CN114386314A - Non-invasive modeling method of comprehensive energy system based on zero sample learning - Google Patents

Non-invasive modeling method of comprehensive energy system based on zero sample learning Download PDF

Info

Publication number
CN114386314A
CN114386314A CN202111495935.8A CN202111495935A CN114386314A CN 114386314 A CN114386314 A CN 114386314A CN 202111495935 A CN202111495935 A CN 202111495935A CN 114386314 A CN114386314 A CN 114386314A
Authority
CN
China
Prior art keywords
equipment
state
energy system
type
time
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
Application number
CN202111495935.8A
Other languages
Chinese (zh)
Inventor
商文颖
程孟增
王宗元
徐熙林
张娜
邬桐
李金起
胡旌伟
张玫珊
刘凯
杨方圆
杨博
许言路
杨天蒙
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
STATE GRID LIAONING ECONOMIC TECHNIQUE INSTITUTE
State Grid Corp of China SGCC
Original Assignee
STATE GRID LIAONING ECONOMIC TECHNIQUE INSTITUTE
State Grid Corp of China SGCC
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by STATE GRID LIAONING ECONOMIC TECHNIQUE INSTITUTE, State Grid Corp of China SGCC filed Critical STATE GRID LIAONING ECONOMIC TECHNIQUE INSTITUTE
Priority to CN202111495935.8A priority Critical patent/CN114386314A/en
Publication of CN114386314A publication Critical patent/CN114386314A/en
Pending legal-status Critical Current

Links

Images

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
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/29—Graphical models, e.g. Bayesian networks
    • G06F18/295—Markov models or related models, e.g. semi-Markov models; Markov random fields; Networks embedding Markov models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/08—Probabilistic or stochastic CAD

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Artificial Intelligence (AREA)
  • General Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Software Systems (AREA)
  • Molecular Biology (AREA)
  • General Health & Medical Sciences (AREA)
  • Mathematical Physics (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Computing Systems (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Medical Informatics (AREA)
  • Computer Hardware Design (AREA)
  • Geometry (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention belongs to the technical field of energy system modeling, and particularly relates to a non-invasive modeling method of a comprehensive energy system based on zero sample learning. The method classifies the equipment in the self-energy source by using zero sample learning based on sparse coding, trains a classification model for a known data set and auxiliary information of the known data set of the comprehensive energy system, and effectively migrates the knowledge learned by the known data set into an unknown data set to realize classification of the unknown equipment data set; extracting the running states of the equipment by adopting a bidirectional long-short term memory network, and dividing the running states of the electric equipment, the power generation equipment and the energy storage equipment in the comprehensive energy system according to the extraction result; and establishing a multi-mode factor hidden Markov model based on the running state of the equipment to realize non-invasive modeling of the comprehensive energy system containing the equipment of unknown type. The method accurately identifies the unknown type equipment in the comprehensive energy system, effectively reduces the average error of the non-invasive modeling method, and obviously improves the precision and the accuracy of the model.

Description

Non-invasive modeling method of comprehensive energy system based on zero sample learning
Technical Field
The invention belongs to the technical field of energy system modeling, and particularly relates to a non-invasive modeling method of a comprehensive energy system based on zero sample learning.
Background
The comprehensive energy system is a novel open energy ecosystem which takes clean renewable energy sources such as wind and light and the like as main primary energy sources and takes an energy technology, an advanced control technology, an intelligent optimization technology, an information processing technology and the like as implementation means, can realize the high-efficiency utilization of the renewable energy sources, and improves the occupation ratio of the renewable energy sources in the production and consumption of the primary energy sources. The comprehensive energy system modeling is the basis for researching the safety and stability control and the energy optimization scheduling of the energy Internet system.
At present, domestic and foreign scholars mainly concentrate on the research of invasive modeling methods in the aspect of comprehensive energy system modeling, and the methods respectively model equipment contained in an energy system and integrate all models according to energy types so as to represent the integral model of the energy system. However, this modeling approach requires not only the equipment that the exhaustive system contains, but also the mechanism of operation of each equipment, resulting in a number of difficulties in modeling the energy system, including:
(1) the model is more complex along with the increase of internal equipment of the energy system, and the model parameters and state variables are increased when the model is subjected to optimization control;
(2) due to the nonlinearity and uncertainty of the energy system, a unified model capable of reflecting the state of the energy system is difficult to integrate;
(3) the system operation state is diversified, and different models need to be established and controlled.
The non-invasive load monitoring method provides a new solution for energy system modeling, and refined internal load category and use state data of the user can be obtained by decomposing and identifying total data of the user in the non-invasive load monitoring. However, the types of devices included in the integrated energy system are various, and with the updating of various devices and the access of new devices, the existing method needs to continuously update the system structure and the device model, which greatly affects the precision of the integrated energy system model.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a non-invasive modeling method of a comprehensive energy system based on zero sample learning. The aim is to achieve the aim of the invention which is high in precision and accuracy and can carry out non-invasive modeling on an integrated energy system containing unknown equipment.
The technical scheme adopted by the invention for realizing the purpose is as follows:
a non-intrusive modeling method of an integrated energy system based on zero sample learning comprises the following steps:
step 1, classifying known type equipment and unknown type equipment in the comprehensive energy system;
step 2, extracting the running state of each device in the comprehensive energy system according to the classification result;
step 3, dividing according to the extracted running state;
and 4, step 4: establishing a multi-mode factor hidden Markov model according to the division result;
and 5: performing parameter estimation on the established multi-mode factor hidden Markov model;
step 6: and decoding the hidden state of the multi-mode factor hidden Markov model according to the parameter estimation result, and finally establishing a comprehensive energy system decomposition model.
Further, the known types of devices in the integrated energy system include: photovoltaic power generation equipment, wind power generation equipment, electric energy storage equipment, electric vehicles, rotating electrical machines, electric boilers, micro gas turbines, electric heaters, electric ovens, refrigerators, washing machines and lighting equipment.
Furthermore, the step of classifying the known type equipment and the unknown type equipment is to classify the equipment in the self-energy source by utilizing zero sample learning based on sparse coding, train a classification model by utilizing a known data set of the comprehensive energy system and auxiliary information thereof, and effectively transfer the knowledge learned by the known data set to the unknown data set, thereby realizing the classification of the unknown equipment data set.
Further, the step 1 of classifying the known type device and the unknown type device in the integrated energy system comprises:
step 1.1: n in the integrated energy systemknowThe time series of operating powers of the devices of known type form a known data set
Figure BDA0003400736320000021
Wherein DknowFor a known data set, pknow,iFor operating a power time series for a device of known type, yknow,iFor corresponding equipment tags of known type, YknowSet of device tags for known classes, QknowThe number of devices of known type;
step 1.2: n to be identified in the integrated energy systemXThe running power time sequence of the unknown type equipment forms an unknown data set
Figure BDA0003400736320000022
Wherein DXFor unknown data sets, pX,iTime series of operating powers for devices of unknown type, yknow,iFor corresponding device tags of unknown type, YknowFor unknown classes of device tag sets, QknowNumber of devices of known type, yX,iLabeling the corresponding unknown type device;
step 1.3: performing semantic dictionary learning on the running power time sequence of the known type equipment:
Figure BDA0003400736320000031
wherein L isknowFor known type device semantic dictionaries, HknowFor known type device semantic representation, | ·| non-woven calculationFIs the Frobenius norm,
Figure BDA0003400736320000032
semantic dictionary regularization for known type devicesTerm, λ, controls the strength of the regularizing term, PknowRunning a power time series for a known type of device;
step 1.4: performing semantic dictionary learning on the running power time sequence of the unknown type equipment:
Figure BDA0003400736320000033
wherein,
Figure BDA0003400736320000034
qjis yX,jRepresentation in semantic embedding space, ωijFor inputting a time sequence pX,iBelonging to the label yX,jProbability of (H)XFor semantic representation of unknown types of devices, LXFor a device semantic dictionary of unknown type, siFor the equipment running state, | LX-LknowI is for limiting LXAnd LknowRegular term of fitness, | hi-qjThe method comprises the following steps that I is a regular term used for limiting the similarity between the representation of the power time sequence of the unknown type equipment in the semantic embedding space and the representation of the label of the unknown type equipment in the semantic embedding space;
step 1.5: fixation of H in step 3.3 and step 3.4, respectivelyXAnd LXAnd performing alternate iterative solution, and finding corresponding classification in an embedding space according to the result:
Figure BDA0003400736320000035
Figure BDA0003400736320000036
in the above formula, the first and second carbon atoms are,
Figure BDA0003400736320000037
the optimal solution is expressed semantically for the unknown type of device,
Figure BDA0003400736320000038
representing an optimal solution for unknown type device semantics, | ·| non-calculationFIs Frobenius norm.
Furthermore, the step 2 of extracting the operation states of the devices in the integrated energy system according to the classification result utilizes the Bi-directional long-short term memory network-based device to extract the operation states, the used Bi-LSTM network comprises six layers, wherein the length of an input layer is the length of a time window t, the second layer is a convolutional layer for extracting features from signals, the third and fourth layers are Bi-LSTM, the fifth layer is a convolutional layer, the sixth layer is a fully-connected layer, and the whole network is trained by a time forward and backward Bi-directional propagation method.
Furthermore, the step 3 of dividing according to the extracted operation state includes the following steps:
step 3.1: and (3) dividing the running states of the electric equipment according to the classification result in the step 1:
Figure BDA0003400736320000041
wherein n isdThe number of the electric devices of the type d,
Figure BDA0003400736320000042
the running state of the electric equipment at the time t;
step 3.2: dividing the operating states of the power generation equipment according to the classification result in the step 1:
Figure BDA0003400736320000043
wherein n isDGFor the number of operating modes of the power plant, sDGT is the running state of the power generation equipment at the moment t;
step 3.3: dividing the operating states of the energy storage equipment according to the classification result in the step 1:
Figure BDA0003400736320000044
wherein,
Figure BDA0003400736320000045
for the mode of operation of the energy storage device,
Figure BDA0003400736320000046
for the rated power of the energy storage device, ses,tThe operating state of the energy storage device at time t.
Further, step 4 is to establish a multi-modulus hidden markov model according to the division result, as follows:
Figure BDA0003400736320000047
Figure BDA0003400736320000048
Figure BDA0003400736320000049
in the above formula, the first and second carbon atoms are,
Figure BDA00034007363200000410
operating state of the Q +1 st device at the initial moment, ptFor the operating power of the device at time t,
Figure BDA00034007363200000411
for the operating state of the Q +1 st device at time t,
Figure BDA00034007363200000412
operation status of the Q +1 th device at time t-1, N (μ)i,εi) To a desired value of muiStandard deviation of epsiloniThe normal distribution of (c),
Figure BDA00034007363200000413
for the operating state of the 1 st device at time t,
Figure BDA00034007363200000414
and the operation state of the 2 nd equipment at the moment t, wherein pi is the probability distribution of the initial state, A is a state transition matrix, and B is an observation matrix.
Further, the step 5 of performing parameter estimation on the established multi-modulus hidden markov model includes the following steps:
step 5.1: the multi-modulus hidden Markov model parameter estimation equation is established as follows:
Figure BDA0003400736320000051
in the above formula, θ*The observed multi-mode factor hidden Markov model parameter with the maximum probability of the time series of the operating power of the equipment, P is the operating power of the equipment, PtIs the running power of the equipment at the moment t, S is the running state of the equipment, StTheta is the operation state of the equipment at the time t, theta is a multi-modulus hidden Markov model parameter, | P1:TI is the running power time sequence of the equipment from the initial moment to the t moment;
step 5.2: introducing a forward auxiliary variable
Figure BDA0003400736320000052
Is represented by the timetQ-type equipment operation power time sequence under operation state i
Figure BDA0003400736320000053
Given an initial parameter theta0，
Figure BDA0003400736320000054
Expressed as:
Figure BDA0003400736320000055
under the initial conditions of the process, the process is carried out,
Figure BDA0003400736320000056
is shown as
Figure BDA0003400736320000057
In the above formula, the first and second carbon atoms are,
Figure BDA0003400736320000058
for q type equipment operation power time sequence under t time and operation state i
Figure BDA0003400736320000059
Of joint probability of theta0For the multi-modulus hidden markov model initial parameters,
Figure BDA00034007363200000510
the method comprises the following steps that (1) the operation state of q type equipment at an initial moment is obtained, and i is the operation state of the equipment at a t moment;
step 5.3: based on
Figure BDA00034007363200000511
Forward recursion principle acquisition
Figure BDA00034007363200000512
Figure BDA00034007363200000513
In the above formula, the first and second carbon atoms are,
Figure BDA00034007363200000514
for q type equipment operation power time sequence under t time and operation state i
Figure BDA00034007363200000515
The joint probability of (a) is determined,
Figure BDA00034007363200000516
for the state transition probability of a q-type device,
Figure BDA00034007363200000517
the q type device operating power for time t +1,
Figure BDA00034007363200000518
the operating state of the q-type equipment at the moment t +1, the operating state of j at the moment t +1,
Figure BDA00034007363200000519
operating power time sequence of q type equipment at t +1 moment and in operating state j
Figure BDA00034007363200000520
A joint probability of (a);
step 5.4: introducing backward auxiliary variables
Figure BDA00034007363200000521
Representing a time series of operating powers of a device of type q observed at time t, operating state i
Figure BDA00034007363200000522
Given an initial parameter θ0，
Figure BDA00034007363200000523
Expressed as:
Figure BDA0003400736320000061
step 5.5: subtending a back variable using a recursive formula
Figure BDA00034007363200000626
And (3) calculating:
Figure BDA0003400736320000062
wherein the initial value
Figure BDA0003400736320000063
In the above formula, the first and second carbon atoms are,
Figure BDA0003400736320000064
for the state transition probability of a q-type device,
Figure BDA0003400736320000065
the q type device operation state at the time t +1,
Figure BDA0003400736320000066
the operating power of the q-type equipment at the moment t +1 is obtained;
step 5.6: for a given initial parameter θ0And observation sequence
Figure BDA0003400736320000067
Computing the slave state of a q-type device
Figure BDA0003400736320000068
Transition to a State
Figure BDA0003400736320000069
Probability of (2)
Figure BDA00034007363200000610
And presenting state at time t
Figure BDA00034007363200000611
Probability of (2)
Figure BDA00034007363200000612
Figure BDA00034007363200000613
Figure BDA00034007363200000614
In the above formula, the first and second carbon atoms are,
Figure BDA00034007363200000615
for the state transition probability of a q-type device,
Figure BDA00034007363200000616
is the backward auxiliary variable at time t +1,
Figure BDA00034007363200000617
for the backward auxiliary variable at time t,
Figure BDA00034007363200000618
presenting state for time t
Figure BDA00034007363200000619
The probability of (a) of (b) being,
Figure BDA00034007363200000620
slave status for q-type devices
Figure BDA00034007363200000621
Transition to a State
Figure BDA00034007363200000622
The probability of (d);
step 5.7: recalculating the model parameters for the q hidden markov chains:
Figure BDA00034007363200000623
Figure BDA00034007363200000624
Figure BDA00034007363200000625
Figure BDA0003400736320000071
in the above formula, the first and second carbon atoms are,
Figure BDA0003400736320000072
for the initial state probability estimate for a q-type device,
Figure BDA0003400736320000073
for the q-type device state transition probability estimation,
Figure BDA0003400736320000074
for an estimate of the expected values of the observation matrix for a q-type device,
Figure BDA0003400736320000075
an estimated value of standard deviation of an observation matrix of the q-type equipment is obtained;
step 5.8: iterative computation of forward variables from new parameter loops
Figure BDA0003400736320000076
Backward variation
Figure BDA0003400736320000077
Figure BDA0003400736320000078
And
Figure BDA0003400736320000079
until convergence.
Further, the step 6 of decoding the hidden state of the hidden markov model with the multi-modulus factor for the parameter estimation result to finally establish the comprehensive energy system decomposition model is to decode the hidden state of the hidden markov model with the multi-modulus factor by applying a Viterbi algorithm to the summarized power consumption sequence parameter estimation result to finally establish the comprehensive energy system decomposition model; the method comprises the following steps:
step 6.1: introducing a variable deltat(i) Representing all states s at time t1,s1,...,stTo an observation sequence p1,p2,...,pTMaximum value of probability:
Figure BDA00034007363200000710
n is a device hidden layer state number, T is 1, 2.
Step 6.2: feeding in the product obtained in step 5.7
Figure BDA00034007363200000711
And the output power time sequence P of the integrated energy system1:T＝{p1,p2,...,pT}；
Step 6.3: initialization
Figure BDA00034007363200000712
Step 6.4: recursion to each other
Figure BDA00034007363200000713
Step 6.5: optimal path backtracking:
Figure BDA00034007363200000714
a computer storage medium having a computer program stored thereon, the computer program when executed by a processor implementing the steps of a zero sample learning based non-intrusive modeling method for an integrated energy system.
The invention has the following beneficial effects and advantages:
the invention provides a non-invasive modeling method of a comprehensive energy system based on zero sample learning, which can be used for carrying out non-invasive modeling on the comprehensive energy system containing unknown equipment. Firstly, extracting the running characteristics of known type equipment and unknown type equipment accessed to the comprehensive energy system through dictionary learning; further classifying equipment in the comprehensive energy system by using zero sample learning based on sparse coding; on the basis, the running state of the equipment is extracted by using a bidirectional long-short term memory network, and the modeling is carried out on the comprehensive energy system based on a multi-mode factor hidden Markov model. The method accurately identifies the unknown type equipment in the comprehensive energy system, and the established model effectively reduces the average error of the non-invasive modeling method, so that the precision and the accuracy of the model are obviously improved.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of a non-intrusive modeling method of an integrated energy system based on zero sample learning according to the present invention;
FIG. 2 is a schematic diagram of the integrated energy system of the present invention;
FIG. 3 is a schematic diagram of the zero sample learning based integrated energy system classification of the present invention;
FIG. 4 is a block diagram of the bidirectional long short term memory network of the present invention;
FIG. 5 is a diagram of a multi-modal equipment operating status breakdown of the present invention;
FIG. 6 is a hidden Markov model with multi-modal factors in accordance with the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
The solution of some embodiments of the invention is described below with reference to fig. 1-6.
Example 1
The invention provides an embodiment, which is a non-intrusive modeling method of an integrated energy system based on zero sample learning, and the non-intrusive modeling method is realized by the non-intrusive modeling integrated energy system based on zero sample learning, as shown in fig. 1, and fig. 1 is a flow chart of the non-intrusive modeling method of the integrated energy system based on zero sample learning.
The invention specifically comprises the following steps:
step 1, classifying known type equipment and unknown type equipment in the comprehensive energy system;
the invention relates to equipment of known type in a non-intrusive modeling comprehensive energy system based on zero sample learning, which comprises: photovoltaic power generation equipment, wind power generation equipment, electric energy storage equipment, electric vehicles, rotating electrical machines, electric boilers, micro gas turbines, electric heaters, electric ovens, refrigerators, washing machines and lighting equipment.
The method comprises the following steps:
step 1.1: n in the integrated energy system shown in FIG. 2knowThe time series of operating powers of the devices of known type form a known data set
Figure BDA0003400736320000091
Wherein DknowFor a known data set, pknow,iFor operating a power time series for a device of known type, yknow,iFor corresponding equipment tags of known type, YknowSet of device tags for known classes, QknowThe number of devices of known type;
step 1.2: n to be identified in the integrated energy system shown in FIG. 2XThe running power time sequence of the unknown type equipment forms an unknown data set
Figure BDA0003400736320000092
Wherein DXFor unknown data sets, pX,iTime series of operating powers for devices of unknown type, yknow,iFor corresponding device tags of unknown type, YknowFor unknown classes of device tag sets, QknowNumber of devices of known type, yX,iAnd is labeled with the corresponding unknown type device.
Step 1.3: performing semantic dictionary learning on the running power time sequence of the known type equipment:
Figure BDA0003400736320000093
wherein L isknowFor known type device semantic dictionaries, HknowFor known type device semantic representation, | ·| non-woven calculationFIs the Frobenius norm,
Figure BDA0003400736320000094
controlling the strength of the regularization term, P, for the known type device semantic dictionary regularization term, λknowA power time series is run for a known type of device.
Step 1.4: performing semantic dictionary learning on the running power time sequence of the unknown type equipment:
Figure BDA0003400736320000095
wherein,
Figure BDA0003400736320000096
qjis yX,jRepresentation in semantic embedding space, ωijFor inputting a time sequence pX,iBelonging to the label yX,jProbability of (H)XFor semantic representation of unknown types of devices, LXFor a device semantic dictionary of unknown type, siThe equipment running state is set; l | |X-LknowI is for limiting LXAnd LknowRegular term of fitness, | hi-qjAnd | l is a regular term used for limiting the similarity between the representation of the power time sequence of the unknown type device in the semantic embedding space and the representation of the tag of the unknown type device in the semantic embedding space.
Step 1.5: fixation of H in step 3.3 and step 3.4, respectivelyXAnd LXAlternate iterative solution is performed, and the corresponding classification is found in the embedding space shown in fig. 3 according to the result:
Figure BDA0003400736320000101
Figure BDA0003400736320000102
in the above formula, the first and second carbon atoms are,
Figure BDA0003400736320000103
the optimal solution is expressed semantically for the unknown type of device,
Figure BDA0003400736320000104
representing an optimal solution for unknown type device semantics, | ·| non-calculationFIs Frobenius norm.
Step 2, extracting the running state of each device in the comprehensive energy system according to the classification result;
the operation state extraction is carried out by using a Bi-directional Long Short-Term Memory network (Bi-LSTM) device based on fig. 4, the Bi-LSTM network comprises six layers in total, wherein the length of an input layer is the length of a time window t, the second layer is a convolutional layer and is used for extracting characteristics from signals, the third layer and the fourth layer are Bi-LSTM, the fifth layer is a convolutional layer and the sixth layer is a full-connection layer, and the whole network is trained by a time forward and backward bidirectional propagation method.
And 3, dividing according to the extracted running state.
The method comprises the following steps of dividing the running states of electric equipment, power generation equipment and energy storage equipment in the comprehensive energy system according to the running states of the equipment extracted by Bi-LSTM, and converting the 0-1 switch running state division problem of the equipment into a multi-mode division problem of the same type of equipment according to the equipment type, as shown in FIG. 5, wherein FIG. 5 is a multi-mode equipment running state division diagram of the invention, and the method comprises the following steps:
step 3.1: and (3) dividing the running states of the electric equipment according to the classification result in the step 1:
Figure BDA0003400736320000105
wherein n isdThe number of the electric devices of the type d,
Figure BDA0003400736320000106
and (3) the running state of the electric equipment at the time t.
Step 3.2: dividing the operating states of the power generation equipment according to the classification result in the step 1:
Figure BDA0003400736320000111
wherein n isDGFor the number of operating modes of the power plant, sDG,tIs the operating state of the power generating equipment at the time t.
Step 3.3: dividing the operating states of the energy storage equipment according to the classification result in the step 1:
Figure BDA0003400736320000112
wherein,
Figure BDA0003400736320000113
for the mode of operation of the energy storage device,
Figure BDA0003400736320000114
for the rated power of the energy storage device, ses,tThe operating state of the energy storage device at time t.
And 4, step 4: a multi-modulus hidden markov model is built according to the division result, as shown in fig. 6:
Figure BDA0003400736320000115
Figure BDA0003400736320000116
Figure BDA0003400736320000117
in the above formula, the first and second carbon atoms are,
Figure BDA0003400736320000118
operating state of the Q +1 st device at the initial moment, ptFor the operating power of the device at time t,
Figure BDA0003400736320000119
for the operating state of the Q +1 st device at time t,
Figure BDA00034007363200001110
operation status of the Q +1 th device at time t-1, N (μ)i,εi) To a desired value of muiStandard deviation of epsiloniThe normal distribution of (c),
Figure BDA00034007363200001111
for the operating state of the 1 st device at time t,
Figure BDA00034007363200001112
and the operation state of the 2 nd equipment at the moment t, wherein pi is the probability distribution of the initial state, A is a state transition matrix, and B is an observation matrix.
And 5: the parameter estimation is carried out on the established multi-modulus factor hidden Markov model, and the method comprises the following steps:
step 5.1: the multi-modulus hidden Markov model parameter estimation equation is established as follows:
Figure BDA00034007363200001113
in the above formula, θ*The observed multi-mode factor hidden Markov model parameter with the maximum probability of the time series of the operating power of the equipment, P is the operating power of the equipment, PtIs the running power of the equipment at the moment t, S is the running state of the equipment, StTheta is the operation state of the equipment at the time t, theta is a multi-modulus hidden Markov model parameter, | P1:TAnd | is the running power time sequence of the equipment from the initial moment to the t moment.
Step 5.2: introducing a forward auxiliary variable
Figure BDA0003400736320000121
Representing the operating power time series of a q-type device at time t in operating state i
Figure BDA0003400736320000122
Given an initial parameter theta0，
Figure BDA0003400736320000123
Expressed as:
Figure BDA0003400736320000124
under the initial conditions of the process, the process is carried out,
Figure BDA0003400736320000125
is shown as
Figure BDA0003400736320000126
In the above formula, the first and second carbon atoms are,
Figure BDA0003400736320000127
for q type equipment operation power time sequence under t time and operation state i
Figure BDA0003400736320000128
Of joint probability of theta0For the multi-modulus hidden markov model initial parameters,
Figure BDA0003400736320000129
the device running state is the initial time q type device running state, and i is the t time device running state.
Step 5.3: based on
Figure BDA00034007363200001210
Forward recursion principle acquisition
Figure BDA00034007363200001211
Figure BDA00034007363200001212
In the above formula, the first and second carbon atoms are,
Figure BDA00034007363200001213
for q type equipment operation power time sequence under t time and operation state i
Figure BDA00034007363200001214
The joint probability of (a) is determined,
Figure BDA00034007363200001215
for the state transition probability of a q-type device,
Figure BDA00034007363200001216
the q type device operating power for time t +1,
Figure BDA00034007363200001217
the operating state of the q-type equipment at the moment t +1, the operating state of j at the moment t +1,
Figure BDA00034007363200001218
operating power time sequence of q type equipment at t +1 moment and in operating state j
Figure BDA00034007363200001219
The joint probability of (c).
Step 5.4: introducing backward auxiliary variables
Figure BDA00034007363200001220
Representing a time series of operating powers of a device of type q observed at time t, operating state i
Figure BDA00034007363200001221
Given an initial parameter θ0，
Figure BDA00034007363200001222
Expressed as:
Figure BDA00034007363200001223
step 5.5: subtending a back variable using a recursive formula
Figure BDA00034007363200001224
And (3) calculating:
Figure BDA00034007363200001225
wherein the initial value
Figure BDA00034007363200001226
In the above formula, the first and second carbon atoms are,
Figure BDA00034007363200001227
for the state transition probability of a q-type device,
Figure BDA00034007363200001228
the q type device operation state at the time t +1,
Figure BDA00034007363200001229
and (5) operating power of the type q equipment at the moment t + 1.
Step 5.6: for a given initial parameter θ0And observation sequence
Figure BDA0003400736320000131
Computing the slave state of a q-type device
Figure BDA0003400736320000132
Transition to a State
Figure BDA0003400736320000133
Probability of (2)
Figure BDA0003400736320000134
And presenting state at time t
Figure BDA0003400736320000135
Probability of (2)
Figure BDA0003400736320000136
Figure BDA0003400736320000137
Figure BDA0003400736320000138
In the above formula, the first and second carbon atoms are,
Figure BDA0003400736320000139
for the state transition probability of a q-type device,
Figure BDA00034007363200001310
is the backward auxiliary variable at time t +1,
Figure BDA00034007363200001311
for the backward auxiliary variable at time t,
Figure BDA00034007363200001312
presenting state for time t
Figure BDA00034007363200001313
The probability of (a) of (b) being,
Figure BDA00034007363200001314
slave status for q-type devices
Figure BDA00034007363200001315
Transition to a State
Figure BDA00034007363200001316
The probability of (c).
Step 5.7: recalculating the model parameters for the q hidden markov chains:
Figure BDA00034007363200001317
Figure BDA00034007363200001318
Figure BDA00034007363200001319
Figure BDA00034007363200001320
in the above formula, the first and second carbon atoms are,
Figure BDA00034007363200001321
for the initial state probability estimate for a q-type device,
Figure BDA00034007363200001322
for the q-type device state transition probability estimation,
Figure BDA00034007363200001323
for an estimate of the expected values of the observation matrix for a q-type device,
Figure BDA00034007363200001324
and (4) observing the estimated value of the standard deviation of the matrix for the q-type equipment.
Step 5.8: iterative computation of forward variables from new parameter loops
Figure BDA00034007363200001325
Backward variation
Figure BDA00034007363200001326
Figure BDA0003400736320000141
And
Figure BDA0003400736320000142
until convergence.
Step 6: and decoding the hidden state of the multi-mode factor hidden Markov model by applying a Viterbi algorithm to the summarized power consumption sequence parameter estimation result, and finally establishing a comprehensive energy system decomposition model.
The method specifically comprises the following steps:
step 6.1: introducing a variable deltat(i) Representing all states s at time t1,s1,...,stTo an observation sequence p1,p2,...,pTMaximum value of probability:
Figure BDA0003400736320000143
n is the device hidden layer state number, and T is 1, 2.
Step 6.2: feeding in the product obtained in step 5.7
Figure BDA0003400736320000144
And the output power time sequence P of the integrated energy system1:T＝{p1,p2,...,pT}；
Step 6.3: initialization
Figure BDA0003400736320000145
Step 6.4: recursion to each other
Figure BDA0003400736320000146
Step 6.5: optimal path backtracking:
Figure BDA0003400736320000147
the embodiment achieves the following technical effects:
(1) the unknown equipment type accessed into the comprehensive energy system can be accurately identified;
(2) the running states of the electric equipment, the energy storage equipment and the power generation equipment in the comprehensive energy system can be accurately extracted, and the running states are divided according to the equipment types;
(3) based on equipment type identification and equipment running state extraction, the running state of each equipment in the comprehensive energy system and the overall energy output condition of the comprehensive energy system can be accurately reflected by adopting a factor hidden Markov model.
(4) According to the method, the operation states of the equipment in the comprehensive energy system are divided, so that the calculation complexity and the calculation time of the comprehensive energy system modeling are effectively reduced, and meanwhile, the unknown equipment is classified by adopting zero-sample learning, so that the precision and the accuracy of the model are improved.
Example 2
Based on the same inventive concept, an embodiment of the present invention further provides a computer storage medium, where a computer program is stored on the computer storage medium, and when the computer program is executed by a processor, the computer program implements the steps of the non-invasive modeling method for an integrated energy system based on zero sample learning according to embodiment 1.
As will be appreciated by one skilled in the art, 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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (10)

1. A non-invasive modeling method of a comprehensive energy system based on zero sample learning is characterized by comprising the following steps: the method comprises the following steps:
step 1, classifying known type equipment and unknown type equipment in the comprehensive energy system;
step 2, extracting the running state of each device in the comprehensive energy system according to the classification result;
step 3, dividing according to the extracted running state;
and 4, step 4: establishing a multi-mode factor hidden Markov model according to the division result;
and 5: performing parameter estimation on the established multi-mode factor hidden Markov model;
step 6: and decoding the hidden state of the multi-mode factor hidden Markov model according to the parameter estimation result, and finally establishing a comprehensive energy system decomposition model.
2. The non-invasive modeling method of the integrated energy system based on the zero sample learning as claimed in claim 1, wherein: the known types of devices in said integrated energy system comprise: photovoltaic power generation equipment, wind power generation equipment, electric energy storage equipment, electric vehicles, rotating electrical machines, electric boilers, micro gas turbines, electric heaters, electric ovens, refrigerators, washing machines and lighting equipment.
3. The non-invasive modeling method of the integrated energy system based on the zero sample learning as claimed in claim 1, wherein: the step of classifying the known type equipment and the unknown type equipment is to classify the equipment in the self-energy source by utilizing zero sample learning based on sparse coding, train a classification model by utilizing a known data set and auxiliary information of a comprehensive energy system, and effectively transfer the knowledge learned by the known data set to the unknown data set, thereby realizing the classification of the unknown equipment data set.
4. The non-invasive modeling method of the integrated energy system based on the zero sample learning as claimed in claim 1, wherein: the step 1 of classifying the known type equipment and the unknown type equipment in the comprehensive energy system comprises the following steps:
step 1.1: n in the integrated energy systemknowThe time series of operating powers of the devices of known type form a known data set
Figure FDA0003400736310000011
Wherein DknowFor a known data set, pknow,iFor operating a power time series for a device of known type, yknow,iFor corresponding equipment tags of known type, YknowSet of device tags for known classes, QknowThe number of devices of known type;
step 1.2: n to be identified in the integrated energy systemXThe running power time sequence of the unknown type equipment forms an unknown data set
Figure FDA0003400736310000021
Wherein DXFor unknown data sets, pX,iTime series of operating powers for devices of unknown type, yknow,iFor corresponding device tags of unknown type, YknowFor unknown classes of device tag sets, QknowNumber of devices of known type, yX,iLabeling the corresponding unknown type device;
step 1.3: performing semantic dictionary learning on the running power time sequence of the known type equipment:
Figure FDA0003400736310000022
wherein L isknowFor known type device semantic dictionaries, HknowFor known type device semantic representation, | ·| non-woven calculationFIs the Frobenius norm,
Figure FDA0003400736310000023
controlling the strength of the regularization term, P, for the known type device semantic dictionary regularization term, λknowRunning a power time series for a known type of device;
step 1.4: performing semantic dictionary learning on the running power time sequence of the unknown type equipment:
Figure FDA0003400736310000024
wherein,
Figure FDA0003400736310000025
qjis yX,jRepresentation in semantic embedding space, ωijFor inputting a time sequence pX,iBelonging to the label yX,jProbability of (H)XFor semantic representation of unknown types of devices, LXFor a device semantic dictionary of unknown type, siFor the equipment running state, | LX-LknowI is for limiting LXAnd LknowRegular term of fitness, | hi-qjThe method comprises the following steps that I is a regular term used for limiting the similarity between the representation of the power time sequence of the unknown type equipment in the semantic embedding space and the representation of the label of the unknown type equipment in the semantic embedding space;
step 1.5: fixation of H in step 3.3 and step 3.4, respectivelyXAnd LXAnd performing alternate iterative solution, and finding corresponding classification in an embedding space according to the result:
Figure FDA0003400736310000026
Figure FDA0003400736310000027
in the above formula, the first and second carbon atoms are,
Figure FDA0003400736310000028
the optimal solution is expressed semantically for the unknown type of device,
Figure FDA0003400736310000029
representing an optimal solution for unknown type device semantics, | ·| non-calculationFIs Frobenius norm.
5. The non-invasive modeling method of the integrated energy system based on the zero sample learning as claimed in claim 1, wherein: and 2, extracting the running state of each device in the comprehensive energy system according to the classification result, namely extracting the running state by using the device based on the bidirectional long-short term memory network, wherein the used Bi-LSTM network comprises six layers, the length of an input layer is the length of a time window t, the second layer is a convolutional layer and is used for extracting characteristics from signals, the third layer and the fourth layer are Bi-LSTM, the fifth layer is a convolutional layer, the sixth layer is a full-connection layer, and the whole network is trained by a time forward-backward bidirectional transmission method.
6. The non-invasive modeling method of the integrated energy system based on the zero sample learning as claimed in claim 1, wherein: the step 3 of dividing according to the extracted running state comprises the following steps:
step 3.1: and (3) dividing the running states of the electric equipment according to the classification result in the step 1:
Figure FDA0003400736310000031
wherein n isdThe number of the electric devices of the type d,
Figure FDA0003400736310000032
the running state of the electric equipment at the time t;
step 3.2: dividing the operating states of the power generation equipment according to the classification result in the step 1:
Figure FDA0003400736310000033
wherein n isDGFor the number of operating modes of the power plant, sDG,tThe operation state of the power generation equipment at the time t is shown;
step 3.3: dividing the operating states of the energy storage equipment according to the classification result in the step 1:
Figure FDA0003400736310000034
wherein,
Figure FDA0003400736310000035
for the mode of operation of the energy storage device,
Figure FDA0003400736310000036
for the rated power of the energy storage device, ses,tThe operating state of the energy storage device at time t.
7. The non-invasive modeling method of the integrated energy system based on the zero sample learning as claimed in claim 1, wherein: step 4, establishing a multi-modulus factor hidden Markov model according to the division result, as follows:
Figure FDA0003400736310000037
Figure FDA0003400736310000038
Figure FDA0003400736310000039
in the above formula, the first and second carbon atoms are,
Figure FDA00034007363100000310
operating state of the Q +1 st device at the initial moment, ptFor the operating power of the device at time t,
Figure FDA0003400736310000041
for the operating state of the Q +1 st device at time t,
Figure FDA0003400736310000042
operation status of the Q +1 th device at time t-1, N (μ)i,εi) To a desired value of muiStandard deviation of epsiloniThe normal distribution of (c),
Figure FDA0003400736310000043
for the operating state of the 1 st device at time t,
Figure FDA0003400736310000044
and the operation state of the 2 nd equipment at the moment t, wherein pi is the probability distribution of the initial state, A is a state transition matrix, and B is an observation matrix.
8. The non-invasive modeling method of the integrated energy system based on the zero sample learning as claimed in claim 1, wherein: and 5, performing parameter estimation on the established multi-modulus factor hidden Markov model, comprising the following steps of:
step 5.1: the multi-modulus hidden Markov model parameter estimation equation is established as follows:
Figure FDA0003400736310000045
in the above formula, θ*The observed multi-mode factor hidden Markov model parameter with the maximum probability of the time series of the operating power of the equipment, P is the operating power of the equipment, PtIs the running power of the equipment at the moment t, S is the running state of the equipment, StTheta is the operation state of the equipment at the time t, theta is a multi-modulus hidden Markov model parameter, | P1:TI is the running power time sequence of the equipment from the initial moment to the t moment;
step 5.2: introducing a forward auxiliary variable
Figure FDA0003400736310000046
Representing the operating power time series of a q-type device at time t in operating state i
Figure FDA0003400736310000047
Given an initial parameter theta0，
Figure FDA0003400736310000048
Expressed as:
Figure FDA0003400736310000049
under the initial conditions of the process, the process is carried out,
Figure FDA00034007363100000410
is shown as
Figure FDA00034007363100000411
In the above formula, the first and second carbon atoms are,
Figure FDA00034007363100000412
for q type equipment operation power time sequence under t time and operation state i
Figure FDA00034007363100000413
Of joint probability of theta0For the multi-modulus hidden markov model initial parameters,
Figure FDA00034007363100000414
the method comprises the following steps that (1) the operation state of q type equipment at an initial moment is obtained, and i is the operation state of the equipment at a t moment;
step 5.3: based on
Figure FDA00034007363100000415
Forward recursion principle acquisition
Figure FDA00034007363100000416
Figure FDA00034007363100000417
In the above formula, the first and second carbon atoms are,
Figure FDA0003400736310000051
for q type equipment operation power time sequence under t time and operation state i
Figure FDA0003400736310000052
The joint probability of (a) is determined,
Figure FDA0003400736310000053
for the state transition probability of a q-type device,
Figure FDA0003400736310000054
the q type device operating power for time t +1,
Figure FDA0003400736310000055
the operating state of the q-type equipment at the moment t +1, the operating state of j at the moment t +1,
Figure FDA0003400736310000056
operating power time sequence of q type equipment at t +1 moment and in operating state j
Figure FDA0003400736310000057
A joint probability of (a);
step 5.4: introducing backward auxiliary variables
Figure FDA0003400736310000058
Representing a time series of operating powers of a device of type q observed at time t, operating state i
Figure FDA0003400736310000059
Given an initial parameter θ0，
Figure FDA00034007363100000510
Expressed as:
Figure FDA00034007363100000511
step 5.5: subtending a back variable using a recursive formula
Figure FDA00034007363100000512
And (3) calculating:
Figure FDA00034007363100000513
wherein the initial value
Figure FDA00034007363100000514
In the above formula, the first and second carbon atoms are,
Figure FDA00034007363100000515
for the state transition probability of a q-type device,
Figure FDA00034007363100000516
the q type device operation state at the time t +1,
Figure FDA00034007363100000517
the operating power of the q-type equipment at the moment t +1 is obtained;
step 5.6: for a given initial parameter θ0And observation sequence
Figure FDA00034007363100000518
Computing the slave state of a q-type device
Figure FDA00034007363100000519
Transition to a State
Figure FDA00034007363100000520
Probability of (2)
Figure FDA00034007363100000521
And presenting state at time t
Figure FDA00034007363100000522
Probability of (2)
Figure FDA00034007363100000523
Figure FDA00034007363100000524
Figure FDA00034007363100000525
In the above formula, the first and second carbon atoms are,
Figure FDA00034007363100000526
for the state transition probability of a q-type device,
Figure FDA00034007363100000527
is the backward auxiliary variable at time t +1,
Figure FDA00034007363100000528
for the backward auxiliary variable at time t,
Figure FDA00034007363100000529
presenting state for time t
Figure FDA00034007363100000530
The probability of (a) of (b) being,
Figure FDA00034007363100000531
slave status for q-type devices
Figure FDA00034007363100000532
Transition to a State
Figure FDA00034007363100000533
The probability of (d);
step 5.7: recalculating the model parameters for the q hidden markov chains:
Figure FDA0003400736310000061
Figure FDA0003400736310000062
Figure FDA0003400736310000063
Figure FDA0003400736310000064
in the above formula, the first and second carbon atoms are,
Figure FDA0003400736310000065
for the initial state probability estimate for a q-type device,
Figure FDA0003400736310000066
for the q-type device state transition probability estimation,
Figure FDA0003400736310000067
for an estimate of the expected values of the observation matrix for a q-type device,
Figure FDA0003400736310000068
an estimated value of standard deviation of an observation matrix of the q-type equipment is obtained;
step 5.8: iterative computation of forward variables from new parameter loops
Figure FDA0003400736310000069
Backward variation
Figure FDA00034007363100000610
Figure FDA00034007363100000611
And
Figure FDA00034007363100000612
until convergence.
9. The non-invasive modeling method of the integrated energy system based on the zero sample learning as claimed in claim 1, wherein: step 6, decoding the hidden state of the multi-mode factor hidden Markov model of the parameter estimation result, and finally establishing a comprehensive energy system decomposition model, namely decoding the hidden state of the multi-mode factor hidden Markov model by applying a Viterbi algorithm to the summarized power consumption sequence parameter estimation result, and finally establishing the comprehensive energy system decomposition model; the method comprises the following steps:
step 6.1: introducing a variable deltat(i) Representing all states s at time t1,s1,...,stTo an observation sequence p1,p2,...,pTMaximum value of probability:
Figure FDA00034007363100000613
n is a device hidden layer state number, T is 1, 2.
Step 6.2: feeding in the product obtained in step 5.7
Figure FDA00034007363100000614
And the output power time sequence P of the integrated energy system1:T＝{p1,p2,...,pT}；
Step 6.3: initialization
Figure FDA00034007363100000615
Step 6.4: recursion to each other
Figure FDA0003400736310000071
Step 6.5: optimal path backtracking：
Figure FDA0003400736310000072
10. A computer storage medium, characterized by: the computer storage medium has stored thereon a computer program that, when executed by a processor, performs the steps of a method for non-intrusive modeling of an integrated energy system based on zero sample learning of claims 1-9.
CN202111495935.8A 2021-12-09 2021-12-09 Non-invasive modeling method of comprehensive energy system based on zero sample learning Pending CN114386314A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111495935.8A CN114386314A (en) 2021-12-09 2021-12-09 Non-invasive modeling method of comprehensive energy system based on zero sample learning

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111495935.8A CN114386314A (en) 2021-12-09 2021-12-09 Non-invasive modeling method of comprehensive energy system based on zero sample learning

Publications (1)

Publication Number Publication Date
CN114386314A true CN114386314A (en) 2022-04-22

Family

ID=81196719

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111495935.8A Pending CN114386314A (en) 2021-12-09 2021-12-09 Non-invasive modeling method of comprehensive energy system based on zero sample learning

Country Status (1)

Country Link
CN (1) CN114386314A (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115146876A (en) * 2022-09-05 2022-10-04 常州威图维亚电机科技有限公司 Electric vehicle power optimal control method based on energy model
CN116595488A (en) * 2023-07-19 2023-08-15 青岛鼎信通讯股份有限公司 Non-invasive load identification method based on intelligent ammeter
CN117728583A (en) * 2023-12-27 2024-03-19 中节能甘肃武威太阳能发电有限公司 Distributed photovoltaic cluster energy control monitoring system based on transfer learning

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115146876A (en) * 2022-09-05 2022-10-04 常州威图维亚电机科技有限公司 Electric vehicle power optimal control method based on energy model
CN115146876B (en) * 2022-09-05 2022-11-11 常州威图维亚电机科技有限公司 Electric vehicle power optimal control method based on energy model
CN116595488A (en) * 2023-07-19 2023-08-15 青岛鼎信通讯股份有限公司 Non-invasive load identification method based on intelligent ammeter
CN116595488B (en) * 2023-07-19 2023-11-14 青岛鼎信通讯股份有限公司 Non-invasive load identification method based on intelligent ammeter
CN117728583A (en) * 2023-12-27 2024-03-19 中节能甘肃武威太阳能发电有限公司 Distributed photovoltaic cluster energy control monitoring system based on transfer learning
CN117728583B (en) * 2023-12-27 2024-05-31 中节能甘肃武威太阳能发电有限公司 Distributed photovoltaic cluster energy control monitoring system based on transfer learning

Similar Documents

Publication Publication Date Title
Zhou et al. One fits all: Power general time series analysis by pretrained lm
CN114386314A (en) Non-invasive modeling method of comprehensive energy system based on zero sample learning
Chang et al. Chinese named entity recognition method based on BERT
CN112784965A (en) Large-scale multi-element time series data abnormity detection method oriented to cloud environment
Abdel-Galil et al. Disturbance classification using hidden Markov models and vector quantization
Sun et al. TEST: Text prototype aligned embedding to activate LLM's ability for time series
Liu et al. Dynamic knowledge graph reasoning based on deep reinforcement learning
Ma et al. A survey on time-series pre-trained models
CN116468298A (en) GPT network model-based automobile technology planning and decision-making method and system
CN113868432B (en) Automatic knowledge graph construction method and system for iron and steel manufacturing enterprises
CN113934887A (en) No-proposal time sequence language positioning method based on semantic decoupling
Liao et al. A machine learning approach to case adaptation
CN116108143B (en) Digital economic monitoring method and system based on block chain technology
CN112835585A (en) Program understanding method and system based on abstract syntax tree
Eldele et al. Label-efficient time series representation learning: A review
Lin et al. Research on Named Entity Recognition Method of Metro On‐Board Equipment Based on Multiheaded Self‐Attention Mechanism and CNN‐BiLSTM‐CRF
CN118335190A (en) Method and system for generating amino acid sequence of protein with specific functions and properties by using deep learning technology
Chen et al. Knowledge-aware textual entailment with graph attention network
Wang et al. Out-of-distributed semantic pruning for robust semi-supervised learning
Zhang et al. Cross-Supervised multisource prototypical network: A novel domain adaptation method for multi-source few-shot fault diagnosis
Zhang Machine Learning and Visual Perception
CN114596473A (en) Network embedding pre-training method based on graph neural network hierarchical loss function
Gao et al. Imprecise deep Forest for partial label learning
Liu et al. Deep learning based appliance identification
Li et al. Graph Intelligence with Large Language Models and Prompt Learning

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