JP2005157829A - Method for creating equipment model, program for constructing equipment model, method for creating building model, program for creating building model, method for evaluating equipment repair, and method for evaluating building repair - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a method and a program for creating an equipment model such that equipment for use in a building is expressed by an arithmetic expression, and a method and a program for creating a building model using the method for creating the equipment model. <P>SOLUTION: The method for creating an equipment model includes a step for accepting input variables selected as factors matching purposes of analysis are classified in terms of time; a step for accepting basic data measured as to the input variables selected; a step (S17) for extracting an explanation variable by excluding the input variables that do not substantially work from the selected input variables according to the basic data; a step (S18) for forcibly restoring the excluded input variables so that the extracted explanation variable continues with the current time in terms of time if there is discontinuity in terms of time between the explanation variable and the current time; and a step (S19) for identifying an equipment model by repeating the previous two steps until the equipment model reaches a predetermined accuracy. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to a facility model creation method and a facility model creation program for creating a facility model in which equipment used in a building is expressed by an arithmetic expression. The present invention also relates to a building model creation method and a building model creation program using such an equipment model creation method. Furthermore, the present invention relates to a facility repair evaluation method and a building repair evaluation method using such a facility model creation method.

  In recent years, with the development of network technology, etc., a plurality of sensors arranged in equipment to monitor the operational status of various equipment such as lighting equipment and air conditioning equipment installed in buildings such as office buildings, apartment houses and factories. 2. Description of the Related Art A data collection device that connects terminals via a local network and collects output data from sensor terminals is known. Furthermore, a database server that accumulates the output data from a data collection device via a global network is also known (see, for example, Patent Document 1).

  Collected data is not only accumulated, but also for various analysis purposes, such as an analysis purpose to investigate the comfort of the living environment obtained by properly operating the facility, and an analysis purpose to examine the degree of achievement in energy saving of the facility. By analyzing the data, various information can be obtained and it becomes more meaningful. Such an analysis is usually performed based on reference data, such as calculating a difference between reference data (reference data, baseline) serving as a reference for comparison and an actual measurement value or a predicted value. Conventionally, this reference data has been obtained by averaging measured data for the past several years collected in the past.

Non-Patent Document 1 discloses a method for identifying a model by assuming a linear arithmetic expression model in advance and obtaining a coefficient by a least square method using an actual measurement value.
JP 2002-354553 A Yasuyoshi Akashi and three others "Study on identification of thermal load and thermal characteristics of buildings", Architectural Institute of Japan, Planning report, August 1993, No. 450, P19-27

  By the way, the equipment is operated according to the environment surrounding it. For example, in the case of an air conditioner, the number of working days is relatively small in a warm winter year, and the number of working days is relatively large in a severe winter year. For example, in the case of lighting equipment, the number of working days is relatively small in years when there are many sunny days, and the number of working days is relatively large in years when there is much clouding or rain. In this way, data collected in the past is strongly influenced by the environment surrounding the facility, such as the weather conditions at that time. For this reason, the method obtained by averaging the reference data as in the prior art does not necessarily accurately reflect the influence of different environments from time to time. Therefore, the reliability of the reference data is low, and the analysis result obtained by comparing with the reference data having such low reliability has to be low in reliability.

  In particular, the energy consumed in a building is strongly influenced by the environment such as weather conditions, so it is not easy to derive and specify reference data.

  The present invention has been made in view of the above circumstances, and an object thereof is to provide a facility model creation method and a facility model construction program for creating a facility model in consideration of the influence of the environment surrounding the facility. And this invention provides the building model creation method and building model construction program which create the building model in consideration of the influence of the environment surrounding a building by utilizing such an equipment model creation method and equipment model creation program. For the purpose. Furthermore, an object of the present invention is to provide a facility repair evaluation method for evaluating the effect of repair performed on a facility by using such a facility model creation method and facility model creation program. Another object of the present invention is to provide a building renovation evaluation method for evaluating the effect of renovation applied to a building by using such a facility model creation method and facility model creation program.

  In order to achieve the above object, an equipment model creation method for creating an equipment model, which is an arithmetic expression indicating a relationship between an analysis purpose for equipment and a factor acting on the equipment, according to the present invention depends on the analysis purpose. An input variable accepting step for accepting an input variable selected by selecting the factor and distinguishing the selected factor further in time, and a basis for accepting basic data obtained by measuring the selected input variable A data receiving step, an extraction step of extracting an explanatory variable by excluding an input variable that does not substantially act on the equipment from the selected input variable based on the basic data, and the extracted explanatory variable; If there is a temporal discontinuity with the explanatory variable at the present time, the input is excluded so as to be continuous in time. A restoration step for forcibly restoring the number, and an identification step for identifying the equipment model by repeating the extraction step and the restoration step until the equipment model to be constructed has a predetermined accuracy. To do.

  And in the above-mentioned equipment model creation method, it further includes a section dividing step of dividing the space in the building where the equipment is arranged into a plurality of sections, and sorting the basic data into the plurality of sections, the plurality of sections It is characterized by building an equipment model for each.

  Further, in the above equipment model creation method, when a period for constructing the equipment model is set, the period is divided into a plurality of sub-periods, and the basic data is sorted into the plurality of sub-periods. The method further includes a step of constructing an equipment model for each small period.

  Furthermore, the above-described equipment model creation method further includes an excluding step of excluding specific data that does not meet the purpose of analysis from the basic data.

  In addition, the facility model creation program for creating the facility model, which is an arithmetic expression indicating the relationship between the analysis purpose for the facility and the factor acting on the facility, according to the present invention, selects the factor according to the analysis purpose. An input variable receiving step for receiving an input variable selected by further distinguishing the selected factors in time; a basic data receiving step for receiving basic data obtained by measuring the selected input variable; and Based on basic data, an extraction step for extracting explanatory variables by excluding input variables that do not substantially act on the equipment from the selected input variables, and between the extracted explanatory variables and the current time point If there is a temporal discontinuity, the excluded input variable is forcibly restored to be continuous in time. An identification step for identifying the facility model by repeating a liveness step, and the extraction step and the restoration step until the facility model to be constructed has a predetermined accuracy. It is a program.

  On the other hand, according to the present invention, by creating an equipment model for equipment arranged in a building and calculating the sum of the created equipment models, calculation showing the relationship between the analysis purpose for the building and the factors acting on the building A building model creation method for creating a building model that is an expression is characterized in that the equipment model is created by any one of the equipment model creation methods described above.

  Then, according to the present invention, an equipment model is created by the equipment model creation program for the equipment arranged in the building, and by calculating the sum of the created equipment models, an analysis purpose for the building and factors acting on the building The building model creation program for creating a building model that is an arithmetic expression indicating the relationship is that the equipment model creation program is the equipment model creation program described above.

  Further, according to the present invention, the equipment repair evaluation method for evaluating the effect of the repair applied to the equipment by giving the same data to the equipment model before and after the repair and calculating the difference thereof, the equipment model includes: It is created by any one of the above-described equipment model creation methods.

  Furthermore, the building remodeling evaluation method for evaluating the effect of the renovation applied to the building by giving the same data to the building model before and after the renovation and calculating the difference thereof according to the present invention includes: It is created by the above-mentioned building model creation method.

  The facility model creation method and the facility model creation program having such a configuration select the factors according to the analysis purpose as long as they are assumed as candidates for the explanatory variables, and further distinguish the selected factors in terms of time to input variables. Since the explanatory variable is extracted by excluding the input variable that does not substantially act on the equipment from the selected input variable based on the basic data, the explanatory variable can be appropriately extracted. And when there is a temporal discontinuity between the extracted explanatory variable and the current time point, the equipment model creation method and the equipment model creation program will remove the input variable that has been excluded so as to be temporally continuous. Since it is forcibly revived, it can be an explanatory variable that is consistent in time. Furthermore, the equipment model creation method and the equipment model creation program identify the equipment model by repeating the extraction step and the restoration step until the equipment model to be constructed has a predetermined accuracy. A model can be obtained. As a result, it is possible to obtain a highly reliable equipment model that appropriately considers the influence of the environment surrounding the equipment, and thus it is possible to obtain highly reliable reference data.

  And since the building model creation method and building model creation program of such a configuration create a building model by calculating the sum of the equipment models created using the building model creation method and building model creation program described above, Since it is possible to obtain a highly reliable building model that appropriately considers the influence of the environment surrounding the building, it is possible to obtain highly reliable reference data.

  In addition, the equipment repair evaluation method having such a configuration evaluates the effect of the repair using the equipment model created by using the equipment model creation method and the equipment model creation program described above, so that quantitative evaluation with high reliability is possible. The result can be obtained.

  Furthermore, the building renovation evaluation method having such a configuration evaluates the effect of renovation using the building model created by using the building model creation method and the building model creation program described above. The result can be obtained.

Embodiments according to the present invention will be described below with reference to the drawings. In addition, the structure which attached | subjected the same code | symbol in each figure shows that it is the same structure, The description is abbreviate | omitted.
(Configuration of the embodiment)
FIG. 1 is a block diagram showing the configuration of the equipment model creation device. In FIG. 1, the equipment model creation device 1 includes a central processing unit 11, an input unit 12, a display unit 13, an internal storage unit 14, an external storage unit 15, an auxiliary storage unit 16, and a bus 17.

  The central processing unit 11 includes, for example, a microprocessor and the like, and includes a model creation unit 111 that functionally creates a facility model by executing a facility model creation program described in a program language for processing described later. The input unit 12, the display unit 13, the internal storage unit 14, the external storage unit 15, the auxiliary storage unit 16, and the communication interface 27 are controlled according to the control program.

  The input unit 12 is a device for inputting various commands such as an instruction to execute an equipment model creation program and various data such as basic data for creating an equipment model to the equipment model creation device 1, for example, a keyboard, a mouse, and the like It is. The display unit 13 is a device that displays commands and data input from the input unit 12 and the operation status of the equipment model creation device 1, and is a CRT display, LCD, organic EL display, plasma display, or the like.

  The internal storage unit 14 reads the equipment model creation program and control program executed by the central processing unit 11 from the auxiliary storage unit 16 and temporarily stores each data during execution of the equipment model creation program and control program (RAM) Random Access Memory). The external storage unit 15 stores data with a storage medium such as a flexible disk, a CD-ROM (Compact Disc Read Only Memory), a CD-R (Compact Disc Recordable), and a DVD-R (Digital Versatile Disc ReWritable). An apparatus that performs reading and / or writing, such as a flexible disk drive, a CD-ROM drive, a CD-R drive, and a DVD-R drive. The external storage device 15 is provided in the equipment model creation device 1 as necessary.

  The auxiliary storage unit 16 is a device that stores data such as a hard disk, for example, and includes a data storage unit 161, a schedule storage unit 162, a period division data storage unit 163, and a partition division data storage unit 164, as well as an equipment model creation program, Each program (not shown) such as a control program for operating the equipment model creation device 1 and data (not shown) during and after execution of each program are stored. When each program is not stored, it is installed in the auxiliary storage unit 16 via the external storage unit 15 from the recording medium on which these programs are recorded.

  It should be noted that a communication interface (not shown) for transmitting and receiving communication signals to / from external devices is further provided so that each program is downloaded via a communication network and this communication interface from a server computer (not shown) that manages these programs. It may be configured. In addition, a communication interface (not shown) that transmits and receives communication signals to and from external devices is further provided. Sensors for measuring basic data are arranged at various locations in facilities and buildings, and the sensor is connected to the communication interface via a transmission line. By doing so, the equipment model creation device 1 may be connected, and the basic data may be configured to be collected by the equipment model creation device 1 from the sensor via the transmission path and the communication interface and stored in the auxiliary storage unit 16. The sensor is determined by the purpose of the equipment model to be constructed. For example, a power meter, a gas meter, a water meter, a flow meter, a wind speed sensor, a flow rate sensor, a temperature sensor, a humidity sensor, an air volume sensor, an illuminance sensor, and the like A human sensor or the like.

  The data storage unit 161 stores basic data such as actually measured values necessary for creating an equipment model. The basic data is stored in the data storage unit 161 so that the type of basic data, measurement location, measurement date, etc. can be known.

  The schedule storage unit 162 stores a date (singular date) for eliminating unique basic data that does not match the analysis purpose of the facility model to be created in the target facility for which the facility model is to be created. . For example, when the purpose of analyzing the equipment model is energy consumption in an office building, the operation of the facility is usually different between the working day and the non-working day because the operation of the building is different. Energy consumption is extremely low. Therefore, a non-working day becomes a specific day for the purpose of the analysis. Therefore, the non-working day data is stored in the schedule storage unit 162, and the model creation unit 111 excludes the basic data measured on the non-working day when creating the equipment model, and the basic data remaining after the exclusion Create an equipment model based on On the contrary, the schedule storage unit 162 may be configured to store the date (normal day) for leaving the basic data in accordance with the analysis purpose of the equipment model.

  The period-divided data storage unit 163 generates basic data in the target equipment for which the equipment model is to be created, according to the analysis purpose of the equipment model to be constructed, and the basic data change pattern depending on the operating state of the equipment and changes in weather conditions, etc. Considering the above, data for dividing each period (period divided data) is stored. For example, when the purpose of analyzing the equipment model is energy consumption in the lighting equipment, the sunshine hours differ according to the four seasons, so that the lighting time of the lighting equipment usually varies according to the four seasons. Therefore, each season is divided for the purpose of analysis. Therefore, the period-divided data storage unit 163 stores the data for the four seasons or the data for the four seasons, and the model creation unit 111 creates the equipment model based on the basic data for each season when creating the equipment model. Create

  The partition division data storage unit 164 considers the structure, room configuration, arrangement, etc. of the space in which the equipment acts in accordance with the analysis purpose of the equipment model to be constructed in the target equipment for which the equipment model is to be created. Thus, data for partitioning (partition division data) is stored. For example, when the purpose of analysis of the equipment model is energy consumption in the air conditioning equipment, the data is used to delimit the air conditioning range of the air conditioning equipment, and the purpose of analysis of the equipment model is energy consumption in the lighting equipment Is data for delimiting a range illuminated by the lighting equipment.

  The central processing unit 11, the input unit 12, the display unit 13, the internal storage unit 14, the external storage unit 15, and the auxiliary storage unit 16 are respectively connected to the bus 17 so that data can be exchanged with each other. .

Next, the operation of this embodiment will be described.
(Operation of the embodiment)
In the following description of the equipment model creation device 1, it is assumed that basic data is stored in the data storage unit 161. For example, the user inputs basic data obtained by measuring a selected input variable at predetermined time intervals in a predetermined period using a sensor capable of measuring a physical quantity related to the input variable from the input unit 12 to the equipment model creation device 1. The basic data may be stored in the data storage unit 161 by inputting. In addition, for example, the equipment model creation device 1 further includes a communication interface for transmitting and receiving communication signals, and is connected to the sensor via the transmission path using the communication interface, and the equipment model creation device 1 is predetermined for a predetermined period. The basic data measured by the sensor at time intervals may be collected and stored in the data storage unit 161. Further, for example, the basic data from the sensor is collected by a data collecting device that collects the basic data via a transmission path and stored in a storage medium. The equipment model creation device 1 stores the basic data from the storage medium via the external storage unit 15. You may comprise so that it may memorize | store in the memory | storage part 161. FIG.

  FIG. 2 is a flowchart showing an equipment model creation method. In FIG. 2, first, a model creation period is input by the user from the input unit 12 (S11). Next, partition division data is input (S12). When the partition division data is input, the central processing unit 11 of the equipment model creation device 1 stores the partition division data in the partition division data storage unit 164. Next, data on a peculiar day is input (S13). When the unique day data is input, the central processing unit 11 stores the unique day data in the schedule storage unit 162. Next, it is determined whether or not the period division is necessary (S14). If necessary (Yes), the process S16 is executed after the period division data is inputted (S15), and when it is unnecessary (No). Step S16 is executed. In process S16, a dependent variable of the equipment model (output of the equipment model) and factors that affect the equipment as long as it is assumed from the viewpoint of analysis are input as input variables (candidates for explanatory variables of the equipment model). When the input variable is input, the model creation unit 111 of the central processing unit 11 stores the basic data related to the facility model to be created based on the partition division data stored in the partition division data storage unit 164. Extracted from the basic data stored in the unit 161. The model creation unit 111 excludes the basic data measured on the specific date from the extracted basic data based on the specific date data stored in the schedule storage unit 162. Then, when the period division data is stored in the period division data storage unit 163, the model creation unit 111 sorts the basic data from which the basic data on the specific day is excluded for each period according to the period division data.

When the above processing is completed, the model creation unit 111 uses the excluded basic data or the sorted basic data in Equation 1, for example, by the stepwise method or the forced input method. An effective explanatory variable is selected by excluding input variables that do not substantially act on the equipment from the variables (S17).
Y = Σ (A _{a, t} · X _{a, t} + A _{b, t} · X _{b, t} + A _{c, t} · X _{c, t} +...) + A ₀
... (Formula 1)
Here, A _{a, t} is a coefficient of input variable (explanatory variable) X _{a, t} , A _{b, t} is a coefficient of input variable (explanatory variable) X _{b, t} , and A _{c, t} is , The coefficient of the input variable (explanatory variable) _{Xc, t} . Therefore, the inside of () of (SIGMA) is represented by the sum of all the input variables multiplied by the coefficient. A ₀ is a constant term. The subscript t indicates an input variable (explanatory variable) before time t. For example, when t = 0 is the current time and the time step of the equipment model is in units of one hour, t = 1 is set. One hour ago, t = 2 is two hours ago. Therefore, the input variables in the equipment model are also distinguished in terms of time. When the equipment model considers up to n unit times before, Σ in Equation 1 calculates the sum from t = 0 to t = n.

  Here, the input variables are also distinguished in terms of time in consideration of the fact that the past state (output of the equipment model) up to a certain time affects the current state.

Next, if there is a temporal discontinuity between the explanatory variable selected in process S17 and the explanatory variable at the current time point, the model creation unit 111 eliminates the input excluded in process S17 so as to be temporally continuous. The variable is forcibly restored (S18). For example, if only the A _{c, 2} · X _{c, 2} term of t = 2 remains for the input variable X _c by the process S17, the term A _c up to the current time point is considered in view of physical common sense. _{, 0} · X _{c, 0} and A _{c, 1} · X _{c, 1} should also affect the current state, so these two terms A _{c, 0} · X _{c, 0} and A _c, Forcibly restore ₁ · X _{c, 1} .

  And the model preparation part 111 identifies an equipment model by repeating until the equipment model which should construct | assemble process S17 and process S18 becomes a predetermined precision (S19). The predetermined accuracy is a value determined by an error required for the equipment model, which is a difference between the equipment model and the actual measurement value, and is set to 5%, 3%, or 1%, for example.

  Thus, since the equipment model creation apparatus 1 according to the present invention performs partition division, it is possible to use basic data relating to equipment that is a target of the equipment model for creation of the equipment model. For this reason, a highly accurate equipment model can be created. And since the equipment model creation apparatus 1 which concerns on this invention removes the basic data measured on the peculiar day from the basic data used for preparation of an equipment model, it can create the equipment model suitable for the analysis objective. Moreover, since the equipment model creation apparatus 1 according to the present invention divides the period, it is possible to create an equipment model in consideration of a state of acting on equipment different in each period. For this reason, a highly accurate equipment model can be created.

  Furthermore, the equipment model creation apparatus 1 according to the present invention selects factors as long as it is assumed as candidates for explanatory variables, selects input variables, selects input variables from here, and substantially acts on the equipment based on basic data therefrom. Since explanatory variables are extracted by eliminating input variables that have not been input, it is possible to appropriately extract explanatory variables. And when there is a temporal discontinuity, the equipment model creation device 1 according to the present invention forcibly restores the input variables that have been excluded so as to be temporally continuous. It can be taken as an explanatory variable. In addition, the equipment model creation device 1 according to the present invention identifies the equipment model by repeating the processes S17 and S18 until the equipment model to be constructed has a predetermined accuracy. A model can be obtained.

  Thus, since the equipment model creation device 1 according to the present invention can obtain a highly reliable equipment model that appropriately considers the influence of the environment surrounding the equipment, it obtains highly reliable reference data in the equipment analysis. be able to.

  Here, when analyzing the comfort and energy consumption of a building, etc., the equipment related to these will be analyzed, so this is an arithmetic expression showing the relationship between the analysis purpose for the building and the factors acting on this building A building model can be created by creating an equipment model for equipment arranged in a building and calculating the sum of the created equipment models. By using the equipment model creation device 1 described above for this equipment model, a highly reliable equipment model can be obtained. Therefore, a highly reliable building model can be created, and highly reliable reference data in building analysis. Can be obtained.

  In this case, the data storage unit 161, the schedule storage unit 162, the period division data storage unit 163, and the division division data storage unit 164 store basic data, singular day data, period division data, and division division data related to facilities to be created. Each model is configured to be stored, and the model creation unit 111 is configured to create a facility model of each facility by the above-described processing using these data, and the model creation unit 111 calculates the sum of the created facility models. With this configuration, the facility model creation device 1 can be used as a building model creation device.

Next, an embodiment will be described.
(Example)
In this example, the behavior of an air conditioning facility that handles the intrusion and outflow of heat from the outside world and the heat generated by residents and OA equipment (referred to as the air conditioning heat load) is modeled on a section of one floor of an office building. Is.

  FIG. 3 is a plan view showing one floor of an office building that is a target of the building model in the present embodiment. FIG. 3A is an overall plan view of one floor of an office building, and FIG. 3B is a partially enlarged view of a section targeted for a building model. FIG. 4 is a diagram for explaining measurement items necessary for modeling the total amount of heat processed by the air conditioning equipment into a building model. FIG. 5 is a diagram showing the behavior during a certain period (November 2001 to July 2002) of the total amount of heat processed by the air conditioner (total heat load) in the interior NE section.

  In FIG. 3A, one floor of the office building that is the object of the building model in the present embodiment is roughly divided into three areas: an east area, a central area, and a west area. The east area and the west area are areas where work is mainly performed, and the central area is an area where common facilities are located where machine rooms, stairs, toilets, conference rooms, and elevator halls are located. In this embodiment, this east area is set as a model creation target space SP which is a target for building a building model.

  In this east area, as shown in FIG. 3 (A) and FIG. 3 (B), as an air conditioner, 11 fan coil units (hereinafter abbreviated as “FCU”) 21 (21 -1 to 21-11) are arranged substantially in the north-south direction, and two air conditioners (hereinafter abbreviated as “AHU”) 22 (22-1 and 22-2) are located on the south side at the boundary with the central area. They are located on the north side. Briefly, the FCU 21 is a device that exchanges heat with room air so that the blown air reaches a predetermined temperature. The AHU 22 is a device that takes in outside air, mixes outside air and room air, and performs heat exchange with the mixed air so that the blown air becomes a predetermined temperature and a predetermined humidity.

  Since this embodiment models the total amount of heat processed by the air conditioning equipment composed of the FCU 21 and the AHU 22, first, it is necessary to measure the total amount of heat processed by the FCU 21 and the total amount of heat processed by the AHU 22 as the output of the equipment model, respectively. There is. This measurement is calculated from the difference between the temperature of the cold water and the temperature of the hot water in the heat exchanger of the air conditioning equipment. In order to perform this measurement, temperature sensors are arranged in the FCU 21 and the AHU 22 respectively in the water supply (inlet) and return water (outlet) of the coil section that performs heat exchange, and the flow rate sensor is arranged.

  Each of these temperature sensors and flow rate sensors are configured to be connected to a communication interface (not shown) further provided in the equipment model creation device 1 via a transmission line, and are measured at intervals of 1 to 10 minutes. The measured data is collected in the equipment model creation device 1 from these temperature sensors and flow sensors. The central processing unit 11 of the equipment model creation device 1 averages and integrates the collected data in a time unit that is considered appropriate for model creation, for example, an hour unit. The amount of heat is calculated from the temperature and flow rate of the hot water, and the total amount of heat processed by the FCU 21 and the total amount of heat processed by the AHU 22 are calculated. The central processing unit 11 of the equipment model creation device 1 is configured to store the calculated total processing heat amount of the FCU 21 and the calculated total processing heat amount of the AHU 22 in the data storage unit 161 in the auxiliary storage unit 16 as basic data. .

  On the other hand, since the present embodiment models the total amount of heat processed by the air conditioning equipment for the purpose of analysis, it is necessary to consider factors that affect the total amount of heat processed by the air conditioning equipment, that is, factors that affect the air conditioning equipment. This factor corresponds to an input variable that is a candidate for an explanatory variable of the equipment model. As for this factor, first, the outside air temperature, the outside air absolute humidity, and the intake air volume taken into the AHU 22 from the heat treatment method of the AHU 22 can be mentioned. Moreover, the heat source currently installed in the east area is mentioned, The lighting installation, OA apparatus, and person in the east area become a heat source. Furthermore, it is necessary to consider the influence that the east area receives from the outside, that is, the outside air temperature near the window and the outside wall, the outside air absolute humidity, and the amount of solar radiation from the window.

  Therefore, as shown in FIG. 4, in order to measure these physical quantities such as temperature, humidity, air volume, heat quantity and solar radiation, sensors (not shown) are arranged at various places. That is, a temperature sensor, a humidity sensor, and an air volume sensor are respectively arranged to measure the outside air temperature, the outside air absolute humidity, and the intake air volume that are taken into the AHU 22. In order to indirectly measure the calorific value of the lighting equipment, the power consumption of the lighting equipment is measured, and for this purpose, a power meter is arranged in the lighting equipment. In order to indirectly measure the amount of heat generated by the OA device, the power consumption at the outlet to which the OA device is connected is measured, and for this purpose, a power meter is arranged at the outlet. In order to measure the room temperature and the room absolute humidity, a temperature sensor and a humidity sensor are arranged. In order to measure the outdoor outdoor air temperature and the outdoor outdoor absolute humidity near the window, a temperature sensor and a humidity sensor are arranged near the outdoor window. In addition, a pyranometer is arranged to measure the amount of solar radiation from the window. In this embodiment, the human calorific value is treated as constant, but means for detecting the number of residents, for example, a human sensor utilizing a camera or an infrared sensor is arranged, and the number of residents is estimated from the output or Weighing and indirectly calculating the amount of heat generated by the human body.

  In each of these temperature sensors, humidity sensors, wattmeters, and pyranometers, the measurement data measured at intervals of 1 to 10 minutes are collected in the equipment model creation device 1 in the same manner as described above, and the center of the equipment model creation device 1 is collected. The processing unit 11 averages and integrates these data in an appropriate time unit, for example, one hour unit, generates basic data for constructing the equipment model, and stores it in the data storage unit 161 in the auxiliary storage unit 16. Remember.

These FCU21 total processing heat, AHU22 total processing heat, AHU22 outside air temperature, AHU22 outside air absolute humidity, AHU22 outside air intake airflow, lighting equipment power, outlet power, indoor temperature, indoor absolute humidity, outdoor outdoor air temperature, outdoor By constructing an equipment model for the entire east area by repeatedly selecting effective explanatory variables and forcibly reinstating the input variables until the equipment model with the specified accuracy is obtained, using absolute outside humidity and solar radiation as input variables. However, in this embodiment, in order to construct a more accurate equipment model, it is considered that the indoor environment in the east area is adjusted by 11 FCUs 21 and two AHUs 22, and FIG. As shown in Fig. 3, there are three perimeter sections Zp, interior NE (North East) section Z _INE, and interior SE (South East) section Z _ISE . The east area was divided into areas, and equipment models of FCU21, AHU22-1 and AHU22-2 were constructed for each section. In this example, the equipment model was created by regarding 11 FCUs 21 as one FCU. The facility model creation method according to the present invention can also create a facility model by regarding a plurality of facilities as one facility. The perimeter generally refers to a section of indoor space in the vicinity of a window or an outer wall that is in contact with the outside world, and in reality, it is a range of several meters from a wall or window that is in contact with the outside world.

  The perimeter section Zp is a section in which 11 FCUs 21 mainly adjust the indoor environment, and is an area that is affected by the outside through windows and outer walls. Accompanied by. For this purpose, the FCU 21 is arranged separately from the AHU 22. Therefore, the dependent variable in the equipment model of the perimeter section Zp is the total amount of heat processed by the FCU 21, and the input variables are lighting equipment power perimeter, outlet power perimeter, indoor temperature perimeter, indoor absolute humidity perimeter, outdoor outdoor air temperature, outdoor outdoor air absolute Humidity and solar radiation. Here, the lighting equipment power perimeter is the power in the lighting equipment installed in the perimeter section Zp, the outlet power perimeter is the power in the outlet installed in the perimeter section Zp, and the indoor temperature perimeter is the perimeter section. Is the room temperature in Zp, and the room absolute humidity perimeter is the room absolute humidity in the perimeter compartment Zp.

The interior NE section Z _INE is a section in which the north side AHU 22-1 mainly adjusts the indoor environment, and is a range in which the influence from the outside does not substantially reach through the window or the outer wall. For this reason, the dependent variable in the equipment model of the interior NE section Z _INE is the total processing heat amount of the AHU 22-1, and the input variables are the outside air temperature of the AHU 22, the outside air absolute humidity of the AHU 22, the outside air intake air volume of the AHU 22-1, The lighting equipment power NE, the outlet power NE, the room temperature NE, and the room absolute humidity NE. Here, the lighting equipment power NE is the power in the lighting equipment installed in the interior NE section Z _INE , and the outlet power NE is the power in the outlet installed in the interior NE section Z _INE , and the room temperature NE Is the room temperature in the interior NE section Z _INE , and the room absolute humidity NE is the room absolute humidity in the interior NE section Z _INE .

The interior SE section Z _ISE is a section in which the AHU 22-2 on the south side mainly adjusts the indoor environment, and is a range in which the influence from the outside does not substantially reach through the window or the outer wall. Therefore, the dependent variable in the equipment model of the interior SE section Z _ISE is the total amount of heat processed by the AHU 22-2, and the input variables are the outside air temperature of the AHU 22, the outside air absolute humidity of the AHU 22, the outside air intake air volume of the AHU 22-2, The lighting equipment power SE, the outlet power SE, the room temperature SE, and the room absolute humidity SE. Here, the lighting equipment power SE is power in the lighting equipment installed in the interior SE section Z _ISE , and the outlet power SE is power in the outlet installed in the interior SE section Z _ISE , and the room temperature SE. Is the room temperature in the interior SE section Z _ISE , and the room absolute humidity SE is the room absolute humidity in the interior SE section Z _ISE .

In this embodiment, the outside air temperature of the AHU 22 and the outside air absolute humidity of the AHU 22 are common to the interior NE section Z _INE and the interior SE section Z _ISE , but may be separately measured.

As an example of the basic data, FIG. 5 is a graph showing the total amount of heat processed in the interior NE section Z _INE .

The boundary between the perimeter section Zp, the interior NE section Z _INE, and the interior SE section Z _ISE is determined in consideration of the size of the window, the heat insulation performance of the window and the wall, etc. It is set at a position of about 2 to 3 meters.

  In order to partition the east area in this manner, the partition division data storage unit 164 stores data for identifying which partition the basic data of the data storage unit 161 belongs to as the partition division data. The For example, data for associating an identifier (for example, its name or MAC address) for individually identifying each flow sensor, each temperature sensor, each humidity sensor, each wattmeter, and a pyranometer and each section is stored.

  On the other hand, since the object of the building model in this embodiment is an office building, the office model is not used on non-working days such as Saturdays, Sundays, holidays, and establishment anniversary, so the equipment model of the total amount of heat processed by the air conditioning equipment It will be a peculiar day in building. Furthermore, because the equipment that is the target of the equipment model is air conditioning equipment, there are days that are not used depending on the temperature, and these days are also specific days in building the equipment model for the total amount of heat processed by the air conditioning equipment. .

  Moreover, when creating an equipment model, it is also possible to perform modeling using basic data of the entire target period for modeling. In this embodiment, the total amount of heat processed by the air conditioning equipment is modeled. However, the air conditioning equipment generally has different operating conditions depending on the season. For example, in the winter and summer seasons, the air conditioning equipment is operated almost continuously, but in the spring and autumn seasons, the air conditioning equipment is operated intermittently according to the temperature of the day, and that day. Depending on the air temperature, the cooling operation and the heating operation may be switched. Furthermore, in the spring and autumn seasons, the first half and the second half have different intermittent operation times, and the heating operation and cooling operation have different frequencies. The actual condition of the operating condition of such an air conditioner is grasped by analyzing a trend (corresponding to a basic data change pattern) from a graph of basic data for one year, for example, and the target period for modeling is further divided.

In the present embodiment, the basic NE data from November 1, 2001 to October 31, 2002, the interior NE section Z _INE sets the first autumn period from 11/1 to 11/15 and the winter period to 11/11. 19 to 3/4, 1st spring and 2nd spring from 3/5 to 5/21, summer from 5/22 to 10/10, second fall from 10/11 to 10/31 It was. In the interior SE section Z _ISE , the first autumn period is from 11/1 to 12/14, the winter period is from 12/17 to 2/25, and the first spring period and the second spring period are from 2/26 to 6/7. The summer season was from 6/10 to 10/7, and the second fall season was from 10/8 to 10/31. In perimeter section Zp, the first autumn period is from 11/1 to 11/22, the winter period is from 1/7 to 3/6, the first spring period is from 4/4 to 5/9, and the second spring period is 5 / 13 to 6/7, the summer season from 6/10 to 10/10, and the second fall season from 10/11 to 10/31.

The interior NE section Z _INE and the interior SE section Z _ISE were treated as one period without being divided into periods from the analysis of the trend in the graph of basic data. Moreover, the place where the boundary of each period is discontinuous corresponds to a peculiar day.

  When modeling the total amount of heat processed by the air conditioning equipment as an equipment model, the user first inputs a model construction period from the input unit 12. Next, the user inputs the above-described partition division data from the input unit 12. When input, the central processing unit 11 of the equipment model creation device 1 stores the partition division data in the partition division data storage unit 164. Next, the user inputs the above-mentioned specific date data from the input unit 12. When the singular date data is input, the central processing unit 11 of the equipment model creation device 1 stores the singular date data in the schedule storage unit 162. Next, when the period division is necessary in relation to the model construction period, the user inputs the above-described period division data from the input unit 12. When the period division data is input, the central processing unit 11 of the equipment model creation device 1 stores the period division data in the period division data storage unit 163. Next, the user inputs the dependent variable and the input variable of the equipment model from the input unit 12.

When the dependent variable and the input variable are input, the model creation unit 111 of the equipment model creation device 1 extracts basic data corresponding to the model construction period from the basic data stored in the data storage unit 161. Next, the model creation unit 111 generates basic data for model creation by excluding the basic data corresponding to the singular day based on the singular day data stored in the schedule storage unit 162 from the extracted basic data. To do. Next, the model creation unit 111 uses the generated basic data for model construction based on the partition division data stored in the partition division data storage unit 164, the perimeter section Zp, the interior NE section Z _INE, and the interior SE section Z. _The period is divided into each period using the period division data stored in the period division data storage unit 163. Then, using the basic data obtained by these processes, the model creation unit 111 repeats the process of selecting effective explanatory variables and the process of forced input of the input order until the equipment model has a predetermined accuracy (“ By performing “continuous order input process”, an equipment model for each processing heat amount of each equipment is created.

  FIG. 6 is a diagram for explaining the operation of the continuous order input process in the equipment model of the interior NE section.

  In FIG. 6, in the initial state (S101) where the continuous order input process is started, all input variables (outside air temperature NE, outside air absolute humidity NE, outside air intake air volume NE, room temperature NE, room absolute value humidity NE, lighting equipment power NE are shown. And the outlet power NE) are executed (S102). As explanatory variables, in this embodiment, for example, the outside air temperature NE (0), the outside air absolute humidity NE (0), and the outside air intake air volume NE (0) are used. ) And four explanatory variables of the room temperature NE (0) remain (S103). In addition, the numerical value in () is an order. This order is t in Formula 1, for example, t = 0 represents the current point in time with order 0, t = 1 represents one hour before with order 1, and t = 2 represents two hours ago. For example, for () described after the explanatory variable, (0) represents information at the current time point, (0, 1) represents information at one hour before the current time point, and (0, 1, 2) represents It represents the information at the current time point, one hour ago and two hours ago.

  Here, in order to clarify the influence of each piece of information one hour ago, 4 of the outside air temperature NE (1), the outside air absolute humidity NE (1), the outside air intake air volume NE (1), and the room temperature NE (1). The explanatory variables are forcibly added (S104), the outside air temperature NE (0, 1), the outside air absolute humidity NE (0, 1), the outside air intake air volume NE (0, 1), and the room temperature NE (0, 0). The stepwise method is executed using the eight explanatory variables of 1) (S105), the outside air temperature NE (0), the outside air absolute humidity NE (1), the outside air intake air volume NE (0), and the room temperature NE (0, The five explanatory variables 1) remain (S106).

  The absolute outside humidity at a time after the remaining outside absolute humidity (1) remaining as an explanatory variable, that is, the outside absolute humidity (0), is forced because it should affect the current operating state of the AHU 22-1. In addition, the absolute outside humidity (2) and the indoor temperature (2) are compulsorily determined to determine whether the information one hour before and two hours before the current time has an effect. To the explanatory variable (S107). A stepwise method using the eight explanatory variables of the outside air temperature NE (0), the outside air absolute humidity NE (0, 1, 2), the outside air intake air amount NE (0), and the room temperature NE (0, 1, 2). Is executed (S108), and three explanatory variables of the outside air temperature NE (0) and the room temperature (0, 2) remain (S109).

  The room temperature after the room temperature (2) out of the room temperatures (0, 2) remaining as explanatory variables, that is, the room temperature (1) affects the current operating state of the AHU 22-1. Therefore, the outside air temperature (1) is forcibly added to the explanatory variable in order to determine whether or not the information about one hour before the outside temperature is necessary. S110). A stepwise method is executed using these five explanatory variables of the outside air temperature NE (0, 1) and the room temperature NE (0, 1, 2) (S111), and the outside air temperature NE (0) and the room temperature (0). 2) the three explanatory variables remain (S112), which coincides with the result of the previous stepwise method, and thus the stepwise method ends.

  Next, the room temperature at a time later than the room temperature (2) of the room temperatures (0, 2) remaining as explanatory variables, that is, the room temperature (1) affects the current operating state of the AHU 22-1. Is forcibly added to the explanatory variable (S113). Then, processing by the forced injection method is performed (S114), accuracy verification is performed, and an equipment model is derived by satisfying a predetermined accuracy (S115).

As an example, the facility model for the first fall of AHU22-1 of the interior NE section Z _INE thus created has a constant term of 113.656 and a coefficient of outside air temperature (0) of 0.796. Yes, the coefficient of the room temperature NE (0) was −9.418, and the coefficient of the room temperature NE (1) was 1.966. Similarly, an equipment model of another period in the AHU 22-1 of the interior NE section Z _INE , an equipment model of the AHU 22-2 of the interior SE section Z _ISE , and an equipment model of the FCU 21 of the perimeter section Zp are created. In this example, about 85% of the basic data for one year was used for creating the equipment model, and the remaining about 15% of basic data was used for accuracy verification.

  FIG. 7 is a graph showing the equipment model and the actual measurement value for each period. The horizontal axis in FIG. 7 is time, and the vertical axis is the total heat load (corresponding to the total amount of heat processed) expressed in kW. The upper part of FIG. 7 is a graph from 6/4 to 6/7 in the summer, and the lower part is a graph from 1/8 to 1/10 in the winter. As can be seen from FIG. 7, the value calculated by the equipment model (broken line) and the actual measurement value (solid line) are substantially the same. Furthermore, in order to show the effect of period division, the value (dashed line) calculated by the equipment model similarly created using the basic data for one year without performing period division is also shown in FIG. As can be seen from FIG. 7, the value calculated for the equipment model divided into periods as in the present embodiment is closer to the actual measurement value than in the case of the equipment model not divided into periods.

  The building model in the total processing heat quantity of the air conditioning equipment in the east area is obtained by calculating the sum of the created equipment model of AHU 22-1, equipment model of AHU 22-2 and equipment model of FCU 21.

  As shown in the embodiment, in the present invention, a factor that acts on an object for creating an equipment model (model creation target) is selected as an input variable, and a factor that does not substantially act on the model creation target is excluded. An equipment model is created by extracting valid explanatory variables. Therefore, the equipment model and the building model created by the method according to the present invention are models that take into consideration various disturbance factors such as weather conditions that differ depending on the day, month, and year, and the operational state of the model creation target.

  For this reason, for example, as shown in Fig. 8 (A-1), the basic data of the year before last when there were many relatively clear days, and the last year when there were many relatively cloudy days as shown in Fig. 8 (A-2). Even if basic data is used, substantially the same equipment model and building model can be constructed. Therefore, by predicting the basic data for this year, the model creation target for this year in FIG. 8A-3 can be simulated.

  In addition, when the model itself is subjected to remodeling of the building itself such as the outer wall or heat insulating material or remodeling of the facilities in the building, the effect of the renovation can be appropriately evaluated because the model takes into account disturbance factors. That is, in the equipment model creation method according to the present invention, even if different basic data is used, it is possible to construct an equipment model that expresses the model creation target with high accuracy, so that the effect of the repair can be evaluated appropriately. .

  In this case, first, as shown in FIG. 8 (B-1), the current building model in the model creation target is constructed by using past basic data. Next, as shown in FIG. 8 (B-2), the building model after the repair in the model creation target is constructed by using the basic data after the repair. Then, the same data is given to the current building model and the modified building model, and their outputs are compared. As a result, the effect of the repair can be quantitatively evaluated with high reliability.

  Similarly, the current equipment model in the model creation target is constructed by using past basic data. By using the basic data after the repair, the equipment model after the repair in the model creation target is constructed. Then, the same data is given to the current equipment model and the equipment model after the repair, and their outputs are compared. This makes it possible to quantitatively evaluate the effect of the equipment renovation with high reliability.

  In the above-described embodiments and examples, the equipment model is created on the assumption that there is no interaction between the divided sections. However, considering actual events, there are many cases where there is an interaction between the sections. Therefore, the facility model may be created using the interaction between sections as the input variable. In the above-described embodiment, for example, heat is conducted between adjacent areas, so the adjacent area temperature may be used as the input variable. FIG. 9 shows the results when the adjacent area temperature is used as the input variable and when it is not used.

FIG. 9 is a diagram showing a comparison result between the case where the adjacent area temperature is the input variable (■) and the case where it is not (♦). 9A and 9B are diagrams showing comparison results of R ² values in the interior section NE, interior section SE, and perimeter section from the left in the figure, and FIG. 9 (B) shows by month. R ² value is the freedom the adjusted R ² value in consideration of the number of explanatory variables is calculated by Equation 2.
R ² value = 1− (A (p) / B (p)) (1-r ² ) (Formula 2)
Here, A (p) = (n−1) × (n + p + 1), B (p) = (n + 1) × (n−p−1), and n is used to create an equipment model. P is an explanatory variable, r ² is an R ² value before adjusting the degree of freedom, and R is a correlation coefficient between the actually measured value and the equipment model. The larger the R ² value, the better the accuracy of the equipment model. As can be seen from FIGS. 9A and 9B, the accuracy of the equipment model is better when the interaction between the sections is added to the input variable.

  FIG. 9C is a graph showing the equipment model and the actual measurement value for each presence / absence of the adjacent area temperature. The horizontal axis in FIG. 9 is time, and the vertical axis is the total heat load (corresponding to the total amount of heat processed) expressed in kW. The upper part of FIG. 9 is a graph from 2/4 to 2/6 in winter, and shows a 2/6 graph in which the scale of the vertical axis is expanded in the balloon. The bottom graph is from summer 6/4 to 6/6. The thick solid line is the actual measurement value, the thin solid line is the case of the equipment model that does not consider the adjacent area temperature, and the broken line is the case of the equipment model that considers the adjacent area temperature. As can be seen from FIG. 9C, in the winter, the value calculated by the equipment model considering the adjacent area temperature is closer to the actually measured value than the case of the equipment model not considering the adjacent area temperature. .

It is a block diagram which shows the structure of an equipment model creation apparatus. It is a flowchart which shows an equipment model creation method. It is a top view which shows one floor of the office building which is the object of the equipment model in a present Example. It is a figure for demonstrating the measurement item required in order to model the total process calorie | heat amount of an air conditioning equipment to an equipment model. It is a figure which shows the behavior in the period (November 2001-July 2002) in the period with the total amount of heat processing (total heat load) of the air conditioner in the interior NE section. It is a figure for demonstrating the operation | movement of the continuous order injection | throwing-in process in the equipment model of interior NE division. It is the figure which showed the equipment model and measured value for every presence or absence of a period division with the graph. It is a figure for demonstrating the relationship between a disturbance factor, a building model, and the effectiveness of quantitative evaluation. It is a figure which shows the comparison result with the case where an adjacent area temperature is made into an input variable, and the case where it is not.

Explanation of symbols

1 Equipment Model Creation Device 11 Central Processing Unit 16 Auxiliary Storage Unit 111 Model Creation Unit 161 Data Storage Unit 162 Schedule Storage Unit 163 Period Division Data Storage Unit 164 Partition Division Data Storage Unit

Claims (9)

In an equipment model creation method for creating an equipment model that is an arithmetic expression indicating a relationship between an analysis purpose for equipment and a factor that acts on the equipment,
An input variable accepting step of accepting an input variable selected by selecting the factor according to the analysis purpose and further distinguishing the selected factor temporally;
A basic data receiving step for receiving basic data obtained by measuring the selected input variable;
Based on the basic data, an extraction step of extracting explanatory variables by excluding input variables that do not substantially act on the equipment from the selected input variables;
When there is a temporal discontinuity between the extracted explanatory variable and the explanatory variable at the current time point, a resurrection step for forcibly reviving the excluded input variable to be continuous in time;
An identification method of identifying the facility model by repeating the extraction step and the restoration step until the facility model to be constructed has a predetermined accuracy. The method further comprises a partition division step of dividing a space in a building where the equipment is arranged into a plurality of sections, and sorting the basic data into the plurality of sections, and constructing an equipment model for each of the plurality of sections. The equipment model creation method according to claim 1. When a period for constructing the facility model is set, the method further includes a period dividing step of dividing the period into a plurality of sub-periods and sorting the basic data into the plurality of sub-periods. The facility model creation method according to claim 1, wherein a model is constructed. The equipment model creation method according to claim 1, further comprising an exclusion step of excluding specific data that does not meet the analysis purpose from the basic data. In an equipment model creation program for creating an equipment model that is an arithmetic expression indicating a relationship between an analysis purpose for equipment and factors that act on the equipment,
An input variable accepting step of accepting an input variable selected by selecting the factor according to the analysis purpose and further distinguishing the selected factor temporally;
A basic data receiving step for receiving basic data obtained by measuring the selected input variable;
Based on the basic data, an extraction step of extracting explanatory variables by excluding input variables that do not substantially act on the equipment from the selected input variables;
If there is a temporal discontinuity between the extracted explanatory variable and the current time point, a resurrection step for forcibly reviving the excluded input variable to be continuous in time;
An identification method for identifying the facility model by repeating the extraction step and the restoration step until the facility model to be constructed has a predetermined accuracy. The facility model creation program for causing a computer to execute . Create a facility model for the equipment to be placed in the building, and calculate the sum of the created facility models to create a building model that is an arithmetic expression that indicates the relationship between the analysis purpose for the building and the factors that affect the building In the building model creation method to
The building model creation method, wherein the facility model is created by the facility model creation method according to any one of claims 1 to 4. By creating an equipment model for equipment to be placed in a building using the equipment model creation program and calculating the sum of the created equipment models, an arithmetic expression indicating the relationship between the analysis purpose for the building and the factors acting on the building In a building model creation program that creates a building model,
The building model creation program according to claim 5, wherein the facility model creation program is the facility model creation program according to claim 5. In the facility repair evaluation method for evaluating the effect of the repair performed on the facility by giving the same data to the facility model before and after the repair and calculating the difference,
The equipment repair evaluation method, wherein the equipment model is created by the equipment model creation method according to any one of claims 1 to 4. In the building renovation evaluation method for evaluating the effect of the renovation applied to the building by giving the same data to the building model before and after the renovation and calculating the difference between them,
The building model is created by the building model creating method according to claim 6.

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