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A power load peak forecasting method and device based on a Bayesian network model

A Bayesian network and power load technology, applied in prediction, character and pattern recognition, instruments, etc., can solve the problems of slow learning rate, large deviation of prediction results, inability to handle large-scale samples, etc., and achieve fast learning rate, The effect of small deviations in prediction results

Pending Publication Date: 2018-12-14
CHINA ELECTRIC POWER RES INST
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Problems solved by technology

[0003] In order to overcome the deficiencies in the above-mentioned prior art that the deviation of the prediction result is large, the learning rate is slow, and the large-scale samples cannot be processed when the external influencing factors change greatly, the present invention provides a method for predicting the peak value of electric load based on the Bayesian network model and the device, first clustering the obtained load data with the obtained meteorological data and the numerically processed time data as clustering features, and then predicting the peak power load according to the pre-built Bayesian network model, where The Bayesian network model is constructed according to the clustering results. The present invention has a small prediction result deviation when the external influence factors change greatly, and the learning rate is fast, and it can handle large-scale samples

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  • A power load peak forecasting method and device based on a Bayesian network model
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  • A power load peak forecasting method and device based on a Bayesian network model

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Embodiment 1

[0048] Embodiment 1 of the present invention provides a method for predicting peak power loads based on a Bayesian network model. The specific flow chart is as follows figure 1 As shown, the specific process is as follows:

[0049] S101: Using the acquired meteorological data and numerically processed time data as clustering features, cluster the acquired load data;

[0050] S102: Predict the peak power load according to the pre-built Bayesian network model, wherein the Bayesian network model is constructed according to the clustering results obtained in S102.

[0051]In S101, the acquired meteorological data and numerically processed time data can be used as clustering features, and before clustering the acquired load data, the meteorological data, time data and load data can be obtained first, and the time data can be numerically processed .

[0052] The acquired meteorological data includes temperature, air pressure, wind speed and humidity; the time data includes month, ...

Embodiment 2

[0084] Based on the same inventive concept, Embodiment 2 of the present invention also provides a Bayesian network model-based power load peak prediction device, including a clustering module and a prediction module. The functions of the above-mentioned modules are described in detail below:

[0085] The clustering module is used to cluster the acquired load data by taking the acquired meteorological data and the numerically processed time data as clustering features;

[0086] The prediction module therein predicts the peak value of electric load according to the pre-built Bayesian network model, and the Bayesian network model is constructed according to the clustering results.

[0087] The above meteorological data includes temperature, air pressure, wind speed and humidity, and the time data includes month, week, day and holidays.

[0088] The power load peak prediction device based on the Bayesian network model provided in Embodiment 2 of the present invention also includes...

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Abstract

The invention provides a power load peak forecasting method and device based on a Bayesian network model. The method comprises the steps of: taking the obtained meteorological data and the time data after numerical processing as clustering features, clustering the load data obtained, and then predicting the peak value of the electric load according to the pre-constructed Bayesian network model, wherein the Bayesian network model is constructed according to the clustering result, and the invention has the advantages of small deviation of the prediction result when the external influencing factors change greatly, fast learning speed, and large-scale sample processing. The present invention can effectively process incomplete data, combine with other technologies for causal analysis, effectively combine prior knowledge and data, effectively avoid transitional fitting of data, and still have high accuracy when environmental factors vary greatly.

Description

technical field [0001] The invention relates to the technical field of electricity consumption forecasting, in particular to a method and device for forecasting peak power loads based on a Bayesian network model. Background technique [0002] In recent years, with the rapid growth of the national economy and the continuous improvement of residents' living standards, the proportion of electric energy in the final energy consumption has also become higher and higher. The overall power load has shown a relatively high growth rate, especially the growth rate of the peak load faster than the growth rate of electrical load. At present, the peak load in China continues to hit new highs, and the imbalance between supply and demand during the peak period continues to deepen. Therefore, it is very necessary to strengthen the prediction research on the peak value of electric power load, so as to provide more scientific and effective decision-making basis for the operation and transact...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06K9/62
CPCG06Q10/04G06Q50/06G06F18/23G06F18/24155
Inventor 田世明王文秀卜凡鹏覃剑龚桃荣
Owner CHINA ELECTRIC POWER RES INST
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