CN111767951A - Method for discovering abnormal data by applying isolated forest algorithm in residential electricity safety analysis - Google Patents
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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 resident electricity utilization safety analysis aims at collecting a large amount of resident electricity utilization load data through terminal equipment such as an intelligent electric meter, and then carrying out abnormal value monitoring, cluster analysis, time series and other methods to obtain electricity utilization habits of different residents, and discovering electricity utilization abnormal conditions. The invention discloses a method for discovering abnormal data by applying an isolated forest algorithm to resident electricity load data in resident electricity utilization safety analysis, which aims to report abnormal behaviors in resident electricity utilization and early warn abnormal electricity utilization conditions in communities.
Description
Technical Field
The invention relates to the technical field of electric power safety analysis, in particular to a method for finding abnormal data by applying an isolated forest algorithm in residential electricity load data analysis.
Background
In recent years, with the further deepening of innovation and opening, the number of enterprises is greatly increased, the living quality of residents is greatly improved, so that a new round of electricity utilization is increased, and the safety problem of community electricity utilization is further highlighted. 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. In group renting in the community, the condition of the industrialized application of the residential electricity consumption is infinite, and the residential electricity consumption safety analysis of habit analysis can be used for discovering the violation condition of the city manager at the first time of abnormity occurrence by imaging the residential electricity consumption. The resident electricity utilization safety analysis aims at collecting a large amount of resident electricity utilization load data through terminal equipment such as an intelligent electric meter, and then carrying out abnormal value monitoring, cluster analysis, time series and other methods to obtain electricity utilization habits of different residents, and discovering electricity utilization abnormal conditions. The invention discloses a method for discovering abnormal data by applying an isolated forest algorithm to resident electricity load data in resident electricity utilization safety analysis, which aims to report abnormal behaviors in resident electricity utilization and early warn abnormal electricity utilization conditions in communities.
Disclosure of Invention
The invention provides a method for screening abnormal data of resident electricity load data based on an isolated forest algorithm, which is characterized by having a function of finding abnormal data and reporting the abnormal data by applying the isolated forest algorithm.
The isolated forest algorithm is a machine learning algorithm for anomaly detection, is an unsupervised learning algorithm, and is used for identifying anomalies through outliers in isolation data based on a decision tree algorithm. Outliers are isolated by randomly selecting features from a given set of features and then randomly selecting a segmentation value between the maximum and minimum values of the features. This random division of features makes the paths that the outlier data points generate in the tree shorter, separating them from other data. In solitary forest, an anomaly is defined as "outlier that is easily isolated", which can be understood as a point that is sparsely distributed and is far from a population with high density. In the feature space, sparsely distributed regions indicate that events have a low probability of occurring in the regions, and thus data falling in these regions can be considered abnormal. Isolated forest is an anomaly detection method suitable for continuous data, i.e. marked samples are not needed for training, but features need to be continuous. The isolated forest algorithm uses a set of very efficient strategies on how to find which points are easily isolated. In solitary forest, the data set is recursively randomly partitioned until all sample points are isolated. Under this strategy of random segmentation, outliers typically have shorter paths. Statistically, if there are only sparsely distributed points in a region in the data space, the probability that the data point falls in the region is very low, and therefore, the points in the regions can be considered as abnormal. Intuitively, the clusters with high density need to be cut many times to be isolated, but the points with low density can be easily isolated and considered as outliers.
The actually collected historical data of the electricity load of the residents can be calculated and analyzed through an isolated forest, the abnormal value of electricity utilization can be found, the abnormal occurrence time can be found through the timestamp, and the high-efficiency management on the electricity utilization safety of the community can be realized by locking the electricity utilization residents and the abnormal electricity utilization behaviors.
Drawings
Fig. 1 is a schematic processing flow diagram of a method for removing abnormal data and denoising historical load data in the embodiment of the invention.
FIG. 2 is a schematic diagram of a process for cutting a sub-sample 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. As shown in fig. 1, the present invention involves the following steps.
The first step,Data preprocessing:the collected original historical power load historical data are arranged according to a time sequence, the start and stop time of a data set is determined, and a data timestamp and the serial number of the electricity using residents are marked.
Step two,Removing abnormal values by an isolated forest algorithm:and (4) marking a timestamp on the historical power load data preprocessed in the first step, and inputting the serial number of the residents into the isolated forest algorithm model. Firstly, training a single tree on data:
1) randomly selecting n points from training data as subsamples, and putting the subsamples into a root node of an isolated tree;
2) randomly appointing a dimension, and randomly generating a cutting point p in the range of the current node data, wherein the cutting point is generated between the maximum value and the minimum value of the appointed dimension in the current node data;
3) the selection of the cut point generates a hyperplane, which divides the current node data space into 2 subspaces: placing points smaller than p in the currently selected dimension on the left branch of the current node, and placing points larger than or equal to p on the right branch of the current node;
recursion steps 2) and 3) on the left branch node and the right branch node of the node, and continuously constructing new leaf nodes until only one piece of data (cutting can not be continued) is on the leaf nodes or the tree grows to the set height;
FIG. 2 shows the process of training the cutting of the sub-samples, wherein Xi of the left image is in a region with a higher density, so that the left image is cut ten or more times and is divided into separate subspaces, and Xo of the right image falls in a region with sparsely distributed edges and is "isolated" after only four cuts;
the results of all the isolated trees are integrated after the isolated trees are respectively calculated, and since the cutting process is completely random, the results need to be converged by using a set method, namely, cutting is repeatedly started from the beginning, and then the average value of the results of each cutting is calculated. After t isolated trees are obtained, the training of a single tree is finished. The test data can then be evaluated using the generated orphan tree, i.e., an anomaly score s is calculated. For each sample x, the results for each tree need to be computed in combination, and the anomaly score is computed by the following formula:
h (x) is the height of x in each tree, c (Ψ) is the average of the path lengths at a given number of samples Ψ, and is used to normalize the path length h (x) of sample x;
analyzing the calculated abnormal score, wherein if the abnormal score is close to 1, the abnormal score must be an abnormal point; if the anomaly score is much less than 0.5, then it must not be an anomaly point; if the scores of all points for an outlier are around 0.5, then there is a high probability that an outlier is not present in the sample. And counting the abnormal score of each data point of the historical load data, and setting different thresholds to tighten or loosen the abnormal value removing conditions to remove the abnormal value according to the expected effect. The rejected abnormal value is marked according to the time stamp and the resident serial number and is input to the next step to supplement the missing value.
Step three,Abnormal resident electricity consumption timestamp mark: and marking abnormal electricity utilization conditions and positioning electricity utilization residents according to the resident sequence number and the timestamp one-to-one correspondence of the abnormal electricity utilization load data selected by the isolated forest algorithm.
The invention provides a method for screening abnormal data of residential electricity load data, which is characterized in that the abnormal data is found by applying an isolated forest algorithm and the abnormal data is reported, the occurrence time and place of abnormal electricity consumption behaviors of residents in a community are accurately positioned, the labor input and time cost for checking abnormal electricity consumption are reduced, the efficiency of community safety electricity consumption management is improved, and the method has wide application space in the field of electricity consumption safety management which shows more and more importance. Outliers in the isolated forest isolated data points, rather than analyzing normal data points. Compared with other normal data points, the tree path of the abnormal data points is shorter, so that the tree in the solitary forest does not need too much depth, and the method has the advantages of low memory requirement, high calculation speed and the like. By applying the invention, the efficiency of power utilization safety management can be greatly improved.
Claims (1)
1. The invention discloses a method for discovering abnormal data by applying an isolated forest algorithm in resident electricity safety analysis, which is characterized by comprising the following steps of:
the first step,Data preprocessing:arranging the collected historical data of the original historical power load according to a time sequence, determining the starting and stopping time of a data set, and marking a data timestamp and the serial number of a power consumer;
step two,Removing abnormal values by an isolated forest algorithm:marking a timestamp on the historical power load data preprocessed in the first step, and inputting the serial number of residents into an isolated forest algorithm model;
firstly, training a single tree on data:
1) randomly selecting n points from training data as subsamples, and putting the subsamples into a root node of an isolated tree;
2) randomly appointing a dimension, and randomly generating a cutting point p in the range of the current node data, wherein the cutting point is generated between the maximum value and the minimum value of the appointed dimension in the current node data;
3) the selection of the cut point generates a hyperplane, which divides the current node data space into 2 subspaces: placing points smaller than p in the currently selected dimension on the left branch of the current node, and placing points larger than or equal to p on the right branch of the current node;
recursion steps 2) and 3) on the left branch node and the right branch node of the node, and continuously constructing new leaf nodes until only one piece of data (cutting can not be continued) is on the leaf nodes or the tree grows to the set height;
FIG. 2 shows the process of training the cutting of the sub-samples, wherein Xi of the left image is in a region with a higher density, so that the left image is cut ten or more times and is divided into separate subspaces, and Xo of the right image falls in a region with sparsely distributed edges and is "isolated" after only four cuts;
integrating the results of all the isolated trees after respectively calculating the isolated trees, and because the cutting process is completely random, a set method is needed to make the results converge, namely, cutting is repeatedly started from the beginning, and then the average value of each cutting result is calculated;
after t isolated trees are obtained, training of a single tree is finished, and then the generated isolated trees can be used for evaluating test data, namely calculating an abnormal score s, for each sample x, calculating the result of each tree comprehensively, and calculating the abnormal score through the following formula:
h (x) is the height of x in each tree, c (Ψ) is the average of the path lengths at a given number of samples Ψ, and is used to normalize the path length h (x) of sample x; analyzing the calculated abnormal score, wherein if the abnormal score is close to 1, the abnormal score must be an abnormal point; if the anomaly score is much less than 0.5, then it must not be an anomaly point; if the scores of all the points of the abnormal score are about 0.5, the abnormal point is probably not present in the sample; counting the abnormal score of each data point of the historical load data, and eliminating the abnormal value according to the expected effect by setting different thresholds and tightening or loosening the abnormal value elimination condition; marking the removed abnormal value according to the timestamp and the resident serial number and inputting the marked abnormal value to the next step to supplement the missing value;
step three,Abnormal resident electricity consumption timestamp mark: and marking abnormal electricity utilization conditions and positioning electricity utilization residents according to the resident sequence number and the timestamp one-to-one correspondence of the abnormal electricity utilization load data selected by the isolated forest algorithm.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112381610A (en) * | 2020-11-16 | 2021-02-19 | 国网上海市电力公司 | Prediction method of group lease risk index and computer equipment |
| CN113125903A (en) * | 2021-04-20 | 2021-07-16 | 广东电网有限责任公司汕尾供电局 | Line loss anomaly detection method, device, equipment and computer-readable storage medium |
| CN113187650A (en) * | 2021-04-07 | 2021-07-30 | 武汉四创自动控制技术有限责任公司 | Intelligent hydraulic power plant whole-plant hydraulic turbine speed regulation system and diagnosis method |
| CN114124482A (en) * | 2021-11-09 | 2022-03-01 | 中国电子科技集团公司第三十研究所 | Access flow abnormity detection method and device based on LOF and isolated forest |
| CN116451168A (en) * | 2023-06-15 | 2023-07-18 | 北京国电通网络技术有限公司 | Abnormal power information generation method, device, electronic equipment and readable medium |
| CN116911806A (en) * | 2023-09-11 | 2023-10-20 | 湖北华中电力科技开发有限责任公司 | Internet + based power enterprise energy information management system |
| CN117913996A (en) * | 2024-01-24 | 2024-04-19 | 江苏同合电气有限公司 | Intelligent monitoring management method and system for operation of power distribution cabinet based on data analysis |
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| CN117913996A (en) * | 2024-01-24 | 2024-04-19 | 江苏同合电气有限公司 | Intelligent monitoring management method and system for operation of power distribution cabinet based on data analysis |
| CN117913996B (en) * | 2024-01-24 | 2024-06-07 | 江苏同合电气有限公司 | Intelligent monitoring management method and system for operation of power distribution cabinet based on data analysis |
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