Multi-interval-prediction-result weighting integration method

Abstract

The invention discloses a multi-interval-prediction-result weighting integration method. The method specifically comprises the steps of (1) acquiring multiple interval prediction results of one prediction object, wherein each interval prediction result contains a prediction expectation and the variance of the prediction expectation; (2) setting different weights for different interval prediction results; (3) calculating the expectation and variance of a comprehensive prediction result with weight considered; (4) calculating a new weighted interval prediction result according to the expectation and variance of the comprehensive prediction result obtained in the step (3), and ending calculation. According to the method, aiming at the multiple interval prediction results acquired through different approaches and different prediction methods, a new interval prediction result is formed according to the probability distribution information of multiple prediction results preserved currently or user tendency, and then limitation of a single prediction algorithm interval prediction result is avoided and practicality of prediction is improved greatly.

Description

technical field [0001] The invention relates to a weighted integration method of multi-interval prediction results, belonging to the technical field of prediction analysis. Background technique [0002] Traditional forecasting is often based on definite values ​​for analysis and modeling, and the obtained forecast results are also definite values. With the increasing number of forecasting methods, some scholars have proposed a method of combining multiple sets of definite forecast results to obtain a set of relatively more accurate forecasting results. Good final deterministic numerical predictions. [0003] However, the prediction of a certain value is not conducive to subsequent applications to consider the risk of prediction result errors. For example, in order to ensure the balance of power generation and electricity consumption in the power industry, when compiling power generation plans, not only consider meeting the determined load forecast, but also ensure that the p...

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Problems solved by technology

[0003] However, the prediction of a certain value is not conducive to subsequent applications to consider the risk of prediction result errors. For example, in order to ensure the balance of power generation and electricity consumption in the power industry, when compiling power generation plans, not only consider meeting the determined load forecast, but also ensure that the power load is under certain fluctuations. Still able to adjust

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