Representative data reconstruction-based incremental SVR (support vector regression) load prediction method

Abstract

The invention discloses a representative data reconstruction-based incremental SVR (support vector regression) load prediction method. The method includes the following steps that: electric load data are acquired; multiple-input-single-output pattern data are obtained through using a phase-space reconstruction theory; a support vector regression model is established by using the obtained pattern data and a particle swarm algorithm; newly-increased electric power load prediction data are obtained in real time; an optimal representative data subset is updated through using an incremental learning algorithm; model parameters are updated by using a nested particle swarm method; a support vector regression model is established by using the updated model parameters and optimal the representative data subset; and incremental load prediction is determined, and an incremental load prediction value is outputted. According to the method of the invention, support vectors of support vector regression are applied to the knowledge understanding research of massive data. With the method adopted, newly increased data-caused representative data reconstruction can be realized; the problems of high calculation complexity of massive data and difficulty in knowledge extraction can be effectively solved; the updating of the model parameters is realized in a nested manner; and references can be provided for the planning and operation of an electric power system.

Description

technical field [0001] The invention relates to the field of rapid analysis of computer data, in particular to an incremental SVR load prediction method based on representative data reconstruction. Background technique [0002] Since electric energy is a kind of energy that is difficult to store in large quantities, the production, transmission, distribution and consumption of electric energy must be carried out at the same moment, which determines that the result of electric load forecasting is the premise of safe, stable and economical operation of the electric power system. At present, the typical load forecasting methods mainly include statistical methods based on parameter assumptions, neural network methods, gray methods, etc. These methods can only train models under given data, but cannot extract representative data from large amounts of data, because only Identifying a small number of representative data in a large amount of training data can produce knowledge for h...

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Problems solved by technology

However, the current support vector regression method needs to re-perform model selection and model training, whi

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