CN112152840B - Sensor deployment method and system based on BIM and analog simulation - Google Patents

Sensor deployment method and system based on BIM and analog simulation Download PDF

Info

Publication number
CN112152840B
CN112152840B CN202010879473.9A CN202010879473A CN112152840B CN 112152840 B CN112152840 B CN 112152840B CN 202010879473 A CN202010879473 A CN 202010879473A CN 112152840 B CN112152840 B CN 112152840B
Authority
CN
China
Prior art keywords
simulation
hvac
variable
hot zone
bim
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
CN202010879473.9A
Other languages
Chinese (zh)
Other versions
CN112152840A (en
Inventor
徐占伯
申沅均
吴江
刘坤
高峰
管晓宏
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.)
Xian Jiaotong University
Original Assignee
Xian Jiaotong 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 Xian Jiaotong University filed Critical Xian Jiaotong University
Priority to CN202010879473.9A priority Critical patent/CN112152840B/en
Publication of CN112152840A publication Critical patent/CN112152840A/en
Application granted granted Critical
Publication of CN112152840B publication Critical patent/CN112152840B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14Network analysis or design
    • H04L41/145Network analysis or design involving simulating, designing, planning or modelling of a network
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W84/00Network topologies
    • H04W84/18Self-organising networks, e.g. ad-hoc networks or sensor networks

Abstract

The invention discloses a sensor deployment method and a system based on BIM and simulation, the method is based on BIM information, under the simulation environment, indoor environment variables in divided hot zones are used for representing sensor data, the influence components of the hot zone environment variables on an HVAC terminal are used as decision variables, the optimization target of the minimum HVAC maladjustment rate and the minimum quantity of configured sensors is adopted, and a random search algorithm is adopted to obtain the optimal deployment scheme of the indoor sensors. The invention can complete the deployment of the indoor sensor in the building design stage, thus obviously saving the cost of data collection and scheme test; meanwhile, the clear and adaptive optimization target is adopted, so that the optimization result has higher practicability.

Description

Sensor deployment method and system based on BIM and analog simulation
Technical Field
The invention belongs to the technical field of building sensor network deployment and HVAC (heating ventilation and air conditioning) system control, and particularly relates to a building sensor network node deployment method and system based on BIM (building information model) and simulation.
Background
HVAC systems are the primary indoor environment conditioning devices in buildings, bearing most of the indoor human comfort requirements. Especially in today's urban heat island environment, HVAC systems have become an indispensable device in every building. In recent years, with the increasing demand for comfort in buildings, intelligent and refined indoor environment control becomes more and more important, and with the increasing computing power brought by the progress of computer technology and information technology, the realization of the demand is more feasible. Being a negative feedback system, the ability to accurately and efficiently capture the indoor environmental conditions is a prerequisite for each HVAC system to operate efficiently. The HVAC system generally uses the sensor to acquire the environmental information, and besides the sensing precision and accuracy of the sensor, the position arrangement of the sensor can also have a great influence on the acquisition of the environmental information of the HVAC system.
Conventional sensor deployment decision methods often require a large amount of high spatial granularity historical data as a decision basis, and the resulting deployment decisions also require long-term verification in the field, which can take a significant amount of time and money. There is therefore a need to propose a more efficient sensor deployment method.
Disclosure of Invention
In order to solve the above problems, the present invention provides a sensor deployment method and system based on BIM and simulation, which can simultaneously consider the effectiveness of sensor data on HVAC systems and the economic requirements of sensor deployment, and obtain an effective sensor deployment scheme in the building design stage.
In order to achieve the purpose, the invention adopts the following technical scheme:
a sensor deployment method based on BIM and analog simulation comprises the following steps:
and S1, dividing the building into full-coverage and non-coincident hot areas according to the building structure in the BIM file and based on the maximum sensing range of the sensor or the artificial set value. Environmental variables such as indoor air temperature and humidity, carbon dioxide concentration and PM2.5 concentration which can be detected by the sensor to be deployed. In the method, the output data of the environment variable of the hot area is directly used as the sensor data of the area, and the environment variable Z of the hot area is set as follows:
Z=[z1,…,zi,…,zN]T
wherein N is the number of hot zone environment variables.
And S2, configuring HVAC terminals in hot zones divided by S1 according to the building structure in the BIM file and the planned building environment regulation and control requirement. And controlling the terminal air outlet state of the corresponding region, such as air outlet flow, air outlet temperature and humidity and the like, according to the received environment variable as a control signal in the given indoor environment variable limiting range by the HVAC system, so as to regulate and control the indoor environment. Setting the controlled variable of the HVAC terminal as the HVAC terminal control variable:
Y=[y1,…,yj,…,yM]T
wherein M is the number of HVAC terminal control variables.
S3, constructing a linear mapping matrix of the hot zone environment variables to the HVAC terminal control variables according to the hot zone environment variables divided by the S1 and the HVAC terminals distributed by the S2:
wherein N is the hot zone environment variable number, M is the HVAC terminal control variable number, xijRepresenting the component of the ith hot zone environment variable controlling the jth HVAC terminal control variable, where X needs to satisfy the constraint:
that is, the component of any hot zone environment variable to the HVAC terminal must be greater than or equal to 0, and the sum of the components of all the hot zone environment variables corresponding to any HVAC terminal must be 1. These two constraints reflect the finite nature of the impact of the various hot zone environmental variables on the HVAC terminal.
And S4, setting a building energy consumption simulation model according to the building structure in the BIM file, the predicted environment parameters and the use condition of the building, the hot zone division of S1 and the HVAC terminal distribution of S2.
S5, combining the mapping matrix in S3, operating the simulation model in S4, and using the indoor environment variable X after mapping in the simulation processTZ is used as the control basis of the HVAC terminal, namely the terminal control variable of the HVAC system is as follows: y ═ XTAnd Z. And according to the simulation result, calculating the ratio of the time period exceeding the limit range of the preset environment variable value in all the hot zone full simulation time periods to the total simulation time of all the hot zones, which is called as HVAC imbalance ratio:
wherein T is the total time step number of the simulation,and itzand respectively limiting the upper limit and the lower limit of the range for the t-th simulation time of the hot zone environment variable i.
S6, calculating the influence of the hot zone environment variables on the HVAC terminal according to the mapping matrix in S3, and calculating whether the hot zone environment variables influence the HVAC terminal through the following functions:
Aiand the idle condition of the sensor corresponding to the ith hot zone environment variable is reflected and is called as a sensor deployment index. For all hotspot variables, let the expression of sensor deployment rate a be as follows:
this value reflects the usage of the sensor for the hot zone environmental variables. Only if an entire row in the X matrix is 0, i.e., the hot zone variable corresponding to the row has no effect on any HVAC terminal, in practice corresponds to a room or zone where no sensor is installed; and when a row in the X matrix has a value other than zero, the value u is 1 regardless of the values other than 0, i.e., the hot zone environment variable corresponding to the row has an effect on at least one HVAC terminal, and in practice corresponds to the room or zone in which the sensor is installed.
S7, optimizing by taking the mapping matrix X in S3 as a decision variable and taking the HVAC maladjustment rate H in S5 and the sensor deployment rate A in S6 as the minimum, wherein the multi-objective optimization problem can be expressed as follows:
xij≥0,1<i<N,1<j<M
where H (x) is the HVAC imbalance rate H obtained after adding decision variables to the simulation. In the optimization process, H and A only change with X, and are denoted as H (X) and A (X), respectively.
Because the two objective functions H (X) and A (X) of the problem are not analyzed, a random search algorithm, such as a genetic algorithm, a simulated annealing algorithm, a particle swarm algorithm and the like, is adopted to solve the problem, and a decision variable with an optimal solution is obtained. These random search algorithms contain the following basic steps:
s701, generating a plurality of decision variables, and calculating the corresponding values of the objective functions H (X) and A (X) for the current decision variable set { X }.
S702, updating the decision variable set according to the objective function value of the current decision variable set.
S703, judging whether the search state meets the termination condition: if so, ending the step, wherein the decision variable or the decision variable set with the optimal objective function value is the optimal solution; otherwise, proceed to S704.
S704, generating a new decision variable set according to the current search state, and repeating S701 to S703.
S8, solving the decision variables with the optimal solution, namely mapping moments according to S7And (3) array X, a sensor is not required to be configured in the hot zone corresponding to the hot zone environment variable which does not influence the HVAC terminal, and other areas are deployment node positions of the sensor: for the area corresponding to the environment variable i of the heat area in the building, when A isiWhen 1, install the sensor, when AiWhen the value is 0, the device is not installed. Meanwhile, the element value which is not 0 in the mapping matrix is the proportion of the influence of the corresponding sensor on the HVAC terminal, and can be used for controlling the HVAC system.
Further, the building structure in the BIM file described in S1 includes the basic geometry of each room in the building, the distribution of floors in the building, the distribution of building orientation, the distribution of doors and windows of the room, and the like.
Further, the planned building environment regulation and control requirements in S2 include human comfort requirements of each area including temperature, humidity and CO and normal operation environment requirements of indoor equipment2And (4) concentration.
Further, the predicted environment parameters of the building in S4 include weather conditions, the surrounding environment of the building, and the like, and the predicted use conditions of the building include indoor personnel activity conditions, indoor facility operation conditions, and the like.
Further, the settings of the building energy consumption simulation model in S4 include parameter settings in the BIM file, the thermal zones described in S1, the HVAC terminal configuration described in S2, HVAC system settings, environmental variable limits for HVAC system control, simulation variable input and output interfaces, and the like.
Wherein, the simulation input and output interface comprises each hot zone environment variable Z ═ Z1,…,zi,…,zN]TAnd the indoor environment variable actually controlled by the HVAC system, i.e. the HVAC terminal control variable Y ═ Y1,…,yj,…,yM]T
Further, a sensor network node deployment system based on BIM and simulation comprises a simulation operation module, a simulation interface module and an optimization search module;
the simulation operation module simulates the influence of an HVAC system on the indoor environment of the building through air outlet in a specific simulation environment, indirectly controls the HAVC through exchanging hot zone environment variables and HVAC terminal control variables with the simulation interface module, and obtains the HVAC imbalance ratio based on the simulation result;
the simulation interface module realizes the mapping Y-X of the hot zone environment variable Z input and output by the simulation operation module to the HVAC terminal control variable according to the given mapping matrixTZ;
And the optimization searching module evaluates the optimization target HVAC maladjustment rate H and the sensor deployment rate A through an optimization algorithm, searches a new mapping matrix X as the input of the simulation interface module, and finally obtains an optimization result which is used as a node deployment strategy of the building sensor.
Compared with the prior art, the invention has at least the following beneficial technical effects:
the sensor deployment method takes a mapping matrix formed by components of control generated by data detected by a sensor on an HVAC terminal as a decision variable, so that the decision variable can reflect the distribution of the sensor and the control of the HVAC terminal at the same time; and then taking the number of deployed sensors and the control effectiveness of the HVAC system as optimization targets, wherein the obtained optimal decision is a multi-objective optimization result. Because the influence size of the sensors is adopted as a decision, and the sensors are not simply deployed or not, the optimization of the sensor deployment is extensible, namely the number and the position of the deployed sensors are not required to be given in stages and then optimized, and the optimal number and the optimal position of the sensor deployment can be directly obtained. Since the other objective of the optimization is the control effect of the HVAC, the control algorithm of the HVAC does not need to be designed after the data of the sensor is obtained, and the control can be directly carried out according to the mapping matrix obtained by the optimal decision. The method used by the invention can be completed through simulation and optimization software, and the used data can also be obtained from computer files such as BIM and the like, so that the design, optimization and verification of the scheme can be completed in the design stage, and the cost for executing and verifying the scheme is obviously reduced.
The system comprises a simulation operation module, a simulation interface module and an optimization search module which are respectively used for executing the steps, and the cost of data acquisition and scheme inspection is obviously reduced by verifying the effect of the candidate deployment scheme through simulation.
The invention can complete the deployment of the indoor sensor in the building design stage, thus obviously saving the cost of data collection and scheme test; meanwhile, the clear and adaptive optimization target is adopted, so that the optimization result has higher practicability.
Drawings
FIG. 1 is a schematic flow chart of a sensor deployment method based on BIM and simulation provided by the present invention;
FIG. 2 is a block diagram of a BIM and simulation based architecture sensor network node deployment system provided in the present invention;
FIG. 3 is a schematic block diagram of EnergyPlus;
FIG. 4 is a flow chart of a genetic algorithm.
Detailed Description
In order to make the objects and technical solutions of the present invention clearer and easier to understand. The present invention will be described in further detail with reference to the following drawings and examples, wherein the specific examples are provided for illustrative purposes only and are not intended to limit the present invention.
In the description of the present invention, it is to be understood that the terms "first", "second" and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless otherwise specified; unless expressly stated or limited otherwise, the terms "mounted," "connected," and "connected" are intended to be inclusive and mean, for example, that they may be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Fig. 1 is a schematic flow chart of the method of the present invention, and an embodiment of the present invention provides a sensor deployment method based on BIM and simulation, which specifically includes the following steps:
s1, for a pending building design, the BIM file generally contains the building structure of the building, which includes the basic geometry of each room in the building, the distribution of floors in the building, the distribution of facing of the building, and the distribution of doors and windows of the room. According to the information, the building is divided into hot zones which are fully covered and not overlapped according to the maximum sensing range of the sensor or the artificial set value. Dividing a space with uniform and consistent environmental variables corresponding to the same sensor into the same hot area; for example, an office is divided into a hot zone if the air temperature in the office is considered uniform; if a conference room is not considered to be uniform but is divided into two parts, which are then respectively uniform, it is divided into two hot zones. The environmental variable of the hot zone was used as the data collected by the sensors of this zone, in this example only the temperature sensor was studied, and the hot zone environmental variable Z was taken as the room air temperature (c):
Z=[z1,…,zi,…,zN]T
where N is the number of hot zone environment variables, which in this example is equal to the number of partitioned hot zones, ziIs the ith hot zone chamber air temperature.
And S2, configuring an HVAC terminal-an air conditioner indoor unit in the hot zone divided by the S1 according to the building structure in the BIM file and the planned building environment regulation and control requirement. The building environmental regulatory requirements, which typically include human comfort requirements for each thermal zone and the operating environment requirements for the indoor equipment, in this example, consider only air temperature. The HVAC system controls the air outlet state of the HVAC terminal in the corresponding area, such as air outlet flow, air outlet temperature and humidity and the like, and regulates and controls the indoor environment according to the received environment variable as a control signal in a given indoor environment variable limiting range. In this example, for convenience, HVAC terminals corresponding one-to-one to the hot zones are set, and the HVAC terminal control variable Y is set to the air temperature (° c):
Y=[y1,…,yj,…,yM]T
wherein, yjControlling a variable for the ith HVAC terminal; m is the number of HVAC terminal control variables, in this example M ═ N.
S3, constructing a linear mapping matrix X of the hot zone environment variables to the HVAC terminal control variables according to the hot zone environment variables divided by S1 and the HVAC terminals distributed by S2:
wherein, N is M, zijRepresenting the component of the ith hot zone environment variable controlling the jth HVAC terminal control variable, where X needs to satisfy the constraint:
that is, the component of any hot zone environment variable to the HVAC terminal must be greater than or equal to 0 and the sum of the components of all the hot zone environment variables of any HVAC terminal must be 1. These two constraints reflect the finite nature of the impact of the various hot zone environmental variables on the HVAC terminal.
And S4, setting a building energy consumption simulation model according to the building structure in the BIM file, the predicted environment parameters and the predicted use condition of the building, the hot zone division result of S1 and the HVAC terminal distribution result of S2. The building predicted environment parameters comprise weather and the surrounding environment of the building, such as local air pressure, shading conditions of other buildings around the building and the like, and the building predicted use conditions comprise indoor personnel flow conditions, such as the number of people in each room unit area in unit time and the like.
In this example, the building Energy consumption simulation model is created by editing an IDF file of Energy plus, which is an open-source building Energy consumption simulation engine developed by the united states Department of Energy (DOE) and Lawrence Berkeley National Laboratory (LBNL), and fig. 3 shows the main module components of Energy plus.
The building energy consumption simulation model comprises a building model, an HVAC system, an input interface and an output interface. The building model and the HVAC system are given by their design. Taking the indoor average air temperature of each hot zone as the environmental variable Z ═ Z of the hot zone1,…,zi,…,zN]TAs an output, the indoor air temperature at which the HVAC system is actually controlled, i.e., the HVAC terminal control variable Y ═ Y1,…,yj,…,yM]TAs an input.
S5, running the simulation model in S4 in combination with the mapping matrix X in S3, i.e., running the edited IDF file. In the simulation process, the mapped indoor environment variable is used as a control basis of the HVAC terminal, namely the terminal control variable input of the HVAC system is as follows: y ═ XTAnd Z. The matrix multiplication operation is completed outside EnergyPlus through an input-output interface at each time node in simulation operation.
And finally, obtaining the HVAC imbalance ratio in the building according to the simulation result, wherein the HVAC imbalance ratio is represented by the ratio of the time period exceeding the limit range of the preset environment variable value in all the hot zone full simulation time periods to the total simulation time of all the hot zones, and is called as the HVAC imbalance ratio:
wherein T is the total time step number of the simulation,and itzand respectively limiting the upper limit and the lower limit of the range for the t-th simulation time of the hot zone environment variable i.
S6, calculating the influence of the hot zone environment variables on the HVAC terminal according to the mapping matrix in S3, and calculating whether the hot zone environment variables influence the HVAC terminal through the following functions:
wherein A isiAnd the idle condition of the sensor corresponding to the ith hot zone environment variable is reflected and is called as a sensor deployment index. For all hotspot variables, let the expression of sensor deployment rate a be as follows:
this value reflects the usage of the sensor for the hot zone environmental variables. Only when an entire row in the X matrix is 0, i.e., the hot zone variable corresponding to the row has no effect on any HVAC terminal, it is in practice equivalent to no sensor deployed in the room or zone corresponding to the hot zone; and when a row in the X matrix has a value other than zero, the value u is 1 regardless of the values other than 0, i.e., the hot zone environment variable corresponding to the row has an effect on at least one HVAC terminal, and in practice corresponds to the room or zone in which the sensor is installed.
S7, optimizing by taking the mapping matrix in S3 as a decision variable and taking the HVAC maladjustment rate in S5 and the sensor deployment rate A in S6 as the minimum, wherein the multi-objective optimization problem can be expressed as follows:
xij≥0,1<i<N,1<j<M
where h (X) is a decision variable added to the simulation, i.e. the mapping Y of the mapping matrix X is XTAfter Z, the resulting HVAC imbalance ratio H. In this optimization process, H and A are the only independent variables that vary with X, and are labeled as H (X) and A (X).
In this example, the original multi-objective optimization problem is converted into a single-objective optimization problem with a weighting factor α, 0< α < 1:
the specific value of alpha is determined by balancing the control effect of the HVAC system and the cost of the sensor configuration, or a plurality of alpha values can be selected for multiple calculations, and X when the minimum value of the optimization result is taken as the final decision result.
For the optimization problem, a random search algorithm is adopted for solving, and specifically, a genetic algorithm, a simulated annealing algorithm, a particle swarm algorithm and the like can be adopted.
In this example, a genetic algorithm is used to solve, as shown in FIG. 4, the basic steps are as follows:
s701, coding the decision variable, namely the mapping matrix X. In order to make the encoding process relatively simple, while also facilitating the search for genetic algorithms for which unconstrained optimization is more applicable, the decision variable X and its constraints are improved in this example: removing two constraints of variable X, defining each element X of XijOnly rational numbers can be taken; in order to satisfy a second constraint in the optimization, Y is equal to X each timeTBefore the calculation of Z, all columns of X are normalized, i.e.
WhereinValues are taken for the integer matrix elements.
And combining the characteristics of the decision variable X, and coding each element of X according to a binary system:
B=(B11B12…B1MB21…B2M…Bij…BN1BNM)2
wherein, BijIs xijB is the encoded individual of the decision variable X, k is the number of bits of the binary number,this is to exhaust the possible combinations of values of all sensors affecting a component of an HVAC terminal.
And S702, initializing a population. A certain number of individuals B satisfying the conditions described in S701 are randomly generated as an initial population.
And S703, evaluating the fitness. Because the fitness is in negative correlation with the optimization target, the following fitness function is adopted:
D(X)=1-αH(X)+(1-α)A(X)
obviously, the larger the fitness of a certain individual is, the smaller the objective function value is, and the optimization objective is met. H (X) and A (X) are calculated by the methods in S5 and S6.
S704, judging whether a termination condition is met. When the population fitness variance is smaller than a set value or the cycle times reach a certain number, outputting an optimal solution and ending the cycle; otherwise, the flow is continued.
And S705, selecting. In the example, random sampling selection is adopted, namely random selection is carried out according to the probability that the individual fitness accounts for the sum of the population fitness as the selected probability. And (4) keeping the selected individuals and deleting the individuals.
And S706, crossing. And carrying out local binary code exchange on the reserved partial individuals to generate new individuals.
And S707, mutation. And randomly selecting a binary bit for 0/1 replacement on the retained part of individuals. Then, the process goes to S703.
S8, according to the mapping matrix with the optimal solution obtained by solving S7, the sensor does not need to be configured for the hot zone corresponding to the hot zone environment variable which does not affect the HVAC terminal, and other areas are the deployment node positions of the sensor: for the area corresponding to the hot zone i in the building, when AiWhen 1, install the sensor, when AiWhen the value is 0, the device is not installed. Meanwhile, the element value which is not 0 in the mapping matrix is the proportion of the influence of the corresponding sensor on the HVAC terminal, and can be used for controlling the HVAC system.
A sensor network node deployment system based on BIM and simulation is structurally shown in FIG. 2 and comprises a simulation operation module, a simulation interface module and an optimization search module;
the simulation operation module simulates the influence of an HVAC system on the indoor environment of a building through air outlet under a specific simulation environment, and interacts a hot zone environment variable and an HVAC terminal control variable through an input/output interface and a simulation interface module to realize indirect control of the hot zone environment variable on the HAVC, and obtains an HVAC imbalance ratio based on a simulation result;
the simulation interface module realizes the hot zone environment variable and HVAC terminal control variable Y which are input and output by the simulation operation module according to the given mapping matrixTZ;
The optimization searching module evaluates optimization targets H and A through random search optimization algorithms, such as genetic algorithm, annealing algorithm, particle swarm algorithm and the like, searches a new mapping matrix X as the input of the simulation interface module, and finally obtains an optimization result which is used as a node deployment strategy of the building sensor.
The embodiment of the invention provides a sensor network node deployment system based on BIM and simulation, which is used for executing the sensor network node deployment method based on BIM and simulation. The sensor deployment scheme that has guiding significance for HVAC system control can be obtained at the building design stage according to the information in the BIM file.
In the above embodiments, all may be implemented by software. When implemented using a software program, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The procedures or functions according to the embodiments of the invention are brought about in whole or in part when the computer program instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable system. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). Computer-readable storage media can be any available media that can be accessed by a computer or can comprise one or more data storage devices, such as servers, data centers, and the like, that can be integrated with the media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
While the invention has been described in connection with various embodiments, other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a review of the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the word "a" or "an" does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.
While the invention has been described in conjunction with specific features and embodiments thereof, it will be evident that various modifications and combinations can be made thereto without departing from the spirit and scope of the invention. Accordingly, the specification and figures are merely exemplary of the invention as defined in the appended claims and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the invention. It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (10)

1. A sensor deployment method based on BIM and analog simulation is characterized by comprising the following steps:
s1, according to the building structure in the BIM file, performing full-coverage and non-coincident hot area division on the building, and dividing the building into K hot areas;
s2, configuring HVAC terminals in all hot zones divided by S1 according to the building structure in the BIM file and the planned building environment regulation and control requirement;
s3, constructing a linear mapping matrix X of the hot zone environment variable to the HVAC terminal control variable according to the hot zone environment variable divided by the S1 and the HVAC terminal distributed by the S2;
s4, setting a building energy consumption simulation model according to the building structure, the predicted building environment parameters and the use condition in the BIM file, the hot zone divided by S1 and the HVAC terminal distribution of S2;
s5, operating the building energy consumption simulation model set in S4, and obtaining the HVAC imbalance ratio in the building according to the operation result; calculating whether each hot zone environment variable influences the HVAC terminal according to the mapping matrix X in the S3;
s6, optimizing by taking the mapping matrix X in S3 as a decision variable and taking the minimum HVAC maladjustment rate in S5 and the minimum hot zone environment variable affecting an HVAC terminal as targets, and solving by adopting a random search algorithm to obtain a mapping matrix with an optimal solution;
and S7, according to the mapping matrix with the optimal solution obtained by solving in S6, sensors are not configured in the hot zone corresponding to the hot zone environment variable which does not affect the HVAC terminal, and sensors are configured in other zones.
2. The BIM and simulation based sensor deployment method of claim 1, wherein in the step S3, the hot zone environment variable is a sensing variable that can be collected by the sensor to be deployed.
3. The BIM and simulation based sensor deployment method of claim 1, wherein the mapping matrix X in S3 is:
wherein N is the hot zone environment variable number, M is the HVAC terminal control variable number, xijRepresenting the component of the ith hot zone environment variable that produces control over the jth HVAC terminal control variable, X satisfies the following constraint:
4. the BIM and simulation based sensor deployment method of claim 3, wherein in the simulation process of step S5, the terminal control variable inputs of the HVAC system are: y ═ XTZ, wherein Z is the ambient variable of each hot zone, and Z ═ Z1,…,zi,…,zN]T,ziIs the ith hot zone environment variable, and N is the number of hot zone environment variables.
5. The BIM and simulation based sensor deployment method of claim 3, wherein the step S5 is to calculate whether the hot zone environment variables have an effect on the HVAC terminal by the following function:
where Ai is the sensor deployment index, xijA component representing the control of the ith hot zone environmental variable over the jth HVAC terminal control variable, uiAs a function of the pulse.
6. The BIM and simulation based sensor deployment method of claim 3, wherein in step S6, the following equation is solved to obtain a mapping matrix with an optimal solution:
xij≥0,1<i<N,1<j<M;
h (X) is the HVAC imbalance rate obtained after decision variables are added in the simulation; a (X) is the sensor deployment rate.
7. The BIM and simulation based sensor deployment method of claim 1, wherein the expression of HVAC maladjustment rate obtained in step S5 according to the simulation result is as follows:
wherein N is the number of hot zone environment variables, T is the total time step of the simulation,and itzand respectively limiting the upper limit and the lower limit of the range for the t-th simulation time of the hot zone environment variable i.
8. The BIM and simulation based sensor deployment method of claim 1, wherein the stochastic search algorithm in S6 comprises the following steps:
s601, calculating a target function value of each decision variable in a current decision variable set;
s602, updating a search state according to the objective function values of the decision variables obtained in S601;
s603, judging whether the search state meets the termination condition: if yes, ending the step, wherein the decision variable with the optimal objective function value is the optimal solution; otherwise, go to step S704;
s604, generating a new decision variable set according to the current search state, and repeating S701 to S703.
9. The BIM and simulation based sensor deployment method of claim 1, wherein in S7, for the area corresponding to the environment variable of the heat area in the building, when A isiWhen 1, install the sensor, when AiWhen the sensor is not installed, Ai is a sensor deployment index.
10. A sensor deployment system based on BIM and simulation is characterized by comprising a simulation operation module, a simulation interface module and an optimization search module; the simulation operation module is used for executing steps S1-S3 in the sensor deployment method of claim 1, the simulation interface module is used for executing steps S4-S5 in the sensor deployment method of claim 1, and the optimization search module is used for executing steps S6-S7 in the sensor deployment method of claim 1.
CN202010879473.9A 2020-08-27 2020-08-27 Sensor deployment method and system based on BIM and analog simulation Active CN112152840B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010879473.9A CN112152840B (en) 2020-08-27 2020-08-27 Sensor deployment method and system based on BIM and analog simulation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010879473.9A CN112152840B (en) 2020-08-27 2020-08-27 Sensor deployment method and system based on BIM and analog simulation

Publications (2)

Publication Number Publication Date
CN112152840A CN112152840A (en) 2020-12-29
CN112152840B true CN112152840B (en) 2021-07-13

Family

ID=73888619

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010879473.9A Active CN112152840B (en) 2020-08-27 2020-08-27 Sensor deployment method and system based on BIM and analog simulation

Country Status (1)

Country Link
CN (1) CN112152840B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113099408A (en) * 2021-03-15 2021-07-09 西安交通大学 Simulation-based data mechanism dual-drive sensor node deployment method and system

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN205028060U (en) * 2014-03-07 2016-02-10 优倍快网络公司 Interactive sensing and seeing and hearing node equipment, digital temperature control equipment , accent optical equipment
CN106170792A (en) * 2014-03-03 2016-11-30 飞利浦灯具控股公司 For the method disposing sensor
CN106663142A (en) * 2014-06-26 2017-05-10 三星电子株式会社 Method and apparatus for detecting building information

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9342928B2 (en) * 2011-06-29 2016-05-17 Honeywell International Inc. Systems and methods for presenting building information

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106170792A (en) * 2014-03-03 2016-11-30 飞利浦灯具控股公司 For the method disposing sensor
CN205028060U (en) * 2014-03-07 2016-02-10 优倍快网络公司 Interactive sensing and seeing and hearing node equipment, digital temperature control equipment , accent optical equipment
CN106663142A (en) * 2014-06-26 2017-05-10 三星电子株式会社 Method and apparatus for detecting building information

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
HVAC系统最优控制模型的开发及应用;董超俊;《工业仪表与自动化装置》;20010319;全文 *

Also Published As

Publication number Publication date
CN112152840A (en) 2020-12-29

Similar Documents

Publication Publication Date Title
Harish et al. A review on modeling and simulation of building energy systems
Asadi et al. Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application
Ye et al. Predicting electricity consumption in a building using an optimized back-propagation and Levenberg–Marquardt back-propagation neural network: Case study of a shopping mall in China
CN105378391B (en) For the on-line optimization scheme of HVAC demand responses
Magnier et al. Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network
Zhang et al. A systematic feature selection procedure for short-term data-driven building energy forecasting model development
Luo et al. Development of an IoT-based big data platform for day-ahead prediction of building heating and cooling demands
Hong et al. Building simulation: an overview of developments and information sources
Pombeiro et al. Dynamic programming and genetic algorithms to control an HVAC system: Maximizing thermal comfort and minimizing cost with PV production and storage
EP3025099B1 (en) Control device and method for buildings
Sha et al. Overview of computational intelligence for building energy system design
Jihad et al. Forecasting the heating and cooling load of residential buildings by using a learning algorithm “gradient descent”, Morocco
Pazhoohesh et al. A satisfaction-range approach for achieving thermal comfort level in a shared office
CN106842914A (en) A kind of temperature control energy-saving processing method, apparatus and system
CN112152840B (en) Sensor deployment method and system based on BIM and analog simulation
Lachhab et al. Energy-efficient buildings as complex socio-technical systems: approaches and challenges
Ghahramani et al. Energy trade off analysis of optimized daily temperature setpoints
Li et al. Multi-objective optimization of HVAC system using NSPSO and Kriging algorithms—A case study
Ghofrani et al. Prediction of building indoor temperature response in variable air volume systems
CN112116153A (en) Park multivariate load joint prediction method for coupling Copula and stacked LSTM network
Delcroix et al. Autoregressive neural networks with exogenous variables for indoor temperature prediction in buildings
CN113112077A (en) HVAC control system based on multi-step prediction deep reinforcement learning algorithm
CN111814388A (en) CFD simulation verification method for lower air supply data center based on neural network
Sha et al. Machine learning-based cooling load prediction and optimal control for mechanical ventilative cooling in high-rise buildings
Hossain et al. Identifying grey-box thermal models with Bayesian neural networks

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