CN110501568A - A kind of non-invasive apparatus load monitoring method based on figure signal processing - Google Patents
A kind of non-invasive apparatus load monitoring method based on figure signal processing Download PDFInfo
- Publication number
- CN110501568A CN110501568A CN201910606143.XA CN201910606143A CN110501568A CN 110501568 A CN110501568 A CN 110501568A CN 201910606143 A CN201910606143 A CN 201910606143A CN 110501568 A CN110501568 A CN 110501568A
- Authority
- CN
- China
- Prior art keywords
- graph
- active power
- time
- signal processing
- moment
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000012544 monitoring process Methods 0.000 title claims abstract description 25
- 238000012545 processing Methods 0.000 title claims abstract description 22
- 238000005457 optimization Methods 0.000 claims abstract description 21
- 238000012549 training Methods 0.000 claims abstract description 21
- 230000008859 change Effects 0.000 claims description 32
- 239000011159 matrix material Substances 0.000 claims description 15
- 238000004364 calculation method Methods 0.000 claims description 9
- 238000013507 mapping Methods 0.000 claims description 6
- 238000007781 pre-processing Methods 0.000 claims description 4
- 238000000926 separation method Methods 0.000 description 9
- 230000008569 process Effects 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000003066 decision tree Methods 0.000 description 2
- 238000000354 decomposition reaction Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 230000008713 feedback mechanism Effects 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 238000002922 simulated annealing Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R21/00—Arrangements for measuring electric power or power factor
- G01R21/133—Arrangements for measuring electric power or power factor by using digital technique
- G01R21/1331—Measuring real or reactive component, measuring apparent energy
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The non-invasive apparatus load monitoring method based on figure signal processing that the present invention relates to a kind of, including data prediction, starting point obtains and three steps of objective function global optimization, data are pre-processed first, choose the suitable period as training set for each known device；Then it determines objective function, minimum value solution is carried out to the regularization term of objective function, obtains analytic solutions；Finally using acquired analytic solutions as starting point, executes gradient projection optimization algorithm and global optimization solution is carried out to objective function.Compared with prior art, the present invention has the advantages that improving algorithm performance, obtaining better electric power separating resulting, realize the load monitoring of the non-invasive apparatus of higher precision.
Description
Technical Field
The invention relates to the field of non-intrusive equipment load monitoring, in particular to a non-intrusive equipment load monitoring method based on graph signal processing.
Background
Non-intrusive application Load Monitoring (NILM), that is, the total power consumption of a household at a certain moment measured by a total circuit ammeter is given, and the actual power consumption of each electrical equipment corresponding to the moment is obtained by applying some analysis and calculation modes. In this process, the power consumption of a particular device need not be measured by means of a power meter. Therefore, the NILM has the advantages of easy use, low cost, no equipment interference, no influence on family life, and the like. From the viewpoint of energy saving, NILM plays an irreplaceable important role. For common household users, the NILM can help the users to know the power consumption conditions of various household appliances, and the users can timely turn off the high-power-consumption appliances on the basis, so that the saving consciousness of people is invisibly aroused. For policy makers, NILM can help them to develop a more macroscopic and in-depth understanding of home electricity usage, helping them to make more reasonable energy feedback mechanisms and demand response strategies.
In the existing research, when the Graph Signal processing is applied to the NILM field, the definition of the Graph Signal (Graph Signal) is a discrete function with values of-1, 0 and 1, so that the final objective function is a discrete and non-derivable function, and the use of an optimization algorithm is limited. Meanwhile, the objective function is a discrete and non-derivable function, so most of the existing methods regard the objective function as two parts, and then carry out segmentation and step processing. Firstly, solving the minimum value of the regularization term which is taken as the second part of the target function, then, taking the solution as a starting point, and optimizing the first part of the target function by adopting heuristic algorithms such as simulated annealing and the like, so that the calculation is complex, and the accuracy of the electric power separation result is not high.
Disclosure of Invention
The present invention is directed to overcoming the above-mentioned drawbacks of the prior art and providing a non-intrusive device load monitoring method based on graph signal processing.
The purpose of the invention can be realized by the following technical scheme:
a non-invasive equipment load monitoring method based on graph signal processing comprises three steps of data preprocessing, initial point obtaining and target function integral optimization, wherein data are preprocessed firstly, and proper time periods are selected for known equipment to serve as training sets; then determining a target function, and solving the minimum value of the regularization item of the target function to obtain an analytic solution; and finally, taking the obtained analytic solution as a starting point, and executing a gradient projection optimization algorithm to carry out overall optimization solution on the objective function.
Further, the method specifically comprises the following steps:
s1, acquiring the total load active power p of the user equipment in the set time period from 1 to NiAs monitoring data; obtaining the single active power of each device in a set time period 1-nAs training data, where N and N are positive integers and N is less than N, i is a time, m is the number of the device, and all devices constitute a device set
S2, calculating the average power consumption of each device based on the training dataAnd according to average power consumptionArranging in descending order from big to small;
s3, obtaining the total load active power change value delta p at each moment according to the monitoring dataiConstructing a graph for each device m by graph signal processing A vertex set comprising a plurality of vertexes, each vertex corresponds to an active power variation value delta piA is a weighted adjacency matrix with a weight Ai,jFor corresponding vertex viAnd vjThe weights of the connecting edges between, i and j represent different time instants;
s4, obtaining the individual active power change value of each device at each moment according to the training dataBased on constructed graphsCalculating all graph signals within 1-N timeThe calculation expression is as follows:
wherein, ThrmIs a threshold value for determining whether the device m has changed the operation state,is the single active power change value of the device m in a set time period 1-n;
s5, according to all graph signals within 1-n timeCalculating and acquiring graph signals from N +1 time to N timeBy passingAndmapping between the devices to obtain the actual operation power consumption change value of each device;
s6, repeating the steps S3 to S5, and removing the average power consumption of the running state of the equipment in the monitoring data after separating one equipment each time until all the equipment is separated;
s7, separating all the devices to obtain a starting point Pn+1,...,PNWherein
S8, obtaining the starting point Pn+1,...,PNSolving the optimization model to obtainAnd outputting the optimized signals from time N +1 to time N
Further, in step S3, the adjacency matrix a is weightedi,jThe calculation expression of (a) is:
wherein, Δ piRepresenting the value of the change in the active power of the total load at time i, Δ pjAnd expressing the change value of the active power of the total load at the moment j, and expressing a scaling factor.
Further, in step S5, the graph signalThe calculation expression of (a) is:
where L is the graph laplacian matrix, which is calculated from the graph adjacency weight matrix a.
Further, in step S7, the mapping process is as follows:
if it is notIt is judged that the device m has changed the operation state at the time i when Δ piIf < 0, the judgment device is turned off at the moment i, thenWhen Δ piWhen the current time is more than 0, the judgment device is turned on at the moment i, and then
If it is notIt is judged that the device m does not change the operation state and accordingly
Further, in step S8, the expression of the optimization model is:
wherein, Δ piRepresenting the value of the total load active power change at the moment i,and the active power change value of the equipment m at the moment i is represented, and omega represents a regularization term weight parameter.
Compared with the prior art, the invention has the following advantages:
the invention uses the graph signal processing technology to establish a model for the NILM problem, and adopts an optimization algorithm to solve an objective function so as to obtain an optimal solution and realize the separation of the total power consumption of the electric power. The invention improves the definition of the graph signal, and defines the graph signal as a continuous function with the value in the range of [ -1,1], so that the final objective function is a continuously derivable function. Therefore, after the solution of the minimized regularization term is obtained by the objective function, the objective function can be used as a starting point to execute a gradient projection method, the objective function is optimized and solved integrally, the algorithm performance is improved, a better power separation result is obtained, and the load monitoring of the non-invasive equipment with higher precision is realized.
Drawings
FIG. 1 is a schematic overall flow diagram of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
As shown in fig. 1, the present embodiment provides a non-intrusive device load monitoring method based on graph signal processing. The method mainly comprises three steps of data preprocessing, initial point acquisition and target function overall optimization. Firstly, preprocessing data, and selecting a proper time period for each known device as a training set; then determining a target function, and solving the minimum value of the regularization item of the target function to obtain an analytic solution; and finally, taking the obtained analytic solution as a starting point, and executing a gradient projection optimization algorithm to carry out overall optimization solution on the objective function.
The solving of the NILM problem by applying the graph signal processing technology is to regard the NILM as a classification problem, that is, to the change of the total power consumption at a certain time, it is determined which known device caused the change, so that the operation state of the device can be determined. The embodiment selects suitable data as the training set.
In order to better capture the running condition of each device and improve the decomposition precision, the selection principle of the device training set follows: the time period in which only the device operates alone is selected as its training set to reduce interference from unrelated operating devices. In addition, for each device with the selected training set, the training set is stored separately, when a new electrical device is installed, the training set is only required to be built for the new device, and the training sets of the rest known devices are not required to be updated again.
The present embodiment employs a REDD (reference energy decomposition data set) database containing data of total active power in a home and active power of each device. I.e. for a certain time i the total active power is piThe active power of a certain device m corresponding to the moment isAll known devices constitute a device set
The embodiment specifically includes the following steps:
step S1, obtaining the total load active power p of the user equipment in the set time period 1-NiAs monitoring data; obtaining the single active power of each device in a set time period 1-nAs training data, where N and N are both positive integers and N is less than N, and m is the number of the device.
Step S2, calculating the average power consumption of each device based on the training dataAnd to make the device power consumption on averageArranged in descending order from large to small.
Step S3, obtaining the total load active power change value delta p at each moment according to the monitoring dataiObtaining the power change value delta p at each momenti＝pi+1-pi。
Step S4, using graph signal processing technology to construct graphWherein,a vertex set comprising a plurality of vertexes, each vertex corresponds to an active power variation value delta piA is a weighted adjacency matrix with a weight Ai,jFor corresponding vertex viAnd vjThe connecting edge weights between, i and j represent different time instants. Weight Ai,jThe value of (b) reflects the similarity of two adjacent vertices, i.e., the greater the weight, the higher the similarity between adjacent vertices. The weight calculation formula typically uses a gaussian kernel weight function, i.e.:
wherein, Δ piAnd Δ pjRespectively representing the total load active power change corresponding to the moment i and the moment jThe normalized value, σ, represents a scaling factor.
Step S5, based on constructed graphCalculating all graph signals within 1-N time
Graph signal s is an important concept used in the present invention and is defined by a mapping of a set of vertices to a set of numbers, each vertex viMarking a picture signal si. Based on constructed graphsPicture signal s of device mmFor a vector of length N, the values are defined as follows
Wherein, ThrmIs a threshold value used to determine whether device m has changed operating conditions, typically at a value of half the difference between the average powers of successive states of device m.As class labels, in the training set (i ≦ n), the individual active power change value of device m at time iIs known, so if the change in total power consumption at time i is caused by a change in state of device m, then there isApproaching +1, otherwiseApproaching to-1; in the test set (N < i ≦ N),is unknown, thereforeTemporarily set to 0.
Step S6, based on the graph total smoothness minimization, obtains an initial analytic solution. The overall smoothness of the graph signal is defined by the graph laplacian matrix as:
wherein, L is a graph laplacian matrix, defined as L ═ D-a, and is an N × N real symmetric matrix; d is a diagonal matrix, i.e. the value of the element on the diagonal is defined as Di,i＝∑j＝1:NAi,jAnd the off-diagonal element is 0. Based on the above definition, similar data points viAnd vjA connection weight value ofi,jLarge and its corresponding map signal value siAnd sjAnd (4) approaching. Thus, for data classification, the graph signal constitutes a smooth signal, and when the graph signal is piecewise smooth, the overall graph smoothness value is small.
For the NILM problem to be solved by the present invention, the corresponding mathematical model is expressed as:
wherein n isiThe noise at the moment i mainly comprises the power consumption of a circuit-based load and the power consumption of other unknown devices. From the viewpoint of variation in power consumption, the formula (4) can be modified into the following form
For the training set (i ≦ n), Δ piAndare all known; for the test set (N < i ≦ N)，ΔpiIs known asIs unknown.
Based on the graph signal processing technique, using the overall smoothness of the graph signal as the regularization term, the final NILM objective function is then defined as:
wherein,smis the map signal of the corresponding device m. Therefore, the NILM task is used as an optimization problem to find the optimal solution, namely the power consumption change value of each device m at each moment i (N is more than i and less than or equal to N)
To obtain an initial point Pn+1,...,PNFor each device m, an optimization problem is required to be solved to obtain the smoothest solution. I.e., the regularization term corresponding to the minimization device m, is expressed as:
since the matrix L is diagonally symmetrical,is a known constant vector, and utilizes a block matrix, and the solving process of the formula is as follows:
solved to obtainThen throughAndcan obtain the required starting point Pn+1,...,PN. The mapping process is as follows: if it is notIt can be concluded that device m has changed operating state at time i, otherwise device m has not changed operating state and is accordinglyIf the device m changes its operating state, when Δ pi< 0, the inference device is turned off at time i, haveWhen Δ p is otherwiseiWhen > 0, the inference device is turned on at time i, have
The above process is repeated each time a device is separated according to its average operating powerIn decreasing order. When a device is detached and gets the correspondingThe average power consumption of the device in the operating state is then subtracted from the total power consumption to minimize interference for subsequent device separation.
After the solution of the minimized regularization term is obtained as a starting point, a gradient projection method is executed to carry out optimization solution on the whole objective function (6), and the solution problem at the moment can be regarded as a constraint nonlinear programming problem and is expressed as a constraint nonlinear programming problem
Where Ω constrains the range of the solution set to ensure that the resulting solution, i.e., the power consumption of each device, does not vary beyond what is actually allowed. Finally obtaining the separation result
Evaluation of Performance
Commonly used performance evaluation criteria in the NILM field are Recall (RE), Precision (PR), and F1-score. Each of which is defined as follows
RE＝TP/(TP+FN)
PR＝TP/(TP+FP)
F1＝2×(PR×RE)/(PR+RE)，
Namely, tp (true positive) represents that the state change of the device is correctly recognized, fp (false positive) represents that the device is erroneously recognized as the state change, and fn (false negative) represents that the state change of the device is not correctly recognized.
Therefore, the recall Rate (RE) measures the proportion of correctly identified events in all device state change events, the accuracy rate (PR) measures the correct rate of identification of device state change events, and F1-score is a trade-off between the two, with higher values indicating better separation performance of the method.
The performance of the method proposed in this example was verified by performing experiments on House 1 and House 2 of the REDD data set. Tables 1 and 2 are a comparison of the performance of the present invention with 3 other prior NILM methods, Graph Signal Processing (GSP) -based, Hidden Markov Model (HMM) -based, and Decision Tree (DT) -based. As can be seen from tables 1 and 2, the accuracy of the power data separation result of the method of the present invention is better.
Table 1 comparison of REDD House 1 power data separation results F1 performance values
Table 2 comparison of REDD House 2 power data separation results F1 performance values
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (6)
1. A non-invasive equipment load monitoring method based on graph signal processing is characterized by comprising three steps of data preprocessing, initial point obtaining and target function integral optimization, wherein data are preprocessed firstly, and proper time periods are selected for known equipment to serve as training sets; then determining a target function, and solving the minimum value of the regularization item of the target function to obtain an analytic solution; and finally, taking the obtained analytic solution as a starting point, and executing a gradient projection optimization algorithm to carry out overall optimization solution on the objective function.
2. The non-intrusive device load monitoring method based on graph signal processing as defined in claim 1, specifically comprising the steps of:
s1, acquiring the total load active power p of the user equipment in the set time period from 1 to NiAs monitoring data; obtaining the single active power of each device in a set time period 1-nAs training data, where N and N are positive integers and N is less than N, i is a time, m is the number of the device, and all devices constitute a device set
S2, calculating the average power consumption of each device based on the training dataAnd according to average power consumptionArranging in descending order from big to small;
s3, obtaining the total load active power change value delta p at each moment according to the monitoring dataiConstructing a graph for each device m by graph signal processing A vertex set comprising a plurality of vertexes, each vertex corresponds to an active power variation value delta piA is a weighted adjacency matrix with a weight Ai,jFor corresponding vertex viAnd vjThe weights of the connecting edges between, i and j represent different time instants;
s4, obtaining the individual active power change value of each device at each moment according to the training dataBased on constructed graphsCalculating all graph signals within 1-N timeThe calculation expression is as follows:
wherein, ThrmIs a threshold value for determining whether the device m has changed the operation state,is the single active power change value of the device m in a set time period 1-n;
s5, according to all graph signals within 1-n timeCalculating and acquiring graph signals from N +1 time to N timeBy passingAndmapping between the devices to obtain the actual operation power consumption change value of each device;
s6, repeating the steps S3 to S5, and removing the average power consumption of the running state of the equipment in the monitoring data after separating one equipment each time until all the equipment is separated;
s7, separating all the devices to obtain a starting point Pn+1,...,PNWherein
S8, obtaining the starting point Pn+1,...,PNSolving the optimization model to obtain and output the optimized time from N +1 to N
3. The non-invasive device load monitoring method based on graph signal processing according to claim 2, wherein in step S3, the adjacency matrix a is weightedi,jThe calculation expression of (a) is:
wherein, Δ piRepresenting the value of the change in the active power of the total load at time i, Δ pjAnd expressing the change value of the active power of the total load at the moment j, and expressing a scaling factor.
4. The non-invasive device load monitoring method based on graph signal processing according to claim 2, wherein in step S5, the graph signal is processedThe calculation expression of (a) is:
where L is the graph laplacian matrix, which is calculated from the graph adjacency weight matrix a.
5. The method for non-intrusive device load monitoring based on graph signal processing as defined in claim 2, wherein in step S7, the mapping procedure is as follows:
if it is notThen judge that the device m is in timeAt moment i, the running state is changed, when delta piIf < 0, the judgment device is turned off at the moment i, thenWhen Δ piWhen the current time is more than 0, the judgment device is turned on at the moment i, and then
If it is notIt is judged that the device m does not change the operation state and accordingly
6. The method for non-intrusive device load monitoring based on graph signal processing as defined in claim 2, wherein in step S8, the expression of the optimization model is:
wherein, Δ piRepresenting the value of the total load active power change at the moment i,and the active power change value of the equipment m at the moment i is represented, and omega represents a regularization term weight parameter.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910606143.XA CN110501568B (en) | 2019-07-05 | 2019-07-05 | Non-invasive equipment load monitoring method based on graph signal processing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910606143.XA CN110501568B (en) | 2019-07-05 | 2019-07-05 | Non-invasive equipment load monitoring method based on graph signal processing |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110501568A true CN110501568A (en) | 2019-11-26 |
| CN110501568B CN110501568B (en) | 2020-11-27 |
Family
ID=68585401
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910606143.XA Active CN110501568B (en) | 2019-07-05 | 2019-07-05 | Non-invasive equipment load monitoring method based on graph signal processing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110501568B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113033076A (en) * | 2021-03-02 | 2021-06-25 | 苏州纽克斯电源技术股份有限公司 | Non-invasive load monitoring method and device based on multi-chain decomposition method |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104408480A (en) * | 2014-11-28 | 2015-03-11 | 安徽师范大学 | Feature selection method based on Laplacian operator |
| CN105913141A (en) * | 2016-04-06 | 2016-08-31 | 上海电器科学研究院 | Load prediction method supporting big data |
| CN106096726A (en) * | 2016-05-31 | 2016-11-09 | 华北电力大学 | A kind of non-intrusion type load monitoring method and device |
| CN106447085A (en) * | 2016-09-05 | 2017-02-22 | 华北电力大学 | Short-term power load forecasting method and device and computation equipment |
| CN106443233A (en) * | 2016-08-26 | 2017-02-22 | 北京电力经济技术研究院 | Non-invasive steady-state load monitoring method |
| CN106600074A (en) * | 2016-12-28 | 2017-04-26 | 天津求实智源科技有限公司 | DFHSMM-based non-intrusion type electric power load monitoring method and system |
| CN107546855A (en) * | 2017-09-28 | 2018-01-05 | 广东工业大学 | A kind of non-intruding decomposition method of residential electricity consumption load |
| CN108899892A (en) * | 2018-06-08 | 2018-11-27 | 上海电力学院 | One kind being based on CNN non-intrusion type power load decomposition method |
| CN109462231A (en) * | 2018-11-15 | 2019-03-12 | 合肥工业大学 | Optimization of Load Dispatching method, system and the storage medium of resident's micro-capacitance sensor |
| CN109508908A (en) * | 2018-12-25 | 2019-03-22 | 深圳市城市公共安全技术研究院有限公司 | Non-invasive load recognition model training method, load monitoring method and device |
-
2019
- 2019-07-05 CN CN201910606143.XA patent/CN110501568B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104408480A (en) * | 2014-11-28 | 2015-03-11 | 安徽师范大学 | Feature selection method based on Laplacian operator |
| CN105913141A (en) * | 2016-04-06 | 2016-08-31 | 上海电器科学研究院 | Load prediction method supporting big data |
| CN106096726A (en) * | 2016-05-31 | 2016-11-09 | 华北电力大学 | A kind of non-intrusion type load monitoring method and device |
| CN106443233A (en) * | 2016-08-26 | 2017-02-22 | 北京电力经济技术研究院 | Non-invasive steady-state load monitoring method |
| CN106447085A (en) * | 2016-09-05 | 2017-02-22 | 华北电力大学 | Short-term power load forecasting method and device and computation equipment |
| CN106600074A (en) * | 2016-12-28 | 2017-04-26 | 天津求实智源科技有限公司 | DFHSMM-based non-intrusion type electric power load monitoring method and system |
| CN107546855A (en) * | 2017-09-28 | 2018-01-05 | 广东工业大学 | A kind of non-intruding decomposition method of residential electricity consumption load |
| CN108899892A (en) * | 2018-06-08 | 2018-11-27 | 上海电力学院 | One kind being based on CNN non-intrusion type power load decomposition method |
| CN109462231A (en) * | 2018-11-15 | 2019-03-12 | 合肥工业大学 | Optimization of Load Dispatching method, system and the storage medium of resident's micro-capacitance sensor |
| CN109508908A (en) * | 2018-12-25 | 2019-03-22 | 深圳市城市公共安全技术研究院有限公司 | Non-invasive load recognition model training method, load monitoring method and device |
Non-Patent Citations (1)
| Title |
|---|
| 郑凯: "配电网能效监测中的居民用户非侵入式负荷分解研究", 《中国优秀硕士学位论文全文数据库》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113033076A (en) * | 2021-03-02 | 2021-06-25 | 苏州纽克斯电源技术股份有限公司 | Non-invasive load monitoring method and device based on multi-chain decomposition method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110501568B (en) | 2020-11-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110610280B (en) | Short-term prediction method, model, device and system for power load | |
| Yunpeng et al. | Multi-step ahead time series forecasting for different data patterns based on LSTM recurrent neural network | |
| Guo et al. | Home appliance load modeling from aggregated smart meter data | |
| Stankovic et al. | A graph-based signal processing approach for low-rate energy disaggregation | |
| Pindoriya et al. | Composite reliability evaluation using Monte Carlo simulation and least squares support vector classifier | |
| CN109145949A (en) | Non-intrusive electrical load monitoring and decomposition method and system based on integrated study | |
| CN112434799B (en) | Non-invasive load identification method based on full convolution neural network | |
| CN109359665B (en) | Household appliance load identification method and device based on support vector machine | |
| CN114330495A (en) | Wind power prediction method and device based on deep learning fusion model | |
| CN113554466B (en) | Short-term electricity consumption prediction model construction method, prediction method and device | |
| Liu et al. | Super resolution perception for smart meter data | |
| CN102855634A (en) | Image detection method and image detection device | |
| CN110416995B (en) | Non-invasive load decomposition method and device | |
| Quek et al. | Load disaggregation using one-directional convolutional stacked long short-term memory recurrent neural network | |
| CN113807225A (en) | Load identification method based on feature fusion | |
| CN114970633B (en) | LSTM-based non-invasive electrical appliance identification method, system and equipment | |
| CN111563827A (en) | Load decomposition method based on electrical appliance physical characteristics and residential electricity consumption behaviors | |
| CN110837915A (en) | Low-voltage load point prediction and probability prediction method for power system based on hybrid integrated deep learning | |
| Tongta et al. | Long short-term memory (LSTM) neural networks applied to energy disaggregation | |
| de Diego-Otón et al. | Recurrent LSTM architecture for appliance identification in non-intrusive load monitoring | |
| CN113010985A (en) | Non-invasive load identification method based on parallel AANN | |
| CN110501568B (en) | Non-invasive equipment load monitoring method based on graph signal processing | |
| CN115100466A (en) | Non-invasive load monitoring method, device and medium | |
| Kumar et al. | Data-driven electrical load disaggregation using graph signal processing | |
| Liu et al. | A single-to-multi network for latency-free non-intrusive load monitoring |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |