Load prediction method and system based on LSSVM optimization

Abstract

The invention discloses an load prediction method and system based on LLSVM optimization. The load prediction method based on LSSVM optimization comprises steps of discriminating and correcting abnormal data after obtaining original history load data, constructing a characteristic vector, performing k average value clustering on characteristic vectors, choosing a input variable according to a clustering effect, 2) using a punishment factor C of an LSSVM model and a kernel function width parameter sigma as position coordinates of a particle in a searching space for a particle swarm algorithm, using a particle coordinate vector value [C,sigma] having a smallest fitness value as an output of the particle swam algorithm, (3) using optimized and processed input variable data as input and output of the LSSVM model, and using the [C,sigma] obtained from the step 2 to solve a load prediction regression equation and using the regression equation to perform load prediction. The load prediction method and system based on LSSVM optimization can correct abnormal data, find the most suitable punishment factor and the kernel function parameter and improve accuracy of the load prediction based on the LSSVM.

Description

technical field [0001] The invention belongs to the field of load forecasting, and more specifically relates to a load forecasting method and system based on LSSVM optimization. Background technique [0002] Among the existing load forecasting technologies, LSSVM has the advantages of fast calculation speed, high forecasting accuracy, and good generalization performance, and is very suitable for nonlinear, high-dimensional, and small-sample load forecasting application scenarios. Through the real-time prediction of building load and comparing the predicted value with the actual load value, abnormal use of building energy consumption and existing equipment failures can be found in time, and remedial measures can be taken to avoid possible losses; analysis of building energy saving status and energy saving Potential, adjust energy-saving strategies, and provide decision-making basis for rational energy allocation. [0003] When load forecasting based on LSSVM technology, the ...

AI Technical Summary

Problems solved by technology

[0006] Aiming at the defects of the prior art, the purpose of the present invention is to solve the problem that the quality of the model data sample is not high, the penalty item parameter C and the kernel function width parameter σ are difficult to select when the prior art load forecasting based on LSSVM technology is based on the real-time load of LSSVM Forecast presents difficult technical issues

Images