JPH0593539A - Controller for air conditioning system - Google Patents
Controller for air conditioning systemInfo
- Publication number
- JPH0593539A JPH0593539A JP3253542A JP25354291A JPH0593539A JP H0593539 A JPH0593539 A JP H0593539A JP 3253542 A JP3253542 A JP 3253542A JP 25354291 A JP25354291 A JP 25354291A JP H0593539 A JPH0593539 A JP H0593539A
- Authority
- JP
- Japan
- Prior art keywords
- air conditioning
- air
- energy consumption
- thermal environment
- value
- 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.)
- Pending
Links
Landscapes
- Air Conditioning Control Device (AREA)
Abstract
Description
【０００１】[0001]
【産業上の利用分野】本発明は、室内を温熱的に快適状
態に制御する空調機器群の空調システム制御装置に関す
るものである。BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an air conditioning system control device for an air conditioning equipment group that controls a room to be in a warm and comfortable state.
【０００２】[0002]
【従来の技術】室内空間の空調系の設備群の自動運転制
御を行う制御装置としては、温熱環境の制御装置として
いろいろ提案されている。例えば、室内の温熱要素を調
整する空調機器と、室内の温熱状態を検出するセンサ情
報からその温熱快適度を計算しその計算された快適度か
ら、換気装置などの空調装置を制御する制御装置（特開
平３−７０９３０公報参照）等がある。これらの制御装
置では、室内の温熱快適度、たとえばＰＭＶ（フ゜レテ゛ィクテ
ット゛ミーンホ゛ウト（Predicted Mean Vote））値やＳＥＴ＊値
など（参考文献、中山昭雄編”温熱生理学”理工学社）
を最適な値にするように、室内の複数空調機器の制御を
行っている。2. Description of the Related Art As a control device for automatically controlling the operation of a group of air-conditioning systems in an indoor space, various control devices for a thermal environment have been proposed. For example, an air conditioner that adjusts a heating element in a room and a controller that controls an air conditioner such as a ventilator from the calculated comfort level based on the calculated comfort level from sensor information that detects a heat state in the room. JP-A-3-70930)). In these control devices, indoor thermal comfort, such as PMV (Predicted Mean Vote) value and SET * value, etc.
A plurality of air conditioners in the room are controlled so that
【０００３】[0003]
【発明が解決しようとする課題】しかしながら、このよ
うな空調システム制御装置では、室内の温熱環境を快適
に制御するために必要となる空調機器の使用エネルギー
量について考慮がなされていない。よって、温熱環境を
最適にするために、非常にエネルギー消費量の大きい機
器の運転を重点的に行うことにより非常にエネルギー効
率の悪い制御をしてしまう可能性があった。However, in such an air conditioning system control device, the amount of energy used by the air conditioning equipment necessary for comfortably controlling the indoor thermal environment is not taken into consideration. Therefore, in order to optimize the thermal environment, there is a possibility that control with extremely low energy efficiency may be performed by focusing on the operation of equipment that consumes a large amount of energy.
【０００４】本発明は上記従来の空調システム制御装置
の課題を解決するもので、室内の温熱環境を最適にする
ために必要とされるエネルギー量を考慮して各空調機器
の設定温度を決定することにより、少ない消費エネルギ
ーで快適な温熱環境を居住者に提供することを目的とす
る。The present invention solves the above-mentioned problems of the conventional air conditioning system control device, and determines the set temperature of each air conditioning equipment in consideration of the amount of energy required to optimize the indoor thermal environment. By doing so, it aims to provide the residents with a comfortable thermal environment with low energy consumption.
【０００５】[0005]
【課題を解決するための手段】この目的を達成するため
に本発明の空調システム制御装置は、まず室内温熱環境
量を得る手段、室内に設置された複数の空調機器の状態
量を得る手段、前記温熱環境量と空調機器の状態量から
各空調機器のエネルギー消費率を計算する手段、前記温
熱環境量から各空調機器の温熱快適度寄与率を計算する
手段、各空調機器の設定値を前記温熱快適度を目標値に
制御するために最もエネルギー効率が高い組合せとなる
ように決定する決定手段、決定された設定値により各空
調機器を制御する機器制御手段とを備える。In order to achieve this object, an air conditioning system control device of the present invention comprises a means for obtaining an indoor thermal environment quantity, a means for obtaining a state quantity of a plurality of air conditioners installed in a room, Means for calculating the energy consumption rate of each air conditioning equipment from the thermal environment quantity and the state quantity of the air conditioning equipment, means for calculating the thermal comfort contribution rate of each air conditioning equipment from the thermal environment quantity, the set value of each air conditioning equipment The control unit includes a determination unit that determines a combination having the highest energy efficiency in order to control the thermal comfort level to a target value, and a device control unit that controls each air conditioner according to the determined set value.
【０００６】[0006]
【作用】この構成により、温熱快適度を最適に制御する
際に、各空調機器で消費されると予想されるエネルギー
消費量を推定計算し、その中から最も効率的に温熱快適
度を目標値に制御することができる機器から優先的に設
定値を変更するように制御を行うことにより、空調シス
テム全体で消費されるエネルギー量を最小に抑えること
が可能になる。With this configuration, when the thermal comfort level is optimally controlled, the energy consumption expected to be consumed by each air conditioner is estimated and calculated, and the thermal comfort level is the most efficient target value among them. It is possible to minimize the amount of energy consumed in the entire air conditioning system by performing control so that the device that can be controlled to change the setting value preferentially.
【０００７】また、エネルギー消費率を計算する手段に
おいて、過去の空調機器の運転実績からエネルギー消費
率を学習する場合は、あらかじめ各空調機器のエネルギ
ー消費率が正確に求められない場合でも、運転していく
うちに正確なエネルギー消費率が求められ、またその値
を用いて空調機器の設定値が決定されるため、より正確
な省エネルギー運転が可能となる。Further, in the means for calculating the energy consumption rate, when the energy consumption rate is learned from the past operation results of the air conditioning equipment, even if the energy consumption rate of each air conditioning equipment cannot be accurately obtained in advance, the operation is performed. Accurate energy consumption rate is obtained over time, and the set value of the air conditioner is determined using the value, which enables more accurate energy saving operation.
【０００８】[0008]
【実施例】以下、本発明の実施例について図面を参照し
て説明する。Embodiments of the present invention will be described below with reference to the drawings.
【０００９】図１は本発明にかかる空調システム制御装
置を利用したシステムの一実施例を示すシステム構成図
である。各空調機器、例えばエアコン７、輻射パネル
８、換気装置９、加湿器１０などの設備機器と、空調シ
ステム制御装置１は、各インターフェースユニット６を
介してＨＢＳ（ホームバスシステム）と呼ばれる家庭内
情報ネットワークシステムによって結ばれている。空調
システム制御装置１は、温度検出器２、湿度検出器３、
輻射温度検出器４、活動量検出器５などから成るセンサ
管理装置１１により検出される室内温熱環境のセンサ情
報をＨＢＳを通じた通信によって取得し、各空調機器
７、８、９、１０の機器制御を同様に通信によって行
う。また、各検出器２、３、４、５と各設備機器７、
８、９、１０は、同一の室内空間１２の中に配置されて
いる。FIG. 1 is a system configuration diagram showing an embodiment of a system using an air conditioning system control device according to the present invention. Each air conditioner, for example, the air conditioner 7, the radiation panel 8, the ventilation device 9, the humidifier 10 and other equipment devices, and the air conditioning system control device 1 are connected to each other via the interface units 6 and are called home information called HBS (home bus system). It is connected by a network system. The air conditioning system control device 1 includes a temperature detector 2, a humidity detector 3,
The sensor information of the indoor thermal environment detected by the sensor management device 11 including the radiation temperature detector 4, the activity amount detector 5, and the like is acquired by communication through the HBS, and the device control of each air conditioner 7, 8, 9, 10 is performed. Is similarly performed by communication. In addition, each detector 2, 3, 4, 5 and each equipment 7,
8, 9, and 10 are arranged in the same indoor space 12.
【００１０】図２に本発明による空調システム制御装置
１を具体化したものを示す。空調システム制御装置１
は、前記したセンサ管理装置１１の一例としての温熱環
境量取得手段１３によって得た温熱情報から室内の温熱
快適度を計算する温熱快適度手段１４、室内空調機器
７、８、９、１０の状態量を取得する空調機器状態量取
得手段１５、温熱環境量取得手段１３からの温熱環境量
と空調機器状態量取得手段１５からの空調機器状態量と
からＰＭＶ寄与率を計算するＰＭＶ寄与率計算手段１
６、温熱環境量取得手段１３からの温熱環境量と空調機
器状態量取得手段１５からの空調機器状態量とＰＭＶ寄
与率計算手段１６からのＰＭＶ寄与率とからエネルギー
消費率を計算するエネルギー消費率計算手段１７、エネ
ルギー消費率計算手段１７からのエネルギー消費率から
ＰＭＶ制御効率を計算するＰＭＶ制御効率計算手段１
８、温熱快適度計算手段１４からの温熱快適度とＰＭＶ
詩得漁港率計算手段１８からのＰＭＶ制御効率から、温
熱快適度を最適に、且つ消費エネルギーを最小にするよ
うな各空調機器７、８、９、１０の設定を計算する空調
機器設定決定手段１９、および空調機器制定決定手段１
９からの出力信号に基づいて各空調機器７、８、９、１
０を実際に制御する空調機器制御手段２０を備えてい
る。FIG. 2 shows an embodiment of the air conditioning system control device 1 according to the present invention. Air conditioning system controller 1
Is the state of the thermal comfort unit 14 for calculating the thermal comfort level in the room from the thermal information obtained by the thermal environment quantity acquisition unit 13 as an example of the sensor management device 11 and the state of the indoor air conditioners 7, 8, 9, 10. A PMV contribution rate calculation means for calculating a PMV contribution rate from the thermal environment quantity from the air conditioning equipment state quantity acquisition means 15 for obtaining the quantity and the thermal environment quantity from the thermal environment quantity acquisition means 13 and the air conditioning equipment state quantity from the air conditioning equipment state quantity acquisition means 15. 1
6. Energy consumption rate for calculating the energy consumption rate from the thermal environment quantity from the thermal environment quantity acquisition means 13, the air conditioning equipment state quantity from the air conditioning equipment state quantity acquisition means 15, and the PMV contribution rate from the PMV contribution rate calculation means 16 PMV control efficiency calculation means 1 for calculating PMV control efficiency from the energy consumption rate from the calculation means 17 and the energy consumption rate calculation means 17.
8. Thermal comfort and PMV from thermal comfort calculator 14
From the PMV control efficiency from the Shitoku fishing port rate calculating means 18, an air conditioning equipment setting determining means for calculating settings of each air conditioning equipment 7, 8, 9, 10 that optimizes thermal comfort and minimizes energy consumption. 19, and air conditioner establishment decision means 1
Air conditioners 7, 8, 9, 1 based on output signals from
The air conditioning equipment control means 20 which actually controls 0 is provided.
【００１１】次に、かかる構成における空調システム制
御装置１の動作を以下に説明する。Next, the operation of the air conditioning system control device 1 having such a configuration will be described below.
【００１２】まず、温熱環境量取得手段１３により、温
熱情報（室温、輻射温度、湿度、活動量）を室内のセン
サ２、３、４、５から直接ＨＢＳを通じて得る。また人
情報のうち衣服量は、空調システム制御装置１に備えら
れたカレンダー機能により、現在の季節から推定する。
つまり、冬は衣服量は、１．５ｃｌｏ程度、夏は、０．
５ｃｌｏ程度の値とする。これらの情報から、温熱快適
度計算手段１４により現在の室内の温熱快適度を計算す
る。ここでは温熱快適度としては、ＩＳＯにより規定さ
れた上記ＰＭＶ値を用いている。率 さらに、図３のフローチャートに示すように、ＰＭＶ寄
与率計算手段１６によって、温熱情報や空調機器状態量
でＰＭＶ寄与率を計算する（ステップＳ１）。すなわ
ち、ＰＭＶ寄与率とは、その空調機器の設定をある量だ
け変えたときにどれだけＰＭＶ値が変化するかを表わす
値として定義される量である。例えば、エアコン７の場
合は、エアコン７の設定温度Ｔの変化量ｄＴに対する、
ＰＭＶの変化量ｄＰＭＶ値を現在の温熱環境（室温Ｔ、
平均輻射温度ＭＲＴ、風速Ｖ、湿度Ｈ、活動量Ｍ、衣服
量Ｃ）からの環境変化分により計算する。ここで、風速
Ｖはエアコン７を構成する冷凍サイクルの過熱度や、コ
ンプレッサー回転数等とともに空調機器状態量取得手段
１５により得られる。First, the thermal environment quantity acquisition means 13 obtains thermal information (room temperature, radiation temperature, humidity, activity amount) from the indoor sensors 2, 3, 4, 5 directly through the HBS. The amount of clothes in the personal information is estimated from the current season by the calendar function provided in the air conditioning system control device 1.
In other words, the amount of clothes is about 1.5 clo in winter and 0.
The value is about 5 cl. From this information, the thermal comfort calculation means 14 calculates the current thermal comfort in the room. Here, the above-mentioned PMV value defined by ISO is used as the thermal comfort level. Rate Further, as shown in the flowchart of FIG. 3, the PMV contribution rate calculation means 16 calculates the PMV contribution rate from the thermal information and the air conditioner state quantity (step S1). That is, the PMV contribution rate is an amount defined as a value indicating how much the PMV value changes when the setting of the air conditioner is changed by a certain amount. For example, in the case of the air conditioner 7, with respect to the change amount dT of the set temperature T of the air conditioner 7,
The change amount of PMV dPMV value is set to the current thermal environment (room temperature T,
It is calculated from the average radiant temperature MRT, the wind speed V, the humidity H, the activity amount M, and the clothing amount C) based on the environmental change. Here, the wind speed V is obtained by the air conditioner state quantity acquisition unit 15 together with the degree of superheat of the refrigeration cycle that constitutes the air conditioner 7, the number of revolutions of the compressor, and the like.
【００１３】ｄＰＭＶ＝ＰＭＶ（Ｔ＋ｄＴ、ＭＲＴ、
Ｖ、Ｈ、Ｍ、Ｃ）−ＰＭＶ（Ｔ、ＭＲＴ、Ｖ、Ｈ、Ｍ、
Ｃ） これにより、現在の温熱環境におけるエアコン７の設定
温度ＴのＰＭＶ値への寄与率ＣＴが以下の式により計算
される。DPMV = PMV (T + dT, MRT,
V, H, M, C) -PMV (T, MRT, V, H, M,
C) Thereby, the contribution ratio CT of the set temperature T of the air conditioner 7 to the PMV value in the current thermal environment is calculated by the following formula.
【００１４】ＣＴ（エアコン）＝ｄＰＭＶ／ｄＴ また輻射パネル８の場合、輻射パネル８の設定温度Ｔの
変化量ｄＴに対し、エアコン７の場合と同様に以下の式
によってＰＭＶの変化量を計算する。ここで、Ｐは輻射
パネル８の占める面積の室内の輻射全方位面積に対する
割合を表わす。CT (air conditioner) = dPMV / dT Further, in the case of the radiation panel 8, the change amount of PMV is calculated by the following formula in the same manner as in the case of the air conditioner 7 with respect to the variation amount dT of the set temperature T of the radiation panel 8. .. Here, P represents the ratio of the area occupied by the radiation panel 8 to the total radiation area in the room.
【００１５】ｄＰＭＶ＝ＰＭＶ（Ｔ、ＭＲＴ＋ｄＴ＊
Ｐ、Ｖ、Ｈ、Ｍ、Ｃ）−ＰＭＶ（Ｔ、ＭＲＴ、Ｖ、Ｈ、
Ｍ、Ｃ） つまり、輻射パネル８の温度を変えた場合、室内の平均
輻射温度（ＭＲＴ）がどの程度変化し、またそれによっ
てＰＭＶ値がどの程度変化するかを計算していることに
なる。また、これらの設備機器以外の場合、例えば換気
装置９の動作に対するｄＰＭＶ値は、外気の温度と室内
の温度の差に換気装置９の風量を係数として掛けるとい
った計算により求められる。また、加湿器１０のｄＰＭ
Ｖ値は、湿度検出器３により得られる湿度と設定湿度の
変更量とから同様に計算される。DPMV = PMV (T, MRT + dT *
P, V, H, M, C) -PMV (T, MRT, V, H,
M, C) That is, when the temperature of the radiation panel 8 is changed, it is calculated how much the average radiation temperature (MRT) in the room changes and how much the PMV value changes accordingly. Further, in the case of equipment other than these equipments, for example, the dPMV value for the operation of the ventilation device 9 is obtained by a calculation in which the difference between the temperature of the outside air and the temperature of the room is multiplied by the air volume of the ventilation device 9 as a coefficient. Also, dPM of the humidifier 10
The V value is similarly calculated from the humidity obtained by the humidity detector 3 and the change amount of the set humidity.
【００１６】次に、エネルギー消費率を計算する（ステ
ップＳ２）。このエネルギー消費率とは、その空調機器
の設定をある量だけ変えたときにどれだけエネルギー消
費量が変化するかを表わす値として定義される。例え
ば、エアコン７の場合は、その設定温度Ｔの変化量ｄＴ
に対し、消費エネルギー量がｄＥだけ変化した場合、以
下のように計算される。Next, the energy consumption rate is calculated (step S2). The energy consumption rate is defined as a value indicating how much the energy consumption changes when the setting of the air conditioner is changed by a certain amount. For example, in the case of the air conditioner 7, the change amount dT of the set temperature T
On the other hand, when the consumed energy amount changes by dE, it is calculated as follows.
【００１７】ＣＥ（エアコン）＝ｄＥ／ｄＴ 求めた値をテーブルの形で記憶しておいてもよいし、も
しくは最後に説明するように運転しながら学習により取
得する方法も考えられる。CE (air conditioner) = dE / dT The calculated value may be stored in the form of a table, or a method of acquiring it by learning while driving as described at the end can be considered.
【００１８】以上のようにして機器毎に計算されたＰＭ
Ｖ寄与率ＣＴ、エネルギー消費率ＣＥの値を用いて以下
の式により、ＰＭＶ制御効率計算手段１８でＰＭＶ制御
効率を計算する（ステップＳ３）。PM calculated for each device as described above
Using the values of the V contribution rate CT and the energy consumption rate CE, the PMV control efficiency calculation means 18 calculates the PMV control efficiency by the following formula (step S3).
【００１９】ＰＭＶ制御効率＝ＣＴ／ＣＥ ＰＭＶ制御効率とは、同じエネルギー量でどの位ＰＭＶ
値を変化させることができるかを表わす指標となる。従
ってこのＰＭＶ制御効率が最大の空調機器の設定をＰＭ
Ｖ値が最適になるまで変化させれば最もエネルギー効率
のよいＰＭＶ制御ができる。そのために、空調機器設定
決定手段２０で各機器毎に計算されたＰＭＶ制御効率値
の大小関係を調べ、その中で最大のものを選択する（ス
テップＳ４）。まず、この機器だけでＰＭＶ制御を行う
場合の機器設定値を以下のようにして計算する（ステッ
プＳ５）。つまり、現在のＰＭＶ値の最適値との偏差量
をｅＰＭＶとおいたときに、その空調機器の設定は、現
在の値よりｅＰＭＶ／ＣＴなる値だけ変化させればよ
い。PMV control efficiency = CT / CE PMV control efficiency means how much PMV with the same energy amount.
It is an index that shows whether the value can be changed. Therefore, the PMV control efficiency is set to the maximum for the air conditioning equipment.
PMV control with the highest energy efficiency can be achieved by changing the V value until it becomes optimum. Therefore, the air-conditioning equipment setting determining means 20 checks the magnitude relationship of the PMV control efficiency values calculated for each equipment, and selects the largest one among them (step S4). First, the device setting value when PMV control is performed only by this device is calculated as follows (step S5). That is, when the deviation amount from the optimum value of the current PMV value is set to ePMV, the setting of the air conditioner may be changed by a value of ePMV / CT from the current value.
【００２０】しかし、一つの空調機器でＰＭＶ値を最適
に制御するのは難しい場合もある。例えば輻射パネル８
の場合、その設定温度の制御範囲はかなり限られている
ため、いくらＰＭＶ制御効率が良かったとしても輻射パ
ネル８だけでＰＭＶ値を最適値にもっていくのは不可能
な場合がある。この点に対する対策として、あらかじめ
各機器の設定温度の上限、下限値を設定し、先に計算さ
れた機器の設定温度をその値と比較する（ステップＳ
６）。その結果、設定温度がその上限値、下限値を越え
た場合には、その設定値を上下限値に抑え（ステップ
７）、その結果生ずるＰＭＶ偏差量に対しては、いま選
択された機器を除いた空調機器群に対し上記ステップＳ
４からの処理を繰り返す。最終的にＰＭＶ偏差量がなく
なるまで機器設定の割当が終了すれば、それらの機器設
定値を各機器に設定する（ステップＳ８）。すなわち、
温熱快適度を最適に、且つ消費エネルギーを最小にする
ような各空調機器７、８、９、１０の設定を計算したこ
とになる。However, it may be difficult to optimally control the PMV value with one air conditioner. For example, radiation panel 8
In this case, since the control range of the set temperature is quite limited, it may be impossible to bring the PMV value to the optimum value only by the radiation panel 8 no matter how good the PMV control efficiency is. As a measure against this point, the upper and lower limits of the set temperature of each device are set in advance, and the previously calculated set temperature of the device is compared with the values (step S
6). As a result, when the set temperature exceeds the upper limit value and the lower limit value, the set value is suppressed to the upper and lower limit values (step 7), and the PMV deviation amount generated as a result is selected by the currently selected device. Step S above for the removed air conditioning equipment group
The processing from 4 is repeated. When the device setting allocation is completed until the PMV deviation amount is finally exhausted, those device setting values are set in each device (step S8). That is,
This means that the settings of the air conditioners 7, 8, 9, 10 are calculated so that the thermal comfort level is optimized and the energy consumption is minimized.
【００２１】同様な計算部分に関しての第２の実施例を
説明する。この例では、第１の実施例とはステップＳ１
からステップＳ３までとステップＳ８は同様である。そ
の他のステップＳ４からステップＳ７の代替手段として
以下のような方法を用いる。各空調機器の設定温度変化
量をＸiとしたときに、以下の式で表される総ＰＭＶ変
化量ＴＣＴおよび総エネルギー消費量ＴＣＥ ＴＣＴ＝ΣＣＴi×Ｘi ＴＣＥ＝ΣＣＥi×Ｘi を考える。今、ＰＭＶ値を最適値０に制御し、しかも総
消費エネルギー量を最小にしたいのであるから、例えば
最小２乗法などを用いて、以下のような評価関数Ｊを最
小にするようなＸiを求め、その値に従って各空調機器
の設定温度を変化させればよい。A second embodiment of the similar calculation part will be described. In this example, step S1 is different from the first embodiment.
From step S3 to step S8 are the same. The following method is used as an alternative to the other steps S4 to S7. When the set temperature change amount of each air conditioner is Xi, consider the total PMV change amount TCT and the total energy consumption amount TCE TCT = ΣCTi × Xi TCE = ΣCEi × Xi expressed by the following equations. Now, since it is desired to control the PMV value to the optimum value 0 and to minimize the total energy consumption, for example, the least square method is used to obtain Xi that minimizes the following evaluation function J. The set temperature of each air conditioner may be changed according to the value.
【００２２】Ｊ＝ＴＣＴ2＋ＴＣＥ2 以上、述べてきたような計算手順においてはエネルギー
消費率ＣＥ値を用いるが、その値をエネルギー消費率計
算手段１７にて計算する方法としては、あらかじめ実験
などにより求めておいた値をエネルギー消費率計算手段
１７内でテーブルとして記憶しておく方法の他に、運転
しながらＣＥ値を学習していく方法がある。図４はエネ
ルギー消費率計算手段１７内でエネルギー消費率の学習
に用いるニューラルネットの構成図である。つまり、ニ
ューラルネットの入力層に空調機器の状態量（過熱度や
コンプレッサー回転数など）と室内の温熱環境量を入力
し、出力層にその時のエネルギー消費率ＣＥ値を与える
ことにより学習を行う。このような学習を繰り返してい
くことによりニューラルネットには、実際の機器のおか
れた状態におけるＣＥ値の情報が記憶されることにな
る。かかる方法により、エネルギー消費率計算手段１７
内で学習後、想起計算されたエネルギー消費率ＣＥ値
は、ＰＭＶ制御効率計算手段１８に与えられ、空調機器
設定の決定のための情報として用いられる。J = TCT 2 + TCE 2 The energy consumption rate CE value is used in the above-described calculation procedure, but the method of calculating the value by the energy consumption rate calculation means 17 is based on experiments in advance. In addition to the method of storing the calculated values as a table in the energy consumption rate calculation means 17, there is a method of learning the CE value while driving. FIG. 4 is a configuration diagram of a neural network used for learning the energy consumption rate in the energy consumption rate calculation means 17. That is, learning is performed by inputting the state quantity of the air conditioner (superheat degree, compressor rotation speed, etc.) and the indoor thermal environment quantity into the input layer of the neural network, and giving the energy consumption rate CE value at that time to the output layer. By repeating such learning, information on the CE value in the state where the actual device is placed is stored in the neural network. By this method, the energy consumption rate calculation means 17
The energy consumption rate CE value calculated after the learning is given to the PMV control efficiency calculation means 18 and used as information for determining the air conditioning equipment setting.
【００２３】[0023]
【発明の効果】以上のように本発明による空調システム
制御装置では、温熱快適度を最適に制御する際に必要と
なる各空調機器の消費エネルギー量を考慮して、各空調
機器の設置値を決定することにより、少ないエネルギー
消費量により快適な室内温熱環境を実現できるものであ
る。As described above, in the air conditioning system control apparatus according to the present invention, the installation value of each air conditioning equipment is taken into consideration in consideration of the energy consumption of each air conditioning equipment necessary for optimally controlling the thermal comfort level. By deciding, a comfortable indoor thermal environment can be realized with a small amount of energy consumption.
【図１】本発明にかかる空調システム制御装置を利用し
た制御システムの全体構成図である。FIG. 1 is an overall configuration diagram of a control system using an air conditioning system control device according to the present invention.
【図２】本発明にかかる空調システム制御装置の一実施
例のブロック図である。FIG. 2 is a block diagram of an embodiment of an air conditioning system control device according to the present invention.
【図３】本発明にかかる空調システム制御装置の空調機
器設定決定手段を中心とする動作を示すフローチャート
である。FIG. 3 is a flowchart showing an operation centered on an air conditioning equipment setting determining means of the air conditioning system control device according to the present invention.
【図４】本発明によるエネルギー消費率計算手段のニュ
ーラルネットを用いた構成を示す構成図である。FIG. 4 is a configuration diagram showing a configuration of an energy consumption rate calculation means according to the present invention using a neural network.
１ 空調システム制御装置 ２ 温度検出器 ３ 湿度検出器 ４ 輻射温度検出器 ５ 活動量検出器 ６ インターフェースユニット ７ エアコン ８ 輻射パネル ９ 換気装置 １０ 加湿器 １１ センサ管理装置 １２ 室内空間 1 Air Conditioning System Controller 2 Temperature Detector 3 Humidity Detector 4 Radiation Temperature Detector 5 Activity Level Detector 6 Interface Unit 7 Air Conditioner 8 Radiation Panel 9 Ventilation Device 10 Humidifier 11 Sensor Management Device 12 Indoor Space
Claims (4)
環境要素を検出するための温熱環境量取得手段と、前記
温熱環境量取得手段により検出された温熱環境要素より
所定の演算により算出した温熱快適度に基づいて、前記
空調機器により消費されるエネルギー量を少なくするよ
うに前記各空調機器の設定を決定する空調機器設定決定
手段とを備えたことを特徴とする空調システム制御装
置。1. A plurality of indoor air conditioners, a thermal environment quantity acquisition means for detecting a thermal environment element in the room, and a thermal environment element detected by the thermal environment quantity acquisition means are calculated by a predetermined calculation. An air-conditioning system control device comprising: an air-conditioning device setting determination means that determines the setting of each of the air-conditioning devices so as to reduce the amount of energy consumed by the air-conditioning device based on the thermal comfort level.
ネルギー量の値を学習機能により取得するエネルギー消
費率計算手段を備えたことを特徴とする請求項１記載の
空調システム制御装置。2. The air conditioning system control device according to claim 1, further comprising an energy consumption rate calculating means for acquiring a value of an amount of energy consumed by a plurality of indoor air conditioners by a learning function.
環境要素を検出するための温熱環境量取得手段と、室内
に設置された複数の空調機器の状態量を得る空調機器状
態量取得手段と、前記温熱環境量と空調機器の状態量か
ら各空調機器のエネルギー消費率を計算するエネルギー
消費率計算手段と、前記温熱環境量取得手段により検出
された温熱環境要素より所定の演算により算出した温熱
快適度及び、前記エネルギー消費率計算手段のエネルギ
ー消費率とに基づいて、各空調機器の設定値を、前記温
熱快適度が目標値に制御するために最もエネルギー効率
が高い組合せとなるように決定する空調機器設定決定手
段と、決定された設定値により各空調機器を制御する機
器制御手段とを備えたことを特徴とする空調システム制
御装置。3. A plurality of indoor air conditioners, a thermal environment quantity acquisition means for detecting a thermal environment element in the room, and an air conditioner status quantity acquisition for obtaining the status quantities of the plurality of air conditioners installed in the room. Means, an energy consumption rate calculation means for calculating the energy consumption rate of each air conditioning equipment from the thermal environment quantity and the state quantity of the air conditioning equipment, and a predetermined calculation from the thermal environment element detected by the thermal environment quantity acquisition means Based on the thermal comfort level and the energy consumption rate of the energy consumption rate calculation means, the set value of each air conditioner is set to be the most energy efficient combination for controlling the thermal comfort level to the target value. 2. An air conditioning system control device comprising: an air conditioning equipment setting determining means for determining each of the air conditioning equipment and an equipment controlling means for controlling each air conditioning equipment according to the determined set value.
器により消費されるエネルギー量の値を学習機能により
取得することを特徴とする請求項３記載の空調システム
制御装置。4. The air conditioning system control device according to claim 3, wherein the energy consumption rate calculation means acquires the value of the amount of energy consumed by the air conditioning equipment by a learning function.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP3253542A JPH0593539A (en) | 1991-10-01 | 1991-10-01 | Controller for air conditioning system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP3253542A JPH0593539A (en) | 1991-10-01 | 1991-10-01 | Controller for air conditioning system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| JPH0593539A true JPH0593539A (en) | 1993-04-16 |
Family
ID=17252821
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP3253542A Pending JPH0593539A (en) | 1991-10-01 | 1991-10-01 | Controller for air conditioning system |
Country Status (1)
| Country | Link |
|---|---|
| JP (1) | JPH0593539A (en) |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000035242A (en) * | 1998-07-16 | 2000-02-02 | Matsushita Electric Ind Co Ltd | Air conditioning system controller |
| JP2004278942A (en) * | 2003-03-17 | 2004-10-07 | Toshiba Kyaria Kk | Air conditioning system for shop |
| JP2006162093A (en) * | 2004-12-02 | 2006-06-22 | Toshiba Corp | Comfortable value control method and comfortable value control system |
| JP2007032933A (en) * | 2005-07-27 | 2007-02-08 | Daikin Ind Ltd | Air conditioner |
| JP2009145033A (en) * | 2007-12-13 | 2009-07-02 | Shijin Kogyo Sakushinkai | Method for controlling comfort in location environment by air conditioning system |
| JP2010038472A (en) * | 2008-08-06 | 2010-02-18 | Takenaka Komuten Co Ltd | Ventilation type radiation air conditioning system |
| JP2010065937A (en) * | 2008-09-11 | 2010-03-25 | Mitsubishi Electric Corp | Air conditioner |
| US7886984B2 (en) | 2007-12-19 | 2011-02-15 | Institute For Information Industry | Method of utilizing air conditioner to control thermal comfort level of environment |
| WO2012029343A1 (en) * | 2010-09-03 | 2012-03-08 | オムロン株式会社 | Energy evaluation device, energy evaluation method, and control program |
| WO2016199280A1 (en) * | 2015-06-11 | 2016-12-15 | 三菱電機株式会社 | Air conditioning system and air conditioning method |
| WO2021107054A1 (en) * | 2019-11-26 | 2021-06-03 | ダイキン工業株式会社 | Machine learning device, demand control system, and air conditioning control system |
| WO2022185633A1 (en) * | 2021-03-04 | 2022-09-09 | 株式会社日立製作所 | Environmental control device, environmental control method, and environmental control program |
-
1991
- 1991-10-01 JP JP3253542A patent/JPH0593539A/en active Pending
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000035242A (en) * | 1998-07-16 | 2000-02-02 | Matsushita Electric Ind Co Ltd | Air conditioning system controller |
| JP2004278942A (en) * | 2003-03-17 | 2004-10-07 | Toshiba Kyaria Kk | Air conditioning system for shop |
| JP4509755B2 (en) * | 2004-12-02 | 2010-07-21 | 株式会社東芝 | Comfort value management method and comfort value management system |
| JP2006162093A (en) * | 2004-12-02 | 2006-06-22 | Toshiba Corp | Comfortable value control method and comfortable value control system |
| JP2007032933A (en) * | 2005-07-27 | 2007-02-08 | Daikin Ind Ltd | Air conditioner |
| JP4581891B2 (en) * | 2005-07-27 | 2010-11-17 | ダイキン工業株式会社 | Air conditioner |
| JP2009145033A (en) * | 2007-12-13 | 2009-07-02 | Shijin Kogyo Sakushinkai | Method for controlling comfort in location environment by air conditioning system |
| US7886984B2 (en) | 2007-12-19 | 2011-02-15 | Institute For Information Industry | Method of utilizing air conditioner to control thermal comfort level of environment |
| JP2010038472A (en) * | 2008-08-06 | 2010-02-18 | Takenaka Komuten Co Ltd | Ventilation type radiation air conditioning system |
| JP2010065937A (en) * | 2008-09-11 | 2010-03-25 | Mitsubishi Electric Corp | Air conditioner |
| WO2012029343A1 (en) * | 2010-09-03 | 2012-03-08 | オムロン株式会社 | Energy evaluation device, energy evaluation method, and control program |
| JP2012058781A (en) * | 2010-09-03 | 2012-03-22 | Omron Corp | Energy evaluation device, energy evaluation method, and control program |
| WO2016199280A1 (en) * | 2015-06-11 | 2016-12-15 | 三菱電機株式会社 | Air conditioning system and air conditioning method |
| WO2021107054A1 (en) * | 2019-11-26 | 2021-06-03 | ダイキン工業株式会社 | Machine learning device, demand control system, and air conditioning control system |
| JP2021103083A (en) * | 2019-11-26 | 2021-07-15 | ダイキン工業株式会社 | Machine learning device, demand control system, and air conditioning control system |
| CN114729762A (en) * | 2019-11-26 | 2022-07-08 | 大金工业株式会社 | Machine learning device, demand control system, and air conditioning control system |
| WO2022185633A1 (en) * | 2021-03-04 | 2022-09-09 | 株式会社日立製作所 | Environmental control device, environmental control method, and environmental control program |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP0191481B1 (en) | Temperature control system | |
| US20120118986A1 (en) | Controlling device and method | |
| US6186407B1 (en) | Humidity control based on an estimation using heating plant cycle, of inside window surface temperature | |
| CN107525236A (en) | Air-conditioner control method and air conditioner based on human comfort | |
| EP2647921B1 (en) | HVAC controller for setting a setback value as a function from a recovery time | |
| WO2002054165A2 (en) | Thermal comfort controller having an integral energy savings estimator | |
| CN107560113A (en) | A kind of intelligent air conditioner control method and air conditioner | |
| JP2011069577A (en) | Air conditioning control system, air conditioning control method, air conditioning control device and air conditioning control program | |
| CN110887180B (en) | Air conditioner control method and device, storage medium and air conditioner | |
| JPH0593539A (en) | Controller for air conditioning system | |
| CN108800470B (en) | Control method and device of air conditioning equipment and air conditioning equipment | |
| US10823446B2 (en) | System of adjusting load of air conditioning and method of adjusting the same | |
| CN113418283B (en) | Control method for air conditioner | |
| JP2556884B2 (en) | Air conditioning system controller | |
| KR101779797B1 (en) | Self-learning HAVC energy management system using big data, and the operating method thereof | |
| JPH02242037A (en) | Air conditioning system controller | |
| JPH06323595A (en) | Operation controller for air conditioner | |
| JP3452831B2 (en) | VAV control system | |
| JP3429397B2 (en) | Air conditioner | |
| CN112212481A (en) | System and method for controlling environmental comfort by deep reinforcement learning | |
| JPH07180887A (en) | Air conditioning control system | |
| JP2003004282A (en) | Operation control method for air conditioner and system therefor | |
| JPH0370930A (en) | Air conditioner control apparatus | |
| CN113324321B (en) | Intelligent control method of air conditioner and air conditioner | |
| EP3065022B1 (en) | Methods and systems for controlling the temperature of an internal space |