Air conditioner energy efficiency prediction and scheduling system

Abstract

The invention discloses an air conditioner energy efficiency prediction and scheduling system, which belongs to the technical field of air conditioner energy efficiency, and comprises a data acquisition module used for acquiring air conditioner monitoring real-time data based on the Internet of Things; the data transmission module is used for transmitting air conditioner monitoring real-time data based on the Internet of Things; and the prediction scheduling platform is used for performing analysis prediction and scheduling management and control on the air conditioner energy efficiency. The problems that in the prior art, real-time monitoring prediction and dispatching management and control cannot be conducted on the energy efficiency of the air conditioner, efficient energy management cannot be achieved, energy waste is increased, and the purpose of saving energy cannot be achieved are solved. According to the optimal air conditioner energy efficiency prediction model, air conditioner energy efficiency analysis prediction is conducted on the air conditioner monitoring feature data, the air conditioner energy efficiency prediction result is determined, the air conditioner energy efficiency scheduling management and control scheme is formulated, intelligent scheduling management and control are conducted on the air conditioner energy efficiency, real-time monitoring prediction and scheduling management and control can be conducted on the air conditioner energy efficiency, and the air conditioner energy efficiency is improved. Efficient energy management can be achieved, energy waste is reduced, and the purpose of saving energy can be achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of air conditioning energy efficiency, and in particular to an air conditioning energy efficiency prediction and scheduling system. Background Art

[0002] With the continuous rise in energy costs and the increase in environmental pressure, people are paying more and more attention to the issue of energy conservation and emission reduction. In the construction industry, air conditioning systems are one of the main sources of energy consumption. Among them, air conditioning energy efficiency refers to the ratio of the cooling capacity of the air conditioner to the input power, which reflects the energy-saving level of the air conditioner. The greater the energy efficiency ratio, the more electricity is saved. Choosing energy-saving air conditioners can reduce electricity bills in daily use, which is in line with the trend of environmental protection and energy conservation. Therefore, how to improve the energy efficiency of air conditioning systems, reduce energy consumption and reduce operating costs has become a problem that needs to be solved urgently.

[0003] The Chinese patent with publication number CN113566388A discloses a power generation device for improving the energy efficiency ratio of air conditioners, including a pressurizing device, a vortex tube, a venturi tube, and a turbine power generation device. The vortex tube is used to separate air into hot and cold air, and the hot air is ejected for power generation, while the cold air is condensed for heat exchange. Thus, energy is saved by utilizing condensation heat for power generation, and the energy efficiency of the unit is improved by reducing the temperature of the incoming condensing air. However, the patent has the following defects:

[0004] The existing system cannot conduct real-time monitoring, prediction, and dispatch control of air conditioning energy efficiency, cannot achieve efficient energy management, increases energy waste, and cannot achieve the purpose of energy saving. Summary of the invention

[0005] The purpose of the present invention is to provide an air conditioning energy efficiency prediction and scheduling system, which can perform real-time monitoring, prediction and scheduling control of air conditioning energy efficiency, realize efficient energy management, reduce energy waste, achieve the purpose of energy saving, and solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An air conditioning energy efficiency prediction and scheduling system, comprising:

[0008] Data acquisition module, used to collect real-time data of air conditioning monitoring based on the Internet of Things;

[0009] Data transmission module, used to transmit real-time data of air conditioning monitoring based on the Internet of Things;

[0010] The prediction and scheduling platform is used to analyze, predict, and schedule air conditioning energy efficiency.

[0011] Preferably, the data acquisition module comprises:

[0012] The temperature collection unit is used to monitor and continuously collect indoor and outdoor temperature parameters in real time during the operation of the air conditioner to obtain the air conditioner monitoring temperature data;

[0013] The humidity collection unit is used to monitor and continuously collect indoor and outdoor humidity parameters in real time during the operation of the air conditioner to obtain the air conditioner monitoring humidity data;

[0014] The wind speed collection unit is used to monitor and continuously collect indoor and outdoor wind speed parameters in real time during the operation of the air conditioner to obtain the air conditioner monitoring wind speed data;

[0015] Among them, based on the air-conditioning monitoring temperature data, air-conditioning monitoring humidity data and air-conditioning monitoring wind speed data, the real-time air-conditioning monitoring data based on the Internet of Things is determined.

[0016] Preferably, the data transmission module includes:

[0017] A data sending unit, used for sending real-time data of air conditioning monitoring based on the Internet of Things;

[0018] A data receiving unit, used for receiving real-time data of air conditioning monitoring based on the Internet of Things;

[0019] wherein a data transmission link is established between the data sending unit and the data receiving unit;

[0020] The data sending unit receives the real-time air conditioning monitoring data based on the Internet of Things transmitted from the data acquisition module, and the data sending unit sends the received real-time air conditioning monitoring data based on the Internet of Things to the data receiving unit;

[0021] The data receiving unit receives the real-time air conditioning monitoring data based on the Internet of Things transmitted from the data sending unit, and the data receiving unit transmits the received real-time air conditioning monitoring data based on the Internet of Things to the prediction and scheduling platform.

[0022] Preferably, a data transmission link is established between the data sending unit and the data receiving unit, and the following operations are performed:

[0023] According to the air conditioner energy efficiency prediction and scheduling requirements, the data sending unit transmits an instruction requesting to establish a data transmission connection to the data receiving unit;

[0024] After the data receiving unit receives the instruction transmitted by the data sending unit requesting to establish a data transmission connection, the data receiving unit checks its own data transmission channel;

[0025] When there is an idle data transmission channel in the data receiving unit, the data receiving unit transmits an instruction to the data sending unit to agree to establish a data transmission connection;

[0026] After the data sending unit receives the instruction transmitted by the data receiving unit agreeing to establish a data transmission connection, the data sending unit establishes a data transmission link with the data receiving unit based on the instruction agreeing to establish a data transmission connection, for transmitting real-time air conditioning monitoring data based on the Internet of Things.

[0027] Preferably, the prediction and scheduling platform includes:

[0028] A data processing module is used to process the real-time data of air conditioning monitoring based on the Internet of Things and determine the characteristic data of air conditioning monitoring based on the Internet of Things;

[0029] Model training module, used to build an air conditioning energy efficiency prediction model based on deep learning according to air conditioning energy efficiency prediction and scheduling requirements;

[0030] A test optimization module is used to test and optimize the air conditioning energy efficiency prediction model based on deep learning, and determine the optimal air conditioning energy efficiency prediction model based on deep learning;

[0031] The energy efficiency prediction module is used to analyze and predict the air conditioning energy efficiency based on the air conditioning monitoring feature data based on the Internet of Things according to the optimal air conditioning energy efficiency prediction model based on deep learning, and determine the air conditioning energy efficiency prediction result based on deep learning;

[0032] The scheduling and control module is used to formulate air-conditioning energy efficiency scheduling and control plans and to perform intelligent scheduling and control of air-conditioning energy efficiency.

[0033] Preferably, the data processing module includes:

[0034] A data cleaning unit is used to clean the real-time data of air conditioning monitoring based on the Internet of Things;

[0035] Obtain real-time data of air conditioning monitoring based on the Internet of Things;

[0036] Clean the real-time data of air conditioning monitoring based on the Internet of Things;

[0037] Determine the real-time air conditioning monitoring data that is useful for air conditioning energy efficiency prediction and scheduling;

[0038] A feature extraction unit, used for extracting features from real-time monitoring data of the air conditioner after cleaning;

[0039] Obtain real-time air conditioning monitoring data that is useful for air conditioning energy efficiency prediction and scheduling;

[0040] Extract features from real-time air conditioning monitoring data that is useful for air conditioning energy efficiency prediction and scheduling;

[0041] Extract key features that can reflect air conditioning energy efficiency prediction and scheduling;

[0042] Determine the characteristic data of air conditioning monitoring based on the Internet of Things.

[0043] Preferably, the model training module includes:

[0044] A data collection unit for collecting historical data of air conditioning monitoring based on the Internet of Things;

[0045] According to the air conditioning energy efficiency prediction and scheduling requirements, the historical data of air conditioning monitoring based on the Internet of Things is collected and reserved;

[0046] A model building unit, used to build an air conditioning energy efficiency prediction model based on deep learning;

[0047] Obtain historical data of air conditioning monitoring based on the Internet of Things;

[0048] Divide the historical data of air conditioning monitoring based on the Internet of Things;

[0049] Determine the air conditioning energy efficiency prediction training set and the air conditioning energy efficiency prediction test set;

[0050] Select the convolutional neural network model framework for air conditioning energy efficiency prediction;

[0051] Based on the air conditioning energy efficiency prediction training set, the selected convolutional neural network model framework for air conditioning energy efficiency prediction is trained;

[0052] An air conditioning energy efficiency prediction model based on deep learning was determined.

[0053] Preferably, the test optimization module includes:

[0054] A test and evaluation unit, used to test and evaluate the air conditioning energy efficiency prediction model based on deep learning;

[0055] Obtain air conditioning energy efficiency prediction model based on deep learning;

[0056] Based on the air conditioning energy efficiency prediction test set, the air conditioning energy efficiency prediction model based on deep learning is tested and evaluated;

[0057] Determine the test evaluation results based on the air conditioning energy efficiency prediction model;

[0058] An adjustment and optimization unit, used to adjust and optimize the air conditioning energy efficiency prediction model based on deep learning;

[0059] Obtain test evaluation results based on air conditioning energy efficiency prediction model;

[0060] According to the test and evaluation results based on the air conditioning energy efficiency prediction model, the air conditioning energy efficiency prediction model based on deep learning is mined and analyzed;

[0061] Determine the adjustment and optimization plan based on the air conditioning energy efficiency prediction model;

[0062] According to the adjustment and optimization scheme based on the air conditioning energy efficiency prediction model, the parameters of the air conditioning energy efficiency prediction model based on deep learning are adjusted and iteratively optimized;

[0063] Determine the optimal air conditioning energy efficiency prediction model based on deep learning.

[0064] Preferably, the energy efficiency prediction module includes:

[0065] A data extraction unit, used for extracting characteristic data of air conditioning monitoring based on the Internet of Things;

[0066] An energy efficiency prediction unit, used to predict energy efficiency based on air conditioning monitoring feature data based on the Internet of Things;

[0067] Obtain the optimal air conditioning energy efficiency prediction model based on deep learning;

[0068] Apply the optimal deep learning-based air conditioning energy efficiency prediction model to the IoT-based air conditioning monitoring feature data;

[0069] According to the optimal air conditioning energy efficiency prediction model based on deep learning, the characteristic data of air conditioning monitoring based on the Internet of Things is mined and analyzed to analyze and predict the air conditioning energy efficiency;

[0070] Determine the air conditioning energy efficiency prediction results based on deep learning.

[0071] Preferably, the scheduling control module includes:

[0072] Strategy formulation unit, used to formulate air conditioning energy efficiency scheduling and control plans;

[0073] Obtain air conditioning energy efficiency prediction results based on deep learning;

[0074] According to the air conditioning energy efficiency prediction results based on deep learning, the characteristic data of air conditioning monitoring based on the Internet of Things is analyzed, and the air conditioning energy efficiency scheduling and control plan is studied and judged;

[0075] Determine the air conditioning energy efficiency scheduling and control plan;

[0076] The dispatching and control unit is used to intelligently dispatch and control the energy efficiency of air conditioners;

[0077] Obtain air conditioning energy efficiency scheduling and control solutions;

[0078] Intelligent scheduling and control of air conditioning energy efficiency is carried out based on the air conditioning energy efficiency scheduling and control solution.

[0079] Compared with the prior art, the present invention has the following beneficial effects:

[0080] The present invention collects real-time air-conditioning monitoring data based on the Internet of Things, processes the real-time air-conditioning monitoring data based on the Internet of Things, determines the air-conditioning monitoring feature data based on the Internet of Things, and constructs an air-conditioning energy efficiency prediction model based on deep learning according to the air-conditioning energy efficiency prediction and scheduling requirements. The air-conditioning energy efficiency prediction model based on deep learning is tested and optimized to determine the optimal air-conditioning energy efficiency prediction model based on deep learning. According to the optimal air-conditioning energy efficiency prediction model based on deep learning, the air-conditioning energy efficiency is analyzed and predicted on the air-conditioning monitoring feature data based on the Internet of Things, the air-conditioning energy efficiency prediction result based on deep learning is determined, and an air-conditioning energy efficiency scheduling and control plan is formulated to perform intelligent scheduling and control on the air-conditioning energy efficiency. The air-conditioning energy efficiency can be monitored, predicted and scheduled in real time, and efficient energy management can be achieved, energy waste can be reduced, and the purpose of energy saving can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 It is a structural block diagram of the air conditioning energy efficiency prediction and scheduling system of the present invention. DETAILED DESCRIPTION

[0082] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0083] In order to solve the existing problems of not being able to monitor, predict, and dispatch the air conditioner energy efficiency in real time, not being able to achieve efficient energy management, increasing energy waste, and not being able to achieve the purpose of energy saving, please refer to Figure 1 , this embodiment provides the following technical solutions:

[0084] An air-conditioning energy efficiency prediction and scheduling system comprises: a data acquisition module, a data transmission module and a prediction and scheduling platform.

[0085] It should be noted that the real-time data of air-conditioning monitoring based on the Internet of Things is collected through the data acquisition module; the real-time data of air-conditioning monitoring based on the Internet of Things is transmitted through the data transmission module; the air-conditioning energy efficiency is analyzed, predicted, and dispatched and controlled through the prediction and scheduling platform, and the air-conditioning energy efficiency can be monitored, predicted, and dispatched and controlled in real time, which can realize efficient energy management, reduce energy waste, and achieve the purpose of energy saving.

[0086] Among them, the data acquisition module is used to collect real-time data of air conditioning monitoring based on the Internet of Things;

[0087] In this embodiment, the data acquisition module includes:

[0088] The temperature collection unit is used to monitor and continuously collect indoor and outdoor temperature parameters in real time during the operation of the air conditioner to obtain the air conditioner monitoring temperature data;

[0089] It should be noted that a temperature sensor is installed in the air-conditioning system, and the indoor and outdoor temperature parameters during the operation of the air-conditioning are monitored and continuously collected in real time based on the temperature sensor, so as to obtain the air-conditioning monitoring temperature data.

[0090] Among them, the temperature sensor refers to a sensor that can sense temperature and convert it into a usable output signal. The temperature sensor is the core part of the temperature measuring instrument and there are many varieties. According to the measurement method, it can be divided into two categories: contact and non-contact. According to the sensor material and electronic component characteristics, it can be divided into two categories: thermal resistors and thermocouples. In the present invention, a non-contact temperature sensor is used to obtain air conditioning monitoring temperature data, which is more convenient during use.

[0091] The humidity collection unit is used to monitor and continuously collect indoor and outdoor humidity parameters in real time during the operation of the air conditioner, and obtain the air conditioner monitoring humidity data;

[0092] It should be noted that a humidity sensor is installed in the air-conditioning system, and the indoor and outdoor humidity parameters during the operation of the air-conditioning are monitored and continuously collected in real time based on the humidity sensor, so as to obtain the air-conditioning monitoring humidity data.

[0093] Among them, the humidity sensitive element is the simplest humidity sensor. The humidity sensitive element mainly includes two categories: resistive and capacitive. In the present invention, a humidity sensitive capacitor is used to obtain the humidity data of air conditioning monitoring. The humidity sensitive capacitor is generally made of a polymer film capacitor. Commonly used polymer materials include polystyrene, polyimide, and acid acetate fiber. When the ambient humidity changes, the dielectric constant of the humidity sensitive capacitor changes, so that its capacitance also changes. The change in capacitance is proportional to the relative humidity. The main advantages of the humidity sensitive capacitor are high sensitivity, good product interchangeability, fast response speed, small humidity hysteresis, easy manufacturing, and easy miniaturization and integration.

[0094] The wind speed collection unit is used to monitor and continuously collect indoor and outdoor wind speed parameters in real time during the operation of the air conditioner to obtain the air conditioner monitoring wind speed data;

[0095] It should be noted that a wind speed sensor is installed in the air-conditioning system, and the indoor and outdoor wind speed parameters during the operation of the air-conditioning are monitored and continuously collected in real time based on the wind speed sensor, so as to obtain the air-conditioning monitoring wind speed data.

[0096] Among them, the wind speed sensor is a device used to measure wind speed, which is small and light, easy to carry and assemble; according to the working principle, it can be roughly divided into mechanical wind speed sensor and ultrasonic wind speed sensor. It can effectively obtain wind speed information, and the shell is made of high-quality aluminum alloy profiles or polycarbonate composite materials, which is rainproof, corrosion-resistant, and anti-aging. It is an easy-to-use, safe and reliable intelligent instrument; in the present invention, an ultrasonic wind speed sensor is used to obtain air conditioning monitoring wind speed data, wherein the air flow of the ultrasonic wind speed sensor passes through the sensor probe measurement area, and 2 pairs of ultrasonic probes are arranged in the area. By calculating the time difference of ultrasonic transmission between two points, the wind speed can be calculated. This method can avoid the influence of temperature on the speed of sound.

[0097] Among them, based on the air-conditioning monitoring temperature data, air-conditioning monitoring humidity data and air-conditioning monitoring wind speed data, the real-time air-conditioning monitoring data based on the Internet of Things is determined.

[0098] Among them, the data transmission module is used to transmit the real-time data of air conditioning monitoring based on the Internet of Things;

[0099] In this embodiment, the data transmission module includes:

[0100] A data sending unit, used for sending real-time data of air conditioning monitoring based on the Internet of Things;

[0101] A data receiving unit, used for receiving real-time data of air conditioning monitoring based on the Internet of Things;

[0102] wherein a data transmission link is established between the data sending unit and the data receiving unit;

[0103] According to the air conditioner energy efficiency prediction and scheduling requirements, the data sending unit transmits an instruction requesting to establish a data transmission connection to the data receiving unit;

[0104] After the data receiving unit receives the instruction transmitted by the data sending unit requesting to establish a data transmission connection, the data receiving unit checks its own data transmission channel;

[0105] When there is an idle data transmission channel in the data receiving unit, the data receiving unit transmits an instruction to the data sending unit to agree to establish a data transmission connection;

[0106] After the data sending unit receives the instruction transmitted by the data receiving unit agreeing to establish the data transmission connection, the data sending unit establishes a data transmission link with the data receiving unit based on the instruction agreeing to establish the data transmission connection, for transmitting the real-time data of air conditioning monitoring based on the Internet of Things;

[0107] Specifically, the data sending unit receives the real-time air conditioning monitoring data based on the Internet of Things transmitted from the data acquisition module, and the data sending unit sends the received real-time air conditioning monitoring data based on the Internet of Things to the data receiving unit;

[0108] Specifically, the data receiving unit receives the real-time air conditioning monitoring data based on the Internet of Things transmitted from the data sending unit, and the data receiving unit transmits the received real-time air conditioning monitoring data based on the Internet of Things to the prediction and scheduling platform.

[0109] Among them, the prediction and scheduling platform is used to analyze, predict, and schedule and control air conditioning energy efficiency.

[0110] In this embodiment, the prediction and scheduling platform includes: a data processing module, a model training module, a test optimization module, an energy efficiency prediction module and a scheduling control module.

[0111] It should be noted that the real-time data of air-conditioning monitoring based on the Internet of Things is processed through the data processing module to determine the characteristic data of air-conditioning monitoring based on the Internet of Things; the air-conditioning energy efficiency prediction model based on deep learning is constructed through the model training module; the air-conditioning energy efficiency prediction model based on deep learning is tested and optimized through the test optimization module to determine the optimal air-conditioning energy efficiency prediction model based on deep learning; the air-conditioning energy efficiency is analyzed and predicted on the characteristic data of air-conditioning monitoring based on the Internet of Things through the energy efficiency prediction module to determine the air-conditioning energy efficiency prediction result based on deep learning; the air-conditioning energy efficiency scheduling and control plan is formulated through the scheduling and control module, and the air-conditioning energy efficiency is intelligently scheduled and controlled.

[0112] Among them, the data processing module is used to process the real-time data of air conditioning monitoring based on the Internet of Things and determine the characteristic data of air conditioning monitoring based on the Internet of Things;

[0113] In this embodiment, the data processing module includes:

[0114] A data cleaning unit is used to clean the real-time data of air conditioning monitoring based on the Internet of Things;

[0115] Obtain real-time data of air conditioning monitoring based on the Internet of Things;

[0116] Conduct consistency checks on real-time data from air conditioning monitoring based on the Internet of Things;

[0117] According to the reasonable value range and mutual relationship of each parameter in the real-time data of air conditioning monitoring based on the Internet of Things, check whether the real-time data of air conditioning monitoring based on the Internet of Things meets the requirements;

[0118] Remove inconsistent data that is beyond the normal range, logically unreasonable or contradictory in the real-time data of air conditioning monitoring based on the Internet of Things;

[0119] It should be noted that if a variable measured with a 1-7 scale has a value of 0 or a negative number appears in the weight, they should be considered to be beyond the normal value range; computer software such as SPSS, SAS, and Excel can automatically identify each variable value that exceeds the range based on the defined value range; logically inconsistent answers may appear in various forms: for example, many respondents said that they drove to work, but reported that they did not have a car; or respondents reported that they were heavy buyers and users of a certain brand, but at the same time gave a very low score on the familiarity scale; by performing a consistency check on the real-time air-conditioning monitoring data based on the Internet of Things, inconsistent data that is beyond the normal range, logically unreasonable or contradictory in the real-time air-conditioning monitoring data based on the Internet of Things can be removed, and the subsequent processing accuracy and efficiency of the real-time air-conditioning monitoring data based on the Internet of Things can be improved.

[0120] Process invalid and missing values ​​of real-time data of air conditioning monitoring based on the Internet of Things;

[0121] According to the data validity and integrity requirements, check whether there are invalid values ​​and missing values ​​in the real-time data of air conditioning monitoring based on the Internet of Things;

[0122] Remove invalid and missing values ​​in the real-time data of air conditioning monitoring based on the Internet of Things that are useless for air conditioning energy efficiency prediction and scheduling;

[0123] Determine the real-time air conditioning monitoring data that is useful for air conditioning energy efficiency prediction and scheduling;

[0124] It should be noted that the invalid values ​​and missing values ​​contained in the real-time data of air-conditioning monitoring based on the Internet of Things are useless for air-conditioning energy efficiency prediction and scheduling. Therefore, it is necessary to remove the invalid values ​​and missing values ​​that are useless for air-conditioning energy efficiency prediction and scheduling, which can improve the subsequent processing accuracy and efficiency of the real-time data of air-conditioning monitoring based on the Internet of Things.

[0125] A feature extraction unit, used for extracting features from real-time monitoring data of the air conditioner after cleaning;

[0126] Obtain real-time air conditioning monitoring data that is useful for air conditioning energy efficiency prediction and scheduling;

[0127] Extract features from real-time air conditioning monitoring data that is useful for air conditioning energy efficiency prediction and scheduling;

[0128] Extract key features that can reflect air conditioning energy efficiency prediction and scheduling;

[0129] Determine the characteristic data of air conditioning monitoring based on the Internet of Things.

[0130] Among them, the model training module is used to build an air-conditioning energy efficiency prediction model based on deep learning according to the air-conditioning energy efficiency prediction and scheduling requirements;

[0131] In this embodiment, the model training module includes:

[0132] A data collection unit for collecting historical data of air conditioning monitoring based on the Internet of Things;

[0133] According to the air conditioning energy efficiency prediction and scheduling requirements, the historical data of air conditioning monitoring based on the Internet of Things is collected and reserved;

[0134] A model building unit, used to build an air conditioning energy efficiency prediction model based on deep learning;

[0135] Obtain historical data of air conditioning monitoring based on the Internet of Things;

[0136] Divide the historical data of air conditioning monitoring based on the Internet of Things;

[0137] Determine the air conditioning energy efficiency prediction training set and the air conditioning energy efficiency prediction test set;

[0138] Select the convolutional neural network model framework for air conditioning energy efficiency prediction;

[0139] Based on the air conditioning energy efficiency prediction training set, the selected convolutional neural network model framework for air conditioning energy efficiency prediction is trained;

[0140] An air conditioning energy efficiency prediction model based on deep learning was determined.

[0141] Among them, the test optimization module is used to test and optimize the air-conditioning energy efficiency prediction model based on deep learning, and determine the optimal air-conditioning energy efficiency prediction model based on deep learning;

[0142] In this embodiment, the test optimization module includes:

[0143] A test and evaluation unit, used to test and evaluate the air conditioning energy efficiency prediction model based on deep learning;

[0144] Obtain air conditioning energy efficiency prediction model based on deep learning;

[0145] Based on the air conditioning energy efficiency prediction test set, the air conditioning energy efficiency prediction model based on deep learning is tested and evaluated;

[0146] Determine the test evaluation results based on the air conditioning energy efficiency prediction model;

[0147] An adjustment and optimization unit, used to adjust and optimize the air conditioning energy efficiency prediction model based on deep learning;

[0148] Obtain test evaluation results based on air conditioning energy efficiency prediction model;

[0149] According to the test and evaluation results based on the air conditioning energy efficiency prediction model, the air conditioning energy efficiency prediction model based on deep learning is mined and analyzed;

[0150] Determine the adjustment and optimization plan based on the air conditioning energy efficiency prediction model;

[0151] According to the adjustment and optimization scheme based on the air conditioning energy efficiency prediction model, the parameters of the air conditioning energy efficiency prediction model based on deep learning are adjusted and iteratively optimized;

[0152] Determine the optimal air conditioning energy efficiency prediction model based on deep learning.

[0153] Among them, the energy efficiency prediction module is used to analyze and predict the air conditioning energy efficiency of the air conditioning monitoring feature data based on the Internet of Things according to the optimal air conditioning energy efficiency prediction model based on deep learning, and determine the air conditioning energy efficiency prediction result based on deep learning;

[0154] In this embodiment, the energy efficiency prediction module includes:

[0155] A data extraction unit, used for extracting characteristic data of air conditioning monitoring based on the Internet of Things;

[0156] An energy efficiency prediction unit, used to predict energy efficiency based on air conditioning monitoring feature data based on the Internet of Things;

[0157] Obtain the optimal air conditioning energy efficiency prediction model based on deep learning;

[0158] Apply the optimal deep learning-based air conditioning energy efficiency prediction model to the IoT-based air conditioning monitoring feature data;

[0159] According to the optimal air conditioning energy efficiency prediction model based on deep learning, the characteristic data of air conditioning monitoring based on the Internet of Things is mined and analyzed to analyze and predict the air conditioning energy efficiency;

[0160] Determine the air conditioning energy efficiency prediction results based on deep learning.

[0161] Among them, the scheduling and control module is used to formulate air-conditioning energy efficiency scheduling and control plans, and to perform intelligent scheduling and control of air-conditioning energy efficiency.

[0162] In this embodiment, the scheduling control module includes:

[0163] Strategy formulation unit, used to formulate air conditioning energy efficiency scheduling and control plans;

[0164] Obtain air conditioning energy efficiency prediction results based on deep learning;

[0165] According to the air conditioning energy efficiency prediction results based on deep learning, the characteristic data of air conditioning monitoring based on the Internet of Things is analyzed, and the air conditioning energy efficiency scheduling and control plan is studied and judged;

[0166] Determine the air conditioning energy efficiency scheduling and control plan;

[0167] The dispatching and control unit is used to intelligently dispatch and control the energy efficiency of air conditioners;

[0168] Obtain air conditioning energy efficiency scheduling and control solutions;

[0169] Intelligently dispatch and control the air conditioning energy efficiency based on the air conditioning energy efficiency dispatch and control solution;

[0170] Among them, by analyzing user usage habits and historical data, the start and stop time of the air conditioning equipment is determined to avoid energy waste, turning off the air conditioning before the user leaves home in the morning and turning it on again after the user returns home in the evening;

[0171] Intelligently switch to the best cooling and heating mode according to outdoor temperature, humidity and wind speed;

[0172] Set reasonable indoor and outdoor target temperatures to provide users with appropriate indoor and outdoor target temperatures based on the balance between comfort and energy consumption;

[0173] Use zone control to divide a large room into multiple zones, with independent temperature and air speed settings for each zone;

[0174] Take advantage of low nighttime electricity prices and use sleep mode at night to lower the temperature and maximize energy efficiency;

[0175] Through the Internet of Things technology, the use of air conditioners is monitored in real time, and when it is found that the temperature set by the user is too low or too high, timely energy-saving reminders are given.

[0176] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0177] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An air conditioning energy efficiency prediction and scheduling system, characterized in that: include: Data acquisition module, used to collect real-time data of air conditioning monitoring based on the Internet of Things; Data transmission module, used to transmit real-time data of air conditioning monitoring based on the Internet of Things; The prediction and scheduling platform is used to analyze, predict, and schedule air conditioning energy efficiency.

2. The air conditioning energy efficiency prediction and scheduling system according to claim 1, characterized in that: The data acquisition module comprises: The temperature collection unit is used to monitor and continuously collect indoor and outdoor temperature parameters in real time during the operation of the air conditioner to obtain the air conditioner monitoring temperature data; The humidity collection unit is used to monitor and continuously collect indoor and outdoor humidity parameters in real time during the operation of the air conditioner to obtain the air conditioner monitoring humidity data; The wind speed collection unit is used to monitor and continuously collect indoor and outdoor wind speed parameters in real time during the operation of the air conditioner to obtain the air conditioner monitoring wind speed data; Among them, based on the air-conditioning monitoring temperature data, air-conditioning monitoring humidity data and air-conditioning monitoring wind speed data, the real-time air-conditioning monitoring data based on the Internet of Things is determined.

3. The air conditioning energy efficiency prediction and scheduling system according to claim 2, characterized in that: The data transmission module comprises: A data sending unit, used for sending real-time data of air conditioning monitoring based on the Internet of Things; A data receiving unit, used for receiving real-time data of air conditioning monitoring based on the Internet of Things; wherein a data transmission link is established between the data sending unit and the data receiving unit; The data sending unit receives the real-time air conditioning monitoring data based on the Internet of Things transmitted from the data acquisition module, and the data sending unit sends the received real-time air conditioning monitoring data based on the Internet of Things to the data receiving unit; The data receiving unit receives the real-time air conditioning monitoring data based on the Internet of Things transmitted from the data sending unit, and the data receiving unit transmits the received real-time air conditioning monitoring data based on the Internet of Things to the prediction and scheduling platform.

4. The air conditioning energy efficiency prediction and scheduling system according to claim 3, characterized in that: To establish a data transmission link between the data sending unit and the data receiving unit, perform the following operations: According to the air conditioner energy efficiency prediction and scheduling requirements, the data sending unit transmits an instruction requesting to establish a data transmission connection to the data receiving unit; After the data receiving unit receives the instruction transmitted by the data sending unit requesting to establish a data transmission connection, the data receiving unit checks its own data transmission channel; When there is an idle data transmission channel in the data receiving unit, the data receiving unit transmits an instruction to the data sending unit to agree to establish a data transmission connection; After the data sending unit receives the instruction transmitted by the data receiving unit agreeing to establish a data transmission connection, the data sending unit establishes a data transmission link with the data receiving unit based on the instruction agreeing to establish a data transmission connection, for transmitting real-time air conditioning monitoring data based on the Internet of Things.

5. The air conditioning energy efficiency prediction and scheduling system according to claim 4, characterized in that: The prediction and scheduling platform includes: A data processing module is used to process the real-time data of air conditioning monitoring based on the Internet of Things and determine the characteristic data of air conditioning monitoring based on the Internet of Things; Model training module, used to build an air conditioning energy efficiency prediction model based on deep learning according to air conditioning energy efficiency prediction and scheduling requirements; A test optimization module is used to test and optimize the air conditioning energy efficiency prediction model based on deep learning, and determine the optimal air conditioning energy efficiency prediction model based on deep learning; The energy efficiency prediction module is used to analyze and predict the air conditioning energy efficiency based on the air conditioning monitoring feature data based on the Internet of Things according to the optimal air conditioning energy efficiency prediction model based on deep learning, and determine the air conditioning energy efficiency prediction result based on deep learning; The scheduling and control module is used to formulate air-conditioning energy efficiency scheduling and control plans and to perform intelligent scheduling and control of air-conditioning energy efficiency.

6. The air conditioning energy efficiency prediction and scheduling system according to claim 5, characterized in that: The data processing module comprises: A data cleaning unit is used to clean the real-time data of air conditioning monitoring based on the Internet of Things; Obtain real-time data of air conditioning monitoring based on the Internet of Things; Clean the real-time data of air conditioning monitoring based on the Internet of Things; Determine the real-time air conditioning monitoring data that is useful for air conditioning energy efficiency prediction and scheduling; A feature extraction unit, used for extracting features from real-time monitoring data of the air conditioner after cleaning; Obtain real-time air conditioning monitoring data that is useful for air conditioning energy efficiency prediction and scheduling; Extract features from real-time air conditioning monitoring data that is useful for air conditioning energy efficiency prediction and scheduling; Extract key features that can reflect air conditioning energy efficiency prediction and scheduling; Determine the characteristic data of air conditioning monitoring based on the Internet of Things.

7. The air conditioning energy efficiency prediction and scheduling system according to claim 6, characterized in that: The model training module includes: A data collection unit for collecting historical data of air conditioning monitoring based on the Internet of Things; According to the air conditioning energy efficiency prediction and scheduling requirements, the historical data of air conditioning monitoring based on the Internet of Things is collected and reserved; A model building unit, used to build an air conditioning energy efficiency prediction model based on deep learning; Obtain historical data of air conditioning monitoring based on the Internet of Things; Divide the historical data of air conditioning monitoring based on the Internet of Things; Determine the air conditioning energy efficiency prediction training set and the air conditioning energy efficiency prediction test set; Select the convolutional neural network model framework for air conditioning energy efficiency prediction; Based on the air conditioning energy efficiency prediction training set, the selected convolutional neural network model framework for air conditioning energy efficiency prediction is trained; An air conditioning energy efficiency prediction model based on deep learning was determined.

8. The air conditioning energy efficiency prediction and scheduling system according to claim 7, characterized in that: The test optimization module comprises: A test and evaluation unit, used to test and evaluate the air conditioning energy efficiency prediction model based on deep learning; Obtain air conditioning energy efficiency prediction model based on deep learning; Based on the air conditioning energy efficiency prediction test set, the air conditioning energy efficiency prediction model based on deep learning is tested and evaluated; Determine the test evaluation results based on the air conditioning energy efficiency prediction model; An adjustment and optimization unit, used to adjust and optimize the air conditioning energy efficiency prediction model based on deep learning; Obtain test evaluation results based on air conditioning energy efficiency prediction model; According to the test and evaluation results based on the air conditioning energy efficiency prediction model, the air conditioning energy efficiency prediction model based on deep learning is mined and analyzed; Determine the adjustment and optimization plan based on the air conditioning energy efficiency prediction model; According to the adjustment and optimization scheme based on the air conditioning energy efficiency prediction model, the parameters of the air conditioning energy efficiency prediction model based on deep learning are adjusted and iteratively optimized; Determine the optimal air conditioning energy efficiency prediction model based on deep learning.

9. The air conditioning energy efficiency prediction and scheduling system according to claim 8, characterized in that: The energy efficiency prediction module comprises: A data extraction unit, used for extracting characteristic data of air conditioning monitoring based on the Internet of Things; An energy efficiency prediction unit, used to predict energy efficiency based on air conditioning monitoring feature data based on the Internet of Things; Obtain the optimal air conditioning energy efficiency prediction model based on deep learning; Apply the optimal deep learning-based air conditioning energy efficiency prediction model to the IoT-based air conditioning monitoring feature data; According to the optimal air conditioning energy efficiency prediction model based on deep learning, the characteristic data of air conditioning monitoring based on the Internet of Things is mined and analyzed to analyze and predict the air conditioning energy efficiency; Determine the air conditioning energy efficiency prediction results based on deep learning.

10. The air conditioning energy efficiency prediction and scheduling system according to claim 9, characterized in that: The scheduling control module includes: Strategy formulation unit, used to formulate air conditioning energy efficiency scheduling and control plans; Obtain air conditioning energy efficiency prediction results based on deep learning; According to the air conditioning energy efficiency prediction results based on deep learning, the characteristic data of air conditioning monitoring based on the Internet of Things is analyzed, and the air conditioning energy efficiency scheduling and control plan is studied and judged; Determine the air conditioning energy efficiency scheduling and control plan; The dispatching and control unit is used to intelligently dispatch and control the energy efficiency of air conditioners; Obtain air conditioning energy efficiency scheduling and control solutions; Intelligent scheduling and control of air conditioning energy efficiency is carried out based on the air conditioning energy efficiency scheduling and control solution.

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