JPH05223323A - Controller for air conditioner - Google Patents

Abstract

PURPOSE:To make the air conditioning comfortable with the indoor atmosphere and the condition regarding the occupant taken into consideration by a method wherein the control signal is produced though inference of the comfort degree for the occupant on the basis of the ambient conditions of both the inside and the outside of the room, their previous conditions determined by a sensor, the location of the remote control, and the temperature set by the user. CONSTITUTION:A sensor 10 detects the ambient conditions around an air conditioner 1b with respect to both the inside and the outside of the room. The previous conditions which have been detected by the sensor 10 are stored in a storage means 13. A location sensor 1d locates the remote control. An inferring means 17 infers the comfort degree for the occupant on the basis of the output of each of the sensor 10 and the storage means 13, the temperature set by the user by means of the remote control or at a control panel 15, and the output of the location sensor 1d. The inferring means 17 consists of a means modeled after the nerve network which has learned man's comfort degrees. In accordance with the comfort degree inferred for the occupant a control signal-producing means 19 produces a control signal in an operating frequency table regarding the compressor-operating frequency of the air conditioner 1b.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention enhances the comfort of a person in a room by controlling the operating frequency of a compressor which automatically causes comfortable air conditioning operation in an air conditioner equipped with a microcomputer. The present invention relates to a control device for an air conditioner.

[0002]

2. Description of the Related Art When controlling the room temperature in an air conditioner, in the case of heating, in order to improve the characteristics when the room temperature rises, control is performed to shift the indoor target temperature of the air conditioner to a higher value for a certain period of time. The method of controlling the compressor operating frequency by temperature has been adopted. That is, FIG. 8 shows such a conventional air conditioner control device. In FIG. 8, the control signal generating means 81 operates after the suction temperature 85 from the sensor 80 and the air conditioner 82 are turned on. A control signal 86 is generated by a timer value 87 from a timer 83 to be operated and a user-set temperature 88 from a remote controller or an operation panel 84 for operating the air conditioner 82 from the outside. As an example, during heating, in order to quickly raise the indoor temperature within 60 minutes after the power is turned on, the indoor target temperature is set to the remote control or the operation panel 8.
The room temperature adjustment 89 of the air conditioner 82 is controlled so as to be set higher by 2 ° C. than the user's set temperature set from No. 4.

Further, as a control of the actual compressor operating frequency, as shown in FIG. 9, a remote controller or an operation panel 84 is used.
The operating frequency of the compressor is uniquely determined from the difference between the set temperature and the suction temperature of the compressor, and a table is created (example: when the suction temperature-the set temperature = + 0.5 to 1.5, the compressor operation is performed. Frequency No. = 2. (For example, No. 2
= 20 Hz)).

[0004]

However, in the above-described conventional control device, since the control is performed only by the time after the power is turned on and the indoor temperature characteristic, the air conditioning load of the room in which the air conditioner is installed is controlled. You cannot deal flexibly with the size of. Therefore, for example, when the load is too low, the room temperature becomes higher than the target temperature, and when the load is too high, it takes a long time for the room temperature to reach the target temperature. There is a problem that when the distance from the air conditioner is different, the set temperature may not be reached when the distance is far from the air conditioner, so that it is not possible to consider the comfort of the person in the room.

Further, the operating frequency of the compressor is uniquely determined from the difference between the set temperature of the remote control or the operation panel and the suction temperature, and only one operating frequency table of the compressor is owned. Hunting of the operating frequency of the compressor occurs depending on the balance with the air conditioning load in the room, noise and noise when the operating frequency of the compressor changes, sudden changes in the blowing temperature (cold draft feeling), and power consumption due to hunting However, there were various problems such as increase of temperature and decrease of room temperature distribution.

(For example, as shown in FIG.
The actual compressor operating frequencies corresponding to 2, 3 and 4 are 2 respectively.
Assuming 0 Hz, 25 Hz, and 35 Hz, the operating frequency No. 2 and No. When the load frequency is balanced with that of No. 3, the operating frequency difference for hunting is 5 Hz. 3 and No. When the load is balanced between 4 and 4, the operating frequency difference for hunting is as large as 10 Hz. )

[0007]

To achieve this object, the present invention provides a plurality of sensor means for detecting environmental conditions inside and outside the air conditioner, and a memory for holding the previous state of the sensor means. Means, a position detecting means for detecting the position of the remote controller, an output from the sensor means and the storage means, a temperature set by the user, and an output from the position detecting means to infer the comfort of a person in the room. And a control signal generation unit that generates a control signal of an operating frequency table of a compressor operating frequency of the air conditioner based on the comfort feeling of the person in the room estimated by the estimating unit. The present invention proposes a control device for an air conditioner.

[0008]

According to the above-described structure of the present invention, the estimating means includes the indoor and outdoor environmental conditions detected by the plurality of sensor means, the previous state of the sensor means held by the storage means, the temperature set by the user, and The comfort of a person in the room is estimated from the position of the remote controller detected by the position detecting means. Then, based on the comfort feeling of the human being estimated by the estimating means, a control signal is generated by the control signal generating means to control the air conditioner, so that the indoor environment and the human state are taken into consideration and the comfort is improved. Air conditioning and living environment can be realized.

[0009]

Embodiments of the present invention will be described in detail below with reference to FIGS. 1 to 6.

FIG. 1 is a block diagram showing the flow of signals in the control device according to the first embodiment of the present invention, and FIG. 2 is a block diagram showing the learning method of the neural network schematic means in FIG. In FIG. 1, 10 is a sensor, 11,
12 is a sensor signal value from the sensor 10, 13 is storage means, 14 is a slope of the suction temperature output from the storage means 13 at intervals of N seconds, 15 is a remote control or operation panel, 1
6 is an output signal from the remote control or the operation panel 15, 1
7 is a neural network model means, 18 is the comfort level output from the means 17 (predicted average number of votes (Predicted)
Mean Vote (hereinafter referred to as PMV) or standard new effective temperature (Standard Effective T
19) is a control signal generating means, 1a is a control signal output from the means 19, 1b is an air conditioner, 1d is a position sensor for detecting the position of a remote controller, 1e is from the position sensor 1d. Is the position signal of the remote control of.

Next, a more detailed structure for explaining the learning method of the neural network model means will be described with reference to FIG. That is, FIG. 2 is a block diagram showing a learning method, 21 is an outdoor temperature, 22 is a suction temperature, 23 is a gradient of the suction temperature, 24 is an air volume, 25 is a user preset temperature, 26 is a human body temperature, and 27 is a body temperature. Neural network model means, 28
Is a control signal generating means, 29 is a measured PMV (or S
ET), 2a is the estimated PMV (or SET), 2c
Is a control signal, 2d is position information of the remote controller, 211, 22
1, 231, 241, 251, 261 and 2d1 are the outdoor temperature 21, the suction temperature 22, the suction temperature gradient 23, the air volume 24, the user set temperature 25, and the human body temperature 2, respectively.
6 and a signal from the remote controller position information 2d.

Since the air conditioner according to the illustrated embodiment is configured as described above, sensor signals are output from a plurality of sensors (outside air temperature sensor, intake temperature sensor, humidity sensor, human body temperature sensor) 10 in the air conditioner 1b. 11 will be output. This signal 11 is the outdoor temperature, the suction temperature,
Humidity, human body temperature, etc. Further, although the sensor 10 outputs a signal 12 similar to the signal 11 and inputs it to the storage means 13, the storage means 13 inputs the sensor output signal 12 for the past N seconds (N is a positive real number).
Memorize the history of. The air volume and the set temperature value 16 set by the user are output from the remote controller or the operation panel 15, and the storage unit 13 stores N seconds (N is a positive real number) of the room temperature from the state before the sensor, for example, the history of N seconds described above. ) Spacing 1
4 is output. The position sensor 1d detects the position of the remote controller and outputs position information 1e. As shown in FIG. 3, the position sensor 1d divides the room into N × M zones and detects in which zone of the room the remote control is present.
For example, in the i zone, the output signal 1e of the position sensor 1d becomes i. The output signals 11, 14, 16, 1e from the respective means 10, 13, 15, 1d are used as input signals.
Input to the neural network model means 17. The neural network model means 17 outputs the predicted average vote number PMV, which is the indoor comfort level, or the estimated value 18 of the standard new effective temperature SET, from the input signal. PMV is an environmental test room in which the combination of 6 elements such as temperature, humidity, air flow velocity, radiant temperature (surrounding wall), metabolic rate, and clothing state is changed as the factors that affect comfort. It was quantified based on the results of the polls about the heat and cold. That is,
Human condition (metabolic and clothing conditions) and indoor environment (temperature,
PM calculated by humidity, machine speed, ambient wall radiation)
The value of V is -3: cold -2: cool -1: slightly cool 0: nothing +1: rather warm +2: warm +3: hot.

On the other hand, SET seeks the amount of thermal stimulation from the physical factors of the environment to predict human physiological state values and sensations. This is one of the physical evaluation methods for comfort. For example, when the PMV is used, the human condition such as the outdoor temperature, the suction temperature, the suction temperature gradient, the air volume, the remote controller position information, the user's set temperature, and the human body temperature and the indoor environment are input to the neural network model 17. By doing so, the estimated value 18 of the PMV is output from the neural network schematic unit 17. The estimated value 18 of the PMV is input to the control signal generating means 19, and the generating means 19 generates the control signal 1a. The control signal generating means 19 generates the control signal 1a that maximizes the performance of the air conditioner 1b when the comfort level is unsatisfactory.

Further, when the comfortable feeling is satisfied, the control signal 1a for maintaining the comfortable feeling is generated. That is, the signal 1a for controlling the air conditioner 1b is generated based on the estimated PMV value 18. The control signal 1a controls the operating frequency of the compressor in the air conditioner 1b. As an example, the respective means 10, 13, 15, 1d
From the outside air temperature, the intake temperature, the air volume, the set temperature, the inclination of the intake temperature, the position information, etc., the shift amount for calculating the indoor target temperature targeted by the air conditioner 1b is obtained. The relationship between the shift amount and the temperature set by the user and the indoor target temperature is: indoor target temperature = user set temperature + shift amount. Therefore, the control signal 1a of the air conditioner 1b generated by the control signal generating means 19 is input to the air conditioner 1b, and as an example, the operation of the air conditioner 1b is executed so as to reach the indoor target temperature based on the above formula.

Through the above process, the room temperature adjustment 1c
Is done. Next, the learning method of the neural network of FIG. 1 will be described with reference to FIG. 2. The outdoor temperature 21 and the suction temperature 22, the suction temperature gradient 23 at N second intervals, the air volume 24,
Signals 211, 221, 231, 24 from user set temperature 25, human body temperature 26, remote controller position information 2d, etc.
1, 251, 261 and 2d1 are neural network schematic means 27
And outputs the estimated value 2a of PMV.

The neural network model means 27 uses the actually measured PMV 29 measured indoors as the learning data 2b.
The estimated value 2a of PMV is learned.

The neural network learning algorithm may be various methods, for example, a backpropagation algorithm (reference: Ramelhart, DE and McClellan, JL “PDP model-cognitive science and neuron network Search "{Runmelhart, DE and
McClelland, J .; L. (Eds.), "Pa
rally Distributed Procedures
sing, Exploration in the M
icrostructure of Cognitoio
n. Vol. 1,2, MIT Press, Cammb
ridge (1986)}) to find the optimal solution by the method of maximum descent. Then, learning is carried out by these algorithms until PMV can be sufficiently estimated by the neural network schematic unit 27. When the learning is completed, the output value 2a of the neural network patterning means 27 causes the control signal generating means 28 to generate the control signal 2c for maximizing the capacity of the air conditioner when the comfort is not satisfied. In addition, if the user feels comfortable, the control signal 2 is used to maintain the comfortable feeling.
c is generated by the control signal generating means 28. That is, the signal 2c for controlling the air conditioner is generated by the control signal generation means 28 based on the PMV value 2a estimated by the neural network schematic means 27. The control signal 2c controls the operating frequency table of the compressor.

As described above, according to this embodiment,
Input from each sensor is input to the neural network schematic means, and P
By estimating the MV and generating the control signal according to the value of the PMV, it is possible to realize a comfortable air conditioning and living environment in consideration of the indoor environment and the human condition.

In the above-mentioned embodiment, the outputs 18, 2a from the neural network schematic means 17, 27 are PMVs.
It is clear that the same effect can be obtained even if the temperature is replaced with ET or the radiation temperature of the surrounding wall.

Next, a second embodiment of the present invention will be described with reference to FIGS. 4 is a block diagram showing the flow of signals in the control device according to the second embodiment of the present invention, and FIG. 5 is a block diagram for explaining the method of creating the lookup table in FIG. At 40
Is a sensor, 41 and 42 are sensor signal values from the sensor 40, 43 is a storage means, 44 is a slope of the suction temperature output from the storage means 43 at intervals of N seconds, 45 is a remote control or operation panel, and 46 is a remote control or operation. An output signal from the panel 45, 47 is a lookup table, 48 is a control signal output from the lookup table 47, 49
Is an air conditioner, 4b is a position sensor, 4c is a position sensor 4
The position signals of the remote control from b are shown respectively.

Next, referring to FIG. 5, the method of creating the lookup table will be described. 51 is the outdoor temperature, 52 is the suction temperature, 53 is the slope of the suction temperature,
54 is the air volume, 55 is the temperature set by the user, 56 is the human body temperature, 57 is the neural network model means, 58 is the control signal generation means, 59 is the measured PMV (or SET), and 5a is the neural network model means. Inferred PMV (or SE
T), 5b is a comfort level (PMV or SET) from the measured PMV (or SET), 5c is a control signal, 5d is position information of the remote controller, 5e is a lookup table, 51
1,521,531,541,551,561,5d1
Are signals from the outdoor temperature 51, the suction temperature 52, the suction temperature gradient 53, the air volume 54, the user set temperature 55, the human body temperature 56, and the position information 5d of the remote controller.

Since the air conditioner according to the second embodiment has the above-mentioned structure, as shown in FIG.
The sensor signals 41 are output from the plurality of sensors 40 in 9. This signal 41 indicates the outside temperature, the intake temperature,
Humidity, human body temperature, etc.

A signal 42 similar to the signal 41 is output from the sensor 40 and input to the storage means 43. The storage means 43 stores the history of the input sensor output signal 42 for the past N seconds (N is a positive real number). The air flow rate and the user's set temperature value 46 are output from the remote controller or the operation panel 45, and the storage means 43 outputs the state before the sensor, for example, the slope 44 of the room temperature at intervals of N seconds (N is a positive real number). . Further, the position sensor 4b outputs position information 4c of the remote controller. The position sensor 4b is the first
Since the embodiment has been described with reference to FIG. 3, the description thereof will be omitted here. Output signals 41, 44 from the respective means 40, 43, 45, 4b
Reference numerals 46 and 4c are lookup tables 4 as input signals.
7, and a control signal 48 for the air conditioner 49 is obtained from the look-up table 47. The control signal 48 controls the operating frequency of the compressor in the air conditioner 49. As an example, the compressor operating frequency table targeted by the air conditioner 49 is obtained from the outside air temperature, the intake temperature, the air flow rate, the set temperature, the inclination of the intake temperature, etc. from the respective means 40, 43, 45, 4b. ..

Therefore, the control signal 48 obtained from the look-up table 47 is input to the air conditioner 49, and as an example, the air conditioner 49 is operated so as to reach the indoor target temperature based on the compressor operating frequency table. ..

Next, the method of creating the lookup table of FIG. 4 will be described with reference to FIG. 5. First, the outdoor temperature 51 and the suction temperature 52, the suction temperature gradient 53 at N second intervals, the air volume 54, and the temperature set by the user. 55, human body temperature 5
6, signals 511, 52 from the remote controller position information 5d, etc.
1,531,541,551,561,5d1 are input to the neural network schematic unit 57, and the estimated value 5a of PMV is output.

In this case, the neural network model means 57
Learns the estimated value 5a of PMV using the actually measured PMV 59 measured indoors as the learning data 5b.

There are various methods for learning the neural network. For example, the optimal solution may be obtained by the descent method by the above-described backpropagation algorithm. Then, learning is carried out by these algorithms until PMV can be sufficiently estimated by the neural network schematic unit 57. When the learning is completed, the neural network schematic unit 5 is executed.
When the comfort level is not satisfied by the output value 5a of No. 7, the control signal generating unit 58 generates the control signal 5c that maximizes the performance of the air conditioner. Further, when the comfortable feeling is satisfied, the control signal 5c is generated by the control signal generating means 58 so that the comfortable feeling can be maintained.

Although the control signal 5c controls the compressor operating frequency table, its neural network model means 57 and control signal generating means 58 for outputting the control signal 5c.
To replace the part of with the lookup table 5e,
Outdoor temperature 51 which is sensor input 1 to position information 5 of remote controller
The input signals 511 to 5d1 of d may be roughly quantized and input to the look-up table 5e, and the result may be written in the look-up table 5e to create the look-up table.

FIG. 6 shows the above-mentioned lookup table 5e.
Of the zone A.
To C, the zone A has set temperatures ti to te, outside temperatures toi to tom, air volumes fi to fn, and suction temperatures si to.
So and the suction temperature gradient ki to kp are written.

Next, with reference to FIG. 7, a method of owning a compressor operating frequency table during heating will be described as an example. As shown in FIG. 7, the operating frequency table of the compressor is uniquely owned in the transition period (at the time of rising or during disturbance) and the stable period, and a plurality of compressor operating frequency tables are provided in each of the transient period and the stable period. Owns When a heating operation starts or when a large load change occurs, as the compressor operation frequency table targeted by the air conditioner, from the outside air temperature, the intake temperature, the air volume, the set temperature, the inclination of the intake temperature, etc., In order to raise the room temperature more quickly, the optimum operating frequency table is selected from the operating frequency tables in the transition period. Similarly, when the room temperature approaches a substantially stable state, the optimum operating frequency table is selected from the operating frequency tables in the stable period.

As described above, since the compressor operation frequency table is independently owned in the transition period and the stable period, the room can be warmed more quickly at the start of operation and the compressor can be warmed during the stable period. By suppressing the hunting of the operating frequency, the room temperature fluctuation is reduced, noise change noise and vibration when the compressor operating frequency changes, prevention of abrupt change of blowout temperature (cold draft feeling), consumption by reducing operating frequency hunting It is possible to reduce electric power and prevent lowering of room temperature distribution. In addition, since multiple compressor operating frequency tables are owned in each of the transitional period and the stable period, more detailed control is performed in order to select the optimal operating frequency table that suits the load of the room in the operating state. You can

As described above, according to the air conditioner of the above-mentioned embodiment, the input from each sensor is input to the neural network schematic means, PMV is estimated, and the control signal is generated according to the value of PMV. This makes it possible to realize a more comfortable air conditioning and living environment in consideration of the indoor environment. Further, the control device can be easily realized by inferring the PMV from the neural network schematic means and replacing the part for converting into the control signal with the look-up table.

[0033]

As described above, according to the first aspect of the present invention.
According to the control device of the air conditioner, the more comfortable air conditioning and living environment can be realized in consideration of the indoor environment and the human condition. According to the specifications of the neural network of the second aspect, control corresponding to a person or a room is individually realized, and comfort can be improved.

According to the specifications of claim 3 (each value of indoor / outdoor temperature, air volume, and humidity is input to the neural network), the control suitable for the person, the room, and the use condition is individually achieved.
You can improve comfort.

By further providing an appropriate time interval in the suction temperature gradient of claim 4, it is possible to accurately determine the load state of the room at the time of use and to improve comfort.

According to the specifications of claim 5, when the neural network is implemented in an air conditioner, the estimated calculation time is long and a large-scale computer is required. Therefore, by storing it in a lookup table, microcomputer processing becomes possible, resulting in a significant cost reduction. Benefits are achieved.

According to the specifications of claim 6, the PMV evaluation, which is the most desirable index for evaluating the human comfort, can be dramatically improved.

According to the specification of claim 7, an effect equivalent to that of the specification of claim 7 is achieved. According to the specification of claim 8, since the state of the person is indirectly estimated by the set temperature set by the user, the comfort can be improved.

According to the specification of claim 9, the state of the person is directly estimated by the temperature of the human body, so that the improvement of comfort can be further achieved.

According to the specification of claim 10, the compressor operating frequency table is uniquely owned in the transition period (at the time of rising or falling) and the stable period, and a plurality of compressions are provided in each of the transient period and the stable period. Since it has a machine operating frequency table, it improves the immediate warming (or promptness) at the start of operation, etc., and when it is stable, it suppresses room temperature fluctuations by suppressing hunting of the compressor operating frequency, and when the compressor operating frequency changes. It is possible to reduce the noise change sound and vibration, to prevent a sudden change in the blowing temperature (cold draft feeling), to reduce the power consumption by reducing the hunting of the operating frequency, and to prevent the room temperature distribution from deteriorating.
In addition, since multiple compressor operating frequency tables are owned in each of the transitional period and the stable period, more detailed control is performed in order to select the optimal operating frequency table that suits the load of the room in the operating state. You can

[Brief description of drawings]

FIG. 1 is a block diagram showing a signal flow of a control device of an air conditioner according to a first embodiment of the present invention.

FIG. 2 is a block diagram for explaining a learning method of a neural network schematic unit in FIG.

FIG. 3 is an explanatory view showing position information of a remote controller in FIGS. 1 and 2.

FIG. 4 is a block diagram showing a signal flow of a control device of an air conditioner according to a second embodiment of the present invention.

5 is a block diagram for explaining a method of creating a lookup table in FIG.

6 is an explanatory diagram showing an example of a lookup table in FIGS. 4 and 5. FIG.

FIG. 7 is an explanatory diagram showing an example of a compressor operating frequency table during heating operation (operating frequency table during transition period and stable period).

FIG. 8 is a block diagram showing a conventional control device.

FIG. 9 is an explanatory diagram showing an example of a compressor operating frequency table during a conventional heating operation.

[Explanation of symbols]

 10, 40 Sensors 11, 12, 41, 42 Sensor signals 13, 43 Storage means 14, 44 Suction temperature gradient at N second intervals 15, 45 Remote control or operation panel 16, 46 Air volume, set temperature, etc. 17 Neural network model means 18 Comfort estimated value 19 Control signal generation means 1a, 48 Control signal 1b, 49 Air conditioner 1c, 4a Indoor temperature adjustment 1d, 4b Position sensor 1e, 4c Remote control position information 21, 51 Outdoor temperature 22, 52 Suction temperature 23, 53 Suction temperature gradient 24,54 Air volume 25,55 User set temperature 26,56 Human body temperature 2d, 5d Remote controller position information 27,57 Neural network model means 28,58 Control signal generation means 29,59 Measured PMV 2a , 5a PMV estimated value 2b, 5b PMV learning data 2c, 5c Control signal 2d, 5 Position information 47,5e lookup table 80 sensor 81 control signal generation unit 82 air conditioner 83 timer 84 remote or suction operation panel 85 Temperature 86 control signals 87 the timer value 88 set temperature of the remote control

Claims (10)

[Claims] 1. A plurality of sensor means for detecting environmental conditions inside and outside of an air conditioner, a storage means for holding a previous state of the sensor means, a position detecting means for detecting a position of a remote controller, and Estimating means for estimating the comfort of a person in the room from the outputs from the sensor means and the storage means, the temperature set by the user, and the output from the position detecting means,
An air conditioner comprising: a control signal generating unit that generates a control signal of an operating frequency table of a compressor operating frequency of the air conditioner based on a comfort feeling of a person in the room estimated by the estimating unit. Control device. 2. The control device for an air conditioner according to claim 1, wherein the estimating means is a neural network model means (neural network) that learns a human comfort feeling. 3. The control device for an air conditioner according to claim 1, wherein the sensor means detects the temperature inside and outside the room, the air volume of the air conditioner, and the humidity. 4. The control device for an air conditioner according to claim 1, wherein the storage means stores the intake air temperature gradient of the air conditioner at intervals of N seconds (N is a positive real value). 5. The control signal generation means is a look-up table storage means for generating a control signal from the output of a function for estimating the comfort of a person in the room. Control device for air conditioner. 6. The comfort level of a person in a room, which is estimated by the estimation means, is a predicted average number of votes (P) calculated according to the state of the person and the environment of the room as an evaluation index for controlling the air conditioner.
The control device for an air conditioner according to claim 1, wherein the control device is an MV) or a standard new effective temperature (SET) that predicts a human physiological condition or sensation. 7. The control device for an air conditioner according to claim 1, wherein the comfort feeling of the person in the room estimated by the estimation means is the radiation temperature of the surrounding wall. 8. The control device for an air conditioner according to claim 1, wherein the estimating means estimates the state of a person in the room from the temperature set by the user. 9. The control device for an air conditioner according to claim 1, wherein the estimating means estimates the state of a person in the room by using a human body temperature sensor as the sensor means. 10. A compressor operating frequency table is uniquely owned in a transition period (at the time of rising or falling) and a stable period, and a plurality of compressor operating frequency tables are provided in each of the transition period and the stable period. The control device for an air conditioner according to claim 1, which is owned.