CN111489103A - System and method for classifying electricity consumption condition of residents based on autoregressive analysis - Google Patents

Abstract

The potential community electricity utilization safety problem is paid more and more attention in the current urbanization management, how to rapidly identify the potential electricity utilization safety problem existing in an electricity utilization unit, and the demand of better managing community energy utilization through big data and an intelligent method is increased in recent years. The invention discloses a system and a method for identifying the potential safety hazard of residential electricity consumption according to the real-time power load condition by using an autoregressive analysis mode.

Description

System and method for classifying electricity consumption condition of residents based on autoregressive analysis

Technical Field

The invention relates to the field of analysis of big data of household and civil electricity, in particular to a system and a method for classifying household electricity consumption conditions based on autoregressive analysis.

Background

Monitoring and managing resident electricity utilization safety by using electricity load data are increasingly applied to community management in recent years, and for potential electricity utilization safety hazards or special conditions which are analyzed, classification management is also increasingly emphasized by community management, so that the problem which needs to be solved in a future intelligent city system is solved. With the advancement of urbanization in China, a large number of population groups live in, and the traditional electricity safety management method becomes lagged and inefficient. For many power utilization units with potential power utilization safety hazards, advance prejudgment and identification cannot be usually performed, for example: illegal group renting houses, solitary old people, illegal community workshops and the like. With the wide popularization and application of the intelligent electric meter in cities, more and more real-time electricity load data can be well collected by a management organization, and the possibility of data is provided for intelligent management. The invention aims to identify the electricity utilization characteristics of users by applying various mathematical statistics models to real-time electricity utilization load data, classify the electricity utilization conditions of the users and provide decision-making opinions for city managers, power supply and power utilization parties to the comprehensive management of resident electricity utilization.

Disclosure of Invention

The invention provides a system and a method for analyzing potential residential electricity utilization potential safety hazards based on power load data. The whole process comprises a data collection module, an extreme value elimination module, an autocorrelation coefficient analysis module and an identification report module, which are shown in figure 1; the electric load data collection module generally collects, stores and processes original residential electricity load data through terminal equipment such as an intelligent electric meter and the like, analyzes, integrates and corrects the data, fills up missing values and carries out standardized processing. The abnormal analysis module eliminates extreme abnormal values in the power load data, wherein the extreme abnormal values include maximum and minimum values and missing values which are formed by reading errors during data entry. And inputting the data after the abnormal analysis into an autoregressive analysis module, performing 48-hour autoregressive analysis on the load data of each power consumption unit by the autoregressive analysis module, and analyzing whether the power consumption rule of the power consumption unit is normal, relatively normal or abnormal by calculating the correlation of the same time period on different days. And finally, determining whether the user is reported to be an abnormal user by comparing historical conditions, on-site investigation and other modes according to the classified user conditions in the electricity utilization type identification module.

Drawings

Fig. 1 is a flow chart of a residential electricity consumption safety analysis module in the embodiment of the invention.

FIG. 2 is a diagram illustrating a normal power utilization behavior according to an embodiment of the present invention.

Fig. 3 is a diagram illustrating a curve showing a normal power consumption behavior according to an embodiment of the present invention.

FIG. 4 is a diagram illustrating an example of a curve of abnormal power consumption behavior according to an embodiment of the present invention.

Detailed Description

In order to make the content, the purpose, the features and the advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the scope of the protection scope of the present invention.

The invention provides a system and a method for classifying electricity consumption of residents based on autoregressive analysis.

1. A data collection module: the electricity consumption data of each electricity consumption unit is collected and stored. The electrical load data may be logged at different frequencies and summed or redistributed to hours before entering the analysis.

2. According to the processed power load data obtained in the step 1, firstly, the power consumption data of each user is subjected to abnormal analysis, and extreme values are removed:

1) the data values are:

2) assuming a gaussian distribution of data load for each user:

3) solving the corresponding parameters:

4) if it is not

Then the value is considered to be extreme.

3. Performing autoregressive analysis on each electricity unit data based on the processed data obtained in step 2:

where k is the value we choose 48.

4. From the results of the autoregressive analysis in step 3, we select the 24 th and 48 th values, and if the sum of the two values is greater than 0.15, the user is considered to be normal in power consumption behavior, as shown in fig. 2;

if the power consumption is less than 0.15 and greater than 0, the user is considered to have normal power consumption behavior, as shown in fig. 3;

if the power consumption is less than 0, the user is considered to be abnormal in power consumption behavior, as shown in FIG. 4;

while the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

The invention provides a method for analyzing abnormal analysis and autoregressive analysis by aiming at users of the same type of power utilization condition in a community area through deep data analysis and mining of power utilization load, establishes a group representative power utilization condition image, has quite a plurality of applications in scale management of the power utilization safety condition of a specific area, saves the workload and screening rate of related personnel to a great extent, and improves the efficiency of administrative management and power utilization safety.

Claims (1)

1. The invention provides a system and a method for classifying electricity consumption of residents based on autoregressive analysis, which are characterized by comprising the following steps of:

step one, a data collection module: collecting and storing the electricity utilization data of each electricity utilization unit, recording the electricity utilization load data by adopting different frequencies, and summing or redistributing the electricity utilization load data into hours before analysis;

step two, according to the processed power load data obtained in the step one, firstly, carrying out abnormity analysis on the power consumption data of each user, and eliminating extreme values:

1) the data values are:

2) assuming a gaussian distribution of data load for each user:

3) solving the corresponding parameters:

4) if it is not

If so, the value is considered as an extreme value;

thirdly, performing autoregressive analysis on each electricity consumption unit data according to the processed data acquired in the second step:

wherein, the k value is selected to be 48;

step four, selecting the 24 th value and the 48 th value according to the result of the autoregressive analysis in the step three, and if the sum of the two values is greater than 0.15, determining that the electricity utilization behavior of the user is normal, as shown in fig. 2;

if the power consumption is less than 0.15 and greater than 0, the user is considered to have normal power consumption behavior, as shown in fig. 3;

if the power consumption is less than 0, the power consumption behavior of the user is considered to be abnormal, as shown in FIG. 4.

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