JP2007133468A - Building environment support system, method, and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a building environment support system for obtaining environmental performance data for small block units of a building, and for collating the obtained data with actually proper use standard or emission standard, and for managing energy consumption and waste emission. <P>SOLUTION: This building environment support system is provided with a sensor group, which is installed in every small block of the building, including a sensor for measuring the usage of an energy medium including at least electricity, gas and water and a plurality of types of sensors for measuring an environment; a database for storing the output of each sensor with a time stamp; and an analyzing part for extracting a linear regression model in which the usage of each energy medium is defined as target variables for each small block unit, and for inputting the measurement data of each sensor of the sensor group as explanatory variables, and for calculating the estimated value of the usage of the energy medium to be controlled by the linear regression model, and to calculate the difference between the actual value and estimated value of the usage of each energy medium, and to calculate the difference as a saving target. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to a building environment support system, method, and program for grasping the amount of energy used, such as electricity, gas, and oil, and the amount of generated waste, and promoting energy saving and waste emission reduction.

In recent years, a system for grasping environmental performance data (energy consumption, CO2 emissions, waste generation, etc.) in units of buildings has come to be used due to increasing environmental awareness and the trend of strengthening environmental laws and regulations. (For example, see Patent Document 1 and Non-Patent Document 1).
JP 2004-170310 A http://www.higu.net/se.html (confirmed September 21, 2004)

  However, in the energy management system shown in the above-described conventional example, the environmental performance data (for example, management items of ISO14001) is grasped in units of buildings. There is a problem that it is difficult to establish a detailed target for energy saving and emission reduction because it is not grasped in units of small sections such as tenants.

  Furthermore, in the energy management system shown in the above-mentioned conventional example, efforts are being made to promote energy saving and environmental load reduction. Since only the level of comparison is performed, and environmental performance data for each subdivision that changes depending on factors such as the outside air temperature and the number of people in the room is not known, there is a rationale for what is actually the appropriate usage standard or emission standard. Certain goals are not clear.

  The present invention has been made in view of such circumstances, and by grasping environmental performance data in units of small sections of a building, it is possible to compare the grasped data with practically appropriate usage standards or emission standards. In addition, an object of the present invention is to provide a building environment support system, method and program capable of managing energy consumption and waste emission.

  The building environment support system according to the present invention includes a sensor for measuring the usage amount of an energy medium including at least electricity, gas, and water, and a plurality of types of sensors for measuring the environment of the small section, which are provided for each small section of the building. A first linear regression model in which the amount of use of each energy medium is a target variable for each subdivision unit, and a database that stores the output of each sensor in correspondence with a time stamp. Then, measurement data of each sensor in the sensor group is input as an explanatory variable, an estimated value of the amount of energy medium used as a control target is calculated by the first linear regression model, and the amount of use of each energy medium is calculated. A first analysis unit that calculates a difference between the actual measurement value and the estimated value and calculates the difference as a saving target (for example, the power consumption analysis unit 11 in the embodiment, the water use The amount analysis unit 13, and a gas consumption analyzer 14).

  The building environment support system of the present invention extracts a second linear regression model for another control target using the target variable of the first linear regression model of each of the energy media as an explanatory variable, and A second analysis unit that calculates an estimated value of another control target based on the estimated value, obtains a difference between the actually measured value of each energy medium and the estimated value, and calculates the difference as a saving target (for example, implementation A CO2 concentration analyzer 12) in the form.

  The building environment support system of the present invention calculates a measured value, an estimated value, and a saving target of the control target for each subdivision unit every predetermined period, and generates a report in a predetermined format Department (for example, report creation section 18 in the embodiment).

  The building environment support system according to the present invention calculates a usage amount in a predetermined period from an actual measurement value of the control target for each small section for each predetermined period, calculates a usage fee from the usage amount, and determines a predetermined format. A bill creation unit (for example, bill creation unit 15 in the embodiment) for generating a bill.

  The building environment support system of the present invention measures the usage amount of energy medium including at least electricity, gas, and water and the environment of the small section provided for each small section of the building by a plurality of types of sensors. The measurement data measured by the above is stored in a database with a time stamp added, the measurement data is read from the database in units of small sections, and the use amount of each energy medium is a target variable using the measurement data as an explanatory variable. First linear regression models are extracted, estimated values of the usage amounts of the energy medium to be controlled are calculated by the first linear regression model, and measured values of the usage amounts of the respective energy media and the estimated values are calculated. A difference between the values is calculated, the difference is calculated as a first saving target, and the target variable of the first linear regression model of each of the energy media is explained. As a result, the second linear regression model for the other control object is extracted, the estimated value of the other control object is calculated from the estimated value of the first linear regression model, A difference between the estimated values is obtained, and the difference is calculated as a second saving target. In the generation of the report, the measured value, the estimated value, and the first and A second reduction target is calculated, a report is generated in a predetermined format, and the report can be viewed on the information communication network for the owner of the building or the owner of the subdivision, for a predetermined period For each sub-compartment, a usage amount in a predetermined period is calculated from the measured value of the control target, a usage fee is calculated from the usage amount, and a bill is generated in a predetermined format.

  The building environment support method of the present invention includes a sensor for measuring the usage amount of an energy medium including at least electricity, gas, and water, and a plurality of types of sensors for measuring the environment of the small section, which are provided for each small section of the building. A storage process of storing the output of each sensor of the sensor group corresponding to the time stamp in a database, and a first linear regression model in which the amount of use of each energy medium is a target variable for each of the small sections Extracting each of them, inputting measurement data of each sensor in the sensor group as an explanatory variable, calculating an estimated value of the amount of energy medium to be controlled by the first linear regression model, and using each energy medium A first analysis step of obtaining a difference between the actually measured value of the quantity and the estimated value and calculating the difference as a saving target.

  The building environment support method of the present invention extracts a second linear regression model for another control object using the target variable of the first linear regression model of each of the energy media as an explanatory variable, and According to the estimated value, there is a second analysis process in which an estimated value of another control target is calculated, a difference between the actually measured value of each energy medium and the estimated value is obtained, and this difference is calculated as a saving target.

  The program of the present invention is a sensor comprising a sensor for measuring the amount of use of an energy medium containing at least electricity, gas and water, and a plurality of types of sensors for measuring the environment of the small section, which are provided for each small section of the building. A process of storing the output of each sensor of the group in correspondence with a time stamp and storing it in a database, and extracting a first linear regression model in which the amount of use of each of the energy media is a target variable for each of the small sections; Measurement data of each sensor in the sensor group is input as an explanatory variable, an estimated value of the usage amount of the energy medium to be controlled is calculated by the first linear regression model, and an actual measurement value of the usage amount of each energy medium is calculated. And calculating a difference between the estimated values and causing the computer to execute a building environment support process including a first analysis process for calculating the difference as a saving target.

  The program of the present invention extracts a second linear regression model for another control target using the target variable of the first linear regression model of each of the energy media as an explanatory variable, and uses the estimated value of the first linear regression model. The building environment support process includes a second analysis process that calculates an estimated value of another control target, obtains a difference between the actually measured value of each energy medium and the estimated value, and calculates the difference as a saving target. Is executed on the computer.

  As described above, according to the present invention, the usage amount of an energy medium such as electricity, gas, and water is set as a target variable that is an optimum value for each small section of the building, and the explanatory variable corresponding to this target variable. The linear regression model is generated by the above, and the explanatory variable at the time of control is input to this linear regression model, so that the estimated value is calculated as the target variable. Therefore, the optimal value (target variable) at the time of control is always obtained, By comparing with the actual measurement value, it can be output as the difference between the actual measurement value and the estimated value, so that the amount of energy medium that can be reduced in real time can be accurately grasped.

In addition, according to the present invention, it is possible to accurately grasp the amount of energy medium that can be reduced as the difference between the actually measured value and the estimated value by comparing with each actually measured value. , Energy performance such as energy usage, gas usage, water usage, etc. can be reduced for each subdivision (such as tenant tenant occupancy) required for energy saving and environmental impact reduction activities Accurate amount).
Furthermore, according to the present invention, as described above, for energy saving and environmental load reduction activities, by grasping the amount of area that can be reduced, the setting of specific and rational targets, and the results of efforts for this setting Can be grasped in real time, and CO2 reduction can be promoted in business departments such as offices that are behind.

Hereinafter, a building environment support system according to an embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a conceptual diagram showing small sections in a building to be controlled according to the embodiment. FIG. 2 is a block diagram illustrating a configuration example of a building environment support system according to an embodiment.
In FIG. 1, an air conditioner 101, an illumination 102, a water supply 103, a gas 104, an electronic device 105, and the like, which are control targets, are installed in a small section.
In addition, a sensor group that acquires data used for control of the control target is provided for each small section.

The sensor group includes a occupancy coefficient sensor 201, a temperature sensor 202, a humidity sensor 203, an illuminance sensor 204, a CO2 sensor 205, a gas meter 206, a water meter 207, a waste amount detection sensor 208, and the like, and the measurement data is identified by itself. It is sent to the management center along with the information.
Here, the occupancy coefficient sensor 201, the temperature sensor 202, the humidity sensor 203, the illuminance sensor 204, and the CO2 sensor 205 are sensors that measure the environment of a small section.
In addition, an electric meter for measuring the electric usage of each of the lighting, OA equipment and air conditioning and an electric meter for measuring the total electric usage of the lighting, OA equipment and air conditioning are provided.
In addition, the usage amount (electricity usage amount, gas usage amount, water usage amount) of an energy medium such as electricity, gas, and water is measured by an electric meter, a gas meter 206, and a water meter 207, respectively.

The occupancy counting sensor 201 detects and outputs the number of people in the small compartment, and the temperature sensor 202 and the humidity sensor 203 are provided at predetermined positions in a plurality of locations in the small compartment.
The CO2 sensor 205 is provided at a predetermined position in a plurality of locations in the small section, and detects the CO2 concentration at each location.
In addition to the numerical value obtained from the CO2 concentration obtained by the CO2 sensor 205, the final CO2 emission amount includes emission at the time of energy conversion, and therefore includes the amount of power used and the amount of gas used converted to CO2 emissions. It will be.
The waste amount sensor 208 is, for example, a weigh scale, and measures the weight of each burning garbage and non-burning garbage to obtain the waste amount.

The above-described occupancy counting sensor 201 can be used to count the number of people in the room (including gender-specific information) in entrance / exit management using, for example, a non-contact IC (integrated circuit) card, or to photograph the entire room such as the ceiling A camera is attached to the camera, image analysis such as contour extraction is performed on the image taken by the camera, a person is extracted, and the number of people in the room is counted. At this time, the ratio of each gender in the total number of people may be detected.
Here, the occupancy counting sensor 201 transmits the number of people in the room when the number of people in the room is changed to the management center C. Further, the occupancy counting sensor 201 may be transmitted to the management center C with the average number of people occupying the room at regular intervals as the number of people in the room.

Each sensor in the sensor group transmits measurement data measured to the management center C via an information communication network I (further, a terminal disposed in each small section) such as a dedicated communication network, a public communication network, and the Internet.
As shown in FIG. 2, the management center C stores a processing server 1 that performs analysis processing for building environment support based on measurement data from the sensor group, and data necessary for the analysis processing. A database 2 is provided.
In addition, the processing server 1 receives, via the information communication network I, weather data including weather (any of sunny, cloudy, rain, snow, etc.), outside air temperature, outside air humidity, etc. from the external system as supplementary data for the above analysis processing. Get as.

As shown in FIG. 3, the processing server 1 includes a DB writing unit 10, a power consumption analysis unit 11, a CO2 concentration analysis unit 12, a water usage analysis unit 13, a gas usage analysis unit 14, and a bill creation unit 15. , A waste analysis unit 16, a distribution unit 17, and a report creation unit 18.
The power consumption analysis unit 11 measures the thermal efficiency (blocking rate with other spaces) of a small section (room, floor, etc.), the number of people in the room that is the number of persons existing in the small section, the set temperature, the external temperature, Explains temperature, set humidity, external humidity, measured humidity, and the feeling of use (numerical data) of people in small sections. Variables, and air conditioning usage (air conditioning power usage) As a target variable, a multiple regression analysis method is used to calculate a reference value for optimum air conditioning, that is, a partial regression coefficient of a regression equation, from the measured values of the explanatory variable and the target variable in the air conditioner 101, and a linear multiple regression model (linear Find the regression model.

At this time, as shown in FIG. 11, the power consumption at the point where the measured temperature and the usability data intersect is defined as the optimum value of the air conditioning usage, and the optimum air conditioning reference value (ie, the optimum value) in the air conditioner 101 is defined. ).
The power consumption analysis unit 11 obtains a predicted value of optimum air conditioning from the linear multiple regression model (for the air conditioner 101) and the measured value of each sensor, that is, the linear multiple regression model from the measured value of each sensor. The air conditioning usage amount obtained by the above is used as a predicted value, and the predicted value is further set as a target value, and a reduction target for each small section is output in comparison with the current air conditioning usage amount.

  In addition, the power consumption analysis unit 11 explains the illuminance of the lighting 102, the number of people in the room, and the feeling of use (numerical data) of the people present in the small sections, and the lighting load (use of the lighting power) (Quantity) as a target variable, and using a method of multiple regression analysis, a linear multiple regression model is obtained by calculating a reference value of optimal illumination, that is, a coefficient of a regression equation, from the above-described explanatory variable and actual measurement value of the target variable in the illumination 102. .

At this time, similarly to the air-conditioning usage amount, the power usage amount at the point where the illuminance and the usability data intersect is defined as the optimum value of the illumination load, and is used as the reference value of the optimum illumination in the illumination 102.
In addition, the power consumption analysis unit 11 obtains a predicted value of optimum illumination from the linear multiple regression model (for the illumination 102) and the measurement value of each sensor, that is, linear from the measurement data (measurement value) of each sensor. The lighting load obtained by the multiple regression model is used as a predicted value, and the predicted value is further used as a target value, and a reduction target for each small section is output in comparison with the current lighting load.

  Furthermore, the power saving analysis unit 11 uses the number of people in each subcompartment and the feeling of use (numerical data) of the people present in the subcompartment as explanatory variables, and OA equipment (personal computer and printer) Using the multiple regression analysis method with the load as the target variable, the optimal reference value of the OA device usage amount, that is, the coefficient of the regression equation is calculated from the above-described explanatory variable and the actual measured value of the target variable in the electronic device 105. Find a linear multiple regression model.

At this time, the power saving analysis unit 11 defines the power usage at the point where the OA equipment load and the usability data intersect as an optimum value of the OA equipment load, and the electronic equipment 105 (there is a plurality in each small section). Use the standard value for the optimal amount of OA equipment used.
Further, the power consumption analysis unit 11 obtains an optimum predicted value of OA device usage from the linear multiple regression model (for the electronic device 105) and the measured value of each sensor, that is, from the measured value of each sensor. The OA equipment load obtained by the linear multiple regression model is used as a predicted value, and the predicted value is further set as a target value, and a reduction target for each small section is output in comparison with the current OA equipment load.

  In addition, the power consumption analysis unit 11 obtains the obtained illumination load, OA equipment load and air conditioning usage, the number of people in the room, and building information (the volume of the small section, the thermal efficiency based on the window ratio of the small section, small The thermal efficiency depending on the floor, ceiling and wall materials of the compartment) and the operating time indicating the time that this occupant used the small compartment was an explanatory variable, and the amount of electricity used in the small compartment was the target variable. Using the technique, the linear multiple regression model is obtained by calculating the optimum reference value of the electric consumption, that is, the coefficient of the regression equation, from the measured values of the explanatory variable and the objective variable in each small section.

  The power consumption analysis unit 11 also includes the linear multiple regression model (units of small sections), the measured values of each sensor (occupancy detection sensor 201), and the explanatory variables (lighting load, OA) corresponding to the occupancy. The predicted value of the optimal OA device usage amount is calculated from the device load and the air conditioning usage amount, that is, the electricity usage amount obtained by the linear multiple regression model is used as the prediction value, and this prediction value is set as the target value, Outputs the saving target for each subdivision compared to the amount used.

  The gas usage amount analysis unit 14 explains the number of people in the room, which is the number of people existing in the small section, the operation time indicating the time when the number of people in the room has used the small section, and hot water supply energy (for example, hot water consumption). Using the multiple regression analysis technique with the variable and gas usage as the objective variable, the optimum gas usage standard from the above-described explanatory variables and the actual measured values of the target variable (gas usage detected by the gas meter 207) in the gas 104 A linear multiple regression model is obtained by calculating a value, that is, a coefficient of a regression equation.

At this time, for example, the gas usage analysis unit 14 calculates the average value of data used for a predetermined period (every month) input from the gas meter 207 (measured when the number of people in the room is the same) as the gas usage. The optimum value is defined as a reference value of the optimum gas usage amount in the gas 104 (one or more in each small section).
The gas usage analysis unit 14 obtains an optimum predicted value of gas usage from the linear multiple regression model (for the gas 104) and the measured value of each sensor, that is, linearly calculates from the measured value of each sensor. Using the gas usage determined by the multiple regression model as a predicted value, and using this predicted value as a target value, a target for saving is output for each small section in comparison with the current gas usage.

  The water usage analysis unit 13 uses the number of people in the room, which is the number of people existing in the small section, and the operation time indicating the time when the number of people in the room is using the small section as explanatory variables, and the water usage is a target variable. Using the multiple regression analysis technique, the optimum water usage reference value, that is, the coefficient of the regression equation, is calculated from the measured values (the water usage detected by the water meter 206) of the explanatory variable and the objective variable in the gas 104. To obtain a linear multiple regression model. Here, the number of people in the room is obtained in addition to the number of men by multiplying the number of women by a coefficient (for example, 1.5). It corresponds to the difference in water usage by gender.

At this time, for example, the water usage analysis unit 13 inputs the average value of data input from the water meter 206 (measured when the number of people in the room is the same) over a predetermined period (every month). And the reference value for the optimal water usage in the water supply 103 (one or more in each small section).
Further, the water usage analysis unit 13 obtains an optimal predicted value of water usage from the linear multiple regression model (for the water supply 103) and the measurement values of each sensor, that is, linearly from the measurement values of each sensor. The water use amount obtained by the multiple regression model is used as a predicted value, and the predicted value is used as a target value, and a target for saving is output for each small section in comparison with the current water use amount.

The waste calculation unit 16 performs multiple regression analysis using the number of people in the room and the usage type of the small section (for example, the type of industry, each industry is quantified) as an explanatory variable, and the amount of waste as a target variable. Using a method, a linear multiple regression model is obtained by calculating a reference value of the optimum amount of waste, that is, a coefficient of a regression equation, from the measured values of the explanatory variable and the objective variable (measured values in the weighing scale 208).
Here, as the amount of waste, burnable garbage and non-burnable garbage (classified by type such as steel cans, aluminum cans, PET bottles, vinyl, etc.) are each measured with a weight scale 208 and multiplied by a predetermined coefficient for each type. And the result of integrating all is used.

The CO2 concentration analysis unit 12 uses a method of multiple regression analysis with the amount of electric power used (electricity consumption), the amount of gas used (gas consumption) and the amount of waste as explanatory variables, and CO2 emissions as the objective variables. Then, a linear multiple regression model is obtained by calculating a reference value of the best CO2 concentration, that is, a coefficient of a regression equation, from the measured values of the explanatory variable and the target variable (CO2 concentration detected by the CO2 sensor 205).
For the CO2 concentration, for example, an average value of concentrations measured by the CO2 sensor 205 within a predetermined time (several hours) is used.

That is, the CO2 concentration analysis unit 12 defines the average value of the data input from the CO2 sensor 205 over a predetermined time (several hours) as the optimum value of the CO2 concentration and sets it as the reference value of the best CO2 concentration.
Further, the CO2 concentration analysis unit 12 uses the linear multiple regression model (for CO2 concentration) to calculate the amount of power used and the amount of gas calculated by each of the other analysis units, and the measured values (waste amount and waste) of each sensor input. The optimal CO2 concentration predicted value is obtained from the CO2 concentration), that is, the CO2 concentration obtained by the linear multiple regression model from the measured values of each sensor is used as the predicted value, and this predicted value is used as the target value to obtain the current CO2 Outputs the saving target for each sub-compartment compared to the concentration.

Based on the target value calculated by each analysis unit and the actual measurement value, the report creation unit 18 calculates the difference between the target power amount, the used power amount, and the used power amount and the target power amount for each small section. A certain amount of electric power that can be saved and, like this electric energy, CO2 concentration, gas usage, water usage, waste, etc. are created in a format that can be viewed on the Web.
The invoice creation unit 15 bills each subcompartment user (for example, tenant owner) according to each usage amount within a predetermined period based on the power consumption, gas usage, and water usage. A report is prepared, and the expected tax amount based on the CO2 emission estimated from the CO2 concentration is calculated and output as the expected billing amount.

At this time, the CO2 concentration analysis unit 12 estimates the CO2 emission amount from the CO2 concentration, for example, the CO2 concentration in the air at night (that is, the ideal CO2 concentration) measured by the CO2 sensor 205, and the date. By calculating the difference from the CO2 concentration when a person is present in the room and multiplying the obtained difference in CO2 concentration by the volume of the room, the CO2 emission amount can be calculated.
Here, the relationship between concentration and mass is conventionally (mg / l), and the numerical value is expressed in% or ppm.
As described above, the CO2 emission amount includes the conversion value from the energy at which CO2 is emitted at the time of energy conversion with respect to the estimated value of the CO2 emission amount obtained from the measurement value of the CO2 sensor 205 described above. It becomes a numerical value.

As an example, the amount of each energy used and the numerical value obtained by converting the amount used to CO2 emissions are shown as follows: electricity: 1 kWh, city gas: 1 m ³ , LPG: 1 kg, water: 1 m ³ , kerosene: 1 l (liter), gasoline: 1 l, light oil: 1 l, burning garbage: 1 kg, 0.12 kg, 0.64 kg, 1.8 kg, 0.16 kg, 0.69 kg, 0.64 kg, 0.72 kg, 0 Converted to 24 kg.
The CO2 concentration analysis unit 12 converts the above-mentioned energy and other energy into the CO2 emission amount in the same manner, and uses the total of these as the CO2 emission amount calculated from the measured value of the CO2 sensor 205. Output as emission amount.

The distribution unit 17 receives the measurement data from the sensors arranged in each small section via the information communication network I and the terminals installed in the small sections (tenants) of each building (building). The report and the bill are transmitted to a terminal installed in the section or a preset transmission destination.
The database 2 includes a plurality of tables created in a predetermined storage area, that is, a building data table 2a, a tenant (small section) individual data table 2b, a reference value data table 2c, a detection data table 2d, an analysis data table 2e, An edit data table 2f and an invoice data table 2g are provided.
The DB writing unit 10 stores the data detected by each sensor provided in each small section for each small section in the detected data table 2d for each time stamp.

Each table provided in the database 2 will be described below.
The building data table 2a stores the building design, age, material (concrete, prefabricated, mortar, wood, etc.) and floor number for each building, and the thermal efficiency of each subdivision for each subdivision. Data such as the window ratio, volume, and the number of floors in which the small sections are located, which is the ratio of the area of the window to the surface area of the room, is stored.
The building data table 2a also stores building information (the volume of the small section, the thermal efficiency by the window ratio of the small section, the thermal efficiency by the material of the floor, ceiling and wall of the small section) for each small section.
The tenant individual data table 2b stores data such as the type of business, the number of employees (by gender), the number of OA devices, etc., in the individual data of tenants residing in this subdivision, and reports analysis results, etc. Report destination data, authority data such as tenant user ID and password (a plurality of authority data exist depending on authority), and the like are stored.

The reference value data table 2c includes the electricity usage (table shown in FIG. 4), gas usage (table shown in FIG. 5), water usage calculated by multiple regression analysis from the past usage data accumulation. Amount (table shown in FIG. 6), CO2 emission amount (table shown in FIG. 7) and the like are stored.
In the detection data table 2d, each sensor (occupied person detection sensor 201, temperature sensor 202, humidity sensor 203, illuminance sensor 204, CO2 sensor 205, water supply for each building and subdivision (floor or tenant) Data detected by the meter 206, the gas meter 207, and the gravimeter 208) is stored corresponding to each time stamp of a predetermined cycle.

The analysis data table 2e includes reference values calculated by the power consumption analysis unit 11, the CO2 concentration analysis unit 12, the water usage analysis unit 13, the gas usage analysis unit 14, and the waste analysis unit 16, and actual measurements. The value and the amount that can be reduced are stored for each time stamp in each small section unit.
By confirming this change in the amount that can be reduced in time series, it is possible to confirm the effect of the countermeasure against the reduction. For example, it can be determined whether or not the direction of the countermeasures has contributed to the reduction of usage.

The edit data table 2f stores a predetermined report format of a report to be sent to a national supervisory authority, a building owner, a user of a subdivision, and the like.
In this report format, fields for inputting the usage amount of each energy medium, the value of CO2 concentration, etc. are prepared, and a report is generated by the report creation unit 18 writing a numerical value corresponding to this field. .
The invoice data table 2g stores the history of invoices sent to users in each subsection, and the creation date and destination of the invoice and the destination are stored for each user in each subsection. .

Next, with reference to FIGS. 1-3 and FIGS. 8-10, operation | movement of the building environment assistance system in the Example mentioned above is demonstrated.
8 is a flowchart showing the flow of analysis processing, FIG. 9 is a flowchart showing the flow of editing processing, and FIG. 10 is a flowchart showing processing for distributing analyzed data and the like.
<Analysis processing>
Each analysis unit, for each floor, that is, for each subdivision, from the building data table 2a, based on the multiple regression analysis model, the reference value of the control target (power consumption, gas consumption, water consumption, CO2 emission, etc.) Data necessary for calculating (target value) is read (step S1).

For each subdivision, in order to calculate a target value as a management target by a linear multiple regression model for each subdivision, that is, to obtain a target variable that becomes a target value by a linear multiple regression model, a reference value that becomes an explanatory variable is set. And read from the reference value data table 2c. Hereinafter, as an example, the description will be given focusing on the operation of the power consumption analysis unit 11, but other analysis units also perform the same operation.
In other words, the power consumption analysis unit 11 reads the linear multiple regression model equation corresponding to the small section from the reference value data table 2c to calculate the reference value of the amount of electricity used, and calculates the amount of electricity used as the reference value. As the objective variables, the number of people in the room corresponding to the electricity usage, the operating time, the lighting load, the OA equipment load, and the air conditioning usage are read as explanatory variables corresponding to the time stamp of the time to be analyzed ( Step S2).
Then, the power consumption analysis unit 11 uses the building information that has already been read out as an explanatory variable to calculate a target value for the amount of electricity used.

Next, the power consumption analysis unit 11 reads the occupancy number detected by the occupancy number detection sensor 201 from the detection data table 2d in correspondence with the time stamp of the analysis time (step S3).
Next, the power consumption analysis unit 11 inputs an analysis result (target value, actual measurement value, difference) of power consumption within a predetermined range in the past from the analysis data table 2e (step S4).

  Hereinafter, target value (optimum value) calculation by linear regression analysis by the power consumption analysis unit 11 in step S5 will be described. Each of the other analysis units also performs similar processing to obtain a target value that is an optimum value. Here, the power consumption analysis unit 11 uses the following equation (1) to calculate a multiple regression analysis model for each small section, that is, a partial regression coefficient a (a0, a1, a2,...) Ask for.

  First, the power consumption analysis unit 11 creates Expression (2) corresponding to each objective variable from the objective variable read from the reference value data table 2c and the explanatory variable corresponding thereto.

  In the above formulas (1) and (2), ei (e1 to en) is a residual, and the formula (2) is a numerical value so that the residual sum of squares SSE in the following formula (3) becomes the minimum value. The partial regression coefficient a is obtained by solving the calculation.

  In Equations (1) and (2), Equation (4) as “Observed Value (Measured Value) = Estimated Value (Target Value) + Residual” is established with the portion excluding the residual ei as an estimated value. become.

  Here, the above equation (4) can be transformed into the following equation (5) in consideration of the average value of the objective variable.

  From the equations (5) and (3), the following equation (6) is obtained which indicates that “the total sum of squares SST = the sum of squares SSR of regression (estimated value) + the residual sum of squares SSE”.

  In the above equation (6), the smaller the residual sum of squares, the higher the degree of completion as a regression model, and the accuracy of the estimated value obtained from the following equation (7) increases.

Here, as shown in the following equation (8), when the determination coefficient R ² is defined, the closer the determination coefficient R ² is to 1, the higher the accuracy of the estimated value obtained from the regression model, and the determination coefficient R ^2. The square root of coincides with the multiple correlation coefficient.

  Then, the power consumption analysis unit 11 reads the building information Xi1 read from the building data table 2a, the occupancy number Xi2 (corresponding to the time stamp) read from the detection data table 2d, and the operation actually used. The lighting load Xi4, the OA equipment load Xi5, and the air conditioning usage Xi6 corresponding to this occupant number Xi2 read from the reference value data table 2c as time variables Xi3 and the explanatory variables are substituted into the equation (7) of the multiple regression model. Then, the target value of the electricity usage at the read time stamp is calculated (step S5). Similarly, the other analysis units generate a multiple regression model and obtain a target value based on each data read from the detection data table 2d.

Then, the power consumption analysis unit 11 obtains a difference between the obtained target value of the electricity usage and the actually measured value of the electricity usage actually used during the operation time Xi3, and calculates it as the electricity usage that can be saved ( Step S6).
At this time, the power consumption analysis unit 11 inputs individual measured values of the lighting load Xi4, the OA equipment load Xi5, and the air conditioning usage amount Xi6 and compares them with the reference values read as explanatory variables. It is also possible to evaluate whether any amount used has increased.
Next, the power consumption analysis unit 11 adds a time stamp to the corresponding subsection in the analysis data table 2e, with respect to the obtained analysis result (target value of electric usage, actual measurement value and difference value thereof). (Step S7).

<Edit processing>
Next, editing processing of the obtained analysis result will be described with reference to the flowchart of FIG.
The report creation unit 18 creates a report to the owner of the building every predetermined period (for example, every month) (step S11).
In other words, the report creation unit 18 analyzes the results of the analysis of the entire building and each subdivision regarding the electricity usage, gas usage, water usage, CO2 emissions, etc. And their difference values).
Here, the report preparation part 18 integrates the analysis results for each subdivision and outputs them as the analysis results of the entire building.

Then, the distribution unit 17 sets the building owner to be able to view the analysis result of the entire building and each small section on the homepage (Web) of the management center C, for example ( Step S12).
Next, the report preparation unit 18 describes the detection data and analysis results of each sensor in the building unit and the subdivision unit on the predetermined format to be submitted to the national supervisory authority, and the report in the above format is written. Generate (step S13).
Then, the bill creation unit 15 calculates a fee based on the amount used for each small section from the data measured by various sensors, and creates a bill requesting payment (step S14).

<Browsing process>
This process is a process performed at each small section or a user terminal arbitrarily provided by the user.
The user who uses the small section requests registration of the small section from the processing server 1 of the management center C via the terminal (step 21).
As a result, the processing server 1 performs registration processing of the requested subsection, that is, creates each data table corresponding to the subsection to be registered in the database 2.

Next, the processing server 1 requests the user to set a user ID and a password.
And according to the said request | requirement, a user transmits a user ID and a password with respect to the processing server 1 via a terminal.
As a result, the processing server 1 stores the user ID and password in the database in association with the registered subsection, and completes the setting of the user ID and password of the user.
And a user will acquire the user ID and password for referring the analysis result of a small division (step S22).

Next, the user accesses the homepage of the management center C via the Internet using application software installed on the terminal, such as hypertext browsing software (step S23), and the user ID and password are entered in the predetermined fields. Are input (step S24).
Then, the processing server 1 determines whether or not the user is a building owner or a tenant administrator from the user ID and password input on the homepage (step S25).
When it is determined that the processing server 1 is the owner of the building or the administrator of the tenant, the analysis result of both the building corresponding to the user ID and the password and the small section in the building and the analysis are used. The actual measurement value of each sensor can be viewed on the homepage (step S26). On the other hand, when it is determined that neither the owner of the building nor the administrator of the tenant is determined, the subsection corresponding to the user ID and password The analysis result and the actual measurement value of each sensor used for the analysis can be browsed on the homepage (step S27).

  1 is recorded in a computer-readable recording medium, and the program recorded in the recording medium is read into a computer system and executed. Thus, the analysis processing, report creation, and bill creation processing of the flowcharts of FIGS. 8 to 10 may be performed. The “computer system” here includes an OS and hardware such as peripheral devices. The “computer system” includes a WWW system provided with a homepage providing environment (or display environment). The “computer-readable recording medium” refers to a storage device such as a flexible medium, a magneto-optical disk, a portable medium such as a ROM and a CD-ROM, and a hard disk incorporated in a computer system. Further, the “computer-readable recording medium” refers to a volatile memory (RAM) in a computer system that becomes a server or a client when a program is transmitted via a network such as the Internet or a communication line such as a telephone line. In addition, those holding programs for a certain period of time are also included.

  The program may be transmitted from a computer system storing the program in a storage device or the like to another computer system via a transmission medium or by a transmission wave in the transmission medium. Here, the “transmission medium” for transmitting the program refers to a medium having a function of transmitting information, such as a network (communication network) such as the Internet or a communication line (communication line) such as a telephone line. The program may be for realizing a part of the functions described above. Furthermore, what can implement | achieve the function mentioned above in combination with the program already recorded on the computer system, and what is called a difference file (difference program) may be sufficient.

It is a conceptual diagram which shows the sensor group used by one Embodiment of this invention, and its monitoring object. It is a conceptual diagram which shows the structural example of the building environment assistance system by one Embodiment of this invention. It is a block diagram which shows the structural example of the processing server 1 of FIG. It is a table of the electricity usage amount memorize | stored in the reference value data table 2c in the database 2 of FIG. It is a table of the gas usage-amount memorize | stored in the reference value data table 2c in the database 2 of FIG. It is a table of the amount of water used memorize | stored in the reference value data table 2c in the database 2 of FIG. 2 is a table of CO2 emission amounts stored in a reference value data table 2c in the database 2 of FIG. 4 is a flowchart illustrating an operation example of the processing server 1. 4 is a flowchart illustrating an operation example of the processing server 1. 3 is a flowchart illustrating an operation example of a processing server 1. It is a conceptual diagram explaining calculation of the optimal value of the air-conditioning usage-amount.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 ... Processing server 2 ... Database 2a ... Building data table 2b ... Tenant individual data table 2c ... Reference value data table 2d ... Detection data table 2e ... Analysis data table 2f ... Editing data table 2g ... Invoice data table 10 ... DB book 11: Power consumption analysis unit 12 ... CO2 concentration analysis unit 13 ... Water usage analysis unit 14 ... Gas usage analysis unit 15 ... Invoice creation unit 16 ... Waste analysis unit 17 ... Distribution unit 18 ... Report creation unit DESCRIPTION OF SYMBOLS 101 ... Air conditioner 102 ... Lighting 103 ... Water supply 104 ... Gas (gas stopper)
201 ... Number of people detection sensor 202 ... Temperature sensor 203 ... Humidity sensor 204 ... Illuminance sensor 205 ... CO2 sensor 206 ... Water meter 207 ... Gas meter 208 ... Weigh scale C ... Management center I ... Information communication network

Claims (9)

A sensor group comprising a plurality of sensors for measuring the usage of an energy medium including at least electricity, gas, and water, and a plurality of types of sensors for measuring the environment of the subdivision, provided for each subdivision of the building;
A database for storing the output of each sensor in correspondence with a time stamp;
A first linear regression model in which the usage amount of each of the energy media is a target variable is extracted for each sub-compartment unit, measurement data of each sensor in the sensor group is input as an explanatory variable, and the first linear model is input. A first analysis that calculates an estimated value of the usage amount of the energy medium to be controlled by the regression model, obtains a difference between the actually measured value of each energy medium and the estimated value, and calculates this difference as a saving target. A building environment support system characterized by comprising:   Using the target variable of the first linear regression model of each of the energy media as an explanatory variable, a second linear regression model for another control object is extracted, and the other control object is estimated based on the estimated value of the first linear regression model. 2. The apparatus according to claim 1, further comprising: a second analysis unit that calculates an estimated value, obtains a difference between the actual measured value of each energy medium and the estimated value, and calculates the difference as a saving target. Building environment support system.   A report creation unit that calculates an actual measurement value, an estimated value, and a saving target of the control target for each subdivision unit for each predetermined period and generates a report in a predetermined format is provided. The building environment support system according to claim 1 or 2.   An invoice for calculating a usage amount in a predetermined period from an actual measurement value of the control target for each subdivision unit in a predetermined period, calculating a usage fee from the usage amount, and generating a bill in a predetermined format The building environment support system according to claim 1, further comprising a creation unit. Measure the usage amount of energy medium including at least electricity, gas and water and the environment of each subdivision by each of several types of sensors.
The measurement data measured by the sensor is added to the database with a time stamp,
Reading out the measurement data from the database to the sub-compartment unit, using the measurement data as explanatory variables, respectively, extracting a first linear regression model in which the usage amount of each energy medium is a target variable,
An estimated value of the usage amount of the energy medium to be controlled is calculated by the first linear regression model, a difference between the actually measured value and the estimated value of each energy medium is obtained, and this difference is reduced to the first saving. As a goal,
Using the target variable of the first linear regression model of each of the energy media as an explanatory variable, extracting a second linear regression model for another controlled object;
Based on the estimated value of the first linear regression model, the estimated value of the other control target is calculated, the difference between the actual measured value of each energy medium and the estimated value is obtained, and this difference is calculated as the second saving target. And
In the generation of a report, the measured value, the estimated value and the first and second saving targets of the control target are calculated for each subdivision unit for each predetermined period, and the report is generated in a predetermined format. And
For the owner of the building or the owner of the parcel, the report can be viewed on the information communication network,
For each subdivision unit, calculate the usage amount in a predetermined period from the measured value of the control target, calculate the usage fee from this usage amount, and generate a bill in a predetermined format for each subdivision unit. The building environment support system according to any one of claims 1 to 4, wherein the building environment support system is characterized. Output of each sensor of a sensor group comprising a sensor for measuring the amount of use of an energy medium including at least electricity, gas, and water and a plurality of types of sensors for measuring the environment of the subdivision provided for each subdivision of the building. A process of storing the data in the database in correspondence with the time stamp,
A first linear regression model in which the usage amount of each of the energy media is a target variable is extracted for each sub-compartment unit, measurement data of each sensor in the sensor group is input as an explanatory variable, and the first linear model is input. A first analysis that calculates an estimated value of the usage amount of the energy medium to be controlled by the regression model, obtains a difference between the actually measured value of each energy medium and the estimated value, and calculates this difference as a saving target. A building environment support method characterized by comprising a process.   Using the target variable of the first linear regression model of each of the energy media as an explanatory variable, a second linear regression model for another control object is extracted, and the other control object is estimated based on the estimated value of the first linear regression model. 7. The method according to claim 6, further comprising: a second analysis step of calculating an estimated value, obtaining a difference between the actual measured value of each energy medium and the estimated value, and calculating the difference as a saving target. Building environment support method. Output of each sensor of a sensor group comprising a sensor for measuring the amount of use of an energy medium including at least electricity, gas, and water and a plurality of types of sensors for measuring the environment of the subdivision provided for each subdivision of the building. Is stored in the database corresponding to the time stamp,
A first linear regression model in which the usage amount of each of the energy media is a target variable is extracted for each sub-compartment unit, measurement data of each sensor in the sensor group is input as an explanatory variable, and the first linear model is input. A first analysis that calculates an estimated value of the usage amount of the energy medium to be controlled by the regression model, obtains a difference between the actually measured value of each energy medium and the estimated value, and calculates this difference as a saving target. A program that causes a computer to execute a building environment support process. Using the target variable of the first linear regression model of each of the energy media as an explanatory variable, a second linear regression model for another control object is extracted, and the other control object is estimated based on the estimated value of the first linear regression model. The building environment support according to claim 9, further comprising a second analysis process for calculating an estimated value, obtaining a difference between the actually measured value of each energy medium used and the estimated value, and calculating the difference as a saving target. A program that causes a computer to execute processing.

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