Real-time electricity price forecasting system and method based on multi-density clustering and multi-core SVM

Abstract

The invention provides a real-time electricity price forecasting system based on multi-density clustering and multi-core SVM, which is characterized in that a database management module is respectively connected with a data acquisition module, a power generation energy consumption statistics module, a real-time electricity price forecasting module and a data visualization module. The method and system is mainly used to analyze the time of real-time electricity price, electricity load, main energy generation quantity and generation cost Spatial distribution characteristics and summed up the law, forecasts the real-time tariff for the selected area, and considers the characteristics of the non-linearity of real-time electricity price, sparsity and volatility, as well as the load, the main energy production and the cost of power generation, which improves the prediction accuracy and adaptability of the system, avoids the over-fitting of the prediction model, improves the distributed processing ability, and reduces the computational complexity and time complexity. The real-time electricity price forecasting method based on multi-density clustering and multi-core SVM which is scientificand reasonable, and strong in is provided.

Description

technical field [0001] The invention belongs to the technical field of electricity price prediction, and relates to a real-time electricity price prediction system and method based on multi-density clustering and multi-core SVM. Background technique [0002] Real-time electricity price refers to the marginal cost of providing electricity to users within a limited period of time, considering the operation of the power system and basic investment. It directly reflects the relationship between the market price and the current or real-time market power purchase cost. It is one of the most ideal electricity price mechanisms. Accurate forecasting of real-time electricity prices, on the one hand, can provide reliable value basis for electricity buyers, so as to formulate scientific electricity consumption strategies; Healthy, stable and orderly development of the electricity market. However, because the real-time electricity price is easily affected by many factors, the real-time...

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Problems solved by technology

[0004] The limitation of the forecasting method based on real-time electricity prices is that: with the integration of new energy and new equipment into the power grid at all levels, the time series of electricity prices presents more complex nonlinear characteristics, which makes it difficult for the time series forecasting method to select appropriate input variables number; and the real-time electricity price prediction method using artificial neural network, it is easy to cause the prediction model to overfit and affect the prediction performance of the model; although the prediction method based on support vector machine overcomes the existing shortcomings in the artificial neural network prediction method. Poor generalization ability, slow convergence and other shortcomings, but large-scale training sample data will lead to a significant increase in its computational complexity

Therefore, it is difficult to achieve the desired effect by using the existing real-time electricity price forecasting method

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