JPH06221648A - Controller and control method of air conditioner - Google Patents

Abstract

PURPOSE:To obtain a comfortable residential space by controlling the operating frequency of a compressor of an air conditioner based on the comfort of persons inside a room. CONSTITUTION:Two or more temperature sensors 10 detect the outside temperature, suction temperature, human body temperatures, etc., and output sensor's signals 12 which are stored in a memory 13. A location sensor 1d detects the location of a remote controller and outputs a location overlap signal 1e. A neuro network device 17 outputs a prospect means number of votes as a comfort degree inside a room PMV or an estimated value 18 of a reference effective temperature SET. The estimated value 18 is input to a control signal generating device 19 which generates control signals 1a. The operating frequency of a compressor of an air conditioner 1b is controlled according to the control signals 1a. thus, the room temperature controls 1c are carried out, so that a more comfortable air conditioning and living environment can be achieved with the environment inside the room and situation of persons taken into the consideration.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an air conditioner control device and control for controlling the operating frequency of a compressor to automatically perform optimum air conditioning operation by, for example, a microcomputer to provide a comfortable living space. Regarding the method.

[0002]

2. Description of the Related Art Conventionally, when controlling the room temperature in an air conditioner, in the case of heating, in order to improve the rising characteristics of the room temperature, control for shifting the target room temperature of the air conditioner to a certain time higher and A method of controlling the compressor operating frequency according to the temperature (for example, Japanese Patent Laid-Open No. 4-225746).
No. gazette) was adopted. FIG. 8 shows the configuration of such a conventional air conditioner control device. As shown in FIG. 8, the control signal generating means 81 controls the suction temperature 8 from the sensor 80.
5, the timer value 87 from the timer 83 that operates after turning on the power of the air conditioner 82, and the air conditioner 82.
A control signal 86 is generated based on information such as a remote control (hereinafter, referred to as a remote control) for externally operating the device or a user set temperature 88 from the operation panel 84. For example, at the time of heating, in order to quickly raise the room temperature within 60 minutes after the power is turned on, the target room temperature is set to 2 ° C. higher than the set temperature set by the user by the remote controller or the operation panel 84. The indoor temperature adjustment 89 of the air conditioner 82 is configured to be controlled so as to be set.

As an example of controlling the actual compressor operating frequency, as shown in FIG. 9, the operating frequency of the compressor is uniquely determined from the difference between the temperature set by the remote controller or the operation panel 84 and the suction temperature. Along with the decision, it is tabulated. For example, suction temperature-set temperature = + 0.
In the case of 5 to 1.5, the operating frequency of the compressor is No. = 2
(For example, No. 2 = 20 Hz) is set.

[0004]

However, in such a conventional control device, since the control is performed only by the time after the power is turned on and the indoor temperature characteristic, the room in which the air conditioner is installed is controlled. It is not possible to flexibly deal with the magnitude of the air conditioning load. Therefore, for example, when the load is too small, the room temperature becomes higher than the target temperature, and when the load is too large, it takes a considerable time for the room temperature to reach the target temperature. In addition, a temperature difference may occur depending on the position in the room, and if the distance from the air conditioner is long, the set temperature may not be reached. Therefore, there is a problem that the comfort of the person living in the room cannot be satisfied.

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. Therefore, hunting of the operating frequency of the compressor occurs depending on the balance with the air conditioning load in the room, and noise changes sound and vibration when the operating frequency of the compressor changes, sudden changes in the blowout temperature (cold draft feeling), and consumption due to hunting. There were various problems such as power increase and room temperature distribution decrease.

For example, the compressor operating frequency N shown in FIG.
o. Assuming that the actual compressor operating frequencies corresponding to 2, 3, and 4 are 20 Hz, 25 Hz, and 35 Hz, respectively. 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 10, the operating frequency difference for hunting is 10H.
Z is much larger.

The present invention solves such a problem, and an object of the present invention is to provide an air conditioner control device and control method for detecting indoor and outdoor environmental conditions and estimating and controlling comfortable conditions. It is a thing.

[0008]

In order to achieve the above object, the present invention provides a plurality of sensor means for detecting environmental conditions inside and outside where an air conditioner is installed, and a state before the sensor means detects them. The storage means for holding the position, the position detection means for detecting the position of the remote control, the output from the sensor means and the storage means, the temperature set by the user, and the output from the position detection means to improve the comfort of the person living in the room. Based on this, the control signal is generated by the control signal generating means from the operating frequency table that determines the compressor operating frequency of the air conditioner, and the compressor is operated by the control signal.

[0009]

According to the above construction, the estimating means detects the indoor and outdoor environmental conditions detected by the plurality of sensor means, the state before the sensor means held by the storage means, the temperature and the position detected by the user. The comfort of a person in the room is estimated from the position of the remote controller detected by the means. Then, the control signal is generated by the control signal generation means on the basis of the human comfort feeling estimated by the estimation means to control the air conditioner. As a result, it is possible to realize a more comfortable air conditioning and living environment in consideration of the indoor environment and the human condition.

[0010]

Embodiments of the present invention will be described below with reference to the drawings.

(Embodiment 1) FIG. 1 shows the configuration of a control device for an air conditioner according to a first embodiment of the present invention. FIG. 2 shows a learning method of the neural network schematic means shown in FIG. In FIG. 1, 10 is a sensor, 11 and 12 are sensor signals 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, 16 Is a remote control or operation panel 1
5 is an output signal from 5, 5 is a neural network model unit, 18 is a comfort level output from the unit 17, and is a predicted average vote (Predicted Mean Vote), hereinafter PM
V) or standard new effective temperature (Standard)
Effective Temperature, hereinafter S
Also called ET). 19 is a control signal generating means,
Reference numeral 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 the remote controller, and 1e is a position signal of the remote controller from the position sensor 1d.

Next, in order to explain the learning method of the neural network model means, a more detailed structure is shown in FIG. Figure 2
21 is an outdoor temperature, 22 is a suction temperature, 23 is a suction temperature gradient, 24 is an air volume, 25 is a user preset temperature, 26 is a human body temperature, 27 is a neural network schematic means, 28
Is a control signal generating means, 29 is a measured PMV or SE
T, 2a is the estimated PMV or SET, 2c is a control signal, 2d is position information of the remote controller, 211, 221, 23
1, 241, 251, 261, and 2d1 are signals from the outdoor temperature 21, the suction temperature 22, the suction temperature gradient 23, the air volume 24, the user set temperature 25, the human body temperature 26, and the position information 2d of the remote controller.

Next, the operation of the air conditioner configured as described above will be described. As shown in FIG. 1, the air conditioner 1b
A sensor signal 11 is output from a plurality of sensors (outside air temperature sensor, suction temperature sensor, humidity sensor, human body temperature sensor) 10 provided inside. The sensor signal 11 is the outdoor temperature, the suction temperature, the humidity, the human body temperature, or the like. Further, the sensor signal 12 similar to the sensor signal 11 is output from the sensor 10 and input to the storage unit 13. The storage unit 13 stores the history of the input sensor signal 12 for the past N seconds (N is a positive real number). 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 value) of the room temperature from the state before the sensor, for example, the history of N seconds described above. Real number)
The interval slope 14 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 exists in FIG. By detecting in which zone of the room the remote control is located, it is possible to easily guess at which position in the room the person is. For example,
If the remote controller is in the H zone, the output signal 1e of the position sensor 1d becomes "H". Each means 10, 13, 15, 1d
The output signals 11, 14, 16 and 1e from the above are input to the neural network model means 17 as input signals. The neural network model 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 a factor that influences comfort, and temperature, humidity, air flow velocity, radiant temperature (surrounding wall), metabolic rate, and clothing condition. It was quantified based on the results of the polls about the feeling of cold weather. That is, the value of PMV calculated according to the human condition (the condition of metabolism and clothing) and the indoor environment (temperature, humidity, air velocity, ambient wall radiation) is: -3: cold -2: cool -1: somewhat Cool 0: Nothing +1: Somewhat warm +2: Warm +3: It can be evaluated as hot.

On the other hand, SET seeks the amount of thermal stimulus from the physical factors of the environment to predict human physiological state values and sensations. The relation between pleasantness and discomfort to heat is physiological response to the physical amount of thermal stimulus. It is one of the physical evaluation methods for comfort that is being grasped in. 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 set temperature of the user, 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 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 indoor target temperature targeted by the air conditioner 1b is determined from the outside air temperature, the intake temperature, the air flow rate, the set temperature, the inclination of the intake temperature, the position information, etc. from each of the means 10, 13, 15, 1d. The shift amount to be calculated is calculated. 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. First, the outdoor temperature 21, the suction temperature 22, the slope 23 of the suction temperature at intervals of N seconds, and the air volume 2
4, signals 211, 221, 231 from the user's set temperature 25, human body temperature 26, remote controller position information 2d, etc.
241, 251, 261 and 2d1 are input to the neural network schematic unit 27, and the estimated value 2a of PMV is output.

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

There are various methods for learning the neural network. 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 schematic unit 27 causes the control signal generation unit 28 to generate the control signal 2c that maximizes the capacity of the air conditioner when the comfort is not satisfied. If the user feels comfortable, the control signal 2 is used to maintain the feeling of comfort.
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 the first embodiment, the inputs from the respective sensors are input to the neural network model means, the PMV is estimated, and the control signal is generated according to the value of the PMV, whereby the indoor environment is determined. It is possible to realize a comfortable air-conditioning and living environment in consideration of human condition.

In the first embodiment, the neural network model means 1
Outputs 18 and 2a from 7 and 27 are PMV, but SE
The same effect can be obtained even if T or the radiation temperature of the surrounding wall is substituted.

(Second Embodiment) A second embodiment of the present invention will be described with reference to FIGS.

FIG. 4 shows a signal flow in the control device according to the second embodiment of the present invention. FIG. 5 is a block diagram for explaining a method of creating a lookup table in the control device shown in FIG. In FIG. 4, 40 is a sensor, 41,
42 is a sensor signal from the sensor 40, 43 is a storage means,
Reference numeral 44 is a slope of the suction temperature output from the storage means 43 at intervals of N seconds, 45 is a remote controller or operation panel, 46 is an output signal from the remote controller or operation panel 45, 47 is a look-up table, and 48 is a look-up table 4.
7 is a control signal output from the air conditioner, 49 is an air conditioner, 4b is a position sensor, and 4c is a position signal of the remote controller from the position sensor 4b.

Next, referring to FIG. 5, a 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 user's set temperature, 56 is the human body temperature, 57 is the neural network model means, 58 is the control signal generation means, 59 is the actually measured PMV or SET, and 5a is estimated from the neural network model means. PMV or SET, 5b is the comfort level (PMV or S
ET), 5c is a control signal, 5d is position information of the remote controller,
5e is a lookup table, 511, 521, 53
1, 541, 551, 561 and 5d1 are the outdoor temperature 51, the suction temperature 52, the suction temperature gradient 53,
It is a signal based on the air volume 54, the set temperature 55 of the user, the human body temperature 56, and the position information 5d of the remote controller.

The operation of the air conditioner of the second embodiment constructed as above will be described. As shown in FIG. 4, the sensor signal 41 is output from the plurality of sensors 40 in the air conditioner 49. The sensor signal 41 includes outside temperature, suction temperature, humidity, human body temperature, and the like.

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 past N in the input sensor output signal 42.
The history for a second (N is a positive real number) is stored. 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 function of the position sensor 4b has been described in the first embodiment shown in FIG. Output signals 41, 44, 4 from the respective means 40, 43, 45, 4b
Reference numerals 6 and 4c are lookup tables 47 as input signals.
The 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, each means 4
The compressor operating frequency table targeted by the air conditioner 49 is obtained from the outside air temperature from 0, 43, 45, 4b, the intake temperature, the air volume, the set temperature, the inclination of the intake temperature, and the like. 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 operation frequency table.

Next, a method of creating the lookup table shown in FIG. 4 will be described with reference to FIG. First, the outdoor temperature 5
1 and a suction temperature 52, a suction temperature gradient 53 at N second intervals, an air volume 54, a user set temperature 55, a human body temperature 56, a signal 511 from the remote controller position information 5d, and the like.
521, 531, 541, 551, 561, and 5d1 are input to the neural network schematic unit 57 to estimate the PMV value 5a.
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 lowest descent method using 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 7, the control signal generation means 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 5
7 and the portion of the control signal generating means 58 for outputting the control signal 5c are replaced with the look-up table 5e, the input signals 511 to 5d1 of the outdoor temperature 51 which is the sensor input to the position information 5d of the remote controller are roughly quantized to look up. The look-up table may be created by inputting the result into the look-up table 5e and writing the result in the look-up table 5e.

FIG. 6 shows a concrete example of the look-up table 5e. Look-up table 5a is zone A-
C, and zone A has set temperatures ti to te and outside temperature t
oi to tom, air volume fi to fn, suction temperature si to s
o 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 has a transition period (at the time of rising or disturbance) and a stable period independently, and a plurality of compressor operating frequency tables are provided in each of the transition period and the stable period. Owns. At the start of heating operation or when a large load change occurs, the air conditioner's target compressor operating frequency table is: outside air temperature, intake temperature, air volume, set temperature,
The optimum operating frequency table is selected from the operating frequency tables in the transition period in order to raise the room temperature more quickly due to the gradient of the suction temperature. 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.

In this way, since the transition period and the stable period are independently owned as the operating frequency table of the compressor, the room can be warmed more quickly at the start of the operation and the compression is performed at the stable time. Room temperature fluctuations are reduced by suppressing hunting of the operating frequency of the machine, noise change noise and vibration when the operating frequency of the compressor changes, prevention of sudden changes in blowing temperature (cold draft feeling), hunting of operating frequency is reduced. It is possible to reduce the power consumption, prevent the room temperature distribution from deteriorating, and the like.
Further, since a plurality of compressor operating frequency tables are owned in each of the transition period and the stable period, more detailed control is performed in order to select the optimum operating frequency table that matches the load of the room in the operating state. be able to.

As described above, according to the air conditioner of the embodiment, the input from each sensor is input to the neural network schematic means, the PMV is estimated, and the control signal is generated according to the value of the PMV. It is 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.

[0034]

As is apparent from the above description, according to the present invention, the compressor operating frequency of the air conditioner is controlled by the control signal from the operating frequency table based on the comfort feeling of the person in the room estimated by the estimating means. By doing so, it is possible to realize a comfortable living space in consideration of the state of people in the room.

Further, by the specification of the neural network model means (hereinafter referred to as a neural network), individual control corresponding to a person or room can be performed, and comfort is improved.

Further, by inputting the indoor / outdoor temperature, the air volume, and the humidity into the neural network, the control suitable for the person, the room, and the use condition can be performed, and the comfort is improved.

Further, by setting an appropriate time interval in the intake temperature gradient of the air conditioner, it is possible to accurately judge the load condition of the room when the air conditioner is used.

Further, by storing the neural network in the look-up table, processing by a microcomputer becomes possible, and the apparatus becomes small and inexpensive.

Further, since a plurality of compressor operation tables are possessed, the immediate warming property or the immediate cooling property at the time of starting the operation of the compressor is improved, and when stable, the hunting of the operating frequency of the compressor is suppressed and a smoother control is achieved. Can be realized.

[Brief description of drawings]

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

FIG. 2 is a block diagram showing a learning method of the neural network schematic means.

FIG. 3 is a diagram showing position information of the remote controller.

FIG. 4 is a block diagram showing a configuration of a control device for an air conditioner according to the second embodiment.

FIG. 5 is a block diagram showing a method of creating the lookup table.

FIG. 6 is a diagram showing an example of the lookup table.

FIG. 7 is a diagram showing an example of a compressor operating frequency table during the heating operation.

FIG. 8 is a block diagram showing a configuration of a conventional air conditioner control device.

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

[Explanation of symbols]

 10, 40 Sensors 13, 43 Storage means 15, 45 Remote control or operation panel 17 Neural network model means 19 Control signal generation means 1b, 49 Air conditioner 1d, 4b Position sensor 21, 51 Outdoor temperature 22, 52 Suction temperature 23, 53 Gradient of suction temperature 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 2d , 5d Remote controller position information 47,5e Look-up table

Claims (11)

[Claims] 1. A plurality of sensor means for detecting environmental conditions inside and outside where an air conditioner is installed, a storage means for holding a state before detection by the sensor means, and a position of a remote control. The position detection means, the estimation means for estimating the comfort of a person in the room from the output from the sensor means and the storage means, the temperature set by the user and the output from the position detection means, and the estimation means An air conditioner control device comprising: a control signal generation unit that generates a control signal from an operation frequency table that determines a compressor operation frequency of the air conditioner based on a feeling of comfort of a person in the room. 2. The control device for an air conditioner according to claim 1, wherein the estimating means is a neural network model means for learning a human comfort feeling. 3. The control device for an air conditioner according to claim 1, wherein the sensor means detects the indoor and outdoor temperatures, the air volume and the humidity of the air conditioner. 4. The control device for an air conditioner according to claim 1, wherein the storage means stores the temperature gradient of the air taken in by the air conditioner at positive real value intervals. 5. The control device for an air conditioner according to claim 1, wherein the control signal generation means is a storage means based on a look-up table that generates a control signal from the output of a function for estimating the comfort of a person in the room. 6. The comfort level of a person in the room, which is estimated by the estimating means, is a predicted average number of votes calculated by the state of the person or the environment of the room as an evaluation index for controlling the air conditioner, or the physiological state of the person. The control device for an air conditioner according to claim 1, wherein the air conditioner control device is set by a standard new effective temperature for which prediction of feeling and feeling is performed. 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 set by the ambient wall radiation temperature. 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 has its own table for a transition period and a stable period at the time of rising or falling, and has a plurality of compressor operating frequencies in each of the transition period and the stable period. The control device for an air conditioner according to claim 1, which owns a table. 11. A plurality of sensor means for detecting environmental conditions inside and outside where an air conditioner is installed, a storage means for holding a state before the detection by the sensor means, and a position of a remote control. Based on the comfort of the person living in the room, the compression of the air conditioner from the position detection means, the outputs from the sensor means and the storage means, the temperature set by the user and the output from the position detection means. A method for controlling an air conditioner, wherein a control signal from a driving frequency table that determines a machine operating frequency is generated by a control signal generating means, and the compressor is driven and controlled by the control signal.