CN111553444A - Load identification method based on non-invasive load terminal data - Google Patents
Load identification method based on non-invasive load terminal data Download PDFInfo
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Abstract
The invention discloses a load identification method based on non-invasive load terminal data, which comprises the following steps: sampling the load data; carrying out mean shift clustering on historical electricity utilization data of various electric appliances of a user to obtain different clusters of a clustered electric appliance data set of the user, extracting load identification characteristics of the electric appliances from the electric appliance clusters, thereby identifying the historical load type loads and establishing a multivariate Gaussian distribution model of the operation characteristics of the electric appliances; on the other hand, load operation data is used as input characteristics, and the probability of occurrence of each cluster corresponding to the operation characteristics of the electric appliance is calculated by a naive Bayes algorithm; and taking a cluster with the maximum probability as the actual category of the electric appliance, and taking the electric appliance type of the cluster as the electric appliance type of the electric appliance. According to the invention, through carrying out cluster modeling on the user historical load operation data sent from the non-invasive load terminal data, the re-identification of the cloud platform on the user load is realized, and the comparison and correction of the electrical appliance attributes sent from the non-invasive terminal can be effectively carried out.
Description
Technical Field
The invention belongs to the technical field of intelligent power utilization, and particularly relates to a load identification method based on non-invasive load terminal data.
Background
The intelligent power utilization enables the load of residents to become a relatively controllable resource, and the load transfer and peak load shifting are realized through means of direct load control, time-of-use electricity price and the like. The load monitoring is a core link of intelligent power utilization, and the intelligent electric meter is used for analyzing internal load components and load characteristics of a user to obtain detailed power utilization behaviors of the user and energy utilization information of the electric meter. Load Monitoring is classified into Intrusive Load Monitoring (ILM) and Non-Intrusive Load Monitoring (NILM). In order to perform online monitoring on all electrical equipment of a user, an existing monitoring system needs to install a plurality of sensors for induction measurement and data transmission devices in the user, so that installation cost and maintenance cost are greatly increased, the production life of the user is interfered by an 'intrusive' installation mode and maintenance management, the satisfaction degree of the user on energy management service is reduced, and therefore the technical level of load monitoring in intelligent power utilization needs to be improved urgently. The non-invasive method is that the monitoring equipment is installed at the electric power entrance of the user, the power consumption information of the user is analyzed through a load identification algorithm, the power consumption condition of each equipment in the user is obtained, the hardware structure is greatly simplified, the cost is reduced, the method is suitable for a large number of scattered user installation modes, benefits are brought to multiple parties such as a power grid and the user, and the method becomes a hot spot of domestic and foreign research.
The process of analyzing the electricity utilization behavior of the user by utilizing the non-invasive load monitoring can be divided into 3 parts, namely load classification, decomposition of the electricity utilization behavior and advanced application. Firstly, household electrical equipment is analyzed through feature extraction and load classification, then, the electricity utilization information of the electrical equipment is counted by using an electricity utilization behavior decomposition algorithm, the electricity utilization information comprises contents such as consumed electric energy, types of start-stop appliances, electricity consumption, start-stop time and the like, and the non-invasive load monitoring and calculation are considered to be complex, so that the operation is generally carried out on a cloud platform, and finally, after cloud analysis, the monitored electricity utilization information of each electrical equipment is fed back to a user, so that the user can conveniently manage household energy and participate in power grid interaction; and on the other hand, the method provides services for power grid companies or other management departments to make incentive policies such as power price and demand response measures.
In conclusion, breakthrough and industrialization of the NILMD technology have important significance on development of intelligent power utilization and improvement of energy conservation and emission reduction benefits. However, the nimld technology has yet to be extensively studied in the technology of identifying the electrical load characteristics of the cloud platform.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the load identification method based on the non-invasive load terminal data is provided, the cloud platform is used for identifying the user load again, and the attributes of the electric appliance sent by the non-invasive terminal can be effectively compared and corrected.
The technical scheme is as follows: in order to achieve the above object, the present invention provides a load identification method based on non-intrusive load terminal data, comprising the following steps:
s1: sampling load data through a non-intrusive load terminal;
s2: the load data is sorted, and the method specifically comprises the following two aspects:
on one hand, the historical electricity utilization data of various electric appliances of the user are subjected to mean shift clustering to obtain different clusters of a clustered electric appliance data set of the user, and load identification characteristics of the electric appliances are extracted from the electric appliance clusters, so that the historical load type load is identified;
on the other hand, a multivariate Gaussian distribution model of the operation characteristics of the electric appliance is established according to the electricity utilization characteristics of the user of each sample point in the cluster, the load operation data is used as input characteristics, and the probability of the operation characteristics of the electric appliance corresponding to each cluster is calculated by a naive Bayes algorithm;
s3: and taking a cluster with the maximum probability as the actual category of the electric appliance, obtaining the electric appliance type of the cluster, and taking the electric appliance type of the cluster as the electric appliance type of the electric appliance.
Further, the specific steps of mean shift clustering in step S2 are as follows:
a1: determining the radius r of the sliding window, and starting clustering by using a randomly selected central point C and a circular sliding window with the radius r;
a2: in each iteration process, calculating the mean value in a new sliding window, namely a new central point of the window, wherein the number of points in the sliding window is the density in the window;
a3: continuing to move the sliding window according to the mean value until the density in the circle is not increased any more;
a4: filtering the sliding windows generated in the steps A2 to A4, and when a plurality of windows are overlapped, reserving the window containing the most points and clustering according to the sliding window where the data points are positioned;
a5: and storing the centroid coordinates of each data set as standard characteristic values of the categories, and counting the distribution condition of each characteristic in the group as the distribution range of the electric appliance characteristics.
Further, the historical electricity consumption data of the electrical appliance in the step S2 includes total electricity consumption, peak power, number of start-stop times, start time, and running time of the electrical appliance.
Further, the load identification features of the electrical appliance cluster in step S2 include load average running time, load average power, and running time distribution.
Further, the determination process of the radius parameter r of the circular sliding window in step S2 is as follows:
given a dataset P ═ { P (i); 0,1, … n, for any point p (i), calculating the distances between all points in the subset S of the set D from the point p (i) to the point p (i), p (2), …, p (i-1), p (i +1), …, p (n), the distances being sorted in order from small to large, assuming that the sorted distance set is D { D (1), D (2), …, D (k-1), D (k +1), …, D (n) }, D (k) is called k-distance, calculating k-distance for each point p (i) in the set to be clustered, and finally obtaining the k-distance set E of all points E ═ E (1), E (2), …, E (n) }, and according to the obtained k-distance set E of all points, sorting the set E in ascending order to obtain the k-distance set E, obtaining the k-distance set E', and fitting a change curve graph of the k-distance in the sorted E' set, and determining the value of the k-distance corresponding to the position where the change occurs sharply as the value of the radius r according to the change curve graph.
Further, the process of establishing the multivariate gaussian distribution model of the operation characteristics of the electrical appliance in step S2 is as follows:
b-1: data preliminary processing:
respectively reading the user electricity utilization characteristics of the points in each cluster according to different clusters of the clustered user electrical appliance data set;
b-2: calculating the characteristic mean value mu of the sample:
wherein k is the total number of the characteristics i in the electrical appliance cluster, muiThe characteristic mean value of the characteristic i in the cluster;
b-3: establishing a covariance matrix sigma of the electrical characteristic distribution, wherein X is a characteristic vector set of the samples, m is the number of the samples,
and obtaining a multivariate Gaussian distribution model of the power utilization behavior of the user.
Further, the specific calculation step of calculating the occurrence probability of each cluster corresponding to the operating characteristics of the electrical appliance by using a naive bayes algorithm in the step S2 is as follows:
c-1: determining the operation characteristics of a sample set, and collecting sample data;
c-2: training samples, and respectively calculating the conditional probability of the characteristics of each category, namely the occurrence probability of the corresponding characteristic value of each characteristic;
c-3: classifying the target, reading the characteristic value X of the target, calculating it in each class CiProbability of p (X | C)i) Targeting the maximum term thereinClass (c).
Has the advantages that: compared with the prior art, the method and the system have the advantages that the clustering modeling is carried out on the historical load operation data of the user sent from the non-invasive load terminal data, the type of the actual electric appliance of the user is further analyzed, the cloud platform can identify the user load again, and the attributes of the electric appliance sent from the non-invasive terminal can be effectively compared and corrected.
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FIG. 1 is a system flow diagram of the method of the present invention;
fig. 2 is a case power diagram for testing the method of the present invention.
Detailed Description
The invention is further elucidated with reference to the drawings and the embodiments.
As shown in fig. 1, the present invention provides a load identification method based on non-intrusive load terminal data, comprising the following steps:
s1: sampling load data through a non-intrusive load terminal;
s2: the load data is sorted through the cloud platform, and the method specifically comprises the following two aspects:
on one hand, the method comprises the steps of performing mean shift clustering on historical electricity utilization data of various electric appliances of a user to obtain different clusters of a clustered electric appliance data set of the user, and extracting load identification characteristics of the electric appliances from the electric appliance clusters to identify historical load type loads, wherein the load identification characteristics of the electric appliance clusters comprise load average running time, load average power and running time distribution;
on the other hand, a multivariate Gaussian distribution model of the operation characteristics of the electric appliance is established according to the electricity utilization characteristics of the user of each sample point in the cluster, and the occurrence probability of the operation characteristics of the electric appliance corresponding to each cluster is calculated by taking load operation data as input characteristics and a naive Bayes algorithm;
s3: and taking a cluster with the maximum probability as the actual category of the electric appliance, obtaining the electric appliance type of the cluster, and taking the electric appliance type of the cluster as the electric appliance type of the electric appliance.
The historical electricity consumption data of the electric appliance in the embodiment comprises the total electricity consumption, the peak power, the starting and stopping times, the starting time, the running time and the like of the electric appliance.
The specific steps of mean shift clustering in step S2 in this embodiment are as follows:
a1: determining the radius r of the sliding window, and starting clustering by using a randomly selected central point C and a circular sliding window with the radius r;
a2: in each iteration process, calculating the mean value in a new sliding window, namely a new central point of the window, wherein the number of points in the sliding window is the density in the window;
a3: continuing to move the sliding window by the mean until there is no direction to accommodate more points in the kernel, i.e., until the density in the circle no longer increases;
a4: filtering the sliding windows generated in the steps A2 to A4, and when a plurality of windows are overlapped, reserving the window containing the most points and clustering according to the sliding window where the data points are positioned;
a5: and storing the centroid coordinates of each data set as standard characteristic values of the categories, and counting the distribution condition of each characteristic in the group as the distribution range of the electric appliance characteristics.
The determination process of the radius parameter r of the circular sliding window is as follows:
given a dataset P ═ { P (i); 0,1, … n, for any point p (i), calculating the distances between all points in the subset S of the set D from the point p (i) to the point p (i), p (2), …, p (i-1), p (i +1), …, p (n), the distances being sorted in order from small to large, assuming that the sorted distance set is D { D (1), D (2), …, D (k-1), D (k +1), …, D (n) }, D (k) is called k-distance, calculating k-distance for each point p (i) in the set to be clustered, and finally obtaining the k-distance set E of all points E ═ E (1), E (2), …, E (n) }, and according to the obtained k-distance set E of all points, sorting the set E in ascending order to obtain the k-distance set E, obtaining the k-distance set E', and fitting a change curve graph of the k-distance in the sorted E' set, then drawing a curve, and determining the value of the k-distance corresponding to the position where the rapid change occurs as the value of the radius r through observation.
In this embodiment, the process of establishing the multivariate gaussian distribution model of the operation characteristics of the electrical appliance in step S2 is as follows:
b-1: data preliminary processing:
respectively reading the user electricity utilization characteristics of the points in each cluster according to different clusters of the clustered user electrical appliance data set;
b-2: calculating the characteristic mean value mu of the sample:
wherein k is the total number of the characteristics i in the electrical appliance cluster, muiThe characteristic mean value of the characteristic i in the cluster;
b-3: establishing a covariance matrix sigma of the electrical characteristic distribution, wherein X is a characteristic vector set of the samples, m is the number of the samples,
and obtaining a multivariate Gaussian distribution model of the power utilization behavior of the user.
In this embodiment, the specific calculation steps of calculating the occurrence probability of each cluster corresponding to the operation feature of the electrical appliance by using the load operation data as the input feature and using the naive bayes algorithm in step S2 are as follows:
c-1: determining the operation characteristics of a sample set, and collecting sample data;
c-2: training samples, and respectively calculating the conditional probability of the characteristics of each category, namely the occurrence probability of the corresponding characteristic value of each characteristic;
c-3: object classification, reading objectsThe characteristic value X is calculated in each category CiProbability of p (X | C)i) The class that targets the maximum term.
Fig. 2 shows the active power of the fixed-frequency air conditioner-a, the electric cooker-B, the microwave oven-C, the electromagnetic oven-D, the electric kettle-E and the electric water heater-F when they are operated individually, in this embodiment, the load identification method based on the non-intrusive load terminal data is used to identify the operating conditions of the individual operation of these electric appliances, and the specific identification results are shown in table 1:
TABLE 1
Table 1 shows the test results of the load identification method based on non-intrusive load terminal data on the individual operation conditions of a typical electrical appliance, and it can be seen that the load identification method can effectively identify the electrical appliance.
Claims (7)
1. A load identification method based on non-intrusive load terminal data is characterized in that: the method comprises the following steps:
s1: sampling load data through a non-intrusive load terminal;
s2: the load data is sorted, and the method specifically comprises the following two aspects:
on one hand, the historical electricity utilization data of various electric appliances of the user are subjected to mean shift clustering to obtain different clusters of a clustered electric appliance data set of the user, and load identification characteristics of the electric appliances are extracted from the electric appliance clusters, so that the historical load type load is identified;
on the other hand, a multivariate Gaussian distribution model of the operation characteristics of the electric appliance is established according to the electricity utilization characteristics of the user of each sample point in the cluster, the load operation data is used as input characteristics, and the probability of the operation characteristics of the electric appliance corresponding to each cluster is calculated by a naive Bayes algorithm;
s3: and taking a cluster with the maximum probability as the actual category of the electric appliance, obtaining the electric appliance type of the cluster, and taking the electric appliance type of the cluster as the electric appliance type of the electric appliance.
2. The method of claim 1, wherein the load identification method based on non-intrusive load terminal data is characterized in that: the specific steps of mean shift clustering in step S2 are as follows:
a1: determining the radius r of the sliding window, and starting clustering by using a randomly selected central point C and a circular sliding window with the radius r;
a2: in each iteration process, calculating the mean value in a new sliding window, namely a new central point of the window, wherein the number of points in the sliding window is the density in the window;
a3: continuing to move the sliding window according to the mean value until the density in the circle is not increased any more;
a4: filtering the sliding windows generated in the steps A2 to A4, and when a plurality of windows are overlapped, reserving the window containing the most points and clustering according to the sliding window where the data points are positioned;
a5: and storing the centroid coordinates of each data set as standard characteristic values of the categories, and counting the distribution condition of each characteristic in the group as the distribution range of the electric appliance characteristics.
3. The method of claim 1, wherein the load identification method based on non-intrusive load terminal data is characterized in that: the historical electricity consumption data of the electrical appliance in the step S2 includes total electricity consumption, peak power, start-stop times, start time, and running time of the electrical appliance.
4. The method of claim 2, wherein the load identification method based on non-intrusive load terminal data is characterized in that: the determination process of the radius parameter r of the circular sliding window in step S2 is as follows:
given a dataset P ═ { P (i); 0,1, … n, for any point p (i), calculating the distances between all points in the subset S of the set D from the point p (i) to the point p (i), p (2), …, p (i-1), p (i +1), …, p (n), the distances being sorted in order from small to large, assuming that the sorted distance set is D { D (1), D (2), …, D (k-1), D (k +1), …, D (n) }, D (k) is called k-distance, calculating k-distance for each point p (i) in the set to be clustered, and finally obtaining the k-distance set E of all points E ═ E (1), E (2), …, E (n) }, and according to the obtained k-distance set E of all points, sorting the set E in ascending order to obtain the k-distance set E, obtaining the k-distance set E', and fitting a change curve graph of the k-distance in the sorted E' set, and determining the value of the k-distance corresponding to the position where the change occurs sharply as the value of the radius r according to the change curve graph.
5. The method of claim 1, wherein the load identification method based on non-intrusive load terminal data is characterized in that: and the load identification characteristics of the electrical appliance cluster in the step S2 include load average running time, load average power and running time distribution.
6. The method of claim 1, wherein the load identification method based on non-intrusive load terminal data is characterized in that: the process of establishing the multivariate Gaussian distribution model of the operation characteristics of the electric appliance in the step S2 is as follows:
b-1: data preliminary processing:
respectively reading the user electricity utilization characteristics of the points in each cluster according to different clusters of the clustered user electrical appliance data set;
b-2: calculating the characteristic mean value mu of the sample:
wherein k is the total number of the characteristics i in the electrical appliance cluster, muiThe characteristic mean value of the characteristic i in the cluster;
b-3: establishing a covariance matrix sigma of the electrical characteristic distribution, wherein X is a characteristic vector set of the samples, m is the number of the samples,
and obtaining a multivariate Gaussian distribution model of the power utilization behavior of the user.
7. The method of claim 1, wherein the load identification method based on non-intrusive load terminal data is characterized in that: the specific calculation steps of calculating the occurrence probability of each cluster corresponding to the operating characteristics of the electrical appliance by using a naive Bayes algorithm in the step S2 are as follows:
c-1: determining the operation characteristics of a sample set, and collecting sample data;
c-2: training samples, and respectively calculating the conditional probability of the characteristics of each category, namely the occurrence probability of the corresponding characteristic value of each characteristic;
c-3: classifying the target, reading the characteristic value X of the target, calculating it in each class CiProbability of p (X | C)i) The class that targets the maximum term.
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| CN112784210A (en) * | 2021-01-25 | 2021-05-11 | 南京智睿能源互联网研究院有限公司 | Load identification method based on multivariate Gaussian discrimination mode |
| CN112801188A (en) * | 2021-01-29 | 2021-05-14 | 国网上海市电力公司 | NILMD method based on affine propagation clustering algorithm and selective Bayesian classification |
| CN113158309A (en) * | 2021-04-09 | 2021-07-23 | 天津大学 | Heating and ventilation equipment operation strategy identification method |
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