the weights of different probability density prediction methods are determined, and therefore the probability loss of the continuous grade of the final integration prediction model is minimum. The method is finally converted into a quadratic programming problem, the global optimal integrated weight is quickly searched by utilizing mature commercial software, the probability density short-termload prediction precision is improved, and the dispatching operation cost of the power system is further reduced.

Description

technical field [0001] The invention relates to an integration method of multiple prediction results of electric load probability density, belonging to the technical field of power system analysis. Background technique [0002] Power load forecasting is an important part of power system planning and the basis of power system economic operation. In order to assist the power system to make optimal decisions to effectively reduce the planning and operating costs of the power system, high-precision load forecasting is essential. In recent years, with the continuous increase in the scale of the power system, the addition of intermittent energy sources such as wind power and solar energy, and the continuous and rapid growth of distributed renewable energy have made the load of the power system show strong uncertainty. The importance of probabilistic load forecasting research to power system planning and operation has begun to emerge. Compared with traditional point forecasting, ...

AI Technical Summary

Problems solved by technology

In the field of electric load forecasting, there is no related research on the integration of probability density forecasting

Images