Construction method and application of resident power consumption mode prediction model

A technology for forecasting models and building methods, applied in forecasting, data processing applications, character and pattern recognition, etc., can solve problems such as difficulty in load forecasting, and achieve the effect of good model stability

Pending Publication Date: 2022-07-12
HUAZHONG UNIV OF SCI & TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] Aiming at the defects and improvement needs of the prior art, the present invention provides a construction method and application of a residential electricity consumption pattern prediction model. The problem of load forecasting is difficult due to the variety of power consumption patterns

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  • Construction method and application of resident power consumption mode prediction model
  • Construction method and application of resident power consumption mode prediction model
  • Construction method and application of resident power consumption mode prediction model

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Embodiment 1

[0045] A method for constructing a prediction model of household electricity consumption pattern 10, such as figure 1 shown, including:

[0046] 110. Smooth and cluster the historical daily load curve of the residents, define the power consumption mode of the residents' daily load curve, and obtain a variety of power consumption modes;

[0047] 120. Establish an overall Markov model M using the historical electricity consumption patterns of each inhabitant 0 , get M 0 The corresponding state transition matrix; at the same time, an individual Markov model M is established based on the historical power consumption pattern of the resident i i , get M i Corresponding state transition matrix; based on the electricity consumption pattern of resident i on the jth day, respectively adopt M 0 The corresponding state transition matrix and M i The corresponding state transition matrix is ​​used to predict the electricity consumption pattern of day j+1, and the weighted average of th...

Embodiment 2

[0087] A method for predicting a residential electricity consumption pattern, comprising:

[0088] The electricity consumption pattern prediction model of resident i constructed by the method for constructing a resident electricity consumption pattern prediction model described in the first embodiment is used, and the electricity consumption pattern of resident i on the current day is input to obtain the electricity consumption pattern probability of resident i on the next day. distribution to complete the prediction of power consumption patterns. The related technical solutions are the same as those in the first embodiment, and are not repeated here.

Embodiment 3

[0090] A method for predicting daily load data of residents, comprising:

[0091] Using the load data predictor corresponding to each power consumption mode that has been trained, input the daily load data of resident i on the current day respectively, and obtain the daily load data corresponding to each power consumption mode of resident i on the next day;

[0092] based on Calculate the daily load data of resident i for the next day;

[0093] in, represents the probability that the electricity consumption pattern of resident i in the next day belongs to electricity consumption pattern k, represents the daily load data corresponding to the predicted electricity consumption pattern k of the next day of resident i; Represents the daily load data corresponding to the electricity consumption pattern k of the next day of the resident i predicted by the integration method.

[0094] That is, in order to solve the above-mentioned problem that a single resident corresponds to ...

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Abstract

The invention belongs to the field of power load prediction, and particularly relates to a construction method and application of a resident power consumption mode prediction model, and the method comprises the steps: obtaining a plurality of power consumption modes corresponding to a resident daily load curve through employing a smoothing clustering method; the method comprises the following steps: constructing a mixed weighted Markov model as a power consumption mode prediction model of a resident i, predicting a future power consumption mode of the resident by adopting the mixed weighted Markov model, predicting a load by adopting a plurality of predictors, and performing weighted combination according to a power consumption mode prediction result of the mixed weighted Markov model, therefore, more accurate and stable resident load prediction is realized, and good data support and convenient analysis basis are provided for application such as demand response. Compared with a resident load prediction method of a single predictor, the resident load prediction integration method provided by the invention can effectively utilize various modes of information of residents to obtain a more accurate and stable load prediction result.

Description

technical field [0001] The invention belongs to the field of power load forecasting, and more particularly, relates to a construction method and application of a forecasting model of a household power consumption pattern. Background technique [0002] Household demand response can shift flexible peak loads to off-peak times, protecting smart grid operations and providing economic benefits to households and grid companies. Therefore, residents whose peak loads coincide with smart grid peak hours are potential demand response targets. In order to find typical residents suitable for household demand response, short-term resident load forecasting is critical. [0003] However, residents' electricity consumption patterns are volatile, making it very difficult to predict residents' electricity loads. These volatile electricity consumption patterns are caused by internal and external factors. Internal factors include family member information and work schedules, etc., which belo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q10/06G06Q50/06G06K9/62
CPCG06Q10/04G06Q10/0639G06Q50/06G06F18/23213Y04S10/50
Inventor 肖江文方宏亮崔世常刘骁康王燕舞刘智伟
Owner HUAZHONG UNIV OF SCI & TECH
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