KR102487067B1 - Method for providing information on air conditioning equipment performance using artificial neural network model, apparatus and inverter thermo-hygrostat using the same - Google Patents

Abstract

Provided are a method for providing information on predicted performance of air conditioning equipment, and an apparatus for predicting performance of air conditioning equipment using the method. The present invention relates to a method for providing information on performance on air conditioning equipment predicted by a processor. According to the present invention, the method for providing information on predicted performance on air conditioning equipment comprises the steps of: receiving sensor data in an air conditioning zone; and predicting performance on air conditioning equipment based on the received sensor data using an artificial neural network-based prediction model trained to predict performance of the air conditioning equipment using sensor data as an input.

Description

A method for providing information on performance prediction of air conditioning equipment using an artificial neural network model, a performance prediction device, and an inverter thermo-hygrostat using the same }

The present invention relates to a method for providing information on performance prediction of air conditioning equipment using an artificial neural network model, a performance prediction device, and an inverter thermo-hygrostat using the same.

A CRAC (clean room air-conditioner) is an air conditioner that is mainly used to cool server rooms or data center equipment, and can precisely control the temperature within a set range and maintain a constant humidity throughout the year.

When using thermo-hygrostats in factories or air conditioning equipment such as CRACs in the field, there are times when it is necessary to immediately know the cooling performance of products, such as air volume and air-side heat capacity.

In order to accurately measure the cooling capacity and efficiency of HVAC products such as air conditioners, the temperature and humidity must be fixed in a defined standard chamber.

The background description of the invention has been prepared to facilitate understanding of the present invention. It should not be construed as an admission that matters described in the background art of the invention exist as prior art.

Current state of the art air conditioners generally only provide information on the environment temperature, humidity and power consumption, not the air volume, cooling capacity and efficiency of the air conditioning equipment.

Meanwhile, in air conditioning equipment, a new method capable of providing information on air volume, cooling capacity, and efficiency of air conditioning equipment is required.

As a result, the inventors of the present invention invented an air conditioning equipment performance predicting device using a predicted model learned to predict air conditioning equipment performance and an inverter constant temperature and humidity controller including an air conditioning equipment performance predicting function.

More specifically, the inventors of the present invention tried to extract cooling capacity, heating and cooling energy efficiency (cop), and air volume of air conditioning equipment using a predictive model.

Furthermore, the inventors of the present invention tried to design a predictive model to predict the performance of air conditioning equipment products even in a field where temperature and humidity conditions are not precise.

Furthermore, based on the prediction result extracted from the prediction model, a system capable of adjusting any one of the temperature, humidity, and indoor pressure of the air conditioning equipment was attempted to be developed.

The tasks of the present invention are not limited to the tasks mentioned above, and other tasks not mentioned will be clearly understood by those skilled in the art from the following description.

In order to solve the above problems, another embodiment of the present invention provides an information providing device for predicting performance of air conditioning equipment and an inverter thermo-hygrostat including functions of the device for providing information for predicting performance of air conditioning equipment. The apparatus includes a communication unit configured to receive sensor data in an air conditioning zone, and a processor connected to communicate with the communication unit. In this case, the processor is configured to predict the performance of the air conditioning equipment based on the received sensor data using an artificial neural network-based prediction model learned to predict the performance of the air conditioning equipment by taking the sensor data as an input.

According to a feature of the present invention, the prediction model may be composed of a model that outputs cooling capacity, cooling and heating energy efficiency (cop), and air volume.

According to another feature of the present invention, the predictive model includes outdoor DB/WB temperature, indoor DB/WB temperature, test product discharge DB/WB temperature, code tester nozzle differential pressure, code tester nozzle inlet temperature, measured current and measured power. It can be composed of a model that takes at least one of the data as an input.

According to another feature of the present invention, the predictive model may use at least one of outdoor unit intake air temperature, indoor unit ventilation temperature, indoor unit ventilation humidity, indoor unit supplied air temperature, indoor unit supplied air humidity, differential pressure, simple wattmeter current, and power data as an input. can be made into a model.

According to another feature of the present invention, the processor may be further configured to adjust any one of temperature, humidity and room pressure of the air conditioning equipment based on the predicted result.

Details of other embodiments are included in the detailed description and drawings.

According to the present invention, information on efficiency of air conditioning equipment may be provided from sensor data in an air conditioning zone based on a predictive model.

More specifically, the present invention can predict the cooling capacity, cooling and heating energy efficiency (cop), and air volume of the air conditioning equipment extracted from the prediction model learned to predict the performance of the air conditioning equipment.

Furthermore, the present invention may adjust any one of temperature, humidity, and indoor pressure of the air conditioning equipment based on the predicted result.

In addition, the present invention can predict the performance of air conditioning equipment products even in a field where temperature and humidity conditions are not precise.

Effects according to the present invention are not limited by the contents exemplified above, and more various effects are included in the present invention.

1 illustrates an air conditioning equipment performance estimation system based on an air conditioning equipment performance estimation apparatus according to an embodiment of the present invention.
2 is a block diagram showing the configuration of an apparatus for predicting performance of air conditioning equipment according to an embodiment of the present invention.
3 is a block diagram showing the configuration of a server for providing information on air conditioning equipment performance prediction according to an embodiment of the present invention.
4 illustrates a procedure of a method for providing information on air conditioning equipment performance prediction according to an embodiment of the present invention.
5A and 5B illustratively illustrate the configuration of a predictive model used in various embodiments of the present invention.
6 illustrates evaluation results of performance prediction models used in various embodiments of the present invention.

Advantages and features of the present invention, and methods for achieving them, will become clear with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but will be implemented in various different forms, and only these embodiments make the disclosure of the present invention complete, and the common knowledge in the art to which the present invention belongs It is provided to fully inform the holder of the scope of the invention, and the present invention is only defined by the scope of the claims. In connection with the description of the drawings, like reference numerals may be used for like elements.

In this document, expressions such as "has," "may have," "includes," or "may include" indicate the existence of a corresponding feature (eg, numerical value, function, operation, or component such as a part). , which does not preclude the existence of additional features.

In this document, expressions such as “A or B,” “at least one of A and/and B,” or “one or more of A or/and B” may include all possible combinations of the items listed together. . For example, “A or B,” “at least one of A and B,” or “at least one of A or B” (1) includes at least one A, (2) includes at least one B, Or (3) may refer to all cases including at least one A and at least one B.

Expressions such as “first,” “second,” “first,” or “second,” used in this document may modify various elements, regardless of order and/or importance, and refer to one element as It is used only to distinguish it from other components and does not limit the corresponding components. For example, a first user device and a second user device may represent different user devices regardless of order or importance. For example, without departing from the scope of rights described in this document, a first element may be named a second element, and similarly, the second element may also be renamed to the first element.

A component (e.g., a first component) is "(operatively or communicatively) coupled with/to" another component (e.g., a second component); When referred to as "connected to", it should be understood that the certain component may be directly connected to the other component or connected through another component (eg, a third component). On the other hand, when an element (eg, a first element) is referred to as being “directly connected” or “directly connected” to another element (eg, a second element), the element and the above It may be understood that other components (eg, a third component) do not exist between the other components.

As used in this document, the expression "configured to" means "suitable for," "having the capacity to," depending on the circumstances. ," "designed to," "adapted to," "made to," or "capable of." The term "configured (or set) to" may not necessarily mean only "specifically designed to" hardware. Instead, in some contexts, the phrase "device configured to" may mean that the device is "capable of" in conjunction with other devices or components. For example, the phrase "a processor configured (or configured) to perform A, B, and C" may include a dedicated processor (e.g., embedded processor) to perform those operations, or by executing one or more software programs stored in a memory device. , may mean a general-purpose processor (eg, CPU or application processor) capable of performing corresponding operations.

Terms used in this document are only used to describe a specific embodiment, and may not be intended to limit the scope of other embodiments. Singular expressions may include plural expressions unless the context clearly dictates otherwise. Terms used herein, including technical or scientific terms, may have the same meaning as commonly understood by a person of ordinary skill in the art described in this document. Among the terms used in this document, terms defined in a general dictionary may be interpreted as having the same or similar meaning as the meaning in the context of the related art, and unless explicitly defined in this document, an ideal or excessively formal meaning. not be interpreted as In some cases, even terms defined in this document cannot be interpreted to exclude the embodiments of this document.

Each feature of the various embodiments of the present invention can be partially or entirely combined or combined with each other, and as those skilled in the art can fully understand, various interlocking and driving operations are possible, and each embodiment can be implemented independently of each other. It may be possible to implement together in an association relationship.

For clarity of interpretation of this specification, terms used in this specification will be defined below.

In the present disclosure, an air conditioning zone sensor device may refer to all sensors that measure sensor data necessary to predict air conditioning equipment performance. For example, the air conditioning zone sensor may be a temperature sensor, a humidity sensor, a differential pressure sensor, or a multi-function sensor, but is not limited thereto. In this case, the air-conditioning zone sensor device disclosed in this specification may be a device installed in an air-conditioning zone, a device installed inside air-conditioning equipment, or an inverter thermo-hygrostat including an air-conditioning zone sensor. In the present disclosure, sensor data may refer to all data necessary to predict air conditioning equipment performance. For example, outdoor DB/WB temperature, indoor DB/WB temperature, test product discharge DB/WB temperature, code tester nozzle differential pressure, code tester nozzle inlet temperature, current, power, outdoor unit intake temperature, indoor unit ventilation temperature, indoor unit ventilation humidity , air temperature supplied to the indoor unit, air humidity supplied to the indoor unit, differential pressure, current of a simple wattmeter, and power data, etc. may be environmental information of the air-conditioning zone, but is not limited thereto.

In the present disclosure, the predictive model is an artificial neural network (ANN) model, and a Fully Convolutional Network (FCN), Deep Neural Network (DNN), and Convolutional Neural Network (CNN) with VGG net, R, DenseNet, and an encoder-decoder structure. , Deep Convolution Neural Network (DCNN), Recurrent Neural Network (RNN), Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), and Single Shot Detector (SSD) models. Furthermore, the prediction models may be ensemble models based on at least two algorithm models among the aforementioned algorithms.

Hereinafter, an air conditioning equipment performance prediction system based on an air conditioning equipment performance prediction apparatus according to an embodiment of the present invention will be described with reference to FIG. 1 .

1 illustrates an air conditioning equipment performance estimation system based on an air conditioning equipment performance estimation apparatus according to an embodiment of the present invention.

First, referring to FIG. 1 , the air conditioning equipment performance prediction system 1000 may be a system configured to provide information related to air conditioning equipment performance based on sensor data in an air conditioning zone, or may be an embedded system.

Based on the sensor data received from the air conditioning zone sensor device, cooling capacity, cooling and heating energy efficiency (cop), and air volume are extracted, and based on this, any one of the temperature, humidity, and indoor pressure of the air conditioning equipment is extracted. It may include an air conditioning equipment performance predicting device 100 configured to adjust one, a user device 200 receiving information on a result of air conditioning equipment performance prediction, and an air conditioning zone sensor device 300.

First, the air conditioning equipment performance predicting device 100 is a general-purpose computer, laptop, and/or data server that performs various calculations to predict the air conditioning equipment performance based on the sensor data received from the air conditioning zone sensor device 300. can include At this time, the user device 200 may be a device for accessing a web server that provides a web page related to predicting the performance of air conditioning equipment or a mobile web server that provides a mobile website. Not limited.

Specifically, the air conditioning equipment performance prediction apparatus 100 receives sensor data from the air conditioning zone sensor device 300, extracts information related to air conditioning equipment performance prediction based on the received sensor data, and finally obtains a performance prediction result. can provide

In this case, the air conditioning equipment performance prediction apparatus 100 may provide data related to the air conditioning equipment performance to the user device 200 .

Data provided from the apparatus 100 for predicting performance of air conditioning equipment in this way may be provided as a web page through a web browser installed in the user device 200, or may be provided in the form of an application or program. In various embodiments, such data may be provided in a form included in a platform in a client-server environment.

Next, the user device 200 is an electronic device that requests provision of information on air conditioning equipment performance prediction and provides a user interface for displaying prediction result data, such as a smartphone, tablet PC (Personal Computer), laptop computer and/or It may include at least one of PC and the like.

The user device 200 may receive the air conditioning equipment performance prediction result from the air conditioning equipment performance prediction device 100 and display the received result through the display unit. Here, the prediction result may include information on cooling capacity, cooling and heating energy efficiency (cop), and air volume determined based on the prediction model. In this embodiment, the air conditioning equipment performance prediction device 100 and the user device 200 are described as being separated, but the air conditioning equipment performance prediction device 100 and the user device 200 may be implemented as one device.

Next, with reference to FIG. 2, components of the apparatus 100 for predicting performance of air conditioning equipment according to the present invention will be described in detail.

2 is a block diagram showing the configuration of an apparatus for predicting performance of air conditioning equipment according to an embodiment of the present invention.

Referring to FIG. 2 , the apparatus 100 for predicting performance of air conditioning equipment includes a storage unit 110 , a communication unit 120 and a processor 130 .

First, the storage unit 110 may store various data for predicting air conditioning equipment performance. In various embodiments, the storage unit 110 is a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (eg SD or XD memory, etc.), RAM, SRAM, ROM, EEPROM, PROM, magnetic memory , magnetic disks, and optical disks, and may include at least one type of storage medium.

The communication unit 120 connects the user device 200 to enable communication with an external device. The communication unit 120 is connected to the user device 200 and further to the air conditioning zone sensor device 300 using wired/wireless communication to transmit/receive various data. Specifically, the communication unit 120 may receive sensor data including environmental information of the air conditioning zone from the air conditioning zone sensor device 300 . Also, the communication unit may transmit information to the user device 200 .

The processor 130 is operatively connected to the storage unit 110 and the communication unit 120, and can execute various commands for estimating air conditioning equipment performance.

Specifically, the processor 130 may be configured to predict air conditioning equipment performance based on sensor data received through the communication unit 120 .

In this case, the processor 130 may obtain a prediction result by predicting air conditioning equipment performance based on a prediction model that takes sensor data acquired from the air conditioning zone sensor device as an input.

Meanwhile, the air conditioning equipment performance predicting apparatus 100 is not limited to hardware design. For example, the processor 130 of the apparatus 100 for predicting performance of air conditioning equipment may be implemented as software. Accordingly, the performance prediction result may be displayed through a display unit (not shown) of the air conditioning zone sensor device 300 to which the software is connected.

The air conditioning equipment performance prediction device may be an inverter thermo-hygrostat, and may be integrated with the air conditioning equipment performance prediction device. In this case, the inverter thermo-hygrostat may perform the functions described above in the air conditioning equipment performance prediction device.

3 is a block diagram showing the configuration of a user device according to an embodiment of the present invention.

Referring to FIG. 3 together, the user device 200 includes a communication unit 210 , a display unit 220 , a storage unit 230 and a processor 240 .

The communication unit 210 connects the user device 200 to enable communication with an external device. The communication unit 210 may be connected to the air conditioning equipment performance estimation apparatus 100 using wired/wireless communication to transmit various data related to air conditioning equipment performance prediction. Specifically, the communication unit 210 may receive information related to predicting the performance of the air conditioning equipment from the apparatus 100 for predicting the performance of the air conditioning equipment, for example, information necessary for the process of predicting the performance of the air conditioning zone, such as temperature, humidity, differential pressure, etc. . The above information may be displayed and provided for the air conditioning zone sensor device 300, but is not limited thereto.

The display unit 220 may display various interface screens for displaying air conditioning equipment performance prediction results.

In various embodiments, the display unit 220 may include a touch screen, and for example, a touch using an electronic pen or a part of the user's body, a gesture, proximity, drag, or swipe A swipe or hovering input may be received.

The storage unit 230 may store various data used to provide a user interface for displaying result data. In various embodiments, the storage unit 230 may be a flash memory type, a hard disk type, a multimedia card micro type, or a card type memory (for example, SD or XD memory, etc.), RAM (Random Access Memory, RAM), SRAM (Static Random Access Memory), ROM (Read-Only Memory, ROM), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory) , a magnetic memory, a magnetic disk, and an optical disk may include at least one type of storage medium.

The processor 240 is operatively connected to the communication unit 210, the display unit 220, and the storage unit 230, and can perform various commands to provide a user interface for displaying result data.

Hereinafter, with reference to FIG. 4 , a method for providing information on air conditioning equipment performance prediction according to an embodiment of the present invention will be described in detail. FIG. 4 illustratively illustrates a procedure of a method for providing information on air conditioning equipment performance prediction according to an embodiment of the present invention.

First, referring to FIG. 4 , a procedure for providing information on air conditioning equipment performance prediction according to an embodiment of the present invention is as follows. First, sensor data in the air conditioning zone is received (S410). Next, the performance of the air conditioning equipment is predicted based on the received sensor data using the artificial neural network-based prediction model trained to predict the performance of the air conditioning equipment (S420).

According to an embodiment of the present invention, in the step of receiving sensor data in the air conditioning zone (S410), the air conditioning zone sensor device is operated so that data measured in real time may be obtained.

In this disclosure, the sensor data may be a data set obtained using an air enthalpy type ISO standard chamber. Data can be collected in a well-defined chamber environment where temperature and humidity conditions are precisely controlled to measure capacity and efficiency. The data obtained by operating the air conditioning zone sensor device are outdoor DB/WB temperature, indoor DB/WB temperature, test product discharge DB/WB temperature, code tester nozzle differential pressure, code tester nozzle inlet temperature, measured current, and measured power. It may be a chamber data set containing data. At this time, the received data may be displayed through the display unit of the user device.

Next, in the step of predicting the performance of the air conditioning equipment ( S420 ), a predicted model learned to predict the performance of the air conditioning equipment by using the received sensor data as an input may be used.

5A and 5B illustratively illustrate the configuration of a predictive model used in various embodiments of the present invention.

For example, referring to FIG. 5A, the prediction model may be a model configured to output cooling capacity, heating and cooling energy efficiency (cop), and air volume by taking a chamber data set as an input, and even at the installation site It may be a model learned to predict the performance of air conditioning equipment.

The prediction model uses a backpropagation neural network including an input layer, a hidden layer, and an output layer, and may be a model calculated using Keras' sequential structure, using an Adam optimizer, a mean square error (MSE) loss function, and a leaky ReLU (rectified linear unit) may be a model using an activation function. Also, it may be a model learned using a training dataset and a validation dataset.

Predictive models can estimate performance data of air conditioning equipment with plug fans installed along with temperature and humidity sensors. At the same time, it can be a model trained to match accurate performance data in standard air enthalpy chambers depicting various indoor and outdoor environmental conditions.

In the step of predicting the performance of the air conditioning equipment ( S420 ), the prediction model may adjust any one of temperature, humidity, and room pressure of the air conditioning equipment based on the predicted result after predicting the performance.

Hereinafter, with reference to FIG. 5B , a method for providing information on air conditioning equipment performance prediction according to another embodiment of the present invention will be described in detail.

First, referring to FIG. 5B , a procedure for predicting performance of air conditioning equipment according to another embodiment of the present invention is as follows. First, sensor data in an air-conditioning zone is received (S410), and air-conditioning equipment performance is predicted based on the received sensor data using an artificial neural network-based prediction model trained to predict air-conditioning equipment performance (S420).

According to another embodiment of the present invention, in the step of receiving sensor data in the air-conditioning zone (S410), the air-conditioning zone sensor device may be operated to acquire data measured in real time.

The data acquired by measuring in real time when the air conditioning zone sensor device is operated is a product that includes outdoor unit intake air temperature, indoor unit ventilation temperature, indoor unit ventilation humidity, indoor unit supplied air temperature, indoor unit supplied air humidity, differential pressure, simple wattmeter current and power data It can be a data set.

For example, referring to FIG. 5B, the prediction model may be a model configured to output cooling capacity, cooling and heating energy efficiency (cop), and air volume by taking a product data set as an input, and even at the installation site. It may be a model learned to predict the performance of air conditioning equipment.

Evaluation 1: Evaluation of predictive models used in various embodiments of the present invention

Hereinafter, evaluation results of a performance prediction model according to various embodiments of the present disclosure will be described with reference to FIG. 6 . 6 illustrates evaluation results of performance prediction models used in various embodiments of the present invention.

At this time, 2816 pieces of chamber data and product data were obtained as a performance prediction model and used as training data to calculate using Keras sequential structure, but the evaluation result should not be interpreted as being limited to the corresponding model.

First, referring to FIG. 6 , a result of predicting performance of air conditioning equipment with respect to sensor data by a predictive model is shown. It shows the result of calculating the error rate by comparing the training data with the validation data.

Test conditions #1 to #6 are conditions when the values of the input data are within the range of the training data. #7~#10 are conditions when the range of training data for some test conditions is set arbitrarily. Test Condition indicates outdoor temperature/humidity, indoor temperature/humidity, and percentage of product's indoor fan output, respectively.

Predicted value is the result calculated using the predictive model, and Product self-data is the product's own data, indicating the calculation result of the non-neural network.

Referring to Test condition #8 of Predicted value, capa 3.96%, AV 4.95%, COP 4.47%, and #10 has a high error rate with capa 10.5%, AV 3.95%, COP 11.4%. Except for this case, the Predicted value shows accurate prediction results for all three Capa, AV, and COP.

Looking at the error rates of Capa, AV, and COP of Predicted value and Product self-data, when using a prediction model than when using product data, which is a non-neural network, all three of the Capa, AV, and COP accuracy results are higher.

That is, the prediction model of the device according to an embodiment of the present invention appears to predict air conditioning equipment performance with high accuracy.

Although one embodiment of the present invention has been described in more detail with reference to the accompanying drawings, the present invention is not necessarily limited to these embodiments, and may be variously modified and implemented without departing from the technical spirit of the present invention. . Therefore, the embodiments disclosed in the present invention are not intended to limit the technical idea of the present invention, but to explain, and the scope of the technical idea of the present invention is not limited by these embodiments. Therefore, the embodiments described above should be understood as illustrative in all respects and not limiting. The protection scope of the present invention should be construed according to the claims below, and all technical ideas within the equivalent range should be construed as being included in the scope of the present invention.

100: air conditioning equipment performance prediction device
110, 230: storage unit
120, 210: communication department
130, 240: processor
200: user device
220: display unit
300: air conditioning zone sensor device

Claims (15)

A communication unit configured to receive a sensor data set in an air conditioning zone, and
A processor coupled to communicate with the communication unit;
the processor,
Outdoor DB / WB temperature, indoor DB / WB temperature, test product discharge DB / WB temperature, code tester nozzle differential pressure, code tester nozzle inlet temperature, measured current and measured power data obtained through any air conditioning equipment Air conditioning including cooling capacity, cooling and heating energy efficiency (cop) and air volume based on the sensor data set using a prediction model configured to predict air conditioning equipment performance based on the chamber data set Inverter thermo-hygrostat using an artificial neural network model configured to predict equipment performance. delete delete According to claim 1,
The prediction model is composed of a model that receives at least one of outdoor unit intake air temperature, indoor unit ventilation temperature, indoor unit ventilation humidity, indoor unit supplied air temperature, indoor unit supplied air humidity, differential pressure, simple wattmeter current, and power data. According to claim 1,
An inverter thermo-hygrostat configured to adjust any one of temperature, humidity, and room pressure of air conditioning equipment based on a predicted result. A method for providing information on air conditioning equipment performance prediction implemented by a processor,
Receiving a sensor data set in an air conditioning zone;
Outdoor DB / WB temperature, indoor DB / WB temperature, test product discharge DB / WB temperature, code tester nozzle differential pressure, code tester nozzle inlet temperature, measured current and measured power data obtained through any air conditioning equipment Air conditioning including cooling capacity, cooling and heating energy efficiency (cop) and air volume based on the sensor data set using a prediction model configured to predict air conditioning equipment performance based on the chamber data set predicting equipment performance; A method for providing information on air conditioning equipment performance prediction using an artificial neural network model comprising a. delete delete According to claim 6,
The prediction model uses an artificial neural network model, which is a model that takes at least one of outdoor unit intake air temperature, indoor unit ventilation temperature, indoor unit ventilation humidity, indoor unit supplied air temperature, indoor unit supplied air humidity, differential pressure, simple wattmeter current, and power data as an input. A method for providing information about predicting air conditioning equipment performance. According to claim 6,
A method of providing information about air conditioning equipment performance prediction using an artificial neural network model, further comprising adjusting one of temperature, humidity, and indoor pressure of the air conditioning equipment based on the predicted result. A communication unit configured to receive a sensor data set in an air conditioning zone, and
A processor coupled to communicate with the communication unit;
the processor,
Outdoor DB / WB temperature, indoor DB / WB temperature, test product discharge DB / WB temperature, code tester nozzle differential pressure, code tester nozzle inlet temperature, measured current and measured power data obtained through any air conditioning equipment Air conditioning including cooling capacity, cooling and heating energy efficiency (cop) and air volume based on the sensor data set using a prediction model configured to predict air conditioning equipment performance based on the chamber data set An air conditioning equipment performance prediction device using an artificial neural network model configured to predict equipment performance. delete delete According to claim 11,
The prediction model is composed of a model that receives at least one of outdoor unit intake air temperature, indoor unit ventilation temperature, indoor unit ventilation humidity, indoor unit supplied air temperature, indoor unit supplied air humidity, differential pressure, simple wattmeter current, and power data, and predicts performance of air conditioning equipment. Device. According to claim 11,
An air conditioning equipment performance prediction device configured to adjust any one of temperature, humidity, and room pressure of the air conditioning equipment based on the predicted result.

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