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A Method for Predicting Power Load Conditional Density

A technology of power load and condition density, applied in electrical digital data processing, special data processing applications, instruments, etc., can solve problems such as undiscovered, achieve good scalability, avoid overly complex models, and improve computing speed.

Active Publication Date: 2017-09-05
STATE GRID CORP OF CHINA +1
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  • A Method for Predicting Power Load Conditional Density
  • A Method for Predicting Power Load Conditional Density
  • A Method for Predicting Power Load Conditional Density

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[0037] The method for predicting the density of electric load conditions provided by the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0038] The power load condition density prediction method provided by the present invention includes the following steps performed in order:

[0039] Step 1) model establishment: based on the neural network structure and the quantile regression model, establish the quantile regression model of the electric load neural network;

[0040] Step 2) Model solution: In the above-mentioned electric load neural network quantile regression model, since the asymmetric "check function" function (check function) is used as the loss function, it will be non-differentiable at point 0, which brings great difficulties to the model solution. come difficult; the present invention uses the Huber norm to correct the asymmetric "tick" function in the electric load neural network quantile...

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Abstract

A method for power load condition density prediction comprises the steps of model building, model solution, model selection and condition density prediction. The method has the advantages that a power load neural network quantile regression model is built, the advantages of a neural network model and a quantile regression model are combined, the variation law of the power load can be depicted accurately, and the method shows powerful functions. A standard gradient optimization algorithm of the power load neural network quantile regression model is given; and on the premise that model estimation accuracy is not affected, the model computation speed is improved. An AIC criterion of the neural network quantile regression model selection of the power load is established, and the problem that the model is too complex and is subject to overfitting is effectively solved. The power load condition density prediction method is established on the basis of the neural network quantile regression, not only is the model prediction accuracy remarkably improved, but also whole probability density prediction results of the power load are obtained, and the method can provide more useful information and facilitate scientific decision-making.

Description

technical field [0001] The invention belongs to the technical field of application of prediction theory and method in electric power system, and in particular relates to a method for predicting electric load condition density. Background technique [0002] Power load forecasting is an important part of power system planning, and whether it can be accurately predicted is related to the success or failure of power system planning and economic operation. Power load forecasting is based on the past change law of power load, and predicts the future time distribution and spatial distribution characteristics, which has the characteristics of uncertainty and conditionality. [0003] Electric load forecasting has its own complexity, which can be divided into two types: deterministic load forecasting method and uncertain load forecasting method. The deterministic load forecasting method mainly uses one or a set of equations to describe the power load variation law to achieve the purp...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/00
Inventor 刘树勇王磊许启发何耀耀李娜穆健
Owner STATE GRID CORP OF CHINA
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