Method and system for controlling indoor thermal environment of pension building

Abstract

The invention relates to the technical field of building environment control, in particular to a control method and system for an indoor thermal environment of a pension architecture.The method comprises the steps that influence factors used for indoor temperature prediction are obtained firstly, then values of the influence factors are collected, the values of the influence factors are preprocessed, and in the preprocessing operation process, the influence factors are extracted; the numerical values of the influence factors are preprocessed by using the correlation coefficients, so that when a deep learning model is adopted for prediction, input data can reflect the importance degree of data participating in calculation; the prediction accuracy is improved, and the thermal environment control accuracy is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of building environment control, and in particular to a method and system for controlling the indoor thermal environment of a nursing home building. Background Art

[0002] Energy is an important material basis for human progress and development, a basic condition for human survival, and one of the most influential issues in the world today. With the progress of society, people's living standards have also improved, so people's requirements for comfort in buildings have become higher and higher, especially the living and living needs of the elderly have become the focus of current social attention, the equipment load used to maintain indoor thermal comfort has increased, and the unit load of various buildings has increased. HVAC is an important means for people to adjust the indoor environment, and the requirement for high-precision regulation is a major difficulty.

[0003] There are technical solutions in the prior art for controlling air conditioning and thus achieving indoor thermal environment control. For example, a Chinese invention patent (CN110671798A) discloses an indoor thermal environment control system based on artificial intelligence technology to predict thermal sensation. The indoor thermal environment control based on artificial intelligence technology to predict thermal sensation includes step 1. setting the initial value of the user's comfortable skin temperature according to the recommended comfortable temperature value. An infrared thermal imaging camera is used to capture an infrared image of the user, and the skin temperature is identified based on the thermal imaging. Step 2. If the user inputs thermal sensations such as "too cold" or "too hot" through the HMI, the comfort zone of the user's skin temperature is updated accordingly. However, the above scheme has the problem of relatively low control accuracy when performing indoor thermal environment control. Summary of the invention

[0004] In view of this, the present invention proposes a method and system for controlling the indoor thermal environment of a nursing home building, so as to improve the accuracy of indoor thermal environment control.

[0005] In order to achieve the above object, a method for controlling the indoor thermal environment of a nursing home building is provided, the method comprising:

[0006] S1: Obtain influencing factors for indoor temperature prediction;

[0007] S2: Collecting the values ​​of the influencing factors for indoor temperature prediction;

[0008] S3: performing a preprocessing operation on the value of the influencing factor for indoor temperature prediction to obtain the preprocessed value of the influencing factor for indoor temperature prediction;

[0009] The step S3 specifically includes: performing a correlation analysis on the influencing factors for indoor temperature prediction, and then performing a preprocessing operation on the influencing factors for indoor temperature prediction according to the magnitude of the correlation coefficient, to obtain the values ​​of the influencing factors for indoor temperature prediction after preprocessing;

[0010] S4: Build a model for predicting indoor temperature;

[0011] S5: training the model for predicting indoor temperature;

[0012] S6: inputting the preprocessed values ​​of the influencing factors for indoor temperature prediction into the model for predicting indoor temperature to obtain indoor temperature prediction results;

[0013] S7: Control the indoor thermal environment according to the indoor temperature prediction result.

[0014] Preferably, the correlation analysis of the influencing factors for indoor temperature prediction is specifically as follows:

[0015] The indoor temperature value, the outdoor temperature value, the outdoor relative humidity value, the outdoor wind speed value and the solar radiation value are obtained, and then the correlation coefficients of the outdoor temperature value, the outdoor relative humidity value, the outdoor wind speed value and the solar radiation value with the indoor temperature value are calculated respectively by using the product difference method.

[0016] Preferably, the preprocessing operation of the influencing factors for indoor temperature prediction according to the magnitude of the correlation coefficient is specifically as follows:

[0017] Sa: performing normalization processing on the value of the influencing factor for indoor temperature prediction to obtain a normalized value of the influencing factor for indoor temperature prediction;

[0018] Sb: normalizing the values ​​of the influencing factors for indoor temperature prediction to obtain a normalized correlation coefficient value;

[0019] Sc: multiplying the normalized value of the influencing factor for indoor temperature prediction by the corresponding normalized correlation coefficient value to obtain the preprocessed value of the influencing factor for indoor temperature prediction.

[0020] Preferably, in said S1, the influencing factors acquired for indoor temperature prediction include: outdoor temperature, outdoor relative humidity, outdoor wind speed and solar radiation.

[0021] Preferably, in S2, a small meteorological instrument is used to obtain the value of the influencing factor used for indoor temperature prediction; the collected value is the value of the influencing factor a period of time before the prediction moment; the period of time is one of 1 minute, 5 minutes, 10 minutes, and 15 minutes.

[0022] Preferably, the calculation formula for calculating the correlation coefficient r by the product difference method is:

[0023]

[0024] Where x and y are the influencing factor value and indoor temperature value respectively, x′ and y′ are the center points of the influencing factor and indoor temperature value, and n is the number of samples involved in the calculation.

[0025] Preferably, the BP neural network model includes an input layer, a hidden layer and an output layer, the number of nodes in the input layer is 4; the number of nodes in the output layer is 1; the calculation formula of the node h in the hidden layer is:

[0026]

[0027] In the formula, m and n are the number of nodes in the input layer and the output layer respectively, and a is the adjustment coefficient.

[0028] Preferably, the transfer function of the BP neural network model uses a sigmoid function as the transfer function, and its formula is expressed as:

[0029]

[0030] Among them, x is the input data.

[0031] Preferably, the training times of the BP neural network model is set to 50,000 times.

[0032] According to another aspect of the present invention, a control system for the indoor thermal environment of a nursing home building is provided. The system adopts the above-mentioned control method for the indoor thermal environment of a nursing home building. The system comprises:

[0033] An influencing factor acquisition module is used to obtain influencing factors for indoor temperature prediction;

[0034] A data acquisition module, used for acquiring the values ​​of the influencing factors for indoor temperature prediction;

[0035] A preprocessing module, used for performing a preprocessing operation on the influencing factors for indoor temperature prediction to obtain the values ​​of the influencing factors for indoor temperature prediction after preprocessing;

[0036] A model building module, used for building a model for predicting indoor temperature;

[0037] A model training module, used for training the model for predicting indoor temperature;

[0038] A prediction module, which is used to input the pre-processed values ​​of the influencing factors for indoor temperature prediction into the model for predicting indoor temperature to obtain the indoor temperature prediction result;

[0039] The thermal environment control module is used to control the indoor thermal environment according to the indoor temperature prediction result.

[0040] The advantages and beneficial effects of the present invention are:

[0041] The present invention first obtains the influencing factors for indoor temperature prediction, then collects the values ​​of the influencing factors, and then performs a preprocessing operation on the values ​​of the influencing factors. During the preprocessing operation, the numerical values ​​of the influencing factors are preprocessed by using correlation coefficients, so that when a deep learning model is used for prediction, the input data can reflect the importance of the data involved in the calculation; the accuracy of the prediction is improved, thereby improving the accuracy of the thermal environment control. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the description of the present invention or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 A flow chart of a method for controlling the indoor thermal environment of a nursing home building provided by an embodiment of the present invention;

[0044] Figure 2 A flowchart of performing a preprocessing operation on the influencing factors for indoor temperature prediction according to the magnitude of the correlation coefficient provided by an embodiment of the present invention;

[0045] Figure 3 A structural diagram of a BP neural network model provided by an embodiment of the present invention;

[0046] Figure 4 A structural diagram of a control system for the indoor thermal environment of a nursing home building provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] Indoor thermal environment refers to the thermodynamic state of indoor space, including parameters such as temperature, humidity, air flow rate, and thermal radiation. These factors work together on the human senses to determine the thermal comfort of the occupants. The quality of the indoor thermal environment is affected by many factors, including external climate conditions, building design and materials, indoor heat sources (such as electrical appliances and human body), and the configuration of the ventilation system. Temperature is a key factor affecting the indoor thermal environment, which is directly related to energy consumption and the health of the occupants. Temperatures that are too high or too low can cause discomfort to the human body, and appropriate humidity can reduce the survival rate of viruses and the spread of allergens. In this embodiment, a method for controlling the indoor thermal environment of a nursing home building is proposed to achieve dynamic and accurate adjustment of the indoor temperature.

[0049] As attached Figure 1 As shown, a method for controlling the indoor thermal environment of a nursing home building comprises the following steps:

[0050] S1: Obtain influencing factors for indoor temperature prediction;

[0051] The actual heat load of a building includes internal interference and external interference. External interference mainly includes the heat transferred from the outdoor enclosure structure, the solar radiation heat obtained by the outdoor glass skylight and the outdoor curtain wall, and the air heat passing through the gaps between the doors and windows of the building. The heat affecting this part is mainly related to the outdoor temperature, relative humidity, wind speed and solar radiation, heat dissipation area, the number of gaps, etc.; internal interference mainly refers to the number of people, lighting and heat loss of electrical equipment during use;

[0052] Among them, in the field of indoor temperature prediction of buildings, the influencing factors of internal interference are difficult to quantify. Therefore, external interference is generally considered as an influencing factor to achieve thermal control of the indoor environment;

[0053] In this step, the influencing factors obtained for indoor temperature prediction include: outdoor temperature, outdoor relative humidity, outdoor wind speed and solar radiation;

[0054] S2: Collecting the values ​​of the influencing factors for indoor temperature prediction;

[0055] In this step, a small meteorological instrument is used to obtain the value of the influencing factor for indoor temperature prediction;

[0056] It is worth emphasizing that the collected values ​​are the values ​​of the influencing factors in a period of time before the prediction moment; a period of time can be 1 minute, 5 minutes, 10 minutes, 15 minutes; in order to improve the accuracy of the prediction, the values ​​of the predicted influencing factors are selected from the values ​​of the period relatively close to the prediction moment;

[0057] S3: performing a preprocessing operation on the value of the influencing factor for indoor temperature prediction to obtain the preprocessed value of the influencing factor for indoor temperature prediction;

[0058] In the prior art, the influencing factors are generally directly input into the prediction model to obtain the prediction results; this results in low prediction accuracy. Based on the prior art solution, this embodiment performs a correlation analysis on the influencing factors used for indoor temperature prediction, and then performs a preprocessing operation on the influencing factors used for indoor temperature prediction according to the size of the correlation coefficient;

[0059] The correlation analysis of the influencing factors for indoor temperature prediction is specifically as follows:

[0060] Obtain the indoor temperature value and the outdoor temperature value, the outdoor relative humidity value, the outdoor wind speed value and the solar radiation value, and then use the product difference method to calculate the correlation coefficients of the outdoor temperature value, the outdoor relative humidity value, the outdoor wind speed value and the solar radiation value with the indoor temperature value;

[0061] The calculation formula for calculating the correlation coefficient r using the product difference method is:

[0062]

[0063] In the formula, x and y are the value of the influencing factor and the indoor temperature value respectively, x′ and y′ are the center points of the influencing factor and the indoor temperature value, and n is the number of samples involved in the calculation;

[0064] In this embodiment, the correlation coefficients r1, r2, r3, and r4 of the outdoor temperature value, outdoor relative humidity value, outdoor wind speed value, and solar radiation value with the indoor temperature value are 0.983, 0.891, 0.704, and 0.126;

[0065] As attached Figure 2 As shown, the preprocessing operation of the influencing factors for indoor temperature prediction according to the size of the correlation coefficient is specifically as follows:

[0066] Sa: performing normalization processing on the value of the influencing factor for indoor temperature prediction to obtain a normalized value of the influencing factor for indoor temperature prediction;

[0067] Wherein, the maximum and minimum normalization method is used to normalize the values ​​of the influencing factors for indoor temperature prediction;

[0068] Sb: normalizing the values ​​of the influencing factors for indoor temperature prediction to obtain a normalized correlation coefficient value;

[0069] The maximum and minimum normalization method is also used to normalize the correlation coefficients of the outdoor temperature value, outdoor relative humidity value, outdoor wind speed value, solar radiation value and indoor temperature value to obtain a normalized correlation coefficient value r c1 、rc2 、r c3 、r c4 ;

[0070] Sc: multiplying the normalized value of the influencing factor for indoor temperature prediction by the corresponding normalized correlation coefficient value to obtain the value of the influencing factor for indoor temperature prediction after preprocessing;

[0071] In this embodiment, the numerical values ​​of the influencing factors are preprocessed by using the correlation coefficient, so that when the deep learning model is used for prediction, the input data can reflect the importance of the data involved in the calculation, thereby improving the accuracy of the prediction.

[0072] S4: Build a model for predicting indoor temperature;

[0073] Neural network uses the principle of human brain, that is, the human brain is controlled by neurons to receive stimulation and information from the outside world. Studies have shown that there are tens of billions of different neurons in the human brain, and each neuron is closely connected with thousands of other neurons, which together form the neural network of the human brain. When the brain receives external stimulation, neurons will follow certain rules to transmit and accumulate, which is ultimately the response of the human brain to external stimulation. Using the operating principle of the human brain, a biological model is established, which is a neural network model. Neural network is a kind of intelligent computer science theory, which can perform computing functions such as learning, memory, and recognition, and can solve complex, uncertain nonlinear problems. The BP algorithm uses error propagation to form complete interconnections at all levels, and can approximate a variety of complex nonlinear functions with high precision, and obtain high precision with extremely low computational effort; it has the ability to organize learning autonomously and can continuously adapt to changes.

[0074] Therefore, in this embodiment, the model used to predict the indoor temperature is a BP neural network model;

[0075] As attached Figure 3 As shown, the BP neural network model includes an input layer, a hidden layer and an output layer;

[0076] The input data of the input layer is determined by analyzing various indicators. In this embodiment, it is determined by the influencing factors of indoor temperature prediction. Therefore, according to the analysis results of the previous step, the number of nodes in the input layer is 4; the output layer is used to output the predicted temperature, so the node data of the output layer is 1; wherein xi is the input indicator value, f1 is the hidden layer, f2 is the output layer, bi is the hidden layer parameter, and Bk is the output layer parameter;

[0077] The calculation formula of the node h of the hidden layer is:

[0078]

[0079] In the formula, m and n are the number of nodes in the input layer and the output layer respectively, and a is the adjustment coefficient;

[0080] Furthermore, the transfer function of the BP neural network model uses a sigmoid function as a transfer function, and its formula is expressed as:

[0081]

[0082] Where x is the input data;

[0083] The training times of the BP neural network model are set to 50,000 times. In order to obtain a better fitting effect, the learning times of the model are adjusted to a maximum of 50,000 iterations. When the error drops to 0.0001, the model training stops.

[0084] S5: training the model for predicting indoor temperature;

[0085] Wherein, matlabR2014a is used to train the model for predicting indoor temperature;

[0086] With the help of Matlab software, multiple rounds of training and learning are carried out, and the shared value of sample data is accurately output by adjusting factors such as weights and i}} values.

[0087] In this embodiment, the evaluation result output by the evaluation model can be measured by mean square error (MSE). The smaller the MSE, the better the fitting effect of the model.

[0088] S6: inputting the preprocessed values ​​of the influencing factors for indoor temperature prediction into the model for predicting indoor temperature to obtain indoor temperature prediction results;

[0089] In fact, in this step, the indoor temperature prediction result obtained is the prediction result one minute after the prediction;

[0090] S7: Control the indoor thermal environment according to the indoor temperature prediction result;

[0091] The indoor temperature prediction result is input into an air conditioning control system, and the air conditioning control system controls the indoor thermal environment according to the indoor temperature prediction result and a set target temperature.

[0092] Embodiment 2: This embodiment includes a control system for the indoor thermal environment of a nursing home building. The system adopts a control method for the indoor thermal environment of a nursing home building in Embodiment 1. The system includes:

[0093] An influencing factor acquisition module is used to obtain influencing factors for indoor temperature prediction;

[0094] A data acquisition module, used for acquiring the values ​​of the influencing factors for indoor temperature prediction;

[0095] A preprocessing module, used for performing a preprocessing operation on the influencing factors for indoor temperature prediction to obtain the values ​​of the influencing factors for indoor temperature prediction after preprocessing;

[0096] A model building module, used for building a model for predicting indoor temperature;

[0097] A model training module, used for training the model for predicting indoor temperature;

[0098] A prediction module, which is used to input the pre-processed values ​​of the influencing factors for indoor temperature prediction into the model for predicting indoor temperature to obtain the indoor temperature prediction result;

[0099] A thermal environment control module is used to control the indoor thermal environment according to the indoor temperature prediction result;

[0100] Embodiment 3, this embodiment includes a computer-readable storage medium, on which a data processing program is stored, and the data processing program is executed by a processor to provide a method for controlling the indoor thermal environment of a nursing home building according to embodiment 1.

[0101] It will be appreciated by those skilled in the art that the embodiments herein may be provided as methods, devices (equipment), or computer program products. Therefore, this article may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Including but not limited to RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassette, magnetic tape, disk storage or other magnetic storage device, or any other medium that can be used to store desired information and can be accessed by a computer, etc. In addition, it is well known to those of ordinary skill in the art that communication media generally contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0102] This document is described with reference to flowcharts and / or block diagrams of methods, apparatuses (devices) and computer program products according to the embodiments of this document. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0103] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0104] The embodiments and / or implementation methods described above are only used to illustrate the preferred embodiments and / or implementation methods of the technology of the present invention, and are not intended to impose any formal restrictions on the implementation methods of the technology of the present invention. Any person skilled in the art may make some changes or modifications to other equivalent embodiments without departing from the scope of the technical means disclosed in the content of the present invention, but they should still be regarded as the technology or embodiments that are essentially the same as the present invention. Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described here, and that various obvious changes, readjustments and substitutions can be made to those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention is described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may also include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for controlling the indoor thermal environment of a nursing home building, characterized in that: The method comprises the following steps: S1: Obtain influencing factors for indoor temperature prediction; S2: Collecting the values ​​of the influencing factors for indoor temperature prediction; S3: performing a preprocessing operation on the value of the influencing factor for indoor temperature prediction to obtain the preprocessed value of the influencing factor for indoor temperature prediction; The step S3 specifically includes: performing a correlation analysis on the influencing factors for indoor temperature prediction, and then performing a preprocessing operation on the influencing factors for indoor temperature prediction according to the magnitude of the correlation coefficient, to obtain the values ​​of the influencing factors for indoor temperature prediction after preprocessing; S4: Build a model for predicting indoor temperature; S5: training the model for predicting indoor temperature; S6: inputting the preprocessed values ​​of the influencing factors for indoor temperature prediction into the model for predicting indoor temperature to obtain indoor temperature prediction results; S7: Control the indoor thermal environment according to the indoor temperature prediction result.

2. A method for controlling the indoor thermal environment of a nursing home building according to claim 1, characterized in that: The correlation analysis of the influencing factors for indoor temperature prediction is specifically as follows: The indoor temperature value, the outdoor temperature value, the outdoor relative humidity value, the outdoor wind speed value and the solar radiation value are obtained, and then the correlation coefficients of the outdoor temperature value, the outdoor relative humidity value, the outdoor wind speed value and the solar radiation value with the indoor temperature value are calculated respectively by using the product difference method.

3. A method for controlling the indoor thermal environment of a nursing home building according to claim 2, characterized in that: The preprocessing operation of the influencing factors for indoor temperature prediction according to the magnitude of the correlation coefficient is specifically as follows: Sa: performing normalization processing on the value of the influencing factor for indoor temperature prediction to obtain a normalized value of the influencing factor for indoor temperature prediction; Sb: normalizing the values ​​of the influencing factors for indoor temperature prediction to obtain a normalized correlation coefficient value; Sc: multiplying the normalized value of the influencing factor for indoor temperature prediction by the corresponding normalized correlation coefficient value to obtain the preprocessed value of the influencing factor for indoor temperature prediction.

4. The method for controlling the indoor thermal environment of a nursing home building according to claim 1, characterized in that: In S1, the influencing factors obtained for indoor temperature prediction include: outdoor temperature, outdoor relative humidity, outdoor wind speed and solar radiation.

5. The method for controlling the indoor thermal environment of a nursing home building according to claim 1, characterized in that: In S2, a small meteorological instrument is used to obtain the value of the influencing factor used for indoor temperature prediction; the collected value is the value of the influencing factor a period of time before the prediction moment; the period of time is one of 1 minute, 5 minutes, 10 minutes, and 15 minutes.

6. The method for controlling the indoor thermal environment of a nursing home building according to claim 2, characterized in that: The calculation formula for calculating the correlation coefficient r using the product difference method is: Where x and y are the influencing factor value and indoor temperature value respectively, x′ and y′ are the center points of the influencing factor and indoor temperature value, and n is the number of samples involved in the calculation.

7. A method for controlling the indoor thermal environment of a nursing home building according to claim 6, characterized in that: The calculation formula for calculating the correlation coefficient r using the product difference method is: Where x and y are the influencing factor value and indoor temperature value respectively, x′ and y′ are the center points of the influencing factor and indoor temperature value, and n is the number of samples involved in the calculation.

8. The method for controlling the indoor thermal environment of a nursing home building according to claim 1, characterized in that: The model used to predict indoor temperature is a BP neural network model, which includes an input layer, a hidden layer and an output layer. The number of nodes in the input layer is 4; the number of nodes in the output layer is 1; the calculation formula of the node h in the hidden layer is: In the formula, m and n are the number of nodes in the input layer and the output layer respectively, and a is the adjustment coefficient.

9. A method for controlling the indoor thermal environment of a nursing home building according to claim 8, characterized in that: The transfer function of the BP neural network model uses the sigmoid function as the transfer function, and its formula is expressed as: Where x is the input data; The training times of the BP neural network model are set to 50,000 times.

10. A control system for the indoor thermal environment of a nursing home building, characterized in that: The system adopts a method for controlling the indoor thermal environment of a nursing home building according to any one of claims 1 to 9, and the system comprises: An influencing factor acquisition module is used to obtain influencing factors for indoor temperature prediction; A data acquisition module, used for acquiring the values ​​of the influencing factors for indoor temperature prediction; A preprocessing module, used for performing a preprocessing operation on the influencing factors for indoor temperature prediction to obtain the values ​​of the influencing factors for indoor temperature prediction after preprocessing; A model building module, used for building a model for predicting indoor temperature; A model training module, used for training the model for predicting indoor temperature; A prediction module, which is used to input the pre-processed values ​​of the influencing factors for indoor temperature prediction into the model for predicting indoor temperature to obtain the indoor temperature prediction result; The thermal environment control module is used to control the indoor thermal environment according to the indoor temperature prediction result.

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