JP2014134360A - Room temperature estimation device and program - Google Patents

Room temperature estimation device and program Download PDF

Info

Publication number
JP2014134360A
JP2014134360A JP2013003586A JP2013003586A JP2014134360A JP 2014134360 A JP2014134360 A JP 2014134360A JP 2013003586 A JP2013003586 A JP 2013003586A JP 2013003586 A JP2013003586 A JP 2013003586A JP 2014134360 A JP2014134360 A JP 2014134360A
Authority
JP
Japan
Prior art keywords
room temperature
unit
prediction formula
temperature
outside air
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
JP2013003586A
Other languages
Japanese (ja)
Other versions
JP6160945B2 (en
Inventor
Atsushi Mitsuse
農士 三瀬
Naoki Muro
室  直樹
Keiichi Maruyama
敬一 丸山
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Panasonic Corp
Original Assignee
Panasonic Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Panasonic Corp filed Critical Panasonic Corp
Priority to JP2013003586A priority Critical patent/JP6160945B2/en
Priority to PCT/JP2014/000056 priority patent/WO2014109290A1/en
Priority to CN201480004442.1A priority patent/CN104919252B/en
Priority to EP14738327.7A priority patent/EP2944891B1/en
Publication of JP2014134360A publication Critical patent/JP2014134360A/en
Application granted granted Critical
Publication of JP6160945B2 publication Critical patent/JP6160945B2/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24HEATING; RANGES; VENTILATING
    • F24FAIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00Control or safety arrangements
    • F24F11/30Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24HEATING; RANGES; VENTILATING
    • F24FAIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00Control or safety arrangements
    • F24F11/62Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24HEATING; RANGES; VENTILATING
    • F24FAIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00Control or safety arrangements
    • F24F11/89Arrangement or mounting of control or safety devices
    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24HEATING; RANGES; VENTILATING
    • F24FAIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00Control or safety arrangements
    • F24F11/62Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F24F11/63Electronic processing
    • F24F11/64Electronic processing using pre-stored data
    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24HEATING; RANGES; VENTILATING
    • F24FAIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2110/00Control inputs relating to air properties
    • F24F2110/10Temperature
    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24HEATING; RANGES; VENTILATING
    • F24FAIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2110/00Control inputs relating to air properties
    • F24F2110/10Temperature
    • F24F2110/12Temperature of the outside air

Landscapes

  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Fuzzy Systems (AREA)
  • Mathematical Physics (AREA)
  • Signal Processing (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Atmospheric Sciences (AREA)
  • Air Conditioning Control Device (AREA)

Abstract

PROBLEM TO BE SOLVED: To make it possible to estimate the temperature of a room in a building from a measured outside temperature without making a computer simulation.SOLUTION: A room temperature estimation device 10 includes: a storage unit 13; a prediction formula generation unit 15; a prediction transition acquisition unit 16; and a room temperature estimation unit 17. The prediction formula generation unit 15 generates a plurality of regression prediction formulas each representing a relation between room temperature data and outside temperature data for each time using the room temperature and outside temperature data at each of a plurality of time for a plurality of days in a predetermined extraction period stored in the storage unit 13, as prediction formulas, respectively. The room temperature estimation unit 17 obtains an outside temperature on a date of interest from a transition of the outside temperature acquired by the prediction transition acquisition unit 16, and estimates a room temperature by applying the outside temperature to the prediction formula at the time corresponding to the date.

Description

本発明は、着目する日時の室温を予測する室温推定装置、コンピュータを室温推定装置として機能させるプログラムに関する。   The present invention relates to a room temperature estimation device that predicts the room temperature of a date and time of interest, and a program that causes a computer to function as the room temperature estimation device.

従来から、室温の推移と予測される外気温とを用いることにより、予定時刻に室温を希望の設定温度にする技術が知られている(たとえば、特許文献1参照)。また、車両の室温に関して、推定した日射量の変化と、計測した外気温および室温とから室温の変化を予測し、室温が所定の閾値に達することが予測される場合に通知する技術が知られている(たとえば、特許文献2参照)。   2. Description of the Related Art Conventionally, a technique for setting a room temperature to a desired set temperature at a scheduled time by using a transition of the room temperature and a predicted outside air temperature is known (for example, see Patent Document 1). In addition, regarding the room temperature of the vehicle, a technology is known that predicts a change in the room temperature from the estimated change in the amount of solar radiation and the measured outside air temperature and room temperature, and notifies when the room temperature is predicted to reach a predetermined threshold. (For example, refer to Patent Document 2).

特許文献1には、計測される環境の情報が室温であって、計測された室温の履歴に基づいて室温の変化を予測する技術が記載されている。さらに、特許文献1では、予測される室温および外気温の変化と、暖房装置に要求される暖房熱負荷量および暖房能力とから、暖房装置の運転開始時刻および暖房開始時刻が定められている。すなわち、室温と外気温との予測値が求められ、予測値に基づいて室温を希望する設定温度にするための暖房熱負荷量が求められている。   Patent Document 1 describes a technique for predicting a change in room temperature based on the measured history of room temperature when the information on the environment to be measured is room temperature. Further, in Patent Document 1, the operation start time and the heating start time of the heating device are determined from the predicted changes in the room temperature and the outside air temperature, the heating heat load amount and the heating capacity required for the heating device. That is, a predicted value of the room temperature and the outside air temperature is obtained, and a heating heat load amount for making the room temperature a desired set temperature is obtained based on the predicted value.

一方、特許文献2には、計測される環境の情報が室温および外気温であって、計測された室温および外気温と併せて予測される日射量を用いることにより、車室内の温度を予測する技術が記載されている。   On the other hand, Patent Document 2 predicts the temperature in the vehicle interior by using the amount of solar radiation that is predicted in conjunction with the measured room temperature and outside air temperature when the measured environment information is room temperature and outside air temperature. The technology is described.

特開平6−42765号公報JP-A-6-42765 特開2005−343386号公報JP-A-2005-343386

特許文献1に記載された構成は、暖房装置熱負荷量を求めるために、室温および外気温を予測することが記載されているが、室温の予測は履歴に基づいており、室温を定める他の要因を用いて室温を予測する技術は記載されていない。   The configuration described in Patent Document 1 describes that the room temperature and the outside air temperature are predicted in order to obtain the heating device heat load. However, the prediction of the room temperature is based on the history, and other conditions that determine the room temperature are described. A technique for predicting room temperature using factors is not described.

また、特許文献2に記載された構成は、車室内の温度を予測する技術であり、車室内の温度は外気温の変化に短時間で追従するから、外気温および日射量とから車室内の温度を予測することは比較的容易である。しかしながら、建物の部屋の温度は、外気温が変化してもただちに変化することはなく、部屋の断熱性能のような熱特性に依存するから、特許文献2に記載された技術を用いて、建物の室温を外気温から予測することは困難である。   In addition, the configuration described in Patent Document 2 is a technique for predicting the temperature in the vehicle interior, and the temperature in the vehicle interior follows the change in the outside air temperature in a short time. It is relatively easy to predict the temperature. However, the temperature of the building room does not change immediately even if the outside air temperature changes, and depends on the thermal characteristics such as the thermal insulation performance of the room. It is difficult to predict the room temperature from the outside temperature.

一方、建物の室温を、外気温、建物の断熱性能、日射、換気、降雨、人の存否などの種々の要因から求めるために、コンピュータシミュレーションを行う技術が知られている。しかしながら、この種のコンピュータシミュレーションを行うには、多数の情報が必要である上に、正確な値を得るために別の計測が必要になる情報が含まれることがあるから、室温の予測のために簡便に用いることはできない。   On the other hand, a technique for performing computer simulation is known in order to determine the room temperature of a building from various factors such as the outside air temperature, the heat insulation performance of the building, solar radiation, ventilation, rainfall, and the presence of people. However, this kind of computer simulation requires a lot of information and may contain information that needs another measurement to get an accurate value. Cannot be used conveniently.

本発明は、計測された環境の情報に基づいてコンピュータシミュレーションを行うことなく建物内の部屋の室温を推定する室温推定装置を提供することを目的とし、さらに、コンピュータを室温推定装置として機能させるプログラムを提供することを目的とする。   An object of the present invention is to provide a room temperature estimation device that estimates the room temperature of a room in a building without performing computer simulation based on the measured environment information, and further, a program for causing a computer to function as a room temperature estimation device The purpose is to provide.

本発明に係る室温推定装置は、室温計測部から室温のデータを取得する室温取得部と、外気温計測部から外気温のデータを取得する外気温取得部と、前記室温取得部が取得した室温のデータおよび前記外気温取得部が取得した外気温のデータをそれぞれが計測された日時に対応付けて格納する記憶部と、前記記憶部に格納された所定の抽出期間における複数日についてそれぞれ複数の時刻における時刻ごとの室温および外気温のデータを用い、時刻ごとの室温のデータと外気温のデータとの関係を表す複数の回帰予測式をそれぞれ予測式として求める予測式生成部と、外気温について予測される推移を取得する予測推移取得部と、前記予測推移取得部が取得した外気温の推移を用いて着目する時刻の外気温を前記予測式生成部が求めた当該時刻における前記予測式に当て嵌めることにより室温を推定する室温推定部とを備えることを特徴とする。   The room temperature estimation apparatus according to the present invention includes a room temperature acquisition unit that acquires room temperature data from a room temperature measurement unit, an outside air temperature acquisition unit that acquires outside temperature data from an outside air temperature measurement unit, and the room temperature acquired by the room temperature acquisition unit And a storage unit that stores the outside temperature data acquired by the outside temperature acquisition unit in association with the date and time when each is measured, and a plurality of days in a predetermined extraction period stored in the storage unit. About the outside air temperature, which uses the room temperature and outside air temperature data for each time at the time, and obtains a plurality of regression prediction expressions representing the relationship between the room temperature data and the outside air temperature data for each time as a prediction expression, and the outside air temperature A prediction transition acquisition unit that acquires a predicted transition, and a time when the prediction formula generation unit obtains the outside temperature at the time of interest using the transition of the outside temperature acquired by the prediction transition acquisition unit Characterized in that it comprises a room temperature estimating unit for estimating a room temperature by fitting the prediction expression in.

この室温推定装置において、前記抽出期間は、気象環境に基づいて1年を複数に区分した分割期間ごとに定められ、前記室温推定部は、前記分割期間における室温の予測に、当該分割期間に定めた前記抽出期間における室温および外気温のデータを用いて求めた前記予測式を適用することが望ましい。   In this room temperature estimation device, the extraction period is determined for each divided period divided into a plurality of years based on the weather environment, and the room temperature estimating unit determines the room temperature in the divided period for the divided period. It is desirable to apply the prediction formula obtained using room temperature and outside temperature data during the extraction period.

この室温推定装置において、外気温のほかに室温に影響を与え、かつ複数の状態から選択される補正情報を取得する補正情報取得部をさらに備え、前記室温推定部は、前記補正情報取得部が取得した補正情報の状態ごとに求められた複数の前記予測式から当該状態に関して求めた前記予測式を選択し、選択した前記予測式を用いて室温を推定することが好ましい。   The room temperature estimation apparatus further includes a correction information acquisition unit that acquires the correction information that affects the room temperature in addition to the outside temperature and is selected from a plurality of states, and the room temperature estimation unit includes the correction information acquisition unit. It is preferable that the prediction formula obtained for the state is selected from the plurality of prediction formulas obtained for each state of the acquired correction information, and the room temperature is estimated using the selected prediction formula.

この室温推定装置において、前記室温推定部が推定した室温を報知器に出力する報知出力部をさらに備えることが好ましい。   The room temperature estimation apparatus preferably further includes a notification output unit that outputs the room temperature estimated by the room temperature estimation unit to a notification device.

この室温推定装置において、前記外気温取得部は、電気通信回線を通して提供される外気温のデータを取得することが好ましい。   In this room temperature estimating apparatus, it is preferable that the outside air temperature acquisition unit acquires outside air temperature data provided through a telecommunication line.

本発明に係るプログラムは、コンピュータを、上述したいずれかの室温推定装置として機能させるためのものである。   The program according to the present invention is for causing a computer to function as any one of the room temperature estimation devices described above.

本発明の構成によれば、コンピュータシミュレーションを行うことなく、容易に計測可能な情報のみで建物内の部屋の室温を推定することが可能になるという利点を有する。   According to the configuration of the present invention, there is an advantage that the room temperature of a room in a building can be estimated only by information that can be easily measured without performing computer simulation.

実施形態1を示すブロック図である。1 is a block diagram illustrating a first embodiment. 同上の原理を説明するための図である。It is a figure for demonstrating a principle same as the above. 同上に原理を説明するための図である。It is a figure for demonstrating a principle same as the above. 実施形態2を示すブロック図である。FIG. 6 is a block diagram illustrating a second embodiment. 同上の原理を説明するための図である。It is a figure for demonstrating a principle same as the above. 実施形態3を示すブロック図である。FIG. 6 is a block diagram illustrating a third embodiment.

以下では、予測された外気温の推移を用いることにより、室内の冷暖房を行わない場合の室温を推定する技術を例示する。冷暖房を行わない状態において、室温を定める要因は、外気温、部屋の断熱性能、日射、換気、降雨、室内の人数などである。   Below, the technique of estimating the room temperature when not performing indoor air conditioning by using the transition of the predicted outside air temperature will be exemplified. Factors that determine the room temperature in the state where air conditioning is not performed are the outside air temperature, the thermal insulation performance of the room, solar radiation, ventilation, rainfall, the number of people in the room, and the like.

部屋の断熱性能は、住宅の特性であって、住宅の工法や住宅に使用されている建材などから目安は得られるが、定量的に計測することは容易ではない。また、室内の人数は計測できるが、個々人の代謝量や着衣量によって室温を上昇させる程度は一定ではないから、室温に対する人数の関係を理論的に求めることは容易ではない。同様に、日射、換気、降雨は、監視可能であるが、室温への影響を理論的に求めることは容易ではない。   The thermal insulation performance of the room is a characteristic of the house, and can be obtained from the construction method of the house or the building materials used in the house, but it is not easy to measure quantitatively. Although the number of people in a room can be measured, the degree to which the room temperature is raised is not constant depending on the amount of metabolism and the amount of clothes of each person, so it is not easy to theoretically obtain the relationship between the number of people and the room temperature. Similarly, solar radiation, ventilation, and rainfall can be monitored, but it is not easy to theoretically determine the effect on room temperature.

要するに、室温を定める要因を計測することは可能であるが、これらの要因と室温とを結びつける適切なモデルを作成することは容易ではない。したがって、これらの要因についての計測値からコンピュータシミュレーションによって室温を求めることは容易ではない。また、コンピュータシミュレーションで室温を推定する場合には、必要程度の精度を得るために、入力すべき情報が非常に多い上に補正が必要であって、部屋ごとに室温を推定するには専門家による多大な労力を必要とする。   In short, it is possible to measure factors that determine room temperature, but it is not easy to create an appropriate model that links these factors to room temperature. Therefore, it is not easy to obtain the room temperature by computer simulation from the measured values for these factors. In addition, when estimating the room temperature by computer simulation, in order to obtain the required degree of accuracy, there is a lot of information to be input and correction is necessary. Requires a lot of effort.

以下では、複雑なモデルを用いたコンピュータシミュレーションを行うことなく、容易に計測できる情報から室温を比較的よい精度で推定する室温推定装置について説明する。   Hereinafter, a room temperature estimation apparatus that estimates room temperature with relatively good accuracy from information that can be easily measured without performing computer simulation using a complex model will be described.

(実施形態1)
本実施形態では、まず簡単な例として、室温を外気温のみにより推定する技術について説明する。また、後述する実施形態において、日射、換気、降雨、室内の人数を考慮して室温を推定する技術について説明する。本実施形態の室温推定装置は、外気温と室温とを関係付ける予測式を用いて、着目する日時における室温を外気温から推定する。そのため、室温推定装置は、予測式を求める構成と、予測式を用いて外気温から室温を推定する構成とを備える。
(Embodiment 1)
In the present embodiment, as a simple example, a technique for estimating the room temperature only from the outside temperature will be described. In the embodiment described later, a technique for estimating the room temperature in consideration of solar radiation, ventilation, rainfall, and the number of people in the room will be described. The room temperature estimation apparatus of this embodiment estimates the room temperature at the date and time of interest from the outside air temperature using a prediction formula that relates the outside air temperature and the room temperature. Therefore, the room temperature estimation apparatus includes a configuration for obtaining a prediction formula and a configuration for estimating the room temperature from the outside temperature using the prediction formula.

この室温推定装置は、プログラムを実行することにより以下の機能を実現するプロセッサを備えたデバイスと、インターフェイス用のデバイスとを主なハードウェア要素として備える。プロセッサを備えるデバイスは、メモリを内蔵するマイコン、メモリが外付されるプロセッサなどが用いられる。また、以下の機能を実現するプログラムを実行するコンピュータを、室温推定装置として機能させることが可能である。この種のプログラムは、コンピュータで読取可能な記録媒体として提供されるか、あるいは電気通信回線を通して通信により提供される。   This room temperature estimation apparatus includes a device including a processor that realizes the following functions by executing a program and an interface device as main hardware elements. As a device including a processor, a microcomputer incorporating a memory, a processor having an external memory, and the like are used. In addition, a computer that executes a program that implements the following functions can function as a room temperature estimation device. This type of program is provided as a computer-readable recording medium or by communication through an electric communication line.

予測式を求めるには、室温と外気温とを日時に対応付けて計測する必要がある。そのため、室温推定装置は、図1に示すように、室温計測部21から室温のデータを取得する室温取得部11と、外気温計測部22から外気温のデータを取得する外気温取得部12とを備える。   In order to obtain the prediction formula, it is necessary to measure the room temperature and the outside air temperature in association with the date and time. Therefore, as shown in FIG. 1, the room temperature estimation apparatus includes a room temperature acquisition unit 11 that acquires room temperature data from the room temperature measurement unit 21, and an outside air temperature acquisition unit 12 that acquires outside temperature data from the outside air temperature measurement unit 22. Is provided.

室温計測部21および外気温計測部22は、サーミスタのように周囲温度に応じたアナログ出力が得られる温度センサと、温度センサの出力を増幅するセンサアンプとをそれぞれ備える。また、室温計測部21および外気温計測部22は、センサアンプの出力をデジタル値のデータに変換する変換部と、変換部から出力されるデジタル値のデータを室温推定装置10に送信する通信部とを備える。   The room temperature measurement unit 21 and the outside air temperature measurement unit 22 each include a temperature sensor that can obtain an analog output corresponding to the ambient temperature, such as a thermistor, and a sensor amplifier that amplifies the output of the temperature sensor. The room temperature measurement unit 21 and the outside air temperature measurement unit 22 are a conversion unit that converts the output of the sensor amplifier into digital value data, and a communication unit that transmits the digital value data output from the conversion unit to the room temperature estimation device 10. With.

室温計測部21および外気温計測部22は、通信部が省略された構成、あるいは変換部および通信部が省略された構成であってもよいが、室温推定装置10に計測値を正確に伝送するために、変換部および通信部を備えていることが望ましい。なお、変換部が省略されている場合、室温計測部21および外気温計測部22は、アナログ値のデータを室温推定装置10に与えることになる。   The room temperature measurement unit 21 and the outside air temperature measurement unit 22 may have a configuration in which the communication unit is omitted, or a configuration in which the conversion unit and the communication unit are omitted, but accurately transmit the measurement value to the room temperature estimation apparatus 10. Therefore, it is desirable to include a conversion unit and a communication unit. When the conversion unit is omitted, the room temperature measurement unit 21 and the outside air temperature measurement unit 22 provide analog value data to the room temperature estimation device 10.

室温計測部21および外気温計測部22と室温推定装置10との間の通信は、電波を伝送媒体に用いた無線通信路を用いることが望ましいが、有線通信路を用いることも可能である。また、室温計測部21は、室温推定装置10と筐体を共用してもよい。室温計測部21が室温推定装置10と筐体を共用する場合、室温計測部21に通信部は不要である。   The communication between the room temperature measurement unit 21 and the outside air temperature measurement unit 22 and the room temperature estimation apparatus 10 desirably uses a wireless communication path using radio waves as a transmission medium, but can also use a wired communication path. Further, the room temperature measurement unit 21 may share a housing with the room temperature estimation device 10. When the room temperature measurement unit 21 shares the case with the room temperature estimation device 10, a communication unit is not necessary for the room temperature measurement unit 21.

室温取得部11が取得した室温のデータおよび外気温取得部12が取得した外気温のデータは、それぞれが計測された日時に対応付けて記憶部13に格納される。すなわち、記憶部13は、(室温,日時)(外気温,日時)という2種類の対を記憶するか、(室温,外気温,日時)の3つ組を記憶する。後者のほうがデータ量は少なく、記憶部13の記憶容量の節約になる。   The room temperature data acquired by the room temperature acquisition unit 11 and the outside air temperature data acquired by the outside air temperature acquisition unit 12 are stored in the storage unit 13 in association with the date and time when each was measured. That is, the storage unit 13 stores two types of pairs (room temperature, date and time) (outside temperature, date and time), or stores a triple of (room temperature, outside temperature, date and time). The latter has a smaller amount of data and saves the storage capacity of the storage unit 13.

記憶部13に格納される日時は、室温推定装置10に設けられた時計部14が計時する。室温取得部11および外気温取得部12は、室温および外気温のデータを取得する日時があらかじめ設定されており、時計部14が計時する日時を用いて、たとえば毎正時にデータを取得する。この場合、記憶部13は、(室温,外気温,日時)の3つ組を記憶することが望ましい。   The date and time stored in the storage unit 13 is measured by the clock unit 14 provided in the room temperature estimation device 10. The room temperature acquisition unit 11 and the outside air temperature acquisition unit 12 have preset date and time for acquiring room temperature and outside air temperature data, and use the date and time counted by the clock unit 14 to acquire data at, for example, every hour. In this case, the storage unit 13 preferably stores a triplet of (room temperature, outside temperature, date and time).

室温取得部11および外気温取得部12がデータを取得する時間間隔は、1時間ごとである必要はなく、10分、15分、30分、2時間などから必要に応じて選択される。時間間隔が短いと情報量が多くなり、推定精度の高い予測式が得られると考えられるが、記憶部13に格納するデータの量も増加する。そのため、データを取得する時間間隔は、1時間を基準として、1時間の数分の1から数倍程度の範囲で設定することが好ましい。   The time interval at which the room temperature acquisition unit 11 and the outside air temperature acquisition unit 12 acquire data does not have to be every hour, but is selected as needed from 10 minutes, 15 minutes, 30 minutes, 2 hours, and the like. If the time interval is short, the amount of information increases, and a prediction formula with high estimation accuracy can be obtained. However, the amount of data stored in the storage unit 13 also increases. For this reason, it is preferable to set the time interval for acquiring data within a range from a fraction of an hour to several times with respect to one hour.

なお、室温計測部21および外気温計測部22に、それぞれ日時を計時する時計部が設けられていてもよい。この場合、室温計測部21および外気温計測部22が、それぞれの時計部が計時している日時に取得した室温および外気温のデータを室温推定装置10に対して送信する。すなわち、室温計測部21および外気温計測部22は、それぞれの時計部が計時している日時に、室温および外気温のデータを対応付けて室温推定装置10に送信する。   The room temperature measuring unit 21 and the outside air temperature measuring unit 22 may each be provided with a clock unit that measures the date and time. In this case, the room temperature measurement unit 21 and the outside air temperature measurement unit 22 transmit the room temperature and outside air temperature data acquired on the date and time that each clock unit is measuring to the room temperature estimation device 10. That is, the room temperature measurement unit 21 and the outside air temperature measurement unit 22 transmit the room temperature and outside air temperature data to the room temperature estimation apparatus 10 in association with the date and time that each clock unit keeps timing.

この構成では、記憶部13は、(室温,日時)(外気温,日時)という2種類の対を記憶することが望ましい。ここに、室温計測部21および外気温計測部22が室温および外気温のデータを送信するタイミングは、室温および外気温を計測した日時である必要はなく、たとえば半日分あるいは1日分のデータをまとめて送信することが可能である。   In this configuration, the storage unit 13 preferably stores two types of pairs (room temperature, date / time) (outside temperature, date / time). Here, the timing at which the room temperature measuring unit 21 and the outside air temperature measuring unit 22 transmit the room temperature and outside air temperature data does not have to be the date and time at which the room temperature and the outside air temperature are measured. It is possible to send them together.

ところで、室温が日射の影響を受けない状態が継続し、かつ外気温の変化が少ない場合には、部屋に流入する熱量と部屋から流出する熱量とがほぼ平衡状態になり、同時刻における外気温と室温とがほぼ線形関係になるという仮説が得られる。   By the way, when the room temperature remains unaffected by solar radiation and the change in the outside air temperature is small, the amount of heat flowing into the room and the amount of heat flowing out of the room are almost in equilibrium, and the outside air temperature at the same time And the hypothesis that room temperature is almost linear.

本発明者は、比較的長期にわたる複数日について、複数の時刻における室温と外気温とを計測し、時刻ごとの室温と外気温との関係をグラフ化した結果、図2に示すように、特定の時刻では、外気温と室温とがほぼ線形関係になるという知見を得た。すなわち、特定の時刻における室温は、外気温を変数とする一次関数の予測式で表すことができ、この予測式を用いて外気温から室温を推定できることを見出した。   The inventor measured room temperature and outside air temperature at a plurality of times for a plurality of days over a relatively long period, and graphed the relationship between the room temperature and the outside air temperature for each time, as shown in FIG. At the time of, we found that the outside air temperature and room temperature are almost linear. That is, the present inventors have found that the room temperature at a specific time can be expressed by a prediction function of a linear function with the outside air temperature as a variable, and the room temperature can be estimated from the outside air temperature using this prediction expression.

室温推定装置10は、複数日の特定の時刻における外気温と室温とを用いて予測式を生成する予測式生成部15を備えている。予測式生成部15は、記憶部13に格納された所定の抽出期間における複数日について特定の時刻の室温および外気温のデータを抽出し、複数日における同時刻の室温および外気温のデータから回帰式を求め、この回帰式を予測式として用いる。   The room temperature estimation apparatus 10 includes a prediction formula generation unit 15 that generates a prediction formula using the outside air temperature and room temperature at specific times on a plurality of days. The prediction formula generation unit 15 extracts room temperature and outside temperature data at a specific time for a plurality of days in a predetermined extraction period stored in the storage unit 13, and returns from the room temperature and outside temperature data at the same time on a plurality of days. An equation is obtained and this regression equation is used as a prediction equation.

予測式は、外気温の一次関数であることが予測されているから、回帰式を生成するために用いる室温および外気温のデータは、3日分以上が必要である。つまり、抽出期間は3日以上の複数日を含む必要があり、たとえば、15〜90日分の範囲から選択することが望ましい。下限の15日は1つの節気に相当する日数であり、上限の90日は春夏秋冬における1つの季節に相当する日数である。なお、この日数は一例であって、たとえば1〜数年間について1日毎、2日に1回、1週間に1回などに計測した室温および外気温のデータを用いて予測式を生成してもよい。   Since the prediction formula is predicted to be a linear function of the outside air temperature, the room temperature and outside air temperature data used for generating the regression equation needs three days or more. That is, the extraction period needs to include a plurality of days of 3 days or more, and for example, it is desirable to select from the range of 15 to 90 days. The lower limit of 15 days is the number of days corresponding to one moderation, and the upper limit of 90 days is the number of days corresponding to one season in spring, summer, autumn and winter. This number of days is an example, and for example, even if a prediction formula is generated using room temperature and outside temperature data measured every day, once every two days, once a week, etc. for one to several years. Good.

予測式生成部15は、抽出期間において着目する時刻tの室温θ1(t)および外気温θ2(t)の間に線形関係が成立することを利用し、θ1(t)=α・θ2(t)+βという形式の予測式を生成する。予測式の生成に際して、最小二乗法のような周知の方法を用いて室温θ1(t)と外気温θ2(t)とを一次関数に当て嵌める。すなわち、予測式生成部15は、抽出期間において着目する時刻tの室温θ1(t)および外気温θ2(t)のデータから回帰予測式を求める。なお、係数α,βは、通常は、第1の予測式生成部151が生成する予測式に含まれる係数α,βとは異なる値になる。   The prediction formula generation unit 15 uses the fact that a linear relationship is established between the room temperature θ1 (t) and the outside air temperature θ2 (t) at the time t of interest in the extraction period, and θ1 (t) = α · θ2 (t ) Generate a prediction formula of the form + β. When generating the prediction formula, the room temperature θ1 (t) and the outside temperature θ2 (t) are fitted to a linear function using a known method such as the least square method. That is, the prediction formula generation unit 15 obtains a regression prediction formula from data on the room temperature θ1 (t) and the outside air temperature θ2 (t) at the time t of interest in the extraction period. The coefficients α and β are usually different from the coefficients α and β included in the prediction formula generated by the first prediction formula generation unit 151.

この回帰予測式は、当該時刻の外気温を説明変数とし、当該時刻における室温を従属変数とする。ただし、回帰予測式を求める時刻は、室温が日射の影響を受けずに、外気温にのみ依存する時間帯であって、かつ外気温の変動が比較的緩やかである時間帯から選択される。すなわち、日の出前の早朝の時間帯が望ましい。予測式生成部15は、上述のようにして求めた回帰予測式を、外気温から室温を求める予測式に用いる。また、予測式生成部15は、複数の時刻について回帰予測式をそれぞれ求め、各時刻における予測式に用いる。   In this regression prediction formula, the outside air temperature at the time is used as an explanatory variable, and the room temperature at the time is used as a dependent variable. However, the time for obtaining the regression prediction formula is selected from a time zone in which the room temperature is not affected by solar radiation and depends only on the outside air temperature, and the fluctuation of the outside air temperature is relatively gradual. That is, an early morning time zone before sunrise is desirable. The prediction formula generation unit 15 uses the regression prediction formula obtained as described above as a prediction formula for obtaining the room temperature from the outside air temperature. Moreover, the prediction formula production | generation part 15 calculates | requires each regression prediction formula about several time, and uses it for the prediction formula in each time.

室温推定装置10は、上述した方法で予測式を生成し、この予測式を用いて外気温から室温を推定する。以下では、室温推定装置10において、外気温から室温を推定する構成について説明する。室温推定装置10は、外気温取得部12が外気温計測部22から取得した外気温のデータの時系列を用いて外気温の予想される推移を取得する予想推移取得部16と、外気温の推移から室温を推定する室温推定部17とを備える。   The room temperature estimation device 10 generates a prediction formula by the method described above, and estimates the room temperature from the outside air temperature using the prediction formula. Below, the structure which estimates room temperature from external temperature in the room temperature estimation apparatus 10 is demonstrated. The room temperature estimation device 10 includes an expected transition acquisition unit 16 that acquires an expected transition of the outside temperature using a time series of outside temperature data acquired by the outside temperature acquisition unit 12 from the outside temperature measurement unit 22, and an outside temperature And a room temperature estimation unit 17 for estimating the room temperature from the transition.

予測推移取得部16は、外気温のデータの時系列を、あらかじめ登録されている複数種類の外気温の推移の典型(テンプレート)のいずれかに当て嵌め、当て嵌めた典型を用いて外気温の推移を予測する。予測推移取得部16は、外気温のデータの時系列を外気温の推移の典型に当て嵌めるに際して、当日の天候や季節を考慮して、当て嵌めるべき典型を絞り込む。   The predicted transition acquisition unit 16 fits the time series of the outside air temperature data to one of a plurality of types (templates) of the transition of the outside air temperature registered in advance, and uses the fitted representatives to calculate the outside air temperature. Predict the transition. The prediction transition acquisition unit 16 narrows down the typical to be applied in consideration of the weather and season of the day when fitting the time series of the outside temperature data to the typical of the transition of the outside temperature.

外気温の推移は、外気温取得部12が外気温計測部22から取得した外気温のデータを用いる代わりに、外気温取得部12がインターネットのような電気通信回線を通して取得してもよい。すなわち、外気温取得部12は、地域ごとの天候の情報を提供しているサービス提供者から電気通信回線を通して外気温のデータを取得する機能を有している。この構成を採用する場合、予測推移取得部16は、外気温取得部12がサービス提供者から取得した外気温のデータを用いる。   The transition of the outside air temperature may be acquired by the outside air temperature acquiring unit 12 through an electric communication line such as the Internet instead of using the outside air temperature data acquired by the outside air temperature acquiring unit 12 from the outside air temperature measuring unit 22. That is, the outside air temperature acquisition unit 12 has a function of acquiring outside air temperature data through a telecommunication line from a service provider that provides weather information for each region. When this configuration is adopted, the predicted transition acquisition unit 16 uses data on the outside air temperature acquired by the outside air temperature acquisition unit 12 from the service provider.

なお、電気通信回線を通して提供される外気温のデータは、室温を推定しようとする部屋が存在している地域内の特定地点に関するデータであって当該部屋に対する外気温ではないが、当該部屋に対する外気温とは線形関係であることが予想される。したがって、室温推定部17は、外気温のデータから推定される室温を、室温の実測値に基づいて校正すれば、電気通信回線を通して取得した外気温のデータを用いて室温を推定することが可能である。   It should be noted that the outside temperature data provided through the telecommunication line is data on a specific point in the area where the room for which the room temperature is to be estimated exists and is not the outside temperature for the room, but the outside temperature for the room is not It is expected that there is a linear relationship with temperature. Therefore, if the room temperature estimation unit 17 calibrates the room temperature estimated from the outside air temperature data based on the measured room temperature value, the room temperature estimating unit 17 can estimate the room temperature using the outside air temperature data acquired through the telecommunication line. It is.

室温推定部17は、予測推移取得部16が取得した外気温の予測される推移を用い、着目する日時における外気温を求める。外気温が求められると、室温推定部17は、求めた外気温を予測式生成部15が生成した予測式に当て嵌めることにより室温を推定する。すなわち、室温推定部17は、室温を推定しようとする日時について求めた外気温を、外気温の予測される推移を用いて求め、この外気温を予測式に当て嵌めることにより、着目する日時の室温を推定する。   The room temperature estimation unit 17 uses the predicted transition of the outside temperature acquired by the predicted transition acquisition unit 16 to determine the outside temperature at the date and time of interest. When the outside air temperature is obtained, the room temperature estimating unit 17 estimates the room temperature by fitting the obtained outside air temperature to the prediction formula generated by the prediction formula generating unit 15. That is, the room temperature estimation unit 17 obtains the outside air temperature obtained for the date and time when the room temperature is to be estimated using the predicted transition of the outside air temperature, and fits the outside air temperature to the prediction formula, thereby Estimate room temperature.

室温推定装置10は、室温推定部17が推定した室温を報知器23に出力する報知出力部18を備えることが望ましい。報知器23は、ディスプレイ装置を備える専用装置のほか、スマートフォン、タブレット端末、パーソナルコンピュータのように、ディスプレイ装置を備え、かつ通信機能を備えた装置であってもよい。これらの装置を報知器23として用いる場合、報知出力部18は、これらの装置と通信するように構成される。なお、報知器23は、図1に破線で示す報知器23のように、室温推定装置10の筐体に一体に設けられていてもよい。   The room temperature estimation device 10 preferably includes a notification output unit 18 that outputs the room temperature estimated by the room temperature estimation unit 17 to the alarm device 23. In addition to a dedicated device provided with a display device, the alarm device 23 may be a device provided with a display device and having a communication function, such as a smartphone, a tablet terminal, and a personal computer. When these devices are used as the alarm device 23, the notification output unit 18 is configured to communicate with these devices. Note that the alarm 23 may be provided integrally with the casing of the room temperature estimation device 10 like the alarm 23 indicated by a broken line in FIG.

また、室温推定部17が推定した室温は、報知器23によって利用者に通知されるだけではなく、換気扇、空調装置、電動シャッタ、電動カーテン、電動式の窓など、室温に影響を与える装置の制御に用いてもよい。とくに、冷暖房装置(たとえば、空調装置)による冷暖房の制御を行う場合、外気温の推移に基づいて室温を推定すると、冷暖房装置の運転を停止させるタイミングを適切に定めることが可能になり、結果的に冷暖房のために消費するエネルギーの削減が可能になる。   Further, the room temperature estimated by the room temperature estimating unit 17 is not only notified to the user by the alarm device 23 but also of a device that affects the room temperature, such as a ventilation fan, an air conditioner, an electric shutter, an electric curtain, and an electric window. It may be used for control. In particular, when air conditioning is controlled by an air conditioner (for example, an air conditioner), when the room temperature is estimated based on the transition of the outside air temperature, it becomes possible to appropriately determine the timing for stopping the operation of the air conditioner. In addition, it is possible to reduce energy consumed for air conditioning.

たとえば、夏季であれば夜間に室温が低下し冷房装置を停止させても快適な室温に維持できることが予測される場合に、冷房装置の運転を停止させる時刻が決められることにより、冷房装置の無駄な運転を防止して、省エネルギーを図ることが可能になる。同様に、冬季であれば昼間に室温が上昇し暖房装置を停止させても快適な室温に維持できることが予測される場合に、暖房装置の運転を停止させる時刻が決められることにより、暖房装置の無駄な運転を防止して、省エネルギーを図ることが可能になる。   For example, in the summer, when it is predicted that the room temperature can be maintained at a comfortable room temperature even when the cooling apparatus is stopped at night, it is possible to waste the cooling apparatus by determining the time to stop the operation of the cooling apparatus. It is possible to prevent unnecessary operation and save energy. Similarly, if the room temperature rises in the daytime in winter and it is predicted that the room temperature can be maintained at a comfortable level even if the heating system is stopped, the time for stopping the operation of the heating system can be determined. It is possible to save energy by preventing unnecessary driving.

ところで、予測式生成部15が生成する予測式は、季節によって変化することが容易に予想される。たとえば、図2において、左側は冬季における室温と外気温との関係を表し、右側は夏季における室温と外気温との関係を示しており、一見すると、左側のグループと右側のグループとを、同じ一次関数で表すことが可能であるように見える。しかしながら、左側のグループと右側のグループとをそれぞれ一次関数に当て嵌めると、図3に示すように、グループごとに予測式(それぞれ直線で示している)が得られる。   By the way, the prediction formula generated by the prediction formula generation unit 15 is easily predicted to change depending on the season. For example, in FIG. 2, the left side shows the relationship between room temperature and outside temperature in winter, and the right side shows the relationship between room temperature and outside temperature in summer. At first glance, the left group and the right group are the same. It seems that it can be expressed by a linear function. However, when the left group and the right group are respectively fitted to a linear function, prediction formulas (respectively indicated by straight lines) are obtained for each group as shown in FIG.

このことから、予測式の生成に用いる室温および外気温を計測する抽出期間は、季節ごとに定めることが望ましい。そのため、抽出期間は1年を複数に区分して設定された分割期間ごとに設定される。分割期間は、1年を4〜24分割(4分割は春夏秋冬を反映した単位、24分割は節気を反映した単位)した期間から適宜に選択することが望ましい。分割期間の日数は15〜90日になり、抽出期間は分割期間ごとに3日以上の日数であればよく、予測式生成部15は、抽出期間の数に相当する個数の予測式を生成する。   Therefore, it is desirable to determine the extraction period for measuring the room temperature and the outside temperature used for generating the prediction formula for each season. Therefore, the extraction period is set for each divided period set by dividing one year into a plurality of periods. It is desirable that the division period is appropriately selected from a period in which one year is divided into 4 to 24 (4 division is a unit reflecting spring, summer, autumn and winter, and 24 division is a unit reflecting air saving). The number of days in the divided period is 15 to 90 days, and the extraction period may be three days or more for each divided period, and the prediction expression generation unit 15 generates the number of prediction expressions corresponding to the number of extraction periods. .

一方、室温推定部17は、抽出期間ごとに求められた複数の予測式から同じ抽出期間に関して生成した予測式を選択し、選択した予測式を用いて外気温の推移から室温を推定する。   On the other hand, the room temperature estimation unit 17 selects a prediction formula generated for the same extraction period from a plurality of prediction formulas obtained for each extraction period, and estimates the room temperature from the transition of the outside air temperature using the selected prediction formula.

(実施形態2)
実施形態1において、予測式生成部15は、室温が日射の影響を受けない時間帯における室温および外気温を用いて予測式を生成しているから、予測式を用いて外気温から室温を推定することが可能な時間帯に制限がある。すなわち、室温が日射の影響を受ける時間帯には、実施形態1で得られた予測式によって室温を推定することはできず、実施形態1で得られた予測式は、夜半から早朝のように外気温の変化が比較的少ない時間帯に限って採用可能である。
(Embodiment 2)
In the first embodiment, since the prediction formula generation unit 15 generates a prediction formula using the room temperature and the outside temperature in a time zone in which the room temperature is not affected by solar radiation, the room temperature is estimated from the outside temperature using the prediction formula. There is a limit to the time zone that can be done. That is, in a time zone in which the room temperature is affected by solar radiation, the room temperature cannot be estimated by the prediction formula obtained in the first embodiment, and the prediction formula obtained in the first embodiment is from midnight to early morning. It can be used only during times when the outside air temperature changes relatively little.

本実施形態は、室温が日射の影響を受ける昼間の時間帯に使用可能な予測式を設定する技術について説明する。したがって、夜間に日射の影響を考慮する必要がない時間帯に実施形態1の技術で得られた予測式が採用され、昼間に日射の影響を考慮する時間帯には、以下に説明する予測式が採用される。つまり、本実施形態は、日射が室温に影響しない時間帯(日射のない時間帯)と、日射が室温に影響する時間帯とで、予測式を変更する構成を採用している。室温推定装置10における予測式生成部15は、2種類の予測式を生成する機能を備える。   This embodiment demonstrates the technique which sets the prediction formula which can be used in the daytime time slot when room temperature is influenced by solar radiation. Therefore, the prediction formula obtained by the technique of Embodiment 1 is adopted in a time zone in which it is not necessary to consider the influence of solar radiation at night, and the prediction formula described below is used in a time zone in which the influence of solar radiation is considered in the daytime. Is adopted. That is, this embodiment employs a configuration in which the prediction formula is changed between a time zone in which solar radiation does not affect room temperature (a time zone in which solar radiation does not affect the room) and a time zone in which solar radiation affects the room temperature. The prediction formula generation unit 15 in the room temperature estimation apparatus 10 has a function of generating two types of prediction formulas.

室温推定装置10は、図4に示すように、実施形態1と同様の技術を用いて予測式を生成する第1の予測式生成部151と、以下に説明する技術により生成した予測式を生成する第2の予測式生成部152とを備える。   As shown in FIG. 4, the room temperature estimation apparatus 10 generates a prediction formula generated by a first prediction formula generation unit 151 that generates a prediction formula using the same technique as in the first embodiment, and a technique described below. And a second prediction formula generation unit 152.

第1の予測式生成部151は、実施形態1における予測式生成部15と同様にして予測式を生成する。第1の予測式生成部151は、記憶部13に格納された複数日の特定の時刻における室温および外気温のデータを用いて回帰予測式を生成し、生成した回帰予測式を予測式に用いる。   The first prediction formula generation unit 151 generates a prediction formula in the same manner as the prediction formula generation unit 15 in the first embodiment. The first prediction formula generation unit 151 generates a regression prediction formula using room temperature and outside air temperature data at a specific time on a plurality of days stored in the storage unit 13, and uses the generated regression prediction formula as the prediction formula. .

一方、第2の予測式生成部152は、室温と外気温との関係が、部屋の熱特性(断熱性、蓄熱性など)に依存していると考え、以下に説明する方法で予測式を生成する。いま、室温が外気温にのみ依存すると仮定し、部屋を囲む壁、天井、床からなる仕切を通して熱が伝導し、外気温に追従して室温が変化するというモデルを想定する。このモデルによれば、外気温が室温に及ぼす影響は、仕切の熱伝導の程度および仕切の蓄熱の程度に応じて変化すると考えられる。ただし、室温は、仕切からの輻射熱は考慮せず、室内における空気の温度とする。   On the other hand, the second prediction formula generation unit 152 considers that the relationship between the room temperature and the outside air temperature depends on the thermal characteristics of the room (thermal insulation, heat storage, etc.), and calculates the prediction formula using the method described below. Generate. Now, assuming that the room temperature depends only on the outside air temperature, a model is assumed in which heat is conducted through a partition consisting of a wall, ceiling, and floor surrounding the room, and the room temperature changes following the outside air temperature. According to this model, the influence of the outside air temperature on the room temperature is considered to change according to the degree of heat conduction of the partition and the degree of heat storage of the partition. However, the room temperature is the temperature of the air in the room without considering the radiant heat from the partition.

上述したモデルによれば、室温は外気温の変化に遅れて変化すると考えられる。本発明者は、実験の結果、外気温の変化と室温の変化とに相関性があり、かつ室温が外気温に対して、仕切の熱特性(断熱性、蓄熱性など)に応じた遅延時間で遅延して変化するという知見を得た。また、この遅延時間を求めることができれば、着目する時刻の室温と、遅延時間だけずらした時刻における外気温との関係を簡単な予測式で表すことができ、この予測式を用いて外気温から室温を推定できることを見出した。   According to the model described above, the room temperature is considered to change with a delay in the change of the outside air temperature. As a result of the experiment, the present inventor has found that there is a correlation between a change in the outside air temperature and a change in the room temperature, and the room temperature is a delay time corresponding to the outside air temperature according to the thermal characteristics of the partition (insulation, heat storage, etc.) I got the knowledge that it changed with a delay. If this delay time can be obtained, the relationship between the room temperature at the time of interest and the outside air temperature at the time shifted by the delay time can be expressed by a simple prediction formula. We found that room temperature can be estimated.

同日時における室温と外気温との関係の例を図5(a)に示す。図示例を一見しただけでは、室温と外気温との間に関連性を見出すことはできない。一方、本実施形態は、上述したように、外気温の変化と室温の変化との間に、部屋の熱特性に応じた遅延時間をもって相関があるという仮定に基づいている。   An example of the relationship between the room temperature and the outside temperature at the same date and time is shown in FIG. At first glance, it is impossible to find a relationship between room temperature and outside temperature. On the other hand, as described above, the present embodiment is based on the assumption that there is a correlation between the change in the outside air temperature and the change in the room temperature with a delay time corresponding to the thermal characteristics of the room.

そのため、室温推定装置10は、記憶部13に格納された室温のデータと外気温のデータと日時とを用いて、室温と外気温との相関係数が最大になる遅延時間を求める評価部19を備える。評価部19は、着目する日(「抽出日」という)における室温のデータと外気温のデータとについて、室温が計測された日時と外気温が計測された日時とを相対的に偏移させ、室温のデータと外気温のデータとの相関係数が最大になる遅延時間(以下、「時間差」という)を求める。抽出日は、1日とは限らず、複数日であってもよい。以下では、室温を基準にして外気温を偏移させる例を説明するが、外気温を基準にして室温を偏移させてもよい。   Therefore, the room temperature estimation apparatus 10 uses the room temperature data, the outside air temperature data, and the date and time stored in the storage unit 13 to determine the delay time at which the correlation coefficient between the room temperature and the outside air temperature is maximized. Is provided. The evaluation unit 19 relatively shifts the date / time at which the room temperature was measured and the date / time at which the outside temperature was measured for the room temperature data and the outside temperature data on the day of interest (referred to as “extraction date”), A delay time (hereinafter referred to as “time difference”) at which the correlation coefficient between the room temperature data and the outside air temperature data is maximized is obtained. The extraction date is not limited to one day and may be a plurality of days. Hereinafter, an example in which the outside air temperature is shifted based on the room temperature will be described. However, the room temperature may be shifted based on the outside air temperature.

ここでは、日時tにおける室温および外気温のデータをそれぞれθ1(t),θ2(t)と表記し、室温のデータθ1(t)および外気温のデータθ2(t)を取得する時間間隔をpと表記する。日時tは、t=t0+n・pと表され、時間差Δtは、Δt=m・pと表される。t0は抽出日に応じて与えられる基準値であり、m,nは自然数である。   Here, the room temperature and outside air temperature data at date and time t are expressed as θ1 (t) and θ2 (t), respectively, and the time interval for acquiring the room temperature data θ1 (t) and the outside air temperature data θ2 (t) is p. Is written. The date and time t is expressed as t = t0 + n · p, and the time difference Δt is expressed as Δt = m · p. t0 is a reference value given according to the extraction date, and m and n are natural numbers.

上述した表記法を採用すると、室温のデータは、θ1(t0+p),θ1(t0+2p),θ1(t0+3p),…と表され、外気温のデータは、θ2(t0+p),θ2(t0+2p),θ2(t0+3p),…と表される。また、室温のデータθ1(t0+n・p)に対して時間差Δtだけ前の日時の外気温のデータは、θ2(t0+n・p−Δt)=θ1{t0+(n−m)p}になる。   When the above-described notation is adopted, the room temperature data is expressed as θ1 (t0 + p), θ1 (t0 + 2p), θ1 (t0 + 3p),. (T0 + 3p),... Further, the data of the outside air temperature on the date and time before the time difference Δt with respect to the room temperature data θ1 (t0 + n · p) is θ2 (t0 + n · p−Δt) = θ1 {t0 + (n−m) p}.

抽出日の期間を[t0+p,t0+q・p]とすると、抽出日の期間における室温θ1(t)の平均値a(θ1)は、θ1{t0+p},θ1{t0+2p},…,θ1{t0+q・p}の平均値である。また、外気温θ2(t)の平均値a(θ2)は、θ2(t0+(1−m)p),θ2(t0+(2−m)p),…θ2(t0+(q−m)p)の平均値である。なお、[t0+p,t0+q・p]は閉区間であって、t0+p,t0+2p,t0+3p,…,t0+q・pのq個の離散値を表す。   When the extraction date period is [t0 + p, t0 + q · p], the average value a (θ1) of the room temperature θ1 (t) in the extraction date period is θ1 {t0 + p}, θ1 {t0 + 2p},. p} is the average value. The average value a (θ2) of the outside air temperature θ2 (t) is θ2 (t0 + (1-m) p), θ2 (t0 + (2-m) p),... Θ2 (t0 + (q−m) p). Is the average value. [T0 + p, t0 + q · p] is a closed interval, and represents q discrete values of t0 + p, t0 + 2p, t0 + 3p,..., T0 + q · p.

評価部19は、これらの値を用いることにより、日時tの室温θ1(t)と、時間差Δtだけ前の日時(t−Δt)の外気温のデータθ2(t−Δt)との相関係数を求める。相関係数の演算は、一般に知られた演算であって、θ1(t)とθ2(t−Δt)との共分散を、θ1(t)の標準偏差とθ2(t−Δt)の標準偏差との積で除すことにより求められる。ただし、共分散および標準偏差を求めるための平均値a(θ1),a(θ2)は、上述した値が用いられる。室温のデータθ1(t)と外気温θ2(t−Δt)における日時tの範囲は、閉区間[t0+p,t0+q・p]の値が用いられる。   The evaluation unit 19 uses these values to correlate the room temperature θ1 (t) at the date and time t with the outside temperature data θ2 (t−Δt) at the date and time (t−Δt) before the time difference Δt. Ask for. The calculation of the correlation coefficient is a generally known calculation, and the covariance between θ1 (t) and θ2 (t−Δt) is expressed by the standard deviation of θ1 (t) and the standard deviation of θ2 (t−Δt). It is calculated by dividing by the product of. However, the above-described values are used as the average values a (θ1) and a (θ2) for obtaining the covariance and the standard deviation. The value of the closed interval [t0 + p, t0 + q · p] is used for the range of the date and time t in the room temperature data θ1 (t) and the outside air temperature θ2 (t−Δt).

評価部19は、mの値を変化させ、mの値ごとに相関係数を求める。本実施形態において、mの最大値は、時間間隔pとの積m・pが1日を超えないように制限される。たとえば、時間間隔pが1時間である場合、mの最大値は24を超えないように制限される。評価部19は、相関係数が最大になったときのmの値mmを用いて、時間差Δtを、Δt=mm・pとして算出する。   The evaluation unit 19 changes the value of m and obtains a correlation coefficient for each value of m. In the present embodiment, the maximum value of m is limited so that the product m · p with the time interval p does not exceed one day. For example, when the time interval p is 1 hour, the maximum value of m is limited so as not to exceed 24. The evaluation unit 19 calculates the time difference Δt as Δt = mm · p using the value mm of m when the correlation coefficient is maximized.

上述のようにして評価部19が求めた時間差Δtを用い、室温θ1(t)と外気温θ2(t−Δt)との関係を求めると、図5(b)のようになる。図示例では、室温θ1(t)と外気温θ2(t−Δt)とがほぼ線形関係になっており、一次関数に当て嵌め可能であることが予想される。   Using the time difference Δt obtained by the evaluation unit 19 as described above, the relationship between the room temperature θ1 (t) and the outside air temperature θ2 (t−Δt) is obtained as shown in FIG. In the illustrated example, the room temperature θ1 (t) and the outside air temperature θ2 (t−Δt) have a substantially linear relationship, and it is expected that the room temperature θ1 (t) can be applied to a linear function.

第2の予測式生成部152は、図5(b)に示した関係を用いて、外気温から室温を推定するための予測式を生成する。第2の予測式生成部152は、記憶部13に格納された室温のデータおよび外気温のデータのうち着目する抽出日のデータを抽出し、抽出した外気温のデータに、評価部19が求めた時間差Δtを付与する。さらに、第2の予測式生成部152は、室温θ1(t)と外気温θ2(t−Δt)との関係を線形関係とみなして、θ1(t)=α・θ2(t−Δt)+βという形式で表し、最小二乗法のような周知の演算により係数α,βを決定する。このようにして、評価部19が時間差Δtを求め、第2の予測式生成部152が係数α,βを決定すると、予測式が得られる。   The 2nd prediction formula production | generation part 152 produces | generates the prediction formula for estimating room temperature from external temperature using the relationship shown in FIG.5 (b). The second prediction formula generation unit 152 extracts the extraction date data of interest from the room temperature data and the outside air temperature data stored in the storage unit 13, and the evaluation unit 19 obtains the extracted outside air temperature data. A time difference Δt is given. Furthermore, the second prediction formula generation unit 152 regards the relationship between the room temperature θ1 (t) and the outside air temperature θ2 (t−Δt) as a linear relationship, and θ1 (t) = α · θ2 (t−Δt) + β The coefficients α and β are determined by a known calculation such as the least square method. In this way, when the evaluation unit 19 obtains the time difference Δt and the second prediction formula generation unit 152 determines the coefficients α and β, a prediction formula is obtained.

すなわち、第2の予測式生成部152は、記憶部13に格納された所定の抽出日における室温および外気温のデータを用いて、評価部19が時間差を求め、この時間差を付与した室温のデータと外気温のデータとから予測式を生成する。   That is, the second prediction formula generation unit 152 uses the room temperature and outside air temperature data on the predetermined extraction date stored in the storage unit 13 to determine the time difference, and the room temperature data to which the time difference is given. And a prediction formula are generated from the outside temperature data.

第2の予測式生成部152が生成した予測式は、室温が日射の影響を受けるか否かにかかわらず適用可能である。ただし、室温が日射の影響を受けない時間帯であれば、第1の予測式生成部151が生成した予測式によって、比較的よい精度で外気温から室温を推定することが可能である。また、第1の予測式生成部151は、第2の予測式生成部152と比較すると予測式の生成に伴う処理負荷が小さい。   The prediction formula generated by the second prediction formula generation unit 152 can be applied regardless of whether the room temperature is affected by solar radiation. However, if the room temperature is a time zone that is not affected by solar radiation, the room temperature can be estimated from the outside temperature with relatively good accuracy by the prediction formula generated by the first prediction formula generation unit 151. Further, the first prediction formula generation unit 151 has a smaller processing load associated with the generation of the prediction formula than the second prediction formula generation unit 152.

したがって、第1の予測式生成部151が生成した予測式で室温を予測可能な時間帯には、第1の予測式生成部151が生成した予測式を採用し、それ以外の時間帯には、第2の予測式生成部152が生成した予測式を用いるように役割を分担することが望ましい。すなわち、室温が日射の影響を受けない時間帯(日射のない時間帯)には第1の予測式生成部151が生成した予測式を採用し、室温が日射の影響を受ける時間帯には第2の予測式生成部152が生成した予測式を採用する。   Therefore, the prediction formula generated by the first prediction formula generation unit 151 is adopted in the time zone in which the room temperature can be predicted by the prediction formula generated by the first prediction formula generation unit 151, and in other time zones. It is desirable to share the role so that the prediction formula generated by the second prediction formula generation unit 152 is used. That is, the prediction formula generated by the first prediction formula generation unit 151 is adopted in a time zone in which the room temperature is not affected by solar radiation (a time zone in which there is no solar radiation). The prediction formula generated by the second prediction formula generation unit 152 is adopted.

室温推定装置10は、第2の予測式生成部152が生成した予測式を用いて外気温から室温を推定する場合に、着目する日時に対して評価部19が求めた時間差(遅延時間)だけ遡った日時における外気温のデータを取得する必要がある。ここに、着目する日時は、室温を推定しようとする日時を意味している。   When estimating the room temperature from the outside air temperature using the prediction formula generated by the second prediction formula generation unit 152, the room temperature estimation device 10 is only the time difference (delay time) obtained by the evaluation unit 19 with respect to the date and time of interest. It is necessary to acquire data on the outside temperature at the date and time. Here, the date and time of interest means the date and time when the room temperature is to be estimated.

そのため、室温推定部17は、予測推移取得部16が取得した外気温の予測される推移と、評価部19が求めた時間差とを用い、着目する日時から当該時間差だけ遡った外気温を求める。この外気温が求められると、室温推定部17は、求めた外気温を予測式生成部15が生成した予測式に当て嵌めることにより室温を推定する。すなわち、室温推定部17は、室温を推定しようとする日時から評価部19が求めた時間差だけ遡った時点の外気温を、外気温の予測される推移を用いて求め、この外気温を予測式に当て嵌めることにより、着目する日時の室温を推定する。   Therefore, the room temperature estimation unit 17 uses the predicted transition of the outside air temperature acquired by the prediction transition acquisition unit 16 and the time difference obtained by the evaluation unit 19 to obtain the outside air temperature that is traced back by the time difference from the date and time of interest. When the outside air temperature is obtained, the room temperature estimating unit 17 estimates the room temperature by fitting the obtained outside air temperature to the prediction expression generated by the prediction expression generating unit 15. That is, the room temperature estimation unit 17 obtains the outside air temperature at a time point that is back by the time difference obtained by the evaluation unit 19 from the date and time when the room temperature is to be estimated, using the predicted transition of the outside air temperature, and calculates the outside air temperature using a prediction formula. To estimate the room temperature of the date and time of interest.

上述した説明から明らかなように、本実施形態の予測式生成部15は、第1の予測式生成部151と第2の予測式生成部152とを備える。室温推定部17は、時計部14が計時している日時によって、室温が日射の影響を受けない時間帯か室温が日射の影響を受ける時間帯かを判断する。室温が日射の影響を受けない時間帯には、第1の予測式生成部151が生成した予測式が採用され、室温が日射の影響を受ける時間帯には、第2の予測式生成部152が生成した予測式が採用される。   As is clear from the above description, the prediction formula generation unit 15 of the present embodiment includes a first prediction formula generation unit 151 and a second prediction formula generation unit 152. The room temperature estimation unit 17 determines whether the room temperature is not affected by solar radiation or whether the room temperature is affected by solar radiation according to the date and time that the clock unit 14 measures. The prediction formula generated by the first prediction formula generation unit 151 is employed in a time zone in which the room temperature is not affected by solar radiation, and the second prediction formula generation unit 152 is used in a time zone in which the room temperature is affected by solar radiation. The prediction formula generated by is adopted.

実施形態1において、第1の予測式生成部151が生成する予測式には、季節による変化が生じることを説明した。第2の予測式生成部152が生成する予測式についても、季節によって変化することが容易に予想される。そのため、予測式を生成するために用いる室温および外気温を計測する抽出日は、季節ごとに定めることが望ましい。   In the first embodiment, it has been described that the prediction formula generated by the first prediction formula generation unit 151 changes depending on the season. The prediction formula generated by the second prediction formula generation unit 152 is also easily expected to change depending on the season. Therefore, it is desirable to determine the extraction date for measuring the room temperature and the outside temperature used for generating the prediction formula for each season.

すなわち、1年を複数に区分した分割期間が設定され、分割期間ごとに抽出日が定められる。分割期間は、1年を4〜24分割(4分割は春夏秋冬を反映した単位、24分割は節気を反映した単位)した期間から適宜に選択される。第2の予測式生成部152は、分割期間の数に相当する個数の予測式を生成する。   That is, a divided period in which one year is divided into a plurality of periods is set, and an extraction date is determined for each divided period. The division period is appropriately selected from a period in which one year is divided into 4 to 24 (4 division is a unit reflecting spring, summer, autumn and winter, and 24 division is a unit reflecting energy saving). The second prediction formula generation unit 152 generates a number of prediction formulas corresponding to the number of divided periods.

また、室温推定部17は、分割期間ごとに求められた複数の予測式から同じ分割期間に関して生成した予測式を選択し、選択した予測式を用いて外気温の推移から室温を推定する。なお、部屋の熱特性に経年変化が生じる可能性があるから、室温推定部17は、室温を推定する際に、分割期間ごとに求めた時間差を用いることが望ましい。ただし、室温推定部17は、いずれかの分割期間で求めた時間差を用いて室温を推定することが可能であり、また、複数の分割期間で求めた時間差の平均値を用いて室温を推定することが可能である。   Moreover, the room temperature estimation part 17 selects the prediction formula produced | generated regarding the same division period from the several prediction formula calculated | required for every division period, and estimates room temperature from transition of external temperature using the selected prediction formula. Since there is a possibility that the thermal characteristics of the room may change over time, the room temperature estimation unit 17 desirably uses the time difference obtained for each divided period when estimating the room temperature. However, the room temperature estimation unit 17 can estimate the room temperature using the time difference obtained in any divided period, and also estimates the room temperature using the average value of the time differences obtained in a plurality of divided periods. It is possible.

以上説明したように、本実施形態の室温推定部17は、室温が日射の影響を受けるか否かに応じて異なる予測式を用い、予測式に代入する外気温も異ならせているから、実施形態1の構成に比較すると、室温の予測精度が高められる可能性がある。他の構成および動作は実施形態1と同様である。   As described above, the room temperature estimation unit 17 of the present embodiment uses different prediction formulas depending on whether or not the room temperature is affected by solar radiation, and also varies the outside temperature to be substituted into the prediction formula. Compared to the configuration of the first form, the prediction accuracy of the room temperature may be improved. Other configurations and operations are the same as those of the first embodiment.

(実施形態3)
実施形態1、実施形態2では、室温推定装置10は、外気温のみによって室温を推定しているが、上述したように、室温を決める要因は、冷暖房を行わない場合、日射、換気、降雨、室内の人数などを含む。なお、冷暖房を行う場合、冷暖房装置が室温を管理する機能を有していると、室温は冷暖房装置の運転状態に依存し、予測式による室温の予測はできないから、以下では、冷暖房を行う場合については考慮しない。
(Embodiment 3)
In the first embodiment and the second embodiment, the room temperature estimation device 10 estimates the room temperature based only on the outside air temperature, but as described above, the factors that determine the room temperature are solar radiation, ventilation, rain, Includes the number of people in the room. In the case of air conditioning, if the air conditioning unit has a function of managing the room temperature, the room temperature depends on the operating state of the air conditioning unit and the room temperature cannot be predicted by a prediction formula. Is not considered.

外気温のほかに、日射、換気、降雨、人数の情報を考慮するとすれば、これらの情報と室温とを結びつけるモデルを設定し、これらの情報を数値化してモデルに当て嵌めることが考えられる。しかしながら、このようなモデルは、因果関係が複雑であって、コンピュータシミュレーションが必要になり、結果的に、入力すべきパラメータが増加し、処理負荷も大きくなるという問題が生じる。   Considering information on solar radiation, ventilation, rainfall, and number of people in addition to outside air temperature, it is possible to set up a model that links these information and room temperature, and then digitize this information and apply it to the model. However, such a model has a complicated causal relationship and requires computer simulation. As a result, there arises a problem that parameters to be input increase and a processing load increases.

そのため、本実施形態は、これらの情報を補正情報とし、補正情報ごとに予測式を設定することによって、パラメータの増加や処理負荷の増加を防止している。つまり、補正情報を用いる場合、予測式生成部15は、補正情報ごとに補正情報を複数段階に区分し、これらの段階の組み合わせに対応付けた予測式を生成する。なお、図1に示した実施形態1の構成に本実施形態の技術思想を適用した例を説明するが、実施形態2の構成に本実施形態の技術思想を適用することも可能である。   Therefore, in the present embodiment, these pieces of information are used as correction information, and a prediction formula is set for each piece of correction information, thereby preventing an increase in parameters and an increase in processing load. That is, when using the correction information, the prediction formula generation unit 15 classifies the correction information into a plurality of stages for each correction information, and generates a prediction formula associated with a combination of these stages. In addition, although the example which applied the technical idea of this embodiment to the structure of Embodiment 1 shown in FIG. 1 is demonstrated, it is also possible to apply the technical idea of this embodiment to the structure of Embodiment 2. FIG.

ここでは、日射、換気、降雨については、それぞれを有無のみの2段階に分け、また人数については、1人当たり室温を所定温度(たとえば、0.5℃)だけ上昇させるとみなす。このように補正情報の種類を単純化しておけば、補正情報の組み合わせ数は有限であって比較的少数になる。   Here, solar radiation, ventilation, and rainfall are each divided into two stages of presence / absence, and regarding the number of persons, it is assumed that the room temperature per person is increased by a predetermined temperature (for example, 0.5 ° C.). If the types of correction information are simplified in this way, the number of combinations of correction information is finite and relatively small.

予測式生成部15は、個々の組み合わせに応じた予測式を設定する。すなわち、外気温から室温を求める予測式の係数α,βを、日射、換気、降雨に応じて補正した予測式を生成する。なお、室内の人数は、予測式の係数βにのみ反映されるから、人数ごとに予測式を生成する必要なく、室温推定部17において、予測式から求めた室温に、所定温度の人数倍を加算する補正を行えばよい。したがって、上の例では、日射、換気、降雨の補正情報から8通りの予測式が生成される。   The prediction formula generation unit 15 sets a prediction formula corresponding to each combination. That is, a prediction formula is generated by correcting the coefficients α and β of the prediction formula for obtaining the room temperature from the outside air temperature in accordance with solar radiation, ventilation, and rainfall. In addition, since the number of people in the room is reflected only in the coefficient β of the prediction formula, it is not necessary to generate a prediction formula for each number of people, and the room temperature estimation unit 17 adds the number of people at a predetermined temperature to the room temperature obtained from the prediction formula. Correction to be added may be performed. Therefore, in the above example, eight prediction formulas are generated from correction information of solar radiation, ventilation, and rainfall.

室温推定装置10は、図6に示すように、日射検知部33、換気検知部34、降雨検知部35、人数検知部36から補正情報を取得する補正情報取得部32を備える。   As illustrated in FIG. 6, the room temperature estimation apparatus 10 includes a correction information acquisition unit 32 that acquires correction information from a solar radiation detection unit 33, a ventilation detection unit 34, a rainfall detection unit 35, and a number of people detection unit 36.

日射検知部33は、フォトダイオード、フォトトランジスタのような受光素子と、受光素子の出力を閾値と比較して光量を判断する判断部とを備えていればよい。また、部屋への日射の影響は、カーテンやシャッタの開閉の状態にも依存するから、日射検知部33は、カーテンやシャッタの開閉状態を検知する機能を備えていることが望ましい。   The solar radiation detection unit 33 may include a light receiving element such as a photodiode or a phototransistor, and a determination unit that determines the light amount by comparing the output of the light receiving element with a threshold value. In addition, since the influence of solar radiation on the room also depends on the open / closed state of the curtain and shutter, it is desirable that the solar radiation detection unit 33 has a function of detecting the open / closed state of the curtain and shutter.

換気検知部34は、換気扇が運転中か否かを検知する構成、窓の開閉状態を検知する構成、室内の気流を計測する構成などから選択される。降雨検知部35は、所定期間ごとに雨滴を集めて質量を計測する構成、あるいは屋外の画像から雨滴を検出する構成などが採用される。また、降雨に関する補正情報は、サービス提供者がインターネットのような電気通信回線を通して提供している情報によって得るようにしてもよい。人数検知部36は、室内の画像から室内の人数を計測する構成が採用される。   The ventilation detector 34 is selected from a configuration for detecting whether or not the ventilation fan is in operation, a configuration for detecting the open / closed state of the window, a configuration for measuring the airflow in the room, and the like. The rain detection unit 35 employs a configuration that collects raindrops for each predetermined period and measures mass, or a configuration that detects raindrops from an outdoor image. Further, the correction information related to the rain may be obtained by information provided by the service provider through a telecommunication line such as the Internet. The number of persons detecting unit 36 employs a configuration that measures the number of persons in the room from the indoor image.

なお、日射、換気、降雨について、有無だけではなく、程度を3段階以上の複数段階で表すようにしてもよい。たとえば、日射について、強、中、弱、微弱の4段階などに分けることが可能である。換気、降雨についても同様であって、3段階以上の複数段階に分けることが可能である。   In addition, about solar radiation, ventilation, and rainfall, not only the presence / absence, but also the degree may be expressed in a plurality of stages of three or more. For example, it is possible to divide solar radiation into four levels of strong, medium, weak and weak. The same applies to ventilation and rainfall, and it is possible to divide into three or more stages.

室温推定部17は、補正情報取得部32が取得した補正情報に基づいて予測式を選択し、選択した予測式を用いて外気温から室温を推定する。なお、日射、換気、降雨、人数に応じた係数α,βの補正量は、実測値に基づいて統計的に定められる。他の構成および動作は実施形態1、実施形態2と同様である。   The room temperature estimation unit 17 selects a prediction formula based on the correction information acquired by the correction information acquisition unit 32, and estimates the room temperature from the outside temperature using the selected prediction formula. Note that the correction amounts of the coefficients α and β according to solar radiation, ventilation, rainfall, and the number of people are statistically determined based on actually measured values. Other configurations and operations are the same as those in the first and second embodiments.

10 室温推定装置
11 室温取得部
12 外気温取得部
13 記憶部
14 時計部
15 予測式生成部
16 予測推移取得部
17 室温推定部
18 報知出力部
19 評価部
21 室温計測部
22 外気温計測部
23 報知器
31 判定部
32 補正情報取得部
161 第1の予測式生成部
162 第2の予測式生成部
DESCRIPTION OF SYMBOLS 10 Room temperature estimation apparatus 11 Room temperature acquisition part 12 Outside temperature acquisition part 13 Memory | storage part 14 Clock part 15 Prediction formula production | generation part 16 Prediction transition acquisition part 17 Room temperature estimation part 18 Notification output part 19 Evaluation part 21 Room temperature measurement part 22 Outside temperature measurement part 23 Alarm 31 Determination Unit 32 Correction Information Acquisition Unit 161 First Prediction Formula Generation Unit 162 Second Prediction Formula Generation Unit

Claims (6)

室温計測部から室温のデータを取得する室温取得部と、
外気温計測部から外気温のデータを取得する外気温取得部と、
前記室温取得部が取得した室温のデータおよび前記外気温取得部が取得した外気温のデータをそれぞれが計測された日時に対応付けて格納する記憶部と、
前記記憶部に格納された所定の抽出期間における複数日についてそれぞれ複数の時刻における時刻ごとの室温および外気温のデータを用い、時刻ごとの室温のデータと外気温のデータとの関係を表す複数の回帰予測式をそれぞれ予測式として求める予測式生成部と、
外気温について予測される推移を取得する予測推移取得部と、
前記予測推移取得部が取得した外気温の推移を用いて着目する時刻の外気温を前記予測式生成部が求めた当該時刻における前記予測式に当て嵌めることにより室温を推定する室温推定部とを備える
室温推定装置。
A room temperature acquisition unit for acquiring room temperature data from the room temperature measurement unit;
An outside temperature acquisition unit that acquires outside temperature data from the outside temperature measurement unit;
A storage unit that stores the room temperature data acquired by the room temperature acquisition unit and the external temperature data acquired by the outside air temperature acquisition unit in association with the date and time when each was measured;
Using room temperature and outside air temperature data for each time at a plurality of times for a plurality of days in a predetermined extraction period stored in the storage unit, a plurality of times representing the relationship between the room temperature data and the outside air temperature data for each time A prediction formula generation unit that obtains each regression prediction formula as a prediction formula;
A predicted transition acquisition unit for acquiring a predicted transition of the outside temperature;
A room temperature estimation unit for estimating the room temperature by fitting the outside temperature at the time of interest using the transition of the outside temperature acquired by the prediction transition acquisition unit to the prediction formula at the time obtained by the prediction formula generation unit; Equipped with room temperature estimation device.
前記抽出期間は、気象環境に基づいて1年を複数に区分した分割期間ごとに定められ、
前記室温推定部は、前記分割期間における室温の予測に、当該分割期間に定めた前記抽出期間における室温および外気温のデータを用いて求めた前記予測式を適用する
請求項1記載の室温推定装置。
The extraction period is determined for each divided period divided into one year based on the weather environment,
The room temperature estimation device according to claim 1, wherein the room temperature estimation unit applies the prediction formula obtained by using the room temperature and outside air temperature data in the extraction period determined in the division period to the room temperature prediction in the division period. .
外気温のほかに室温に影響を与え、かつ複数の状態から選択される補正情報を取得する補正情報取得部をさらに備え、
前記室温推定部は、前記補正情報取得部が取得した補正情報の状態ごとに求められた複数の前記予測式から当該状態に関して求めた前記予測式を選択し、選択した前記予測式を用いて室温を推定する
請求項1又は2記載の室温推定装置。
In addition to the outside temperature, it further includes a correction information acquisition unit that affects the room temperature and acquires correction information selected from a plurality of states,
The room temperature estimation unit selects the prediction formula obtained for the state from the plurality of prediction formulas obtained for each state of the correction information acquired by the correction information acquisition unit, and uses the selected prediction formula to select a room temperature The room temperature estimation device according to claim 1 or 2.
前記室温推定部が推定した室温を報知器に出力する報知出力部をさらに備える
請求項1〜3のいずれか1項に記載の室温推定装置。
The room temperature estimation apparatus according to claim 1, further comprising: a notification output unit that outputs the room temperature estimated by the room temperature estimation unit to a notification device.
前記外気温取得部は、電気通信回線を通して提供される外気温のデータを取得する
請求項1〜4のいずれか1項に記載の室温推定装置。
The room temperature estimation device according to any one of claims 1 to 4, wherein the outside air temperature acquisition unit acquires outside air temperature data provided through a telecommunication line.
コンピュータを、請求項1〜5のいずれか1項に記載の室温推定装置として機能させるためのプログラム。   The program for functioning a computer as the room temperature estimation apparatus of any one of Claims 1-5.
JP2013003586A 2013-01-11 2013-01-11 Room temperature estimation device, program Active JP6160945B2 (en)

Priority Applications (4)

Application Number Priority Date Filing Date Title
JP2013003586A JP6160945B2 (en) 2013-01-11 2013-01-11 Room temperature estimation device, program
PCT/JP2014/000056 WO2014109290A1 (en) 2013-01-11 2014-01-09 Room temperature estimating device, program
CN201480004442.1A CN104919252B (en) 2013-01-11 2014-01-09 Room temperature estimation unit and room temperature method of estimation
EP14738327.7A EP2944891B1 (en) 2013-01-11 2014-01-09 Room temperature estimating device, program

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
JP2013003586A JP6160945B2 (en) 2013-01-11 2013-01-11 Room temperature estimation device, program

Publications (2)

Publication Number Publication Date
JP2014134360A true JP2014134360A (en) 2014-07-24
JP6160945B2 JP6160945B2 (en) 2017-07-12

Family

ID=51166940

Family Applications (1)

Application Number Title Priority Date Filing Date
JP2013003586A Active JP6160945B2 (en) 2013-01-11 2013-01-11 Room temperature estimation device, program

Country Status (4)

Country Link
EP (1) EP2944891B1 (en)
JP (1) JP6160945B2 (en)
CN (1) CN104919252B (en)
WO (1) WO2014109290A1 (en)

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104913441A (en) * 2015-05-29 2015-09-16 广东美的制冷设备有限公司 Temperature adjustment time prediction method of air conditioner, controller and air conditioner
JP2016205739A (en) * 2015-04-24 2016-12-08 京セラ株式会社 Power control method, power control device and power control system
JP2019086243A (en) * 2017-11-08 2019-06-06 株式会社東芝 Air conditioning capacity estimation device, air conditioning capacity estimation method and program
JP2019105411A (en) * 2017-12-13 2019-06-27 株式会社デンソーウェーブ Whole building air conditioning system
JP2019120568A (en) * 2017-12-29 2019-07-22 ナガノサイエンス株式会社 Temperature characteristic evaluation method
JP2020154353A (en) * 2019-03-18 2020-09-24 株式会社オーガニックnico Method for generating environment data in house
WO2022145565A1 (en) * 2020-12-30 2022-07-07 엘지전자 주식회사 Cooktop
JP7361625B2 (en) 2020-02-14 2023-10-16 三菱電機ビルソリューションズ株式会社 air conditioning system

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPWO2019146067A1 (en) * 2018-01-26 2020-06-11 三菱電機株式会社 Control systems, air conditioners and servers

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH10205852A (en) * 1997-01-22 1998-08-04 Shinryo Corp Method for air-conditioning control by prediction of heat load in building
JP2001227792A (en) * 2000-02-16 2001-08-24 Daikin Ind Ltd Method of estimating air-conditioning load, and its device
JP2006118836A (en) * 2004-10-25 2006-05-11 Ntt Power & Building Facilities Inc Air conditioner control system and method
JP2012172924A (en) * 2011-02-22 2012-09-10 Panasonic Corp Energy management system
JP2012241916A (en) * 2011-05-16 2012-12-10 Fujitsu General Ltd Air conditioner

Family Cites Families (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2507850B2 (en) 1992-07-24 1996-06-19 ミサワホーム株式会社 Heat pump heating system
JPH1123037A (en) * 1997-06-27 1999-01-26 Mitsubishi Electric Corp Regenerative air conditioner
US7502768B2 (en) * 2004-02-27 2009-03-10 Siemens Building Technologies, Inc. System and method for predicting building thermal loads
JP4393277B2 (en) 2004-06-04 2010-01-06 富士通テン株式会社 Car room temperature monitoring device
JP4964720B2 (en) * 2007-09-14 2012-07-04 新日本製鐵株式会社 Method, apparatus, and computer program for estimating future temperature of measured object
JP2009204195A (en) * 2008-02-26 2009-09-10 Mitsubishi Heavy Ind Ltd Air conditioning system and power consumption estimating device for building air-conditioning equipment
US8315961B2 (en) * 2009-07-14 2012-11-20 Mitsubishi Electric Research Laboratories, Inc. Method for predicting future environmental conditions
CN101929721B (en) * 2010-09-25 2015-04-01 上海建坤信息技术有限责任公司 Predicting method of central air conditioner energy-conservation control autoregressive (AR) model load predicting system
CN102705957B (en) * 2012-06-07 2014-06-11 华南理工大学 Method and system for predicting hourly cooling load of central air-conditioner in office building on line

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH10205852A (en) * 1997-01-22 1998-08-04 Shinryo Corp Method for air-conditioning control by prediction of heat load in building
JP2001227792A (en) * 2000-02-16 2001-08-24 Daikin Ind Ltd Method of estimating air-conditioning load, and its device
JP2006118836A (en) * 2004-10-25 2006-05-11 Ntt Power & Building Facilities Inc Air conditioner control system and method
JP2012172924A (en) * 2011-02-22 2012-09-10 Panasonic Corp Energy management system
JP2012241916A (en) * 2011-05-16 2012-12-10 Fujitsu General Ltd Air conditioner

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2016205739A (en) * 2015-04-24 2016-12-08 京セラ株式会社 Power control method, power control device and power control system
CN104913441A (en) * 2015-05-29 2015-09-16 广东美的制冷设备有限公司 Temperature adjustment time prediction method of air conditioner, controller and air conditioner
CN104913441B (en) * 2015-05-29 2017-06-27 广东美的制冷设备有限公司 The temperature adjustment time forecasting methods of air-conditioner, controller and air-conditioner
JP2019086243A (en) * 2017-11-08 2019-06-06 株式会社東芝 Air conditioning capacity estimation device, air conditioning capacity estimation method and program
JP2019105411A (en) * 2017-12-13 2019-06-27 株式会社デンソーウェーブ Whole building air conditioning system
JP2019120568A (en) * 2017-12-29 2019-07-22 ナガノサイエンス株式会社 Temperature characteristic evaluation method
JP7079955B2 (en) 2017-12-29 2022-06-03 ナガノサイエンス株式会社 Temperature characterization method
JP2020154353A (en) * 2019-03-18 2020-09-24 株式会社オーガニックnico Method for generating environment data in house
JP7361625B2 (en) 2020-02-14 2023-10-16 三菱電機ビルソリューションズ株式会社 air conditioning system
WO2022145565A1 (en) * 2020-12-30 2022-07-07 엘지전자 주식회사 Cooktop

Also Published As

Publication number Publication date
EP2944891A1 (en) 2015-11-18
EP2944891A4 (en) 2016-04-20
CN104919252A (en) 2015-09-16
WO2014109290A1 (en) 2014-07-17
CN104919252B (en) 2017-10-24
EP2944891B1 (en) 2019-09-11
JP6160945B2 (en) 2017-07-12

Similar Documents

Publication Publication Date Title
JP6160945B2 (en) Room temperature estimation device, program
US11835394B2 (en) System and method for evaluating changes in the efficiency of an HVAC system
US11168915B2 (en) System and method for characterization of retrofit opportunities in building using data from interval meters
CN107250928B (en) Optimizing and controlling energy consumption of a building
US10082313B2 (en) Instruction device, and air conditioning system
Ioannou et al. Energy performance and comfort in residential buildings: Sensitivity for building parameters and occupancy
US9714772B2 (en) HVAC controller configurations that compensate for heating caused by direct sunlight
US20150276495A1 (en) Computer-Implemented System And Method For Externally Inferring An Effective Indoor Temperature In A Building
US20130060391A1 (en) Energy management system
US8949051B2 (en) Apparatus and method for sub-metering of household devices
US10754918B2 (en) System for providing remote building efficiency audits
US20170329357A1 (en) System and method for identifying drivers of climate control system demand
JP5988217B2 (en) Room thermal characteristics estimation device, program
JP2013228374A (en) Solar radiation situation prediction system, solar radiation situation prediction apparatus and solar radiation situation prediction method
US10885238B1 (en) Predicting future indoor air temperature for building
JP5635220B1 (en) Heat storage amount prediction device, heat storage amount prediction method and program
JP6384791B2 (en) Thermal insulation performance estimation device, program
US20170242940A1 (en) System for providing remote building efficiency audits for solar sensitivity
US11514537B2 (en) Decoupled modeling methods and systems
US10229464B2 (en) System for providing remote building efficiency audits for wind sensitivity
US20230383976A1 (en) Energy Consumption Estimator for Building Climate Conditioning Systems
JP2015090233A (en) Air conditioning system, and program
Ota et al. Energy efficient residential thermal control with wireless sensor networks: A case study for air conditioning in California

Legal Events

Date Code Title Description
A711 Notification of change in applicant

Free format text: JAPANESE INTERMEDIATE CODE: A711

Effective date: 20141006

A621 Written request for application examination

Free format text: JAPANESE INTERMEDIATE CODE: A621

Effective date: 20151009

A131 Notification of reasons for refusal

Free format text: JAPANESE INTERMEDIATE CODE: A131

Effective date: 20160719

A521 Request for written amendment filed

Free format text: JAPANESE INTERMEDIATE CODE: A523

Effective date: 20160920

A02 Decision of refusal

Free format text: JAPANESE INTERMEDIATE CODE: A02

Effective date: 20161220

A521 Request for written amendment filed

Free format text: JAPANESE INTERMEDIATE CODE: A523

Effective date: 20170321

A911 Transfer to examiner for re-examination before appeal (zenchi)

Free format text: JAPANESE INTERMEDIATE CODE: A911

Effective date: 20170330

TRDD Decision of grant or rejection written
A01 Written decision to grant a patent or to grant a registration (utility model)

Free format text: JAPANESE INTERMEDIATE CODE: A01

Effective date: 20170509

A61 First payment of annual fees (during grant procedure)

Free format text: JAPANESE INTERMEDIATE CODE: A61

Effective date: 20170602

R151 Written notification of patent or utility model registration

Ref document number: 6160945

Country of ref document: JP

Free format text: JAPANESE INTERMEDIATE CODE: R151