CN116562113A - Power consumption assessment method, system and medium based on clustering algorithm - Google Patents
Power consumption assessment method, system and medium based on clustering algorithm Download PDFInfo
- Publication number
- CN116562113A CN116562113A CN202211464462.XA CN202211464462A CN116562113A CN 116562113 A CN116562113 A CN 116562113A CN 202211464462 A CN202211464462 A CN 202211464462A CN 116562113 A CN116562113 A CN 116562113A
- Authority
- CN
- China
- Prior art keywords
- curve
- load curve
- data
- load
- curves
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 57
- 238000000034 method Methods 0.000 title claims abstract description 32
- 230000005611 electricity Effects 0.000 claims abstract description 64
- 238000012546 transfer Methods 0.000 claims abstract description 58
- 238000011156 evaluation Methods 0.000 claims abstract description 53
- 238000012545 processing Methods 0.000 claims abstract description 29
- 238000003064 k means clustering Methods 0.000 claims abstract description 14
- 230000008569 process Effects 0.000 claims abstract description 7
- 238000012544 monitoring process Methods 0.000 claims description 22
- 238000004519 manufacturing process Methods 0.000 claims description 13
- 238000004590 computer program Methods 0.000 claims description 11
- 238000004088 simulation Methods 0.000 claims description 7
- 238000007781 pre-processing Methods 0.000 claims description 6
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 238000013016 damping Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 4
- 239000011159 matrix material Substances 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000009412 basement excavation Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000010355 oscillation Effects 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000001502 supplementing effect Effects 0.000 description 1
- 238000005406 washing Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/80—Management or planning
- Y02P90/82—Energy audits or management systems therefor
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- Entrepreneurship & Innovation (AREA)
- Evolutionary Computation (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Health & Medical Sciences (AREA)
- Computer Hardware Design (AREA)
- General Engineering & Computer Science (AREA)
- Game Theory and Decision Science (AREA)
- Geometry (AREA)
- Software Systems (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Medical Informatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a power consumption assessment method, a system and a medium based on a clustering algorithm, which are used for acquiring first data; carrying out iterative updating processing on the first data by adopting an AP clustering algorithm to obtain a first class load curve, a second class load curve and a third class load curve; performing difference value operation on the first type of load curve and the second type of load curve to obtain an interruption curve; adopting a k-means clustering algorithm and a DTW algorithm to process the third class of load curves to obtain transfer curves; evaluating the electricity utilization characteristic and the adjustment capability of a user through the interruption curve and the transfer curve; the method has the beneficial effects that an interruption curve and a transfer curve which can integrate various indexes are constructed by a secondary clustering method, and the evaluation of the electricity utilization characteristics is realized by the two curves, so that the error of the electricity utilization characteristic evaluation is reduced, and the accuracy of an evaluation result is improved.
Description
Technical Field
The invention relates to the technical field of electricity utilization evaluation, in particular to an electricity utilization evaluation method, system and medium based on a clustering algorithm.
Background
At present, a large amount of new energy is connected into a power system, so that the stable operation of a power grid is influenced, the adjustment capability of a power generation side is weakened, and the adjustment capability of an industrial user at a load side is required to be researched, so that an interruptible and transferable curve is excavated. The existing research on the adjustment capability of industrial users is mostly based on the capacity of the users, but does not consider the electricity utilization characteristics such as time and the like, and the excavation of the electricity utilization characteristics is insufficient; and the actual production capacity of the user is not considered when the evaluation index is defined; therefore, in the current evaluation of the actual production of the electricity consumption of the user, the electricity consumption characteristic is evaluated only by the capacity of the user, other indexes such as time and the like are ignored, and the evaluation error is easy to be large, so that the evaluation result is inaccurate.
In view of this, the present application is specifically proposed.
Disclosure of Invention
The invention aims to provide a clustering algorithm-based electricity utilization evaluation method, a clustering algorithm-based electricity utilization evaluation system and a clustering algorithm-based electricity utilization evaluation medium, which are used for reducing the evaluation error of the electricity utilization characteristics and improving the accuracy of the evaluation result.
The invention is realized by the following technical scheme:
the electricity utilization evaluation method based on the clustering algorithm comprises the following steps:
acquiring first data, wherein the first data are a plurality of daily load curve data of a user in a historical time period;
carrying out iterative updating processing on the first data by adopting an AP clustering algorithm to obtain a first type of load curve, a second type of load curve and a third type of load curve, wherein the first type of load curve is a load curve during normal production, the second type of load curve is a load curve with interruption property, and the third type of load curve is a load curve with transfer property;
performing difference value operation on the first type of load curve and the second type of load curve to obtain an interruption curve;
adopting a k-means clustering algorithm and a DTW algorithm to process the third class of load curves to obtain transfer curves;
and evaluating the electricity utilization characteristic and the adjustment capability of the user through the interruption curve and the transfer curve.
When the electric characteristics are evaluated, the traditional method usually evaluates the electric characteristics based on the capacity of a user, but when the method is adopted to evaluate the electric characteristics, other evaluation indexes such as time and the like are often ignored, so that the evaluation error is large, and the evaluation result is inaccurate; the invention provides a power consumption evaluation method based on a clustering algorithm, wherein an interruption curve and a transfer curve which can integrate various indexes are constructed by a secondary clustering method, and the evaluation of power consumption characteristics is realized by the two curves, so that the error of power consumption characteristic evaluation is reduced, and the accuracy of an evaluation result is improved.
Preferably, the step of acquiring daily load curve data includes:
acquiring daily electricity monitoring data in a user history time period;
and processing the daily electricity monitoring data by adopting MATLAB simulation, assigning the distortion data and the missing data in the daily electricity monitoring data to be zero, and filling the zero-valued data points by linear filling to obtain the daily load curve data.
Preferably, the substep of obtaining the transfer curve comprises:
performing secondary clustering treatment on the third class load curve by adopting a k-means clustering algorithm to obtain n sub transfer curves;
and comparing the n sub-transfer curves in pairs by adopting a DTW algorithm, and selecting two sub-transfer curves with minimum regular path values to obtain a transfer curve.
Preferably, the second clustering processing is performed on the third class of load curves by adopting a k-means clustering algorithm, and the specific substep of obtaining n sub-transfer curves comprises the following steps:
in the third class of load curves, determining a clustering number k by using a contour coefficient, and selecting k curves as initial clustering centers;
calculating the Euclidean distance between each curve and the initial clustering center, and dividing the third class load curve into m subclass load curves;
and (3) re-calculating the cluster centers of the m subclass load curves, comparing each newly calculated cluster center with the previous value, and ending the iteration after the distance between the newly calculated cluster centers is smaller than the set threshold or the maximum iteration number N is reached, so as to obtain N sub-transfer curves.
Preferably, the specific expression of the AP clustering algorithm is:
s(i,j)＝-||d i -d j || 2 (i≠j)
s (i, j) is a curve d i And curve d j Similarity between r (i, j) is curve d j As curve d i The degree of attraction of the cluster center, a (i, j) is a curve d i Selection curve d j As the degree of attribution of the cluster center, d i For the daily load curve on day i, j=1, 2, …, n, i=j, s (i, j) takes the median of all similarity values.
Preferably, the historical period of time is one year or one month.
Preferably, the electricity consumption characteristic includes an electricity consumption capacity and an electricity consumption period.
The invention also provides a power consumption evaluation system based on the clustering algorithm, which comprises a data acquisition module, an iteration updating module, a difference module, a processing module and an evaluation module;
the data acquisition module is used for acquiring first data, wherein the first data are a plurality of daily load curve data of a user in a historical time period;
the iterative updating module is used for carrying out iterative updating processing on the first data by adopting an AP clustering algorithm to obtain a first type of load curve, a second type of load curve and a third type of load curve, wherein the first type of load curve is a load curve during normal production, the second type of load curve is a load curve with an interruption property, and the third type of load curve is a load curve with a transfer property;
the difference module is used for carrying out difference operation on the first type of load curve and the second type of load curve to obtain an interruption curve;
the processing module is used for processing the third class of load curves by adopting a k-means clustering algorithm and a DTW algorithm to obtain transfer curves;
and the evaluation module is used for evaluating the electricity utilization characteristics and the adjustment capacity of the user through the interruption curve and the transfer curve.
Preferably, the data acquisition module comprises a sub-data acquisition module and a preprocessing module,
the sub-data acquisition module is used for acquiring daily electricity consumption monitoring data in a user history time period;
and the preprocessing module is used for processing the daily electricity monitoring data by adopting MATLAB simulation, assigning the distortion data and the missing data in the daily electricity monitoring data to be zero, and filling the zero-valued data points through linear filling to obtain the daily load curve data.
The invention also provides a computer storage medium having stored thereon a computer program, characterized in that the computer program, when executed by a processor, implements a method as described above.
Compared with the prior art, the invention has the following advantages and beneficial effects:
according to the electricity utilization evaluation method, system and medium based on the clustering algorithm, provided by the embodiment of the invention, the interrupt curve and the transfer curve which can integrate various indexes are constructed by the secondary clustering method, and the evaluation of the electricity utilization characteristics is realized by the two curves, so that the error of the electricity utilization characteristic evaluation is reduced, and the accuracy of the evaluation result is improved.
Drawings
In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings that are needed in the examples will be briefly described below, it being understood that the following drawings only illustrate some examples of the present invention and therefore should not be considered as limiting the scope, and that other related drawings may be obtained from these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of an evaluation method.
Detailed Description
For the purpose of making apparent the objects, technical solutions and advantages of the present invention, the present invention will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present invention and the descriptions thereof are for illustrating the present invention only and are not to be construed as limiting the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that: no such specific details are necessary to practice the invention. In other instances, well-known structures, circuits, materials, or methods have not been described in detail in order not to obscure the invention.
Throughout the specification, references to "one embodiment," "an embodiment," "one example," or "an example" mean: a particular feature, structure, or characteristic described in connection with the embodiment or example is included within at least one embodiment of the invention. Thus, the appearances of the phrases "in one embodiment," "in an example," or "in an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combination and/or sub-combination in one or more embodiments or examples. Moreover, those of ordinary skill in the art will appreciate that the illustrations provided herein are for illustrative purposes and that the illustrations are not necessarily drawn to scale. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
In the description of the present invention, the terms "front", "rear", "left", "right", "upper", "lower", "vertical", "horizontal", "high", "low", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, merely to facilitate description of the present invention and simplify description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the scope of the present invention.
Example 1
When the electrical characteristics are evaluated, the conventional evaluation is usually performed based on the capacity of the user, but when the electrical characteristics are evaluated by adopting the method, other evaluation indexes such as time and the like are often ignored, so that the evaluation error is large, and the evaluation result is inaccurate.
The embodiment discloses a power consumption evaluation method based on a clustering algorithm, by a secondary clustering method, an interruption curve and a transfer curve which can integrate various indexes are constructed, and the evaluation of power consumption characteristics is realized through the two curves, so that the error of the power consumption characteristic evaluation is reduced, the accuracy of an evaluation result is improved, the specific evaluation method of the embodiment is shown in fig. 1, and the method comprises the following steps:
s1: acquiring first data, wherein the first data are a plurality of daily load curve data of a user in a historical time period; the historical time period is one year or one month.
In step S1, the first data obtained is daily load curve data generated in the past on the electric meter, and in this embodiment, by analyzing the data generated in the past, the electricity consumption characteristics of the user can be intuitively evaluated or the actual production situation can be evaluated.
The sub-step of acquiring the daily load curve data comprises the following steps:
acquiring daily electricity monitoring data in a user history time period; and processing the daily electricity monitoring data by adopting MATLAB simulation, assigning the distortion data and the missing data in the daily electricity monitoring data to be zero, and filling the zero-valued data points by linear filling to obtain the daily load curve data.
Selecting 96-point daily electricity consumption monitoring data of industrial users in a certain area, traversing the monitoring data by utilizing a MATLAB simulation tool to find out distortion data and missing data, assigning the distortion data and the missing data to be zero, filling zero-value data points by utilizing linear filling, correcting and supplementing abnormal data points, and finally obtaining a daily load curve data matrix D= (D) by each user ij ) n×m ，d ij The power value of the j monitoring point on the i day is represented as an element in the daily load curve data matrix D. i=1,2, …, n; j=1, 2, …, m; n represents the number of days left after data washing; m represents the number of monitoring points in one day and has a value of 96.
S2: carrying out iterative updating processing on the first data by adopting an AP clustering algorithm to obtain a first type of load curve, a second type of load curve and a third type of load curve, wherein the first type of load curve is a load curve during normal production, the second type of load curve is a load curve with interruption property, and the third type of load curve is a load curve with transfer property;
in the step S2, three different types of curves are obtained through a one-time clustering method, in the step, by adopting an AP clustering method, the class number and the clustering center do not need to be designated in advance, the stability is good, and the data matrix D= { D for the daily load curve is obtained 1 ,d 2 ,…,d n And d is as follows i Day load curves for day i are shown, i=1, 2, …, n. The AP clustering algorithm involves the following 3 variables.
Similarity s (i, j) reflects curve d i And curve d j Similarity between the two, the value is calculated using the 2-norm.
The suction degree r (i, j) reflects the curve d j As curve d i The degree of attraction of the cluster center.
The degree of attribution a (i, j) reflects the curve d i Selection curve d j As the degree of attribution of the cluster center.
After initialization is completed, the values of r (i, j) and a (i, j) are continuously updated, and meanwhile, a damping coefficient lambda can be introduced to solve the problem of non-convergence until the cluster center is not changed any more; the specific expression is:
s(i,j)＝-||d i -d j || 2 (i≠j)
s (i, j) is a curve d i And curve d j Similarity between r (i, j) is curve d j As curve d i The degree of attraction of the cluster center, a (i, j) is a curve d i Selection curve d j As the degree of attribution of the cluster center, d i For the daily load curve on day i, j=1, 2, …, n, i=j, s (i, j) takes the median of all similarity values.
Meanwhile, when the attraction degree r (i, j) and the attribution degree a (i, j) need to be introduced with a damping coefficient lambda in the process of updating each time, the value range of the damping coefficient lambda is 0-1. The introduction of the damping coefficient lambda can avoid data oscillation and can play a role in adjusting convergence speed in the whole iterative updating process. The updated formula after introducing the damping coefficient is:
r t+1 (i,j)＝λr t (i,j)+(1-λ)r t+1 (i,j)
wherein: r is (r) t+1 (i, j) and r t (i, j) respectively represent the attraction degree after the t+1st update and the attraction degree after the t update, and the two matrixes with different iteration times are connected by damping coefficients.
a t+1 (i,j)＝λa t (i,j)+(1-λ)a t+1 (i,j)
Wherein: a, a t+1 (i, j) and a t (i, j) represents the attribution degree after the t+1st update and the attribution degree after the t update, respectively. Also, two matrices of different iteration numbers are related by a damping coefficient.
S3: performing difference value operation on the first type of load curve and the second type of load curve to obtain an interruption curve;
after the primary clustering is completed, a load curve d of a user in normal production is obtained u Load curve { d having interrupt properties p1 ,d p2 ,…,d pe Sum of load curves { d } with transfer properties f1 ,d f2 ,…,d fc }. Load curve d at normal production u Respectively subtracting load curves { d } having interrupt properties p1 ,d p2 ,…,d pe Obtaining an interruptible curve { d } l1 ,d l2 ,…,d le }. Where e is the number of load curves with interrupt nature; c is the number of load curves with transfer properties; the subscripts u, p, f, l are used to distinguish between different types of load curves.
S4: adopting a k-means clustering algorithm and a DTW algorithm to process the third class of load curves to obtain transfer curves;
the substep of transfer curve acquisition includes: performing secondary clustering treatment on the third class load curve by adopting a k-means clustering algorithm to obtain n sub transfer curves; and comparing the n sub-transfer curves in pairs by adopting a DTW algorithm, and selecting two sub-transfer curves with minimum regular path values to obtain a transfer curve.
The k-means clustering algorithm is adopted to carry out secondary clustering processing on the third class of load curves, and the specific substeps of obtaining n sub-transfer curves comprise the following steps: in the third class of load curves, determining a clustering number k by using a contour coefficient, and selecting k curves as initial clustering centers;
calculating the Euclidean distance between each curve and the initial clustering center, and dividing the third class load curve into m subclass load curves;
and (3) re-calculating the cluster centers of the m subclass load curves, comparing each newly calculated cluster center with the previous value, and ending the iteration after the distance between the newly calculated cluster centers is smaller than the set threshold or the maximum iteration number N is reached, so as to obtain N sub-transfer curves.
In the production process, the industrial users have a time coupling relation between different pipelines, and the load transfer is difficult. Subject to this feature, the transferable characteristics of industrial users need to have a transfer property curve { d } on a one-time clustering basis f1 ,d f2 ,…,d fc And (3) carrying out secondary clustering on all load curves in the class by using a k-means clustering algorithm, and further mining transferable characteristics.
Load curve sequence { d } with transfer characteristic selected for primary clustering f1 ,d f2 ,…,d fc All load curves { d } within a class b1 ,d b2 ,…,d bg Determining a final cluster number k by using a contour coefficient, setting the maximum iteration number N, and selecting k curves as an initial cluster center { d } a1 ,d a2 ,…,d ak Calculating Euclidean distance between each curve and the initial clustering center, so as to divide the category; and then, repeatedly calculating the clustering center and comparing the clustering center with the previous value until the distance between the clustering center and the previous value is smaller than a threshold value or the maximum iteration number is reached. The Euclidean distance calculation formula is as follows:
wherein: i=1, 2, …, g; j=1, 2, …, k; g represents the number of all curves within the class having the transfer characteristic load curve; k represents the number of clusters finally optimized by the contour coefficients; the subscripts a, b are used to distinguish between different types of load curves.
After the initial class division of the data points is completed, calculating a clustering center again, comparing each newly calculated clustering center with the previous value, and ending iteration if the distance between the newly calculated clustering centers is smaller than a set threshold value, namely the convergence state is trended; or the iteration is likewise ended after the maximum number of iterations N has been reached.
Obtaining a transfer curve { d }, through secondary clustering q1 ,d q2 ,…,d qk In this case, there are several transfer curves, which cannot be used for defining an evaluation index, and two of the transfer curves need to be finally taken as mutually transferable curves.
Transfer curve { d } q1 ,d q2 ,…,d qk And comparing every two by using a DTW algorithm, and judging the loads corresponding to two transfer curves with the minimum regular path value as transferable curves.
S5: evaluating the electricity utilization characteristic and the adjustment capability of a user through the interruption curve and the transfer curve; the electricity usage characteristics include electricity usage capacity and electricity usage period.
According to the electricity utilization evaluation method based on the clustering algorithm, through the secondary clustering method, an interruption curve and a transfer curve which can integrate various indexes are constructed, the electricity utilization characteristics are evaluated through the interruption curve and the transfer curve, errors in the electricity utilization characteristic evaluation are reduced, and accuracy of an evaluation result is improved.
Example two
The embodiment discloses a power consumption evaluation system based on a clustering algorithm, which is used for realizing the evaluation method in the first embodiment, and comprises a data acquisition module, an iteration updating module, a difference module, a processing module and an evaluation module;
the data acquisition module is used for acquiring first data, wherein the first data are a plurality of daily load curve data of a user in a historical time period;
the iterative updating module is used for carrying out iterative updating processing on the first data by adopting an AP clustering algorithm to obtain a first type of load curve, a second type of load curve and a third type of load curve, wherein the first type of load curve is a load curve during normal production, the second type of load curve is a load curve with an interruption property, and the third type of load curve is a load curve with a transfer property;
the difference module is used for carrying out difference operation on the first type of load curve and the second type of load curve to obtain an interruption curve;
the processing module is used for processing the third class of load curves by adopting a k-means clustering algorithm and a DTW algorithm to obtain transfer curves;
and the evaluation module is used for evaluating the electricity utilization characteristics and the adjustment capacity of the user through the interruption curve and the transfer curve.
The data acquisition module comprises a sub-data acquisition module and a preprocessing module,
the sub-data acquisition module is used for acquiring daily electricity consumption monitoring data in a user history time period;
and the preprocessing module is used for processing the daily electricity monitoring data by adopting MATLAB simulation, assigning the distortion data and the missing data in the daily electricity monitoring data to be zero, and filling the zero-valued data points through linear filling to obtain the daily load curve data.
Example III
The present embodiment discloses a computer storage medium having stored thereon a computer program which, when executed by a processor, implements the method according to embodiment one.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. The electricity utilization evaluation method based on the clustering algorithm is characterized by comprising the following steps:
acquiring first data, wherein the first data are a plurality of daily load curve data of a user in a historical time period;
carrying out iterative updating processing on the first data by adopting an AP clustering algorithm to obtain a first type of load curve, a second type of load curve and a third type of load curve, wherein the first type of load curve is a load curve during normal production, the second type of load curve is a load curve with interruption property, and the third type of load curve is a load curve with transfer property;
performing difference value operation on the first type of load curve and the second type of load curve to obtain an interruption curve;
adopting a k-means clustering algorithm and a DTW algorithm to process the third class of load curves to obtain transfer curves;
and evaluating the electricity utilization characteristic and the adjustment capability of the user through the interruption curve and the transfer curve.
2. The electricity usage assessment method based on a clustering algorithm according to claim 1, wherein the sub-step of daily load curve data acquisition comprises:
acquiring daily electricity monitoring data in a user history time period;
and processing the daily electricity monitoring data by adopting MATLAB simulation, assigning the distortion data and the missing data in the daily electricity monitoring data to be zero, and filling the zero-valued data points by linear filling to obtain the daily load curve data.
3. The electricity usage assessment method based on a clustering algorithm according to claim 1, wherein the sub-step of transfer curve acquisition comprises:
performing secondary clustering treatment on the third class load curve by adopting a k-means clustering algorithm to obtain n sub transfer curves;
and comparing the n sub-transfer curves in pairs by adopting a DTW algorithm, and selecting two sub-transfer curves with minimum regular path values to obtain a transfer curve.
4. The electricity utilization evaluation method based on a clustering algorithm according to claim 1, wherein the step of performing secondary clustering processing on the third class of load curves by using a k-means clustering algorithm to obtain n sub-transfer curves comprises the following specific sub-steps:
in the third class of load curves, determining a clustering number k by using a contour coefficient, and selecting k curves as initial clustering centers;
calculating the Euclidean distance between each curve and the initial clustering center, and dividing the third class load curve into m subclass load curves;
and (3) re-calculating the cluster centers of the m subclass load curves, comparing each newly calculated cluster center with the previous value, and ending the iteration after the distance between the newly calculated cluster centers is smaller than the set threshold or the maximum iteration number N is reached, so as to obtain N sub-transfer curves.
5. The electricity utilization evaluation method based on the clustering algorithm according to claim 1, wherein the specific expression of the AP clustering algorithm is:
s(i,j)＝-||d i -d j || 2 (i≠j)
s (i, j) is a curve d i And curve d j Similarity between r (i, j) is curve d j As curve d i The degree of attraction of the cluster center, a (i, j) is a curve d i Selection curve d j As the degree of attribution of the cluster center, d i For the daily load curve on day i, j=1, 2, …, n, i=j, s (i, j) takes the median of all similarity values.
6. A method of evaluating electricity consumption based on a clustering algorithm as claimed in claim 2 wherein the historical time period is one year or one month.
7. The electricity consumption assessment method based on the clustering algorithm according to claim 1, wherein the electricity consumption characteristics comprise electricity consumption capacity and electricity consumption time period.
8. The electricity utilization evaluation system based on the clustering algorithm is characterized by comprising a data acquisition module, an iteration updating module, a difference module, a processing module and an evaluation module;
the data acquisition module is used for acquiring first data, wherein the first data are a plurality of daily load curve data of a user in a historical time period;
the iterative updating module is used for carrying out iterative updating processing on the first data by adopting an AP clustering algorithm to obtain a first type of load curve, a second type of load curve and a third type of load curve, wherein the first type of load curve is a load curve during normal production, the second type of load curve is a load curve with an interruption property, and the third type of load curve is a load curve with a transfer property;
the difference module is used for carrying out difference operation on the first type of load curve and the second type of load curve to obtain an interruption curve;
the processing module is used for processing the third class of load curves by adopting a k-means clustering algorithm and a DTW algorithm to obtain transfer curves;
and the evaluation module is used for evaluating the electricity utilization characteristics and the adjustment capacity of the user through the interruption curve and the transfer curve.
9. The electricity consumption assessment system according to claim 8, wherein the data acquisition module comprises a sub-data acquisition module and a preprocessing module,
the sub-data acquisition module is used for acquiring daily electricity consumption monitoring data in a user history time period;
and the preprocessing module is used for processing the daily electricity monitoring data by adopting MATLAB simulation, assigning the distortion data and the missing data in the daily electricity monitoring data to be zero, and filling the zero-valued data points through linear filling to obtain the daily load curve data.
10. A computer storage medium having stored thereon a computer program which, when executed by a processor, implements the method of any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211464462.XA CN116562113A (en) | 2022-11-22 | 2022-11-22 | Power consumption assessment method, system and medium based on clustering algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211464462.XA CN116562113A (en) | 2022-11-22 | 2022-11-22 | Power consumption assessment method, system and medium based on clustering algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116562113A true CN116562113A (en) | 2023-08-08 |
Family
ID=87490402
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211464462.XA Pending CN116562113A (en) | 2022-11-22 | 2022-11-22 | Power consumption assessment method, system and medium based on clustering algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116562113A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117992856A (en) * | 2024-04-03 | 2024-05-07 | 国网山东省电力公司营销服务中心(计量中心) | User electricity behavior analysis method, system, device, medium and program product |
-
2022
- 2022-11-22 CN CN202211464462.XA patent/CN116562113A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117992856A (en) * | 2024-04-03 | 2024-05-07 | 国网山东省电力公司营销服务中心(计量中心) | User electricity behavior analysis method, system, device, medium and program product |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Liu et al. | T-MOEA/D: MOEA/D with objective transform in multi-objective problems | |
| CN114064453B (en) | Software test case automatic generation method based on clustering and evolutionary algorithm | |
| CN113591954B (en) | Filling method of missing time sequence data in industrial system | |
| CN113744089B (en) | Transformer area household variable relation identification method and device | |
| CN113092935A (en) | Method, system, equipment and storage medium for identifying small-load line topology | |
| CN105069290A (en) | Parallelization critical node discovery method for postal delivery data | |
| CN109840353A (en) | Lithium ion battery dual factors inconsistency prediction technique and device | |
| CN107276093B (en) | The Probabilistic Load calculation method cut down based on scene | |
| CN116562113A (en) | Power consumption assessment method, system and medium based on clustering algorithm | |
| CN111834996B (en) | Power grid line loss calculation method and device | |
| CN104821580A (en) | Three-phase reactive power control partitioning method based on reactive power source clustering analysis | |
| CN110571788A (en) | static voltage stability domain boundary coefficient calculation method based on dynamic equivalent circuit | |
| CN110137967B (en) | Large-scale power system power flow convergence adjusting method for key nodes | |
| CN113364058A (en) | Reactive power control method and system for power distribution network | |
| CN116911538A (en) | Reservoir group multi-target scheduling multi-source information joint search method, system and equipment | |
| CN116865318A (en) | Power transmission network and energy storage joint planning method and system based on two-stage random optimization | |
| CN115528684A (en) | Ultra-short-term load prediction method and device and electronic equipment | |
| CN111181166B (en) | Uncertain affine power flow method for prediction correction | |
| CN108717444A (en) | A kind of big data clustering method and device based on distributed frame | |
| CN114781296A (en) | Logic circuit simplification method and system based on matrix decomposition | |
| CN112037289A (en) | Off-line parameter calibration method based on genetic algorithm | |
| CN112488550A (en) | Uncertainty power grid static security analysis method and system based on deep learning | |
| CN112149318B (en) | Coordination state determination method of integrated energy system and related device | |
| CN112036553A (en) | Non-signal injection type user-phase topological relation identification method based on genetic algorithm | |
| CN113890018B (en) | Power distribution network weak point identification method based on data association analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |