Heat supply load interval prediction method based on chaos theory

A technology of chaos theory and load interval, applied in the direction of chaos model, nonlinear system model, etc., can solve the problems that cannot meet the requirements of load forecast reliability, lack of internal regularity description of heat load, etc.

Inactive Publication Date: 2009-02-18
HARBIN INST OF TECH
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  • Abstract
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Problems solved by technology

[0003] The purpose of the present invention is to solve the problem that the existing heat supply load forecasting method relies on a large amount of physical data and weather forecast information, and many point forecasts cannot meet the requirements of thermal dispatching for the reliability of load forecasting, and the existing heat supply load forecast The method lacks the problem of describing the internal regularity of the heat load, and the present invention provides a heating load interval forecasting method based on chaos theory

Method used

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  • Heat supply load interval prediction method based on chaos theory
  • Heat supply load interval prediction method based on chaos theory
  • Heat supply load interval prediction method based on chaos theory

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specific Embodiment approach 1

[0048] Specific implementation mode one: based on the heating load interval forecasting method based on chaos theory, the forecasting method is completed by the following steps: Step 1, the state space reconstruction of the heating load time series: (a) select the embedding of the heating load time series dimension, (b) calculate the delay time of the heating load time series, (c) reconstruct the phase space according to the obtained delay time and embedding dimension; step 2, chaotic identification with the largest Lyapunov exponent: (a) find the reconstructed phase space the nearest neighbors of each point in phase space, and limit the short-term separation of the point from its nearest neighbors, (b) calculate the distance after all discrete time steps of its nearest neighbors for each point in phase space, (c) Calculate the average distance according to the distance from each point to each discrete time step corresponding to this point, (d) make a regression line according ...

specific Embodiment approach 2

[0049] Embodiment two: the difference between this embodiment and embodiment one is: it also includes step 4, adopting the relative error method and the root mean square relative error method to evaluate the Lyapunov index point forecast, and using the interval reliability to evaluate the method's To evaluate the reliability of the forecast interval.

[0050] principle:

[0051] 1. Maximum forecast time scale

[0052] According to the chaotic system theory, if the maximum Lyapunov exponent λ of the time series 1 max , which has with the largest Lyapunov exponent T max = 1 λ 1 established, the maximum Lyapunov exponent interval can be obtained by the prediction algorithm of the maximum Lyapunov exponent interval Therefore, the maximum forecast time scale T max becomes 1 λ 1 m...

specific Embodiment approach 3

[0061] Embodiment 3: The difference between this embodiment and Embodiment 2 is that the method for selecting the embedding dimension of the heating load time series in step 1 (a) is the pseudo-neighborhood method.

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Abstract

A heat load area prediction method based on chaos theory is provided, which relats to a prediction method of heat load. The invention solves the problems that the prediction method of the heat load in prior art depends on a plurality of physical data and weather forecast information, point prediction does not satisfy the requirement of heating power schedule on reliability of load forecast, and the heat load area prediction method is lack of inherent regularity description. The method comprises: 1. state space reconstitution of heat load time sequence; 2. chaos recognition of the largest Lyapunov exponent; 3. span forecast of the largest Lyapunov exponent. The invention is directly applied on heat supply energy saving reconstruction, heating power schedule and heating power station control.

Description

technical field [0001] The invention relates to a method for forecasting heating load. Background technique [0002] The main problems in the prior art are: 1. The existing heat load forecasting method lacks the description of the internal regularity of the heat load. At present, the heating load forecasting methods adopted at home and abroad are mostly independent or combined use of the following methods: time series, neural network, wavelet analysis, support vector machine and so on. Looking at the research status at home and abroad, the "mathematical" tendency of heating load forecast is increasing day by day, and the research on the internal change regularity of heating load is not enough. Although these models and methods are advanced, the forecasting methods are complicated and difficult to master, resulting in It cannot be flexibly applied in practice. 2. The existing heat supply load forecasting methods rely on a large amount of physical data and weather forecast i...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N7/08
Inventor 齐维贵张永明陈烈邓盛川
Owner HARBIN INST OF TECH
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