Method for forecasting short term load under demand response

Abstract

The invention discloses a method for forecasting a short term load under demand response. The method comprises the following steps of: analyzing and selecting influence factors for forecasting the load, and acquiring historical data of the load to obtain a training sample set; generating an input variable according to the historical data of the influence factors and taking the historical data of a corresponding load valve as an output to obtain a training sample; training a support vector machine model by using the training sample to obtain the trained support vector machine model; and generating a test input vector according to actual data of the influence factors at the moment to be forecasted, inputting the test input vector to the trained support vector machine model and taking output of the trained support vector machine model as a load forecasting value of the moment to be predicted. The method for forecasting the short term load under the demand response, which is disclosed by the invention, is a short term load forecasting method based on the support vector machine; and the precision and the generalization capability of a forecasting model are improved by using the favorable nonlinear function approximation capability of the support vector machine.

Description

technical field [0001] The invention belongs to the technical field of electric power systems, and in particular relates to a short-term load forecasting method for predicting electric power system loads. Background technique [0002] Power load forecasting is one of the important tasks of power system dispatching, power consumption, planning, planning and other management departments. Accurate load forecasting is helpful to reasonably arrange the start and stop of generator sets, maintain the safety and stability of power grid operation, reduce unnecessary rotating reserve capacity, reasonably arrange unit maintenance plans, effectively reduce power generation costs, and improve economic and social benefits . Therefore, load forecasting has become one of the important contents to realize the modernization of power system management. [0003] The pressure on global resources and the environment is increasing year by year, and society's requirements for environmental protec...

AI Technical Summary

Problems solved by technology

However, in the actual application process, when there are too many input factors, the prediction model structure will be too complex and the training efficiency will be low. Therefore, it is necessary to make a reasonable selection of the input factors of the model to improve the prediction accuracy.

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