CN113158309B - Heating and ventilation equipment operation strategy identification method - Google Patents

Heating and ventilation equipment operation strategy identification method Download PDF

Info

Publication number
CN113158309B
CN113158309B CN202110383958.3A CN202110383958A CN113158309B CN 113158309 B CN113158309 B CN 113158309B CN 202110383958 A CN202110383958 A CN 202110383958A CN 113158309 B CN113158309 B CN 113158309B
Authority
CN
China
Prior art keywords
power
equipment
group
probability density
devices
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.)
Active
Application number
CN202110383958.3A
Other languages
Chinese (zh)
Other versions
CN113158309A (en
Inventor
刘魁星
黄乙桑
张拓迷
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.)
Tianjin University
Original Assignee
Tianjin University
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 Tianjin University filed Critical Tianjin University
Priority to CN202110383958.3A priority Critical patent/CN113158309B/en
Publication of CN113158309A publication Critical patent/CN113158309A/en
Application granted granted Critical
Publication of CN113158309B publication Critical patent/CN113158309B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/13—Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
    • G06F18/24155—Bayesian classification
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/06—Power analysis or power optimisation

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • General Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Computer Hardware Design (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • Probability & Statistics with Applications (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Biology (AREA)
  • Architecture (AREA)
  • Civil Engineering (AREA)
  • Computing Systems (AREA)
  • Algebra (AREA)
  • Structural Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention provides a heating and ventilation equipment operation strategy identification method, which comprises the following steps: s110, generating probability density distribution of single equipment power; s120, generating probability density distribution of the power of the equipment group; s130, identifying a heating and ventilation equipment strategy of the power line; s140, self-correcting the strategy identification parameters. The invention solves the problem of operation strategy identification of heating and ventilation equipment under the condition that the existing electric power monitoring platform is provided with a plurality of pieces of equipment in a single line.

Description

Heating and ventilation equipment operation strategy identification method
Technical Field
The invention relates to the field of building energy management, in particular to a heating and ventilation equipment operation strategy identification method.
Background
The application and innovation of the algorithm for fusing the building data and the intelligent data mainly focus on improving the comfort level of the building environment and reducing the energy consumption of the building operation, and how to reasonably formulate and adjust the operation strategy of the equipment to maintain the balance of the building environment and the energy consumption of the operation becomes a main research point. Research on BAS system data focuses on the use of data inside building equipment, research and discussion on the relevance of data in BAS data, and prediction of building load or energy consumption, which indirectly improve building environmental comfort or reduce building energy consumption by improving equipment efficiency, diagnosing equipment failure, and modifying system formats.
However, the optimization method of the diagnostic equipment needs building data with various dimensions, and because the equipment parameters of different buildings are different or lack, currently, the related methods are likely to generate results with low robustness and low precision, and the optimization method in the form of a system for modification causes higher modification cost.
Therefore, there is a need in the prior art for a technical solution for guiding improvement of building environmental comfort and improvement of building operation effect by using existing building data, researching identification of heating ventilation operation devices and device groups by a mathematical method, and identifying a current heating ventilation operation strategy so as to integrate with a technical means of an intelligent data algorithm.
Disclosure of Invention
In order to solve the problems, the invention provides a heating and ventilation equipment operation strategy identification method.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a heating and ventilation equipment operation strategy identification method comprises the following steps:
s110, generating probability density distribution of single equipment power;
s120, generating probability density distribution of the power of the equipment group;
s130, identifying a heating and ventilation equipment strategy of the power line;
s140, self-correcting the strategy identification parameters.
In step S110, the single device power includes a fixed frequency device power and a variable frequency device power;
adopting Gaussian distribution as a probability density distribution function of the power of the fixed frequency equipment;
the frequency conversion equipment comprises class fixed frequency equipment and non-class fixed frequency equipment;
wherein, the probability density distribution function of the class fixed frequency equipment power also adopts Gaussian distribution; the probability density distribution of the power of the non-class fixed frequency equipment adopts a statistical method, and the statistical probability density distribution function of the power of the equipment is obtained by counting the actual distribution condition of the power within a certain time.
Step S120 includes the following substeps:
s121, computing equipment group:
calculate all possible device turn-on combinations, where a device group containing n different devices theoretically has 2 n Different equipment opening combinations are planted;
s122, calculating the probability density distribution of each equipment group:
for a group of devices comprising n different devices, the probability density distribution function for the on-power of device n is denoted as p n (x) The probability density distribution function of the group is denoted as p 1,2,3… (x) Wherein 1,2,3, …, n represents the device 1, device in the on state in the device groupPreparing 2, equipment 3, … and equipment n; p represents a probability density function, x represents a given power value;
the power of the equipment group is accumulated by the power of the equipment in the current on state, and the probability density of the power of the equipment group is calculated in the following mode:
Figure BDA0003014105300000021
wherein x is 1 +x 2 +x 3 +...+x n ＝x；
S123, convolution calculation:
decomposing the device group into a device on combination comprising 2 devices and a device on combination comprising n-2 devices; the probability density calculation mode of the device opening combination comprising 2 devices is as follows:
Figure BDA0003014105300000022
considering the device turn-on combination containing 2 devices as 1 new device, then converting the device turn-on combination containing n devices into a device turn-on combination containing n-2+1 devices, and converting the probability density calculation of the power of the device turn-on combination containing n devices into the probability density calculation of the power of the device turn-on combination containing n-1 devices through convolution operation; repeatedly performing convolution operation to realize probability density calculation of power of n equipment combinations; for a device on combination comprising n devices, n-1 convolution operations are required;
s124, calculating a probability density function of the power of the equipment group:
for 2 n And performing multiple convolution calculations on different equipment starting combinations respectively to obtain a probability density function of the power of the equipment group.
Step S130 adopts a bayesian decision method:
let each equipment open combination be denoted by C, C i Showing the ith equipment combination mode, for the equipment comprising n different equipmentThe equipment group is 2 n Seed equipment opening combination, C 1 ,C 2 ,C 3 ,…,C 2 n Denotes the 1 st, 2 nd, 3 rd, … 2 th 2 n The equipment opening combination is set, given X represents the specific power value of the equipment group, the forecast X belongs to the class with the maximum posterior probability under the condition X, and the forecast X belongs to the class C by adopting the Bayesian classification method i And if and only if:
P(C i |X)＞P(C j |X)1≤i,j≤2 n ,i≠j
maximizing P (C) i |X)，P(C i | X) largest class C i Called maximum a posteriori hypothesis, according to bayes theorem:
Figure BDA0003014105300000031
p (X) is constant for all classes, only P (X | C) i )P(C i ) Maximization, if the prior probabilities of all classes are unknown, then it is assumed that these classes are equi-probable, i.e., P (C) 1 )＝P(C 2 )＝…＝P(C 2 n ) (ii) a Otherwise, maximize P (X | C) i )P(C i )。
Step S140 includes the following substeps:
s141: given the rated power of a certain equipment group;
s142: obtaining the equipment opening combination with the maximum probability according to the probability density distribution of the equipment group power;
s143: recalculating the power of the device turn-on combination;
s144: judging whether the power of the device on combination calculated in step S143 is equal to the rated power of the device group given in step S141, if not, returning to step S141, re-giving the rated power of the device group, and correcting the rated power of the device group; if so, go to step S145;
s145: the gaussian distribution parameters of the set of devices are corrected.
Step S141 includes: the desired μ for the device group power is assumed to be the device rated power, and the standard deviation δ is assumed to be 10% of the rated power.
Step S144 includes: calculating probability distribution of each equipment combination by utilizing multiple convolution to finally obtain given power X m×1 Starting and stopping matrix B of lower equipment m×n Wherein X is m×1 Representing m actual line power values, which can be regarded as line power at different times, B m×n Representing the starting and stopping conditions of n line equipment corresponding to the m actual line power values; the rated power of the correction device group is calculated by the following formula:
P'＝B -1 X
wherein B is -1 The pseudo inverse B is used for calculating a power parameter P meeting the power sum X by using a least square method; when the difference between the power parameter P' obtained by the calibration calculation and the given rated power parameter P is smaller than the minimum value required by the iterative calculation, determining that the current given rated power parameter P is approximately equal to the actual power, that is, determining that the power of the device on combination obtained by calculation in step S143 is the rated power of the device group; if the difference between the two values is large, P' is used as the rated power of the device group initially specified in step S141, and the policy identification self-calibration calculation process is continued.
Compared with the prior art, the invention has the beneficial effects that:
1. a typical probability density distribution model of common equipment is designed, the probability density distribution of equipment group power formed by multiple pieces of equipment is calculated by convolution, and the problem of operation strategy identification of heating and ventilation equipment is solved under the condition that an existing power monitoring platform is provided with multiple pieces of equipment in a single line.
2. The policy identification parameter self-correction method can search the equipment power parameter group meeting the actual power consumption performance by an iterative updating method under the condition of giving the initial equipment power parameter group, thereby improving the identification effect of the equipment operation policy.
Drawings
Fig. 1 is a schematic frame diagram of a heating and ventilation equipment operation strategy identification method.
Fig. 2 is a flowchart illustrating the step S120 of the heating and ventilating apparatus operation strategy identification method.
Fig. 3 is a flowchart illustrating the step S140 of the heating and ventilating apparatus operation strategy identification method.
Detailed Description
The technical solutions of the present invention will be clearly and completely described and illustrated below with reference to specific embodiments, but the scope of the present invention is not limited thereto.
The method for identifying the operation strategy of the heating and ventilation equipment shown in fig. 1 to 3 comprises the following steps:
s110, generating probability density distribution of single equipment power;
s120, generating probability density distribution of the power of the equipment group;
s130, identifying a heating and ventilation equipment strategy of the power line;
s140, strategy identification parameter self-correction.
In step S110, the single device power includes a fixed frequency device power and a variable frequency device power;
the probability density distribution function of the power of the fixed frequency equipment adopts Gaussian distribution;
the frequency conversion equipment comprises class fixed frequency equipment and non-class fixed frequency equipment;
wherein, the probability density distribution function of the class fixed frequency equipment power also adopts Gaussian distribution; the probability density distribution of the power of the non-class fixed frequency equipment adopts a statistical method, and the probability density distribution function of the power of the equipment is obtained statistically by counting the actual distribution condition of the power in a certain time.
The expression of the gaussian distribution is as follows:
Figure BDA0003014105300000051
where μ is the average power value of the device and σ is the standard deviation.
In the frequency conversion equipment, the frequency of the variable frequency water pump is artificially set to be 45-47 Hz, and the variable frequency water pump has the property similar to that of fixed frequency equipment, namely similar fixed frequency equipment, and is also suitable for Gaussian distribution.
And the other frequency conversion equipment is non-class fixed frequency equipment, and the probability density distribution of the power of the non-class fixed frequency equipment adopts a statistical method to obtain a statistical equipment power probability density distribution function by counting the actual distribution condition of the power within a certain time. However, for the situation that the non-class fixed frequency equipment accurately operates according to the power meter when the equipment is started, the probability density of the power of the non-class fixed frequency equipment is fit with an actual distribution curve through Gaussian distribution.
Step S120 includes the following substeps:
s121, computing a device group:
calculate all possible device turn-on combinations, where a device group containing n different devices theoretically has 2 n Different equipment starting combinations are planted;
s122, calculating the probability density distribution of each equipment group:
for a group of devices comprising n different devices, the probability density distribution function for the on-power of device n is denoted as p n (x) The probability density distribution function of the group is denoted as p 1,2,3… (x) Wherein 1,2,3, …, n represents device 1, device 2, device 3, …, device n in the on state in the device group; p represents a probability density function, x represents a given power value;
the power of the equipment group is accumulated by the power of the equipment in the current on state, and the probability density of the power of the equipment group is calculated in the following mode:
Figure BDA0003014105300000052
wherein x is 1 +x 2 +x 3 +...+x n ＝x；
S123, convolution calculation:
decomposing the device group into a device on combination comprising 2 devices and a device on combination comprising n-2 devices; the probability density calculation mode of the device opening combination comprising 2 devices is as follows:
Figure BDA0003014105300000053
considering the device turn-on combination containing 2 devices as 1 new device, then converting the device turn-on combination containing n devices into a device turn-on combination containing n-2+1 devices, and converting the probability density calculation of the power of the device turn-on combination containing n devices into the probability density calculation of the power of the device turn-on combination containing n-1 devices through convolution operation; repeatedly performing convolution operation to realize probability density calculation of power of n equipment combinations; for a device on combination comprising n devices, n-1 convolution operations are required;
s124, calculating a probability density function of the power of the equipment group:
for 2 n And respectively carrying out multiple convolution calculations on different equipment starting combinations to obtain a probability density function of the power of the equipment group.
Wherein, when the device group is empty, the probability density distribution of the empty combination is as follows:
Figure BDA0003014105300000061
but there may be some interference sources in the line, which may still result in a small power consumption in the case of the known device being completely off, and in order to improve the stability of the calculation procedure, the probability density distribution of the null combinations may be set to the average probability density distribution around 0.
Step S130 adopts a Bayesian discrimination method:
let each equipment open combination be denoted by C, C i 2 in total for a group of devices including n different devices n Seed equipment opening combination, C 1 ,C 2 ,C 3 ,…,C 2 n Denotes the 1 st, 2 nd, 3 rd, … 2 th 2 n The equipment opening combination is set, given X represents the specific power value of the equipment group, the forecast X belongs to the class with the maximum posterior probability under the condition X, and the forecast X belongs to the class C by adopting the Bayesian classification method i And if and only if:
P(C i |X)＞P(C j |X)1≤i,j≤2 n ,i≠j
maximizing P (C) i |X)，P(C i | X) largest class C i Called maximum a posteriori hypothesis, according to bayes theorem:
Figure BDA0003014105300000062
p (X) is constant for all classes, only P (X | C) i )P(C i ) Maximization, i.e. if the prior probabilities of all classes are unknown, it is assumed that these classes are equiprobable, i.e. P (C) 1 )＝P(C 2 )＝…＝P(C 2 n ) (ii) a Otherwise, maximize P (X | C) i )P(C i )。
Step S140 includes the following sub-steps:
s141: given the power rating of a certain group of devices. Step S141 further includes: the expected μ for the device group power is assumed to be the device rated power and the standard deviation σ is assumed to be 10% of the rated power.
S142: and obtaining the equipment opening combination with the maximum probability according to the probability density distribution of the equipment group power.
S143: the power of the device-on combination is recalculated.
S144: judging whether the power of the device-on combination calculated in the step S143 is equal to the rated power of the device group given in the step S141, if not, returning to the step S141, re-giving the rated power of the device group, and correcting the rated power of the device group; if so, proceed to step S145.
Step S144 includes: calculating probability distribution of each equipment combination by utilizing multiple convolution to finally obtain given power X m×1 Start-stop matrix B of lower equipment m×n Wherein X is m×1 Representing m actual line power values, which may be considered as line power at different times, B m×n Representing the starting and stopping conditions of n line devices corresponding to the m actual line power values; the rated power of the correction device group is calculated by the following formula:
P'＝B -1 X
wherein B is -1 The pseudo inverse of B, namely, the least square method is used for calculating the power parameter P of the equipment which meets the power sum X; when the difference between the power parameter P' calculated by correction and the given rated power parameter P is smaller than the minimum value required by iterative calculation, the current given rated power parameter P is approximately equal to the actual power, that is, the power of the device opening combination calculated in step S143 is determined to be the rated power of the device group; if the difference between the two values is large, P' is used as the rated power of the device group initially specified in step S141, and the policy identification self-calibration calculation process is continued.
S145: and correcting the Gaussian distribution parameters of the equipment group, wherein the Gaussian distribution parameters comprise expected mu and standard deviation sigma of the power of the equipment group.
In step S110, the two parameters, i.e., the expected μ and the standard deviation σ, are used as parameters of gaussian distribution to obtain a probability density distribution function of the power of the single device; and performing convolution calculation in step S120 to obtain a probability density distribution function of the power of the device group. Therefore, through selection of the probability distribution model of the equipment and setting of the parameters, a primary strategy identification result of the heating and ventilation equipment set can be obtained.
The correction calculation process aims to enable the probability distribution of the correction power to be close to the distribution of the actual power, so that more accurate basic information is provided for strategy identification, the starting and stopping strategy of the identification equipment is more accurate, and the self-correction method is established based on the idea of finding the optimal parameters by the iterative loop and the least square method.
Like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
The above description is only for the preferred embodiment of the present invention, but the present invention is not limited to the above specific embodiments, and it will be apparent to those skilled in the art that several variations and modifications may be made without departing from the inventive concept of the present invention, and these modifications and improvements are within the protection scope of the present invention.

Claims (5)

1. A heating and ventilation equipment operation strategy identification method is characterized by comprising the following steps:
s110, generating probability density distribution of single equipment power;
s120, generating probability density distribution of the power of the equipment group;
s130, identifying a heating and ventilation equipment strategy of the power line;
s140, self-correcting the strategy identification parameters;
wherein, step S120 includes the following substeps:
s121, computing equipment group:
calculate all possible device on combinations, where a device group containing n different devices theoretically has 2 n Different equipment opening combinations are planted;
s122, calculating the probability density distribution of each equipment group:
for a group of devices comprising n different devices, the probability density distribution function for the on-power of device n is denoted as p n (x) The probability density distribution function of the group is denoted as p 1,2,3… (x) Wherein 1,2,3, …, n represents device 1, device 2, device 3, …, device n in the on state in the device group; p represents a probability density function, x represents a given power value;
the power of the equipment group is accumulated by the power of the equipment in the current on state, and the probability density of the power of the equipment group is calculated in the following mode:
Figure FDA0003634095020000011
wherein x is 1 +x 2 +x 3 +...+x n ＝x；
S123, convolution calculation:
decomposing the device group into a device on combination containing 2 devices and a device on combination containing n-2 devices; the probability density calculation mode of the device opening combination comprising 2 devices is as follows:
Figure FDA0003634095020000012
regarding the device on combination containing 2 devices as 1 new device, then converting the device on combination containing n devices into a device on combination containing n-2+1 devices, and converting the probability density calculation of the power of the device on combination containing n devices into the probability density calculation of the power of the device on combination containing n-1 devices through convolution operation; repeatedly performing convolution operation to realize probability density calculation of power of n equipment combinations; for a device on combination comprising n devices, n-1 convolution operations are required;
s124, calculating a probability density function of the power of the equipment group:
for 2 n Performing multiple convolution calculation on different equipment starting combinations respectively to obtain a probability density function of the power of the equipment group; and is
Wherein, step S140 includes the following substeps:
s141: given the rated power of a certain equipment group;
s142: obtaining the equipment opening combination with the maximum probability according to the probability density distribution of the equipment group power;
s143: recalculating the power of the device turn-on combination;
s144: judging whether the power of the device on combination calculated in step S143 is equal to the rated power of the device group given in step S141, if not, returning to step S141, re-giving the rated power of the device group, and correcting the rated power of the device group; if so, go to step S145;
s145: the gaussian distribution parameters of the set of devices are corrected.
2. The method for identifying an operation strategy of heating and ventilation equipment as claimed in claim 1, wherein in step S110, the single-equipment power comprises a fixed frequency equipment power and a variable frequency equipment power;
the probability density distribution function of the power of the fixed frequency equipment adopts Gaussian distribution;
the frequency conversion equipment comprises class frequency fixing equipment and non-class frequency fixing equipment;
wherein, the probability density distribution function of the class fixed frequency equipment power also adopts Gaussian distribution; the probability density distribution of the power of the non-class fixed frequency equipment adopts a statistical method, and the statistical probability density distribution function of the power of the equipment is obtained by counting the actual distribution condition of the power within a certain time.
3. The method for identifying the operation strategy of the heating and ventilation equipment as claimed in claim 1, wherein the step S130 adopts a bayesian discrimination method:
let each equipment open combination be denoted by C, C i 2 in total for a group of devices including n different devices n Seed equipment opening combination, C 1 ,C 2 ,C 3 ,…,C 2 n Denotes the 1 st, 2 nd, 3 rd, … 2 nd n The equipment starting combination is set, given X represents the specific power value of the equipment group, the forecast X belongs to the class with the maximum posterior probability under the condition X, and the Bayesian classification method is adopted to forecast that X belongs to the class C i And if and only if:
P(C i |X)＞P(C j |X)1≤i,j≤2 n ,i≠j
maximizing P (C) i |X)，P(C i | X) largest class C i Called maximum a posteriori hypothesis, according to bayes' theorem:
Figure FDA0003634095020000021
p (X) is constant for all classes, only P (X | C) i )P(C i ) Maximization, i.e. if the prior probabilities of all classes are unknown, it is assumed that these classes are equiprobable, i.e. P (C) 1 )＝P(C 2 )＝…＝P(C 2 n )；Otherwise, maximize P (X | C) i )P(C i )。
4. The method for identifying the operation strategy of the heating and ventilation equipment as claimed in claim 1, wherein the step S141 comprises: the expected μ for the device group power is assumed to be the device rated power and the standard deviation δ is assumed to be 10% of the rated power.
5. The method for identifying an operation strategy of heating and ventilation equipment as claimed in claim 1, wherein the step S144 comprises: calculating probability distribution of each equipment combination by utilizing multiple convolutions to finally obtain given power X m×1 Starting and stopping matrix B of lower equipment m×n Wherein X is m×1 Representing m actual line power values, which can be regarded as line power at different times, B m×n Representing the starting and stopping conditions of n line devices corresponding to the m actual line power values; the rated power of the correction device group is calculated by the following formula:
P'＝B -1 X
wherein B is -1 The pseudo inverse of B, namely, the least square method is used for calculating the power parameter P of the equipment which meets the power sum X; when the difference between the power parameter P' calculated by correction and the given rated power parameter P is smaller than the minimum value required by iterative calculation, the current given rated power parameter P is approximately equal to the actual power, that is, the power of the device opening combination calculated in step S143 is determined to be the rated power of the device group; if the difference between the two values is large, P' is used as the rated power of the device group initially specified in step S141, and the policy identification self-calibration calculation process is continued.
CN202110383958.3A 2021-04-09 2021-04-09 Heating and ventilation equipment operation strategy identification method Active CN113158309B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110383958.3A CN113158309B (en) 2021-04-09 2021-04-09 Heating and ventilation equipment operation strategy identification method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110383958.3A CN113158309B (en) 2021-04-09 2021-04-09 Heating and ventilation equipment operation strategy identification method

Publications (2)

Publication Number Publication Date
CN113158309A CN113158309A (en) 2021-07-23
CN113158309B true CN113158309B (en) 2022-08-19

Family

ID=76889754

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110383958.3A Active CN113158309B (en) 2021-04-09 2021-04-09 Heating and ventilation equipment operation strategy identification method

Country Status (1)

Country Link
CN (1) CN113158309B (en)

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111240197A (en) * 2020-01-10 2020-06-05 中国建筑科学研究院有限公司 Energy efficiency deviation rectifying control method and device for electromechanical system of public building
CN111381498A (en) * 2020-03-09 2020-07-07 常熟理工学院 Expectation maximization identification method of multi-sensor based on multi-rate variable time-lag state space model
CN112283890A (en) * 2020-10-26 2021-01-29 济中节能技术（苏州）有限公司 Cold and heat quantity control method and device suitable for building heating and ventilation equipment monitoring system

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2257892A4 (en) * 2008-03-05 2013-07-17 Univ Brown Improved techniques for stochastic combinatorial optimization
CN109871987A (en) * 2019-01-28 2019-06-11 中建八局第三建设有限公司 A kind of intelligent building heating and ventilating equipment synthesis energy saving control method
CN111553444A (en) * 2020-05-15 2020-08-18 东南大学 Load identification method based on non-invasive load terminal data
CN111914375B (en) * 2020-07-07 2022-12-13 贵州电网有限责任公司 Power grid weak link identification method based on Bayesian inference
CN112559963B (en) * 2020-11-20 2024-02-06 国网浙江省电力有限公司绍兴供电公司 Dynamic parameter identification method and device for power distribution network
CN113205259A (en) * 2021-05-07 2021-08-03 国网河北省电力有限公司电力科学研究院 Power grid scheduling decision evaluation method and device and terminal equipment

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111240197A (en) * 2020-01-10 2020-06-05 中国建筑科学研究院有限公司 Energy efficiency deviation rectifying control method and device for electromechanical system of public building
CN111381498A (en) * 2020-03-09 2020-07-07 常熟理工学院 Expectation maximization identification method of multi-sensor based on multi-rate variable time-lag state space model
CN112283890A (en) * 2020-10-26 2021-01-29 济中节能技术（苏州）有限公司 Cold and heat quantity control method and device suitable for building heating and ventilation equipment monitoring system

Also Published As

Publication number Publication date
CN113158309A (en) 2021-07-23

Similar Documents

Publication Publication Date Title
Andrews et al. Inference for parameters defined by moment inequalities using generalized moment selection
CN106026092B (en) It is a kind of for the power distribution network isolated island division methods containing distributed generation resource
CN114705990B (en) Method and system for estimating state of charge of battery cluster, electronic device and storage medium
CN114978956B (en) Method and device for detecting abnormal mutation points of performance of intelligent city network equipment
CN112491360B (en) Fault diagnosis method and system for photovoltaic string and readable storage medium
CN115526286A (en) Differential evolution improvement method based on success history and external archiving mechanism
CN117592010A (en) Temperature prediction method, temperature prediction device, electronic equipment and computer readable storage medium
CN113158309B (en) Heating and ventilation equipment operation strategy identification method
CN117057486B (en) Operation and maintenance cost prediction method, device and equipment for power system and storage medium
CN118525279A (en) Simulation model calibration method, simulation model calibration device, electronic equipment and storage medium
CN114385962A (en) Day-ahead photovoltaic power generation power prediction method and system based on similar days
CN117310533A (en) Service life acceleration test method and system for proton exchange membrane fuel cell
CN116796884A (en) Multi-type low-carbon energy generating capacity prediction method and device based on multi-feature coupling
CN117726478A (en) Intelligent decision-making method for dispatching of power system unit, terminal equipment and storage medium
CN115202915A (en) Disk failure prediction method and device
CN113807019A (en) MCMC wind power simulation method based on improved scene classification and coarse grain removal
Pandit et al. A Deep Neural Network Architecture for Composite Reliability Assessment
CN115641152B (en) Method, device, equipment and storage medium for identifying faults of comprehensive energy system
Jain et al. Simulation Model Calibration with Dynamic Stratification and Adaptive Sampling
CN115496392B (en) Multi-target power grid blocking partition method based on consensus clustering and product
CN114676797B (en) Model precision calculation method and device and computer readable storage medium
CN110912162B (en) Method and device for determining scheme of energy storage device
Xiong et al. Probability Forecast of Photo Voltaic Power with an Adaptive Discretization Algorithm in Higher-Order Markov Chain
CN117272183A (en) Energy storage system performance prediction method and device based on combination feature selection
CN115423211A (en) Method, device, equipment and medium for predicting block random power

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