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Real-time electricity price-based chaotic support vector machine load prediction method

A support vector machine, load forecasting technology, applied in forecasting, instrumentation, data processing applications, etc., can solve problems such as failure to consider the internal evolution of electrical load characteristics

Pending Publication Date: 2021-01-01
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

However, the above load forecasting models fail to take into account the inherent evolution of electrical load characteristics

Method used

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  • Real-time electricity price-based chaotic support vector machine load prediction method
  • Real-time electricity price-based chaotic support vector machine load prediction method
  • Real-time electricity price-based chaotic support vector machine load prediction method

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Embodiment Construction

[0084] The present invention will be further described below in conjunction with specific examples.

[0085] Such as figure 1 , figure 2 , image 3As shown, the real-time electricity price-based chaotic support vector machine load forecasting method provided in this embodiment includes the following steps:

[0086] 1) The improved C-C method is used to reconstruct the multivariate phase space of the load and electricity price time series to obtain high-dimensional phase points. The specific steps are as follows:

[0087] 1.1) For load or electricity price time series X={x 1 , x 2 ,...x i ..., x N}, where x i is the load or electricity value at the i-th moment, with the delay time τ, embedding dimension m, and reconstructing the phase space Y={Y i}, Y i is the i-th phase point in the phase space, and the correlation integral is calculated to get:

[0088]

[0089] Among them, N m is the number of reconstructed phase points, d ij =||Y i -Y j || ∞ Indicates the...

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Abstract

The invention discloses a real-time electricity price-based chaotic support vector machine load prediction method, which comprises the following steps: 1) performing multivariable phase space reconstruction on a load and electricity price time sequence by adopting an improved CC method to obtain a high-dimensional phase point; 2) calculating a Lyapunov index through a small data volume method, calculating a correlation dimension by using a GP method, and quantitatively analyzing the chaotic characteristics of the time sequence; and 3) establishing a model by using a least square support vectormachine improved by an adaptive particle swarm algorithm, and carrying out load prediction. According to the invention, a multivariable phase space reconstruction method is introduced, the influenceof real-time electricity price factors can be effectively considered in load prediction, a least square support vector machine algorithm is used, a regression function is searched in a high-dimensional phase space, the calculation efficiency and precision can be effectively improved, a load prediction technology considering demand side response is realized, and the user load participating in demand side response can be accurately predicted.

Description

technical field [0001] The invention relates to the technical field of demand side response and time series chaotic characteristic analysis and prediction, in particular to a chaotic support vector machine load forecasting method based on real-time electricity price. Background technique [0002] The power industry is an important basic energy industry. In recent years, due to the increase in electricity demand, the scale and level of the power grid have been continuously improved, and users' requirements for the stability, safety and power quality of the power system have also been further improved. . [0003] Load forecasting is the basis for stable, reliable and economical operation of the power grid. It is of great significance in grid construction planning, power deployment planning, and start-stop and maintenance arrangements for generating units. Therefore, accurate load forecasting is of great significance. [0004] Traditional load forecasting methods mainly includ...

Claims

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Application Information

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IPC IPC(8): G06Q50/06G06Q10/04
CPCG06Q10/04G06Q50/06
Inventor 季天瑶王诗雨李志刚
Owner SOUTH CHINA UNIV OF TECH
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