CN104919252A - Room temperature estimating device, program - Google Patents

Abstract

A room temperature estimating device (10) is equipped with a storage unit (13), a prediction formula generating unit (15), a predicted time change obtaining unit (16), and a room temperature estimating unit (17). The prediction formula generating unit (15) generates room temperature data and external air temperature data representing the designated time by using the room temperature data and the external air temperature data at designated times on each of a plurality of days within a predetermined extraction period stored in the storage unit (13). Prediction formula for the relationship between. The room temperature estimating section (17) determines the outside air temperature of the date and time of interest corresponding to the specified time using the time change of the outside air temperature obtained by the forecast time change obtaining section (16), and applies the determined outside air temperature to the forecast formula from which to estimate the room temperature for that date and time of interest.

Description

Room temperature estimation device and program

technical field

The present invention relates to a room temperature estimating device configured to estimate a room temperature on a date and time of interest, and a program for causing a computer to function as the room temperature estimating device.

Background technique

Conventionally, there has been known a technique for adjusting the room temperature to a desired temperature at a predetermined time based on information on temporal changes in room temperature and predicted outside air temperature (see, for example, Japanese Laid-Open Patent Publication Hei 6-42765, hereinafter referred to as "Document 1"). Furthermore, regarding the temperature inside the vehicle, there is also known a technique for predicting the temperature of the interior space of the vehicle based on the estimated change in the amount of sunlight, the measured outside air temperature, and the measured temperature of the interior space of the vehicle. Changes in temperature, and a warning is issued when the interior temperature is predicted to reach a predetermined threshold (for example, see Japanese Laid-Open Patent Publication No. 2005-343386, hereinafter referred to as "Document 2").

Document 1 discloses a technique for measuring room temperature as environmental information and predicting temporal changes in room temperature based on the history of the measured room temperature. Document 1 also discloses a method for determining the temperature of the heating device based on the predicted temporal changes of the room temperature and the outside air temperature, the amount of heat required for heating the room (hereinafter referred to as "heating load energy"), and the heating capacity of the heating device. The technology of the operation start time and heating start time. Specifically, in Document 1, predicted values of room temperature and outside air temperature are determined, and heating load energy for adjusting the room temperature to a desired temperature is calculated based on these predicted values.

In the structure described in Document 1, room temperature is predicted to calculate heating load energy. However, in Document 1, the room temperature is predicted based on historical data of the room temperature. Document 1 does not disclose a technique for predicting room temperature using other factors on which the room temperature depends.

Document 2 discloses a technique for measuring a room temperature and an outside air temperature as environmental information, and estimating a vehicle interior space temperature based on the measured room temperature and the measured outside air temperature and the predicted amount of sunlight.

The structure described in Document 2 is concerned with predicting the interior temperature of a vehicle. It should be noted that the temperature in the vehicle interior follows the outside air temperature in a short period of time when the outside air temperature changes. Therefore, it is relatively easy to predict the interior space temperature based on the outside air temperature and the amount of sunlight. On the other hand, the temperature in a room of a building depends on the thermal characteristics of the room such as thermal insulation performance, and does not change immediately when the outside air temperature changes. Therefore, based on the technique described in Document 2, it is difficult to predict the room temperature in a room of a building from the outside air temperature.

There is also known a technique for predicting the room temperature of a building by using computer simulation based on various factors such as outside air temperature, thermal insulation performance of the building, sunlight, ventilation, rainfall, and the presence or absence of people. However, such computer simulations require a lot of information and may also require specialized measurements to obtain correct values. Therefore, this technique is not convenient for prediction of room temperature.

Contents of the invention

An object of the present invention is to provide a room temperature estimating device for estimating the temperature of a room in a building based on measured environmental information without complicated computer simulation, and a method for causing a computer to be used as the room temperature estimating device. program.

A room temperature estimating device according to the present invention includes: a room temperature obtaining unit configured to obtain room temperature data; an external air temperature obtaining unit configured to obtain external air temperature data; a storage unit configured to obtain the room temperature The room temperature data obtained by the unit and the outside air temperature data obtained by the outside air temperature obtaining unit are stored in association with the measured date and time, respectively; a prediction formula generating unit configured to Stored in the room temperature data and the external air temperature data corresponding to the specified times of each of the multiple days within the predetermined extraction time period, to generate a prediction formula representing the relationship between the room temperature data and the external temperature data at the specified time; a predicted time change obtaining section configured to obtain a predicted time change in outside air temperature; and a room temperature estimating section configured to compare the specified The external air temperature at the time of interest corresponding to the time is applied to the prediction formula, thereby estimating the room temperature at the time of interest.

In the room temperature estimating device, preferably, the predictive formula generator is configured to generate at least a first predictive formula and a second predictive formula respectively corresponding to at least a first designated time and a second designated time as the predictive formula , wherein the first predictive formula is generated based on the room temperature data and the external air temperature data corresponding to the first specified time for each of the days within the extraction time period, and the second predictive formula is based on generated from the room temperature data and the outside temperature data corresponding to the second specified time for each of the plurality of days within the extraction time period, and the room temperature estimating section is configured to: The external air temperature at the first time of interest corresponding to the specified time is applied to the first prediction formula, thereby estimating the room temperature at the first time of interest, and the temperature at the second time of interest corresponding to the second specified time The outside air temperature is applied to the second prediction formula, thereby estimating the room temperature at the second time of interest.

In the room temperature estimating device, preferably, the predictive formula generator is configured to generate a recursive formula as the predictive formula based on room temperature data and outside air temperature data.

In the room temperature estimating device, preferably, the prediction formula generating section is configured to generate the prediction formula by simple linear recursive analysis including outside air temperature data as an independent variable and room temperature data as a dependent variable.

In the room temperature estimating device, preferably, the extraction time period is determined for each divided time period obtained by dividing a year based on the climate environment, and the room temperature estimating unit is configured to The prediction formula generated by extracting the room temperature data and the external air temperature data in the time period determined by the division time period is applied to the prediction of the room temperature in the predetermined division time period.

In the room temperature estimating device, preferably, the room temperature estimating device further includes a correction information obtaining section configured to obtain a condition corresponding to a state selected from a plurality of states other than the outside air temperature that affects the room temperature. Corresponding correction information, wherein, the prediction formula generating part is configured to correct the prediction formula according to the state of the correction information obtained by the correction information obtaining part, thereby generating a corrected prediction formula, and the room temperature estimating part configured to estimate the room temperature based on the corrected prediction formula.

In the room temperature estimating device, preferably, the room temperature estimating device further includes a notification output unit configured to output the room temperature estimated by the room temperature estimating unit to a notifier.

In the room temperature estimating device, preferably, the outside air temperature obtaining unit is configured to obtain outside air temperature data supplied via an electric communication line.

According to a program of the present invention, it is configured to cause a computer to function as any of the room temperature estimating means according to the above.

With the structure of the present invention, the temperature of a room in a building can be estimated based on information that can be easily measured without complicated computer simulations.

Description of drawings

FIG. 1 is a block diagram showing Embodiment 1. As shown in FIG.

FIG. 2 is a diagram for explaining the principle of Embodiment 1. FIG.

FIG. 3 is a diagram for explaining the principle of Embodiment 1. FIG.

FIG. 4 is a block diagram showing Embodiment 2. FIG.

5A and 5B are diagrams for explaining the principle of Embodiment 2. FIG.

FIG. 6 is a block diagram showing Embodiment 3. FIG.

Detailed ways

A technique for estimating the temperature of a room that is not being cooled and heated using the predicted time change of the outside air temperature will be described below. In the state where cooling and heating are not performed, the room temperature depends on factors including the external air temperature, the insulation performance of the room, sunlight (the presence or absence of sunlight and the amount of sunlight), ventilation (the presence or absence of ventilation and the amount of ventilation), Rainfall (presence and amount of rainfall), and the number of people in the room, etc.

The thermal insulation performance of a room is a property specific to a house, and the thermal insulation performance of a room can be roughly estimated based on the building material of the house, the construction method of the house, and the like. However, it is not easy to quantitatively determine the thermal insulation performance of a room. In addition, although the number of people in a room can be counted, it is difficult to theoretically determine the room temperature and the room because the degree of influence on the rise in room temperature varies among individuals depending on the metabolic rate and amount of clothing of those people, etc. The relationship between the number of people in it. In addition, insolation, ventilation, and rainfall can be monitored, but it is not easy to theoretically determine the effect of these factors on room temperature.

That is, the factors that room temperature depends on can be measured, but it is not easy to create an appropriate model that relates these factors to room temperature. Therefore, it is not easy to obtain the room temperature from the measured values of these factors by computer simulation. Furthermore, computer simulations that can estimate the room temperature with a practically required accuracy require input of a large amount of information as well as correction processing. Therefore, estimating the room temperature for each room requires a lot of work by professionals.

A room temperature estimating device that can estimate a room temperature with suitable accuracy based on easily measurable information without computer simulation based on a complicated model will be described below. In Embodiment 1, a technique for estimating room temperature based only on the outside air temperature is described. In Embodiment 2, a technique for estimating the room temperature based on the outside air temperature in consideration of the insulation performance of the room is explained. In Example 3, a technique for estimating room temperature in consideration of the influence of sunlight, ventilation, rainfall, and the number of people in the room will be described.

Example 1

The room temperature estimating device 10 of the present embodiment is configured to estimate the room temperature on the date and time of interest from the outside air temperature based on a prediction formula that correlates the outside air temperature and the room temperature. Therefore, the room temperature estimating device 10 includes a structure configured to generate a prediction formula and a structure configured to estimate the room temperature from the outside air temperature based on the prediction formula.

The room temperature estimating device 10 includes, as main hardware components, a device including a processor configured to execute a program to realize functions described below and a device for an interface. A device including a processor may be a microcomputer having a built-in memory, a processor mounted with an external memory, or the like. Also, a computer that executes a program to realize the functions described below can be used as the room temperature estimating device 10 . These kinds of programs can be provided via a computer-readable storage medium, or provided by communication via an electric communication line.

First, the structure configured to generate the prediction formula in the room temperature estimating device 10 will be explained. In order to generate a prediction formula, it is necessary to measure the room temperature and the outside air temperature while associating them with date and time, respectively. Therefore, as shown in FIG. 1 , the room temperature estimating device 10 includes: a room temperature obtaining unit 11 configured to obtain room temperature data (measured values) from a room temperature measuring unit 21; The measuring unit 22 obtains outside air temperature data (measured value). The room temperature estimating device 10 further includes a storage unit 13 , a timer unit 14 , and a prediction formula generation unit 15 . The storage section 13 is configured to store room temperature data (measured values) and outside air temperature data (measured values) in association with measured dates and times, respectively. The timekeeping section 14 is configured to measure the current date and time. The predictive formula generator 15 is configured to generate a plurality of predictive formulas respectively corresponding to a plurality of times of the day.

The room temperature measuring section 21 is installed inside a room of a building, and is configured to measure the temperature of a place where the room temperature measuring section 21 is installed (ie, measure room temperature). The outside air temperature measurement section 22 is installed outside the building, and is configured to measure the temperature of the place where the outside air temperature measurement section 22 is installed (ie, to measure the outside air temperature).

The room temperature measurement section 21 and the outside air temperature measurement section 22 each include a temperature sensor such as a thermistor configured to generate an analog output reflecting the ambient temperature and a sensor amplifier configured to amplify the output of the temperature sensor. Each of the room temperature measurement section 21 and the outside air temperature measurement section 22 further includes a conversion section configured to convert the output of the sensor amplifier into digital data and a communication section configured to transmit the digital data of the conversion section to the room temperature estimating device 10 .

Each of the room temperature measurement unit 21 and the outside air temperature measurement unit 22 may not include a communication unit or may not include a conversion unit and a communication unit. However, in consideration of correctly transmitting the measured value to the room temperature estimating device 10 , desirably, each of the room temperature measurement section 21 and the outside air temperature measurement section 22 includes a conversion section and a communication section. In the case where the conversion unit is not provided, the room temperature measurement unit 21 and/or the outside air temperature measurement unit 22 supplies the simulation data to the room temperature estimating device 10 .

Desirably, communication between the room temperature measuring section 21 or the outside air temperature measuring section 22 and the room temperature estimating device 10 is performed via a wireless communication channel using radio waves as a transmission medium or via a wired communication channel. The room temperature measuring unit 21 may share a housing with the room temperature estimating device 10 . In the structure in which the room temperature measuring section 21 shares a housing with the room temperature estimating device 10 , the room temperature measuring section 21 does not necessarily include a communication section.

The room temperature data (measured value) obtained by the room temperature obtaining unit 11 and the outside air temperature data (measured value) obtained by the outside air temperature obtaining unit 12 are stored in the storage unit 13 in association with the measured date and time, respectively. . That is, the storage unit 13 is configured to store a set of two types of information (room temperature, date and time) and (outside temperature, date and time), or to store three sets of information (room temperature, outside temperature, date and time). group of information. The latter case is smaller in data volume, and the capacity of the storage section 13 can be saved.

The date and time to be stored in the storage unit 13 are timed by the timekeeping unit 14 provided in the room temperature estimating device 10 . The date and time at which the room temperature data and the outside temperature data are to be obtained are preset in the room temperature obtaining unit 11 and the outside temperature obtaining unit 12 , respectively. The room temperature obtaining unit 11 and the outside air temperature obtaining unit 12 are configured to obtain room temperature data and outside air temperature data at preset dates and times based on the current date and time timed by the timekeeping unit 14 . In this structure, it is desirable that the storage section 13 is configured to store three sets of information (room temperature, outside air temperature, date and time).

For example, each of the room temperature obtaining section 11 and the outside air temperature obtaining section 12 is configured to obtain data for every hour. For example, each of the room temperature obtaining section 11 and the outside air temperature obtaining section 12 is configured to obtain data at respective hours. The room temperature obtaining part 11 and the external air temperature obtaining part 12 need not be configured to obtain data for every hour, but can be for every 10 minutes, every 15 minutes, every 30 minutes or every two hours, etc. (these time intervals can be selected as required, wherein 1) Get the data. The shorter the time interval, the greater the amount of information obtained, which will improve the estimation accuracy of the forecasting formula. However, this leads to an increase in the amount of data to be stored in the storage section 13 . Therefore, it is preferable to set the time interval for obtaining data to a time period of about 1 hour and to be set within a range of a fraction of an hour to several hours. Preferably, each time interval for obtaining data by the room temperature obtaining section 11 and the outside air temperature obtaining section 12 is set to a value obtained by dividing 24 hours by an integer.

Each of the room temperature measuring section 21 and the outside air temperature measuring section 22 may include a dedicated timekeeping section configured to measure the current date and time. In this structure, the room temperature measurement unit 21 and the outside air temperature measurement unit 22 are configured to obtain room temperature data and outside temperature data based on the date and time timed by their own timekeeping units, respectively, and to send the obtained data to the room temperature Estimation means 10. In other words, the room temperature measuring unit 21 and the outside air temperature measuring unit 22 are configured to send the respective room temperature data and outside air temperature data to the room temperature estimating device in a manner associated with the date and time timed by their own timekeeping units. 10.

In this structure, it is desirable that the storage section 13 is configured to store two sets of two kinds of information, (room temperature, date and time) and (outside air temperature, date and time). Note that each of the room temperature measuring section 21 and the outside air temperature measuring section 22 is not limited to be configured to transmit room temperature data or outside air temperature data when the room temperature or outside air temperature is measured, but may also be configured to transmit a set of data for half a day or a day.

It is expected that where there is a difference between the date and time at which room temperature data was obtained and the date and time at which outside air temperature data was obtained, if the difference is less than half of the data acquisition interval (e.g., less than 1/10 of the data acquisition interval), then The data are deemed to have been obtained and associated with the same date and time.

Incidentally, in the case where the room temperature remains unaffected by sunlight and the fluctuation of the outside air temperature is small, the thermal energy introduced into the room and the thermal energy released from the room will be balanced. Therefore, in this case, it can be assumed that the external air temperature and the room temperature at the same time every day show a linear relationship.

The inventors have measured room temperature and outside air temperature at various times of day over a relatively long period of time. Then, by graphically analyzing the relationship between the room temperature and the outside air temperature each for a plurality of times, as shown in FIG. 2 , the present inventors found that the outside temperature and the room temperature showed a linear relationship at a given time. That is, it was found that the room temperature at a given time can be expressed by a prediction formula including a linear function of the outside air temperature as a variable, and the room temperature can be estimated from the outside air temperature based on the prediction formula. Specifically, it was found that the outside air temperature and the room temperature measured at the first specified time of each of the multiple days showed a linear relationship, and also the measured temperature at the second specified time of each of the multiple days The outside air temperature and room temperature show a linear relationship.

Furthermore, in the room temperature estimating device 10 of the present embodiment, the prediction formula generating section 15 is configured to generate a prediction formula based on the outside air temperature and the room temperature at respective designated times on a plurality of days. The prediction formula generating section 15 is configured to extract the room temperature data and the outside air temperature data corresponding to the respective designated times of a plurality of days within a given extraction time period stored in the storage unit 13, so as to extract the data corresponding to the same times of each of the plurality of days based on the data stored in the storage unit 13. The corresponding room temperature data and the external air temperature data generate a recursive formula, and use the recursive formula as a prediction formula. Specifically, the predictive formula generating section 15 is configured to generate the data representing A prediction formula for the relationship between room temperature data and outside temperature data at a given time.

Since the forecast formula is expected to be written as a linear function of the outside air temperature, the room temperature data and the outside air temperature data used to generate the forecast formula should include data for more than three days. Therefore, the extraction time period should be three days or more, and may be selected from a range of 15 to 90 days, for example. The lower limit of the range, 15 days, corresponds to one solar term (half month) in the 24 seasons of the solar year, and the upper limit of the range, 90 days, corresponds to a season (ie, spring, summer, autumn or winter). The number of days in this period is an example, and may be 30 days (about one month), or may be one year if the change in the outside air temperature is not large within one year. The acquisition days of the data used to generate the prediction formula may include a plurality of consecutive days or may be a plurality of discontinuous days. For example, a prediction formula may be generated based on the measured room temperature data and outside air temperature data for every day, every two days, or every week for one to several years.

The prediction formula generating section 15 is configured to generate A prediction formula is generated using the formula "θ1(t)=α*θ2(t)+β". In order to generate the prediction formula, a linear function is generated from the room temperature data θ1(t) and the outside air temperature data θ2(t) based on a known calculation method such as the least square method. That is, the prediction formula generation section 15 is configured to generate a recursive prediction formula from the room temperature data θ1(t) and the outside air temperature data θ2(t) corresponding to the time of interest (specified time) within the extraction period.

The recursive prediction formula includes the outside air temperature at a specified time as an explanatory variable and the room temperature at the specified time as a dependent variable. In other words, the prediction formula generation section 15 is configured to generate a prediction formula by simple linear recursive analysis including the outside air temperature data as an independent variable and the room temperature data as a dependent variable. The time of day (designated time) used to generate the recursive prediction formula is selected from the time zone in which the room temperature is not affected by sunlight and depends only on the outside air temperature, and the change in the outside air temperature is relatively ease. The prediction formula generation section 15 is configured to adopt the generated recursive prediction formula as a prediction formula for determining the room temperature from the outside air temperature.

The prediction formula generating section 15 is configured to generate a plurality of recursive prediction formulas respectively corresponding to a plurality of times of the day. The prediction formula generation section 15 is configured to generate these recursive prediction formulas as prediction formulas for each time of day. Specifically, the prediction formula generating section 15 is configured to generate a plurality of prediction formulas respectively corresponding to a plurality of designated times of the day. Each prediction formula is generated based on room temperature data and outside air temperature data corresponding to a designated time. Specifically, the prediction formula generator 15 is configured to generate at least a first prediction formula and a second prediction formula corresponding to at least a first designated time and a second designated time, respectively. The first prediction formula is generated based on the room temperature data and the outside air temperature data corresponding to the respective first specified times of the plurality of days within the extraction time period. The second prediction formula is generated based on the room temperature data and the outside air temperature data corresponding to the second designated times of the plurality of days in the extraction time period.

The room temperature estimating device 10 generates a plurality of prediction formulas respectively corresponding to a plurality of times of the day in the above-described manner, and then estimates the room temperature from the outside air temperature based on the prediction formulas. Specifically, the room temperature estimating device 10 generates (at least) a first prediction formula corresponding to a first designated time and a second prediction formula corresponding to a second designated time. The room temperature estimating device 10 estimates the room temperature at a time corresponding to the first designated time using the first prediction formula, and estimates the room temperature at the time corresponding to the second designated time using the second prediction formula.

For example, the room temperature estimating device 10 generates a first prediction formula based on the room temperature data and the external air temperature data obtained at 4:00 am (the first designated time) on each of the days, and based on the data at 5:00 am (the second specified time) on the days. The room temperature data and the external air temperature data obtained at a specified time) are used to generate the second prediction formula. The room temperature estimating device 10 uses the first prediction formula to estimate the room temperature at 4 o'clock in the morning (corresponding to the first execution time) of a certain day, and uses the second prediction formula to estimate the room temperature at 5 o'clock in the morning of a certain day (corresponding to the second designated execution time). The room temperature at the time corresponding to the time.

The structure configured to estimate the room temperature from the outside air temperature in the room temperature estimating device 10 will be described in detail below. The room temperature estimating device 10 includes a predicted time change obtaining unit 16 and a room temperature estimating unit 17 . The predicted time change obtaining section 16 is configured to obtain the predicted time change of the outside air temperature based on the time series of outside air temperature data (measured values) obtained by the outside air temperature obtaining section 12 from the outside air temperature measuring section 22 . The room temperature estimating section 17 is configured to estimate room temperature using temporal changes in outside air temperature.

The predicted time change obtaining section 16 is configured to apply the time series of the outside air temperature data to any of a plurality of types of templates of time changes in the outside air temperature registered in advance, and predict the outside air temperature based on the applied template. time changes. The predicted time change obtaining section 16 is configured to limit the template to be applied in consideration of the weather and/or season of the day when applying the time series of outside air temperature data to any of the templates of time change of outside air temperature.

With regard to the predicted change in the outside air temperature, instead of the outside air temperature data (measured value) obtained by the outside air temperature obtaining section 12 from the outside air temperature measuring section 22, the outside air temperature obtaining section 12 may be obtained via an electric communication line such as the Internet or the like. The temporal variation of the external air temperature. That is, the outside temperature obtaining section 12 may have a function configured to obtain outside temperature data from a service provider providing local weather information via an electric communication line. In this configuration, the predicted temporal change obtaining unit 16 uses the outside temperature data obtained by the outside temperature obtaining unit 12 from the service provider.

The outside air temperature data supplied via the electric communication line is data related to a specific position in an area where a target room whose room temperature is to be estimated exists, not the outside air temperature corresponding to the target room. However, it is expected that the provided data will have a linear relationship with the outside air temperature of the room. Therefore, the room temperature estimating section 17 is configured to correct the room temperature estimated using the supplied outside air temperature based on the actual measured value of the room temperature. As a result, the room temperature can be appropriately estimated based on the outside air temperature data supplied via the electric communication line.

The room temperature estimating section 17 specifies the outside air temperature at the date and time of interest based on the predicted time change of the outside air temperature obtained by the predicted time change obtaining section 16 . When the outside air temperature is specified, the room temperature estimating unit 17 estimates the room temperature by applying the specified outside air temperature to the prediction formula generated by the prediction formula generation unit 15 . In short, the room temperature estimating section 17 is configured to estimate the room temperature at the date and time of interest by: determining the outside air temperature at the date and time at which the room temperature is to be estimated based on the predicted time change of the outside air temperature; The outside air temperature of is applied to the prediction formula.

Desirably, the room temperature estimating device 10 includes a notification output section 18 configured to output the room temperature estimated by the room temperature estimating section 17 to the notifier 23 . The notifier 23 may be a dedicated device including a display, or a device including display and communication functions such as a smartphone, a tablet, and a personal computer. In the case of employing these devices as the notifier 23, the notification output section 18 is configured to communicate with these devices. The notifier 23 may be integrally provided in the casing of the room temperature estimating device 10 like the notifier 23 shown by a dotted line in FIG. 1 .

The room temperature estimated by the room temperature estimating unit 17 is not only notified to the user via the notifier 23, but also used to control devices such as ventilation fans, air conditioners, electric blinds, electric curtains, and electric windows that may affect the room temperature. In the case of controlling the heating and/or cooling operation of the cooling and heating equipment (air conditioner), by using the estimated room temperature based on the temporal change of the outside air temperature, it is possible to determine an appropriate timing at which the cooling and heating equipment should be turned off. As a result, energy consumed for cooling and heating can be saved.

For example, in summer, in the case where it is predicted that the room temperature can be kept at a comfortable level without operating the cooling device due to a drop in the room temperature at night, if the timing to turn off the cooling device is determined, useless operation of the cooling device can be prevented to save energy. Likewise, in winter, if it is predicted that the room temperature can be kept at a comfortable level without operating the heating device due to an increase in the room temperature during the day, if the timing to turn off the heating device is determined, it is possible to prevent the heating device from useless operations to save energy.

It is easy to assume that the formula representing the relationship between the outside air temperature data and the room temperature data changes depending on the season. For example, in FIG. 2 , the data points on the left show the relationship between the room temperature and the outside air temperature in winter, and the data points on the right show the relationship between the room temperature and the outside air temperature in summer. Looking at the graph, it appears that the data points in the left group and the data points in the right group can be represented by a single linear function. However, as shown in FIG. 3, by separately generating linear functions from the points in the left group (shown with squares in FIG. 3) and the points in the right group (shown with triangles in FIG. 3), in these Different prediction formulas (shown with straight lines) were obtained between the groups.

Therefore, it is desirable to determine the extraction period for measuring the room temperature and the outside air temperature used to generate the prediction formula for each season. Therefore, in this structure, the extraction time period is given for each divided time period obtained by dividing the time period of one year. Desirably, the length of the divided time period corresponds to a time period appropriately selected from the range of 4 to 24 divisions of a year (a time period obtained by dividing a time period of a year based on the climate environment) (in " In the case of "4 divisions of a year", the divided time periods reflect the four seasons of spring, summer, autumn and winter; and in the case of "24 divisions of a year", each divided time period corresponds to half a month). The length of the divided time period can be set to 15 to 90 days, and the extraction time period for each divided time period can be more than three days. For example, these divided time periods and their extraction time periods are stored in the storage section 13 in advance.

In an example, the prediction formula generating section 15 is configured to generate a plurality of prediction formulas corresponding to the number of divided time periods. Specifically, the prediction formula generation unit 15 is configured to generate a plurality of prediction formulas respectively corresponding to a plurality of times of the day for each divided time period. The room temperature estimating section 17 is configured to, in the case of estimating the room temperature on a certain day and time, select a prediction formula corresponding to the time of interest in the divided time period to which the date of interest belongs, from a plurality of prediction formulas generated for each divided time period. corresponding prediction formula, and estimate the room temperature according to the time change of the outside air temperature based on the selected prediction formula.

In another example, the prediction formula generator 15 is configured to change from one divided time period to the next determined based on the date and time timed by the timekeeping unit 14 (for example, from "summer" to When the time zone of "winter" is changed to the time zone of "winter", a plurality of prediction formulas respectively corresponding to a plurality of times of the day are newly generated. After generating the prediction formula, the room temperature estimating unit 17 estimates the room temperature based on the newly generated prediction formula.

Example 2

In Embodiment 1, the prediction formula generating unit 15 generates prediction formulas each based on the room temperature and the outside air temperature measured in the time zone in which the room temperature is not affected by sunlight. Therefore, the time zone in which the prediction formula can be applied to estimate the room temperature from the outside air temperature is limited. In other words, based on the prediction formula generated according to Embodiment 1, the room temperature in the time zone in which the room temperature is affected by sunlight cannot be accurately estimated. The prediction formula according to Embodiment 1 can be applied only to a time zone in which the change of the outside air temperature is relatively moderate, such as the time period from midnight to early morning.

In this embodiment, a technique for determining a prediction formula suitable for a daytime time zone in which room temperature is affected by sunlight is described. Therefore, the prediction formula generated according to the technique of Embodiment 1 is used for the night time zone where the influence of sunlight is negligible, and the prediction formula described below is used for the daytime when the influence of sunlight should be considered. That is, in this embodiment, different kinds of prediction formulas are employed for the time zone in which the room temperature will not be affected by sunlight (ie, the time zone in which there is no sunlight) and the time zone in which the room temperature will be affected by sunlight. In the room temperature estimating device 10 of the present embodiment, the prediction formula generation section 15 has a function configured to generate two kinds of prediction formulas.

As shown in FIG. 4 , the room temperature estimating device 10 includes a first prediction formula generation unit 151 and a second prediction formula generation unit 152 . The first prediction formula generating section 151 is configured to generate a prediction formula (a first type of prediction formula) based on the same technique as that in Embodiment 1. The second prediction formula generating section 152 is configured to generate a prediction formula (a second type of prediction formula) based on a technique described below.

The first prediction formula generation section 151 is configured to generate prediction formulas in the same manner as the prediction formula generation section 15 in Embodiment 1. The first prediction formula generating section 151 is configured to generate a recursive prediction formula based on the room temperature data and the outside air temperature data corresponding to the respective specified times of multiple days stored in the storage section 13, and adopt the generated recursive prediction formula as Forecast formula.

On the other hand, the second prediction formula generating section 152 is configured to generate a prediction using formula. It will be assumed that the room temperature depends only on the outside air temperature, and a model will be created in which heat is transferred via partition walls of the room such as walls, ceilings, and floors, and the room temperature changes according to changes in the outside air temperature. In this model, the influence of the outside air temperature on the room temperature will vary according to the degree of heat conduction of the partition wall and the degree of heat storage of the partition wall. In this embodiment, the temperature of the air in the room is regarded as room temperature, and radiant heat from the partition wall is not considered.

According to the above model, the room temperature will change later than the outside air temperature. The present inventors checked the experimental results, and found the following: there is a special relationship between the change of the outside air temperature and the change of the room temperature; the change of the room temperature is delayed by a delay time compared with the change of the outside air temperature; The thermal properties of the partition (such as thermal insulation and thermal storage properties, etc.). Furthermore, the present inventors have found the following: by determining the delay time, the relationship between the room temperature at a designated time and the outside air temperature at a time shifted by the delay time from the designated time can be expressed by a simple prediction formula, and can be based on This prediction formula estimates the room temperature at a desired time from the outside air temperature.

FIG. 5A is a graph showing points showing the relationship between room temperature and outside air temperature, each measured on the same date and time. Looking at the graph, it appears that there is no relationship between room temperature and outside air temperature. In contrast, as described above, the present embodiment is realized on the assumption that there is a correlation between the time change of room temperature and the time change of outside air temperature when a delay time depending on the thermal characteristics of the room is set.

Therefore, the room temperature estimating device 10 of the present embodiment includes an evaluation section 19 configured to be based on the room temperature data (measured value), the outside air temperature data (measured value) and the associated date and date stored in the storage section 13. time to determine the delay time for which the correlation coefficient between the room temperature and the outside air temperature becomes maximum. The evaluation section 19 is configured to, based on the room temperature data and the outside air temperature data in a specific period of interest (hereinafter referred to as "extraction period"), by making the date and time of measuring the room temperature data and the date and time of measuring the outside temperature data where One of the relative offsets is used to determine the delay time (hereinafter referred to as "optimum time difference") that maximizes the correlation coefficient between the room temperature data and the outside temperature data. The extraction time period is not limited to one day, but may be multiple days. In the example described below, the date and time of measuring the outside air temperature are shifted from the date and time of measuring the room temperature. However, the reverse case is also possible in which the date and time of measuring the room temperature are shifted from the date and time of measuring the outside air temperature.

In this example, "θ1(t)" and "θ2(t)" are used to denote the room temperature data and the outside air temperature data corresponding to a specific date and time "t", respectively. The data acquisition interval of the room temperature data "θ1(t)" or the outside air temperature data "θ2(t)" is represented by "p". A specific date and time "t" is represented by the formula "t=t0+n*p", and a specific time difference "Δt" is represented by the formula "Δt=m*p", where "t0" is based on the "extraction period" The determined reference value, and "m" and "n" are each a natural number.

According to the above notation, use "θ1(t0+p)", "θ1(t0+2p)", "θ1(t0+3p)", ... to represent room temperature data, and use "θ2(t0+p)", "θ2(t0+2p)", "θ2(t0+3p)", . . . represent the outside air temperature data. Use the formula "θ2(t0+n*p-Δt)=θ2(t0+(n-m)p)" to indicate that the date and time of the room temperature data represented by "θ1(t0+n*p)" is earlier than the time difference" Δt” date and time of outside air temperature data.

The following considers a time period defined as a specific extraction time period of "[t0+p, t0+q*p]". In this case, the average value "a(θ1)" of the room temperature data θ1(t) in the period of the extraction period is calculated as the room temperature data {θ1(t0+p), θ1(t0+2p), ..., the average value of θ1(t0+q*p)}. Calculate the average value "a(θ2)" of the external temperature data θ2(t) in the time period earlier than the "extraction time period" by "time difference Δt" as the external temperature data {θ2(t0+(1-m)p ), the average value of θ2(t0+(2-m)p), ..., θ2(t0+(q-m)p)}. Wherein, [t0+p, t0+q*p] represents a specific closed interval, and includes q discrete values of {t0+p, t0+2p, t0+3p,...,t0+q*p}.

The evaluation section 19 is configured to calculate, based on these values, the room temperature data θ1(t) corresponding to specific dates and times "t" and the date and date earlier than these specific dates and times by a time difference Δt (=m*p). The correlation coefficient between the external air temperature data θ2(t-Δt) corresponding to the time "t-Δt". The correlation coefficient can be calculated using a known calculation method, and is obtained by dividing the covariance of the data θ1(t) and the data θ2(t-Δt) by the standard deviation of the data θ1(t) and the data θ2( The product of the standard deviation of t-Δt) is obtained. The covariance and the standard deviations are calculated using the above-mentioned mean values "a(θ1)" and "a(θ2)". The range of the variable "t" representing the date and time of the room temperature data θ1(t) and the outside air temperature data θ2(t-Δt) is within the period of the extraction period (i.e., the closed interval [t0+p, t0+q*p ])Inside.

The evaluation section 19 is configured to calculate a correlation coefficient for each value of the numerical value "m" in the case of changing the value of the numerical value "m" to change the time difference Δt. In this embodiment, the maximum value of the value "m" is limited so that the product "m*p" of the value "m" and the time interval "p" does not exceed the length of one day. For example, in the case where the time interval "p" corresponds to one hour, the maximum value of the value "m" is limited so as not to exceed "24". The evaluation section 19 is configured to determine the value "mm" of the numerical value "m" corresponding to the largest correlation coefficient. The evaluation unit 19 determines the optimal time difference "Δt _A " by the formula "Δt _A =mm*p".

5B shows points each of which is the relationship between the room temperature data θ1(t) and the outside air temperature data θ2(t−Δt _A ) of the time difference (optimum time difference) Δt _A determined by the evaluation unit 19 . In this illustrative example, it can be found that there is a linear relationship between the room temperature data θ1(t) and the outside air temperature data θ2(t-Δt _A ), and this relationship between the two can be represented by a linear function.

The second prediction formula generation section 152 is configured to generate a prediction formula for estimating the room temperature from the outside air temperature based on the relationship shown in FIG. 5B . The second prediction formula generation part 152 is configured to extract each data within the extraction time period from the room temperature data and the outside air temperature data stored in the storage part 13, and extract the time difference (optimum time difference) determined by the evaluation part 19 Δt _A is provided up to the date and time corresponding to the extracted outside air temperature data. Furthermore, on the assumption that the room temperature data θ1(t) and the outside air temperature data θ2(t-Δt _A ) have a linear relationship, the second prediction formula generating section 152 is configured to use the formula "θ1(t)=α*θ2( t-Δt _A )+β" is used to represent the prediction formula, and the coefficients "α", "β" of the formula are determined using a known calculation method such as the least square method. Specifically, the second prediction formula generation section 152 generates a prediction formula by simple linear recursive analysis including the outside air temperature data set with the time difference Δt _A as an independent variable and the room temperature data as a dependent variable. In this way, the evaluation unit 19 determines the time difference Δt _A and the second prediction formula generation unit 152 determines the coefficients “α”, “β”, and as a result, a prediction formula (a second type of prediction formula) can be generated. Note that the coefficients “α”, “β” in this formula are generally different from the coefficients “α”, “β” in the prediction formula generated by the first prediction formula generating section 151 .

That is to say, according to the second prediction formula generation part 152, the evaluation part 19 determines the time difference (optimum time difference) based on the room temperature data and the outside air temperature data in a given extraction time period stored in the storage part 13, and then the second prediction The formula generating section 152 generates a prediction formula based on one of the room temperature data and the outside air temperature data in which a time difference is set.

The prediction formula generated by the second prediction formula generation unit 152 is applied regardless of the influence of sunlight on the room temperature. Furthermore, room temperature can be estimated with a single prediction formula regardless of the time of day. However, with regard to the time zone in which the room temperature is not affected by sunlight, it is possible to use the prediction formula generated by the first prediction formula generation section 151 (the first type of prediction formula) with appropriate accuracy (possibly at a higher rate than the second prediction formula generation section 152 The precision of the generated prediction formula is high, and the room temperature is estimated from the outside air temperature.

Therefore, it is desirable to adopt the prediction formula generated by the first prediction formula generation part 151 for the time zone in which the room temperature can be estimated using the prediction formula generated by the first prediction formula generation part 151, and to use the second prediction for other time zones. The prediction formula generated by the formula generating unit 152 shares the roles of both. Specifically, for the time zone where the room temperature will not be affected by sunlight (that is, the time zone without sunshine), the prediction formula (first type of prediction formula) generated by the first prediction formula generation unit 151 is used, and for the room temperature that will be affected by sunlight The time zone of influence is the prediction formula (second type of prediction formula) generated by the second prediction formula generation unit 152 .

In the case of estimating the room temperature from the outside air temperature based on the prediction formula generated by the second prediction formula generation unit 152, the room temperature estimation device 10 needs to obtain the time difference determined by the evaluation unit 19 (delay time) earlier than the date and time of interest. ) Δt _A date and time outside air temperature data. Note that the "date and time of interest" is the date and time at which the room temperature is to be estimated.

Therefore, the room temperature estimating unit 17 determines that the time difference ( The outside air temperature (measured or predicted) for the date and time of the best time difference). When the outside air temperature is specified, the room temperature estimating unit 17 estimates the room temperature by applying the specified outside air temperature to the prediction formula generated by the prediction formula generation unit 15 . In short, the room temperature estimating section 17 is configured to determine, based on the predicted time change of the outside air temperature, the outside air temperature at a time point earlier than the date and time at which the room temperature is to be estimated by the time difference determined by the evaluation section 19; and The determined outside air temperature is applied to the prediction formula and the room temperature for the date and time of interest is estimated therefrom.

As described above, the prediction formula generation section 15 of the present embodiment includes the first prediction formula generation section 151 and the second prediction formula generation section 152 . The room temperature estimating unit 17 is configured to determine whether the current time is in a time zone in which the room temperature is affected by sunlight or in a time zone in which the room temperature is not affected based on the date and time measured by the timekeeping unit 14 . The prediction formula generated by the first prediction formula generation unit 151 is used for the time zone in which the room temperature is not affected by sunlight, and the prediction formula generated by the second prediction formula generation unit 152 is used for the time zone in which the room temperature is affected by sunlight.

As described in Embodiment 1, the prediction formula generated by the first prediction formula generation unit 151 changes according to the season. It is also easy to assume that the prediction formula generated by the second prediction formula generation section 152 changes according to the season. Therefore, it is desirable to determine the extraction period for measuring the room temperature and the outside air temperature used to generate the prediction formula for each season.

Therefore, the divided time period obtained by dividing the time period of one year is defined, and the extracted time period is given for each divided time period. The length of the split time period is appropriately selected from the range of 4～24 divisions of a year (in the case of "4 divisions of a year", the split time period reflects the four seasons of spring, summer, autumn and winter; In the case of 24 divisions", each division period corresponds to half a month).

In this configuration, the second prediction formula generation unit 152 generates prediction formulas whose number corresponds to the number of divided time slots. The room temperature estimating section 17 selects a prediction formula corresponding to the division time period to which the date and time of interest belongs from among a plurality of prediction formulas determined for each division time period, and uses the selected prediction formula, the Changes to estimate room temperature.

It should be noted that the thermal characteristics of the room may change due to aging. Therefore, desirably, the room temperature estimating section 17 is configured to employ the time difference determined for each divided period when estimating the room temperature. Specifically, it is desirable that the evaluation unit 19 newly determines the time difference (optimum time difference) Δt _A each time the divided time period elapses, and the second prediction formula generation unit 152 newly generates the prediction formula corresponding to the newly determined time difference Δt _A (the second prediction formula). two prediction formulas), and the room temperature estimating section 17 estimates the room temperature based on the newly generated prediction formula (second prediction formula). However, the room temperature estimating section 17 may be configured to estimate the room temperature based on the time difference determined for any divided period. The room temperature may also be estimated based on an average value of time differences determined for a plurality of divided time periods.

As described above, the room temperature estimating section 17 in this embodiment uses different kinds of prediction formulas depending on whether the room temperature is affected by sunlight. In addition, the outside air temperature to be applied differs depending on the type of prediction formula. Therefore, the prediction accuracy of room temperature can be improved. Other structures and operations are the same as those in Embodiment 1.

Example 3

In Embodiment 1 and Embodiment 2, the room temperature estimating device 10 is configured to estimate the room temperature based only on the outside air temperature. However, as mentioned above, in the absence of cooling and heating, the room temperature depends on factors including sunlight, ventilation, rainfall, and the number of people in the room. Note that if cooling and heating is performed using a cooling and heating device with a function of controlling the room temperature, the temperature in the room depends on the operating state of the cooling and heating device, so the room temperature cannot be predicted by the prediction formula. Therefore, in the following description, it is assumed that cooling and heating are not performed.

In order to consider information of insolation, ventilation, rainfall, and existing number of people in addition to the outside air temperature, creation of a model for associating each information with room temperature is considered, and numerical values related to each information are applied to the model. However, such models require sophisticated computer simulations due to the complex causal relationships among these factors. As a result, such models require the input of a large number of parameters and generate an enormous processing load.

Therefore, in order to prevent an increase in the number of parameters and an increase in processing load, the present embodiment regards each information as correction information, limits the number of possible states of various correction information, and determines a prediction formula for each state of correction information. In the case where there are multiple types of correction information, the predictive formula generating section 15 classifies the status of the correction information into a plurality of levels for each type of correction information. Then, the predictive formula generating section 15 generates predictive formulas (corrected predictive formulas) each corresponding to a specific combination of levels of multiple types of correction information. The technical solution of the present embodiment will be described as being applied to the example of the structure of the first embodiment shown in FIG. 1 , but the technical solution of the present embodiment can also be applied to the structure of the second embodiment.

In this embodiment, two levels (with and without) are respectively defined for sunshine, ventilation and rainfall. Conversely, regarding the number of people present, it is assumed that the room temperature is raised by a predetermined temperature value (for example, 0.5° C.) for each person. By simplifying the kinds of correction information and limiting the number of possible states of various correction information, the number of combinations of individual correction information is limited and relatively small.

The prediction formula generating section 15 is configured to set a prediction formula according to combinations of respective states of various correction information. The number of people in the room is only reflected in the coefficient "β" of the prediction formula. Therefore, it is not necessary to generate different prediction formulas depending on the number of people. Regarding the correction according to the number of people, the room temperature estimating section 17 may be configured to add the product of the existing number of people and the predetermined temperature to the room temperature estimated by the prediction formula. Therefore, in the above-described example, based on these kinds of correction information on insolation, ventilation, and rainfall, eight prediction formulas are generated.

The prediction formula generation section 15 is configured to correct the coefficients "α" and "β" of the prediction formula according to the state of each correction information, and thereby generate the corrected prediction formula. For example, the correction amounts of the coefficients “α” and “β” are associated with the states of the respective correction information and stored in the storage section 13 . In the case where a kind of correction information is in a specific state (for example, in the case of ventilation), the predictive formula generation section 15 reads out from the storage section 13 the values of the coefficients "α" and "β" corresponding to the specific state. correction amount, and apply the read correction amount to the coefficients "α" and "β" of the prediction formula and thereby generate a corrected prediction formula.

As shown in FIG. 6 , the temperature estimating device 10 of this embodiment includes a correction information obtaining part 32, wherein the correction information obtaining part 32 is configured to detect The section 36 acquires each correction information.

The sunlight detecting section 33 may include a photodetector such as a photodiode and a phototransistor, and a judging section configured to compare the output of the photodetector with a threshold to judge the amount of light. The effect of sunlight on a room depends on whether the curtains and/or roller blinds are open or closed. Therefore, it is desirable that the sunshine detection section 33 has a function configured to detect whether the curtain and/or the roller blind is opened or closed.

The ventilation detecting part 34 may be configured to detect whether the ventilation fan is working, and/or detect whether the window is opened or closed and/or measure the airflow in the room. The rain detection part 35 may be configured to collect rainwater for each specific time period to measure the weight of the collected rainwater, and/or detect the presence or absence of raindrops from an outdoor image. Correction information related to rainfall may be obtained from information provided by a service provider via an electrical communication link such as the Internet. The number detection section 36 may be configured to measure the number of people in the room based on the indoor image.

Instead of only two levels of "present" and "absent", the states of correction information related to sunshine, ventilation, and rainfall may be classified into three or more levels according to degrees. The state of sunlight can be classified into, for example, four levels of "strong", "medium", "weak" and "weak". Also, the state of ventilation and/or rainfall may be classified into three or more levels.

The room temperature estimating section 17 corrects the prediction formula based on the correction information obtained by the correction information obtaining section 32 to generate a corrected prediction formula, and estimates the room temperature from the outside air temperature based on the corrected prediction formula. Note that the correction amounts of the coefficients "α" and "β" corresponding to the respective levels of sunshine, ventilation, rainfall, and number of people can be determined statistically based on actual measurement values. Other structures and operations are the same as those in Embodiment 1 or Embodiment 2.

Claims (9)

1. A room temperature estimation device, comprising: a room temperature obtaining unit configured to obtain room temperature data; an external air temperature obtaining unit configured to obtain external air temperature data; a storage unit configured to store the room temperature data obtained by the room temperature obtaining unit and the outside air temperature data obtained by the outside air temperature obtaining unit in association with measured dates and times, respectively; a prediction formula generation section configured to generate, based on the room temperature data and the outside air temperature data corresponding to each specified time on a plurality of days within a predetermined extraction time period stored in the storage section, generating a formula representing the specified time. A prediction formula for the relationship between room temperature data and outside air temperature data; a predicted time change obtaining section configured to obtain a predicted time change of the outside air temperature; and a room temperature estimating section configured to apply the outside air temperature at the time of interest corresponding to the designated time to the prediction formula based on the time change of the outside air temperature obtained by the predicted time change obtaining section, thereby estimating The room temperature at the time of interest. 2. The room temperature estimating device according to claim 1, wherein: The predictive formula generation section is configured to generate at least a first predictive formula and a second predictive formula respectively corresponding to at least a first specified time and a second specified time as the predictive formula, wherein the first predictive formula is based on generated from the room temperature data and the outside air temperature data corresponding to the first specified time for each of the days within the extraction time period, and the second prediction formula is based on the generated from the room temperature data and the outside air temperature data corresponding to the second specified time, respectively, and The room temperature estimating unit is configured to: applying the outside air temperature at a first time of interest corresponding to the first specified time to the first prediction formula, thereby estimating the room temperature at the first time of interest, and Applying the outside air temperature at a second time of interest corresponding to the second designated time to the second prediction formula, thereby estimating the room temperature at the second time of interest. 3. The room temperature estimating device according to claim 1 or 2, wherein the prediction formula generation section is configured to generate a recursive formula as the prediction formula from room temperature data and outside air temperature data. 4. The room temperature estimating device according to any one of claims 1 to 3, wherein: The extraction time period is determined for each division time period obtained by performing multi-division of a year based on the climate environment, and The room temperature estimating section is configured to apply a prediction formula generated based on the room temperature data and the outside air temperature data within the extraction time period determined for a predetermined division time period to the prediction of the room temperature in the predetermined division time period. . 5. The room temperature estimating device according to any one of claims 1 to 4, further comprising a correction information obtaining unit configured to obtain the sum of factors affecting the room temperature other than the outside air temperature from a plurality of states The correction information corresponding to a state selected in , wherein the prediction formula generating section is configured to correct the prediction formula according to the state of the correction information obtained by the correction information obtaining section, thereby generating a corrected prediction formula, and The room temperature estimating section is configured to estimate a room temperature based on the corrected prediction formula. 6. The room temperature estimating device according to any one of claims 1 to 5, further comprising a notification output unit configured to output the room temperature estimated by the room temperature estimating unit to a notifier . 7. The room temperature estimating device according to any one of claims 1 to 6, wherein the outside air temperature obtaining section is configured to obtain outside air temperature data supplied via an electric communication line. 8. The room temperature estimating device according to claim 3, wherein the prediction formula generation section is configured to generate the prediction formula by simple linear recursive analysis including outside air temperature data as an independent variable and room temperature data as a dependent variable . 9. A program configured to cause a computer to function as the room temperature estimating device according to any one of claims 1 to 8.