CN113076354A - User electricity consumption data analysis method and device based on non-invasive load monitoring - Google Patents
User electricity consumption data analysis method and device based on non-invasive load monitoring Download PDFInfo
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Abstract
The invention provides a method and a device for analyzing user electricity consumption data based on non-invasive load monitoring, wherein the method comprises the following steps: collecting power consumption data at a main power supply inlet of a user, wherein the power consumption data comprises a load time sequence; performing waveform segmentation on the load time sequence to generate a plurality of subsequences; and identifying the load equipment for each subsequence to obtain a load equipment identification result. The method avoids the problems that the existing power consumption data analysis method needs a large amount of sample data for training, has high calculation density, high complexity and high requirement on hardware, and limits the application of the methods to the intelligent electric meter, thereby solving the problems of high calculation density, high complexity and high requirement on hardware of the existing power consumption data analysis method.
Description
Technical Field
The invention relates to the technical field of smart power grids, in particular to a user electricity consumption data analysis method and device based on non-invasive load monitoring.
Background
The intelligent power grid and related applications are being developed and deployed in various countries in the world, and the advantages of the intelligent power grid can be exerted to the greatest extent by using data collected by the intelligent electric meters. The data collected by the intelligent electric meter can be used for identifying the type of the electric appliance in the family of the user, namely, the load identification is realized. The load recognition method can be classified into two types, invasive and non-invasive, from the number of sensors. Intrusive load identification requires the installation of a corresponding sensor for each appliance, and additional equipment and higher cost make the intrusive method difficult to popularize. The non-intrusive load identification only needs to collect data from a single bus intelligent electric meter installed in a family, and decomposes the total household power consumption into the energy consumption of a single electric appliance through data analysis of the intelligent electric meter, so that the feedback of the power consumption condition is facilitated, a user is helped to save energy, and meanwhile, the accurate charging of a supply side is facilitated. Compared with invasive load identification, the non-invasive load identification has low cost and is easy to popularize, so that the method is widely researched.
The non-intrusive load monitoring technology can be divided into three major categories, the first category is an identification method based on transient and steady-state electrical characteristics, the second category is a mathematical optimization identification method, and the third category is an intelligent identification method based on active power and reactive power. The three types of load equipment identification methods all belong to calculation-intensive methods, a large amount of sample data is needed for training, the methods are high in calculation complexity and high in hardware requirement, and therefore the application of the method in the intelligent electric meter is limited.
Disclosure of Invention
In view of the above problems, the present invention provides a method and apparatus for analyzing user electricity consumption data based on non-intrusive load monitoring.
In order to solve the technical problems, the invention adopts the technical scheme that: a user electricity consumption data analysis method based on non-intrusive load monitoring comprises the following steps: collecting power consumption data at a main power supply inlet of a user, wherein the power consumption data comprises a load time sequence; performing waveform segmentation on the load time sequence to generate a plurality of subsequences;
and identifying the load equipment for each subsequence to obtain a load equipment identification result.
Preferably, the waveform division of the load time series includes: and determining the starting point of the subsequence according to the jump value of the load power, and determining the ending point of the subsequence according to the fall value of the load power.
Preferably, the identifying the load device for each subsequence includes: matching each subsequence with a known device reference sequence, and recording the mapping relation between the DTW distance value obtained by each matching calculation and sequence matching; selecting a matching result of the minimum DTW distance value as a load equipment identification result; judging whether the minimum DTW distance value is smaller than a set threshold value, if so, finishing the identification, and if so, performing the next step; removing the identified devices from the time series of loads; carrying out waveform segmentation on the load time sequence again to generate a plurality of subsequences; and repeating the steps until the identification is finished.
Preferably, 2 time series Q and C of length n and m, respectively, are given,
Q＝q1，q2，…，qi，…，qn；
C＝c1，c2，…，cj，…，cm；
constructing a distance matrix D of n rows and m columns, wherein the element Di，jRepresenting point qiAnd point cjThe square of the euclidean distance between; the calculation formula of the DTW distance is:
di，j＝(qi－cj)2；
wherein, i is 1, 2, …, n, j is 1, 2, …, m.
Preferably, before the waveform division of the load time series, the method further includes: and carrying out normalization processing on the load time series.
The invention also provides a user electricity consumption data analysis device based on non-invasive load monitoring, which comprises: the power consumption data acquisition module is used for acquiring power consumption data at an inlet of a main power supply of a user, and the power consumption data comprises a load time sequence; the subsequence generating module is used for carrying out waveform segmentation on the load time sequence to generate a plurality of subsequences; and the load equipment identification module is used for identifying load equipment for each subsequence and acquiring a load equipment identification result.
Preferably, the load device identification module includes: the sequence matching unit is used for matching each subsequence with a known device reference sequence and recording the mapping relation between the DTW distance value obtained by each matching calculation and the sequence matching; an identification result determination unit for selecting a matching result of the minimum DTW distance value as a load device identification result; a DTW distance value judging unit for judging whether the minimum DTW distance value is smaller than a set threshold value, if so, finishing the identification, and if so, performing the next step; and the removing unit is used for removing the identified equipment from the load time sequence.
Compared with the prior art, the invention has the beneficial effects that: the method has the advantages that the waveform separation is carried out on the collected load time sequence to generate the subsequence, then the load equipment identification is carried out on the subsequence, the calculation process is simple, no complex calculation method is involved, the calculation program can be conveniently written into a computer chip and is embedded into the intelligent ammeter, the requirement on hardware is not high, the problems that the existing power consumption data analysis method needs a large amount of sample data for training, the calculation density is high, the complexity is high, the requirement on hardware is high, and the methods are limited to be applied to the intelligent ammeter are solved, and the problems that the existing power consumption data analysis method is high in calculation density, complexity and hardware requirement are high are solved.
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The disclosure of the present invention is illustrated with reference to the accompanying drawings. It is to be understood that the drawings are designed solely for the purposes of illustration and not as a definition of the limits of the invention. In the drawings, like reference numerals are used to refer to like parts. Wherein:
fig. 1 is a schematic flowchart of a method for analyzing user electricity consumption data based on non-intrusive load monitoring according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of load device identification for each sub-sequence according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a user electricity consumption data analysis apparatus based on non-intrusive load monitoring according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a load device identification module according to an embodiment of the present invention.
Detailed Description
It is easily understood that according to the technical solution of the present invention, a person skilled in the art can propose various alternative structures and implementation ways without changing the spirit of the present invention. Therefore, the following detailed description and the accompanying drawings are merely illustrative of the technical aspects of the present invention, and should not be construed as all of the present invention or as limitations or limitations on the technical aspects of the present invention.
An embodiment according to the present invention is shown in connection with fig. 1. A user electricity consumption data analysis method based on non-invasive load monitoring comprises the following steps:
and S1, collecting power consumption data at the inlet of the main power supply of the user, wherein the power consumption data comprises a load time sequence.
S2, the load time series is waveform-divided to generate a plurality of subsequences.
Before the waveform segmentation is carried out on the load time sequence, the method further comprises the following steps: and carrying out normalization processing on the load time series.
In order to reduce interference caused by large power difference between different electric equipment, the load time series to be identified is subjected to normalization processing in the embodiment of the invention. Assume that the load to be identified is time-series of
A＝{a1，a2，…ai，…，amAnd m is larger than or equal to 1, the normalization formula is as follows:
wherein A ismeanAs the mean of the time series of the loads to be identified, AvarIs the variance, a ', of the time sequence of the load to be identified'iThe load value in the normalized load time sequence to be identified is A '═ a'1，a'2，…a'i，…，a'm}。
And S3, identifying the load equipment for each subsequence, and acquiring the identification result of the load equipment.
In step S2, the waveform division is performed on the load time series, and includes: and determining the starting point of the subsequence according to the jump value of the load power, and determining the ending point of the subsequence according to the fall value of the load power.
Referring to fig. 2, in step S3, load device identification is performed for each sub-sequence, including:
s301, matching each subsequence with a known device reference sequence, and recording the mapping relation between the DTW distance value obtained by each matching calculation and the sequence matching.
Given 2 time series Q and C of length n and m respectively,
Q＝q1，q2，…，qi，…，qn；
C＝c1，c2，…，cj，…，cm；
constructing a distance matrix D of n rows and m columns, wherein the elements represent points qiAnd point cjThe square of the euclidean distance between; the DTW distance is calculated as:
di，j＝(qi－cj)2；
wherein, i is 1, 2, …, n, j is 1, 2, …, m.
And S302, selecting the matching result of the minimum DTW distance value as the load equipment identification result.
And S303, judging whether the minimum DTW distance value is smaller than a set threshold value, if so, finishing the identification, and if so, performing the next step.
S304, removing the identified devices from the load time sequence, performing waveform segmentation on the load time sequence again to generate a plurality of subsequences, and repeating the steps S2, S301 to S304 until the identification is finished.
Referring to fig. 3, the present invention further provides a user electricity consumption data analysis apparatus based on non-intrusive load monitoring, including:
and the power consumption data acquisition module 1 is used for acquiring power consumption data at the entrance of a main power supply of a user, and the power consumption data comprises a load time sequence.
And the subsequence generating module 2 is used for performing waveform segmentation on the load time sequence to generate a plurality of subsequences.
And the load equipment identification module 3 is used for identifying load equipment for each subsequence and acquiring a load equipment identification result.
Referring to fig. 4, the load device identification module 3 includes:
and a sequence matching unit 301, configured to match each subsequence with a known device reference sequence, and record a mapping relationship between a DTW distance value obtained through each matching calculation and sequence matching.
An identification result determination unit 302 configured to select a matching result of the minimum DTW distance value as a load device identification result.
A DTW distance value determination unit 303, configured to determine whether the minimum DTW distance value is smaller than a set threshold. If the value is smaller than the threshold value, the recognition is finished, and if the value is larger than the threshold value, the rejection unit 304 is operated.
A culling unit 304 for culling the identified devices from the load time series.
In summary, the beneficial effects of the invention include: the method has the advantages that the waveform separation is carried out on the collected load time sequence to generate the subsequence, then the load equipment identification is carried out on the subsequence, the calculation process is simple, no complex calculation method is involved, the calculation program can be conveniently written into a computer chip and is embedded into the intelligent ammeter, the requirement on hardware is not high, the problems that the existing power consumption data analysis method needs a large amount of sample data for training, the calculation density is high, the complexity is high, the requirement on hardware is high, and the methods are limited to be applied to the intelligent ammeter are solved, and the problems that the existing power consumption data analysis method is high in calculation density, complexity and hardware requirement are high are solved.
It should be understood that the integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The technical scope of the present invention is not limited to the above description, and those skilled in the art can make various changes and modifications to the above-described embodiments without departing from the technical spirit of the present invention, and such changes and modifications should fall within the protective scope of the present invention.
Claims (7)
1. A user electricity consumption data analysis method based on non-intrusive load monitoring is characterized by comprising the following steps:
collecting power consumption data at a main power supply inlet of a user, wherein the power consumption data comprises a load time sequence;
performing waveform segmentation on the load time sequence to generate a plurality of subsequences;
and identifying the load equipment for each subsequence to obtain a load equipment identification result.
2. The method of claim 1, wherein the waveform segmenting the load time series comprises: and determining the starting point of the subsequence according to the jump value of the load power, and determining the ending point of the subsequence according to the fall value of the load power.
3. The method for analyzing user electricity consumption data based on non-invasive load monitoring according to claim 1, wherein the identifying load devices for each sub-sequence comprises:
matching each subsequence with a known device reference sequence, and recording the mapping relation between the DTW distance value obtained by each matching calculation and sequence matching;
selecting a matching result of the minimum DTW distance value as a load equipment identification result;
judging whether the minimum DTW distance value is smaller than a set threshold value, if so, finishing the identification, and if so, performing the next step;
removing the identified devices from the time series of loads;
carrying out waveform segmentation on the load time sequence again to generate a plurality of subsequences;
and repeating the steps until the identification is finished.
4. The method of claim 3 wherein 2 time series Q and C of length n and m are given,
Q＝q1，q2，…，qi，…，qn；
C＝c1，c2，…，cj，…，cm；
constructing a distance matrix D of n rows and m columns, wherein the element Di，jRepresenting point qiAnd point cjThe square of the euclidean distance between; the calculation formula of the DTW distance is:
di，j＝(qi－cj)2；
wherein, i is 1, 2, …, n, j is 1, 2, …, m.
5. The method of claim 1, wherein prior to waveform partitioning the load time series, the method further comprises: and carrying out normalization processing on the load time series.
6. A user electricity consumption data analysis device based on non-intrusive load monitoring is characterized by comprising:
the power consumption data acquisition module is used for acquiring power consumption data at an inlet of a main power supply of a user, and the power consumption data comprises a load time sequence;
the subsequence generating module is used for carrying out waveform segmentation on the load time sequence to generate a plurality of subsequences;
and the load equipment identification module is used for identifying load equipment for each subsequence and acquiring a load equipment identification result.
7. The non-intrusive load monitoring based consumer electricity consumption data analysis device of claim 6, wherein the load device identification module comprises:
the sequence matching unit is used for matching each subsequence with a known device reference sequence and recording the mapping relation between the DTW distance value obtained by each matching calculation and the sequence matching;
an identification result determination unit for selecting a matching result of the minimum DTW distance value as a load device identification result;
a DTW distance value judging unit for judging whether the minimum DTW distance value is smaller than a set threshold value, if so, finishing the identification, and if so, performing the next step;
and the removing unit is used for removing the identified equipment from the load time sequence.
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