Energy-saving evaluation and operation strategy optimization method for central air-conditioning system

Abstract

The invention provides an energy-saving evaluation and operation strategy optimization method for a central air-conditioning system. The method comprises the following steps: firstly, building an Internet of Things platform based on the central air-conditioning system, and monitoring and collecting operation parameters of various equipment in the system in real time; unifying formats and preprocessing operation parameters, uploading the operation parameters to a cloud database for storage, and dividing the operation parameters according to categories; performing mapping modeling on the central air-conditioning system in the energy analysis platform, and performing parameter configuration at the same time; then, the divided data in the cloud database are connected according to evaluation categories, and meanwhile, an extensible formula expert database is introduced to evaluate the running state of the central air conditioning system based on an empirical algorithm, and abnormal strategy reminding is conducted; and prompting to adjust the running state of the equipment according to the strategy. According to the method, the problem of strategy missing is effectively solved through an optimization strategy of the extensible expert experience library; and through analysis and verification of seven functions, integrated, refined and systematic analysis of energy consumption and optimization strategy improvement and advanced management are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy conservation, and in particular to a method for energy conservation evaluation and operation strategy optimization of a central air-conditioning system. Background Art

[0002] With the development of Internet of Things technology, the energy consumption management of central air-conditioning systems is becoming more and more automated. However, in the process of energy analysis, energy metering is more often used to analyze the energy usage of equipment, which lacks systematic and procedural analysis. At the same time, after completing the energy analysis, it is necessary to identify the cause through data and manual investigation, which is time-consuming. Moreover, after the cause problem is successfully analyzed, there is no corresponding optimization strategy or the strategy is not targeted. Such problems require a high level of professionalism for operation and maintenance personnel.

[0003] Invention patent CN118446384B proposes an LSTM-based energy-saving optimization method, system and device for building HVAC systems, which predicts energy consumption through a small amount of historical data, equipment operating parameters and simulation data to generate an initial optimization strategy. Its energy-saving method is achieved by adjusting the supply and return water temperatures based on the predicted energy consumption. It uses model training and data algorithms to analyze data, and is a method of predictive result-oriented feedforward regulation. The model training has the problem of overfitting, which will cause deviations in energy consumption predictions; there are missing links in the energy-saving process, and the influence of parameters other than the supply and return water temperatures on the energy-saving steps is not considered. The optimization strategy only targets the regulation of the supply water temperature, and the method is single.

[0004] Invention patent CN117824074A proposes a building HVAC energy-saving optimization method and system, which collects user data, meter data and main equipment parameters for evaluation and maintenance of digital energy-saving methods. Its technical solution has some limitations on the data analyzed for energy saving, and only evaluates and maintains data such as equipment parameters and environmental parameters, without analyzing the characteristics of subsystems at all levels of the building and adjusting energy consumption in combination with subsystem characteristics and equipment characteristics. Summary of the invention

[0005] The purpose of the present invention is to provide a method for energy-saving evaluation and operation strategy optimization of a central air-conditioning system to solve the deficiencies of the above-mentioned prior art.

[0006] To achieve the above object, the present invention adopts the following technical solution:

[0007] A method for energy-saving evaluation and operation strategy optimization of a central air-conditioning system, characterized in that it comprises the following steps:

[0008] S1. Build an IoT platform based on the central air-conditioning system, and use sensors or gateways to monitor and collect the operating parameters of various devices in the central air-conditioning system in real time;

[0009] S2, the IoT platform formats and pre-processes the collected data and uploads it to the cloud database;

[0010] S3, the cloud database categorizes and stores the pre-processed data according to energy sub-items, equipment types, subsystems, spaces and instruments or other categories;

[0011] S4. Build an energy analysis platform with a preset evaluation model, map and model the space and various subsystems, equipment, and instruments of the central air-conditioning system in the energy analysis platform, and configure parameters at the same time;

[0012] S5. In the energy analysis platform, the data divided in the cloud database is connected according to the evaluation model category. At the same time, the expert database is introduced to evaluate the operating status of the central air-conditioning system based on the experience algorithm, and strategic reminders are issued for abnormal operating status;

[0013] S6. Adjust the operating status of the equipment according to the strategy reminder, and optimize the information and strategy reminders in the expert database data according to the actual operating results of the central air-conditioning system.

[0014] Furthermore, the methods of preprocessing the collected data in step S2 include but are not limited to mean interpolation, most likely data prediction, and boxplot method;

[0015] The mean interpolation can fill in the missing of continuous data according to the mean of data of several previous and next time periods;

[0016] The most likely data prediction can predict and fill in missing values ​​for scattered point distribution type data;

[0017] The box plot method can delete or correct abnormal data outside the upper and lower limits according to the data distribution.

[0018] Furthermore, the operating parameters of each device in the central air conditioning system are transmitted to the cloud database via the MAC address, and the MAC address serves as a unique identifier for each device and the IoT layer;

[0019] The operating parameters of each device in the central air conditioning system include the device ID that represents the spatial location of the device;

[0020] The cloud database defines labels for the pre-processed data according to energy sub-items, equipment types, subsystems, spaces and instruments or other categories, and binds them to the equipment ID;

[0021] The cloud database is provided with a classification database table according to energy sub-items, equipment types, subsystems, spaces and instruments or other categories, and the pre-processed data is classified and stored according to the labels.

[0022] Furthermore, in step S4, mapping and modeling the space and various subsystems, equipment, and instruments of the central air-conditioning system in the energy analysis platform specifically includes the following steps:

[0023] S41. Create a space tree in the energy analysis platform, and enter the actual used space into the energy analysis platform according to the structure of area-building-floor-room;

[0024] S42. Enter the number and name of each instrument in the energy analysis platform, bind each instrument to its space and category, and match the unique identifier of the IoT layer with the operating parameters in the cloud database;

[0025] S43. Create various equipment models in the energy analysis platform according to the structure of equipment type-equipment name, and bind each equipment model to the space through the IoT layer address and unique identifier;

[0026] S44, configure the attributes of each device model, add the standard points of the model to each device model, including all parameters of different models and brands of devices;

[0027] S45. Each device model is matched with the operating parameters in the cloud database according to the unique identifier of the IoT layer. If the energy consumption of a device in the IoT layer is detected and provided by an instrument, the operating parameters of the corresponding instrument are bound to the device model of the device.

[0028] Furthermore, the categories of the evaluation model include, but are not limited to: equipment evaluation, indoor environment evaluation, cold and heat source and distribution evaluation, air conditioning evaluation, energy consumption evaluation, energy-saving countermeasure verification, and efficiency evaluation.

[0029] Furthermore, step S5 is specifically implemented by the following method:

[0030] S51. In the energy analysis platform, the data divided in the cloud database is connected according to the evaluation category based on the unique identifier of the IoT layer;

[0031] S52, introducing an expert database, combining the real-time operating parameters obtained for each evaluation category and fitting them into an experience curve based on an experience algorithm;

[0032] S53, judging whether there are abnormal parameters based on the experience curve;

[0033] S54. According to the influencing factors of the abnormal parameters, the optimization strategy and the optimal values ​​of the parameters in the corresponding strategy are searched in the expert database and a reminder is given.

[0034] A central air-conditioning system energy-saving evaluation and operation strategy optimization system, used to implement the above-mentioned central air-conditioning system energy-saving evaluation and operation strategy optimization method, the system is constructed based on the central air-conditioning system combined with the Internet of Things technology, the central air-conditioning system energy-saving evaluation and operation strategy optimization system includes sensors for real-time monitoring and collecting operating parameters of various equipment in the central air-conditioning system, an Internet of Things cloud platform connected to various equipment and sensor networks, a cloud database for processing, analyzing and storing the operating parameters collected by the sensors, an energy analysis platform, and a device control module for remotely regulating various equipment;

[0035] The energy analysis platform includes but is not limited to:

[0036] The system modeling module maps the central air-conditioning system to the energy analysis platform, and connects the operating parameters of the space and various subsystems, equipment, and instruments of the central air-conditioning system with the IoT cloud platform;

[0037] The expert database can be expanded and acquired by web crawler technology, data API interface or data set, including but not limited to the equipment manufacturer's technical documents, operation manuals, historical fault reports, maintenance records, and information obtained through expert interviews and training materials;

[0038] Evaluation models, including but not limited to equipment evaluation models, indoor environment evaluation models, cold and heat source and transmission and distribution evaluation models, air conditioning evaluation models, energy consumption evaluation models, energy-saving countermeasures verification models, and efficiency evaluation models;

[0039] The visualization module visualizes the central air-conditioning system model and the evaluation model in the form of graphics, images, and charts.

[0040] It can be seen from the above technical solutions that the present invention has the following advantages compared with the prior art:

[0041] 1. The present invention uses an optimization strategy of an expandable experience library, based on the abnormal range of parameters according to business logic, and adds similar strategies for similar problems, records each problem that occurs, undertakes part of the training tasks for the project, and solves the problem of missing strategies;

[0042] 2. The present invention checks the system, cold source, transmission and distribution to the terminal one by one, and determines through analysis of the system and the environment, so as to maintain good equipment efficiency and ultimately achieve cooling on demand, energy saving and carbon reduction; through the analysis and verification of the seven major functions, the energy consumption is analyzed in an integrated, refined and systematic manner, and the optimization strategy is improved and advanced management is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic diagram of the steps of the central air-conditioning system energy-saving evaluation and operation strategy optimization method of the present invention;

[0044] Figure 2 It is a structural schematic diagram of the central air-conditioning system energy-saving evaluation and operation strategy optimization system of the present invention. DETAILED DESCRIPTION

[0045] A preferred embodiment of the present invention is described in detail below with reference to the accompanying drawings.

[0046] like Figure 1 The central air conditioning system energy saving evaluation and operation strategy optimization method shown includes the following steps:

[0047] S1. Build an Internet of Things platform based on the central air-conditioning system, and use sensors or gateways to monitor and collect the operating parameters of various equipment in the central air-conditioning system in real time.

[0048] In specific use, each device in the central air-conditioning system has networking or wired transmission functions, or is equipped with sensor equipment to realize the collection of operating parameters.

[0049] S2. The IoT platform formats and pre-processes the collected data in a unified format and then uploads it to the cloud database.

[0050] Since the data of each device needs to be transcoded and transmitted through the IoT protocol, the sources of different parameter devices are inconsistent and the upload methods are inconsistent. Different protocols are required for transmission, resulting in inconsistent data formats and reduced processing speed. Therefore, the data needs to be transmitted to the IoT platform for unified format processing, which can be implemented by the code side.

[0051] Furthermore, when there are new devices, new devices can be created on the IoT platform to connect with the on-site devices; when the new devices require new transmission protocols, new transmission protocols can be added to the gateway through the IoT platform.

[0052] In specific operations, the methods for preprocessing the collected data include but are not limited to mean interpolation, most likely data prediction and box plot method; the mean interpolation can fill in the missing continuous data according to the data means of several previous and subsequent time periods; the most likely data prediction can predict and fill in the missing values ​​of scattered distribution type data; the box plot method can delete or correct abnormal data outside the upper and lower limits according to the data distribution.

[0053] S3, the cloud database categorizes and stores the pre-processed data according to energy sub-items, equipment types, subsystems, spaces and instruments or other categories.

[0054] In specific operations, the operating parameters of each device in the central air-conditioning system are transmitted to the cloud database through the MAC address, and the MAC address serves as the unique identifier of each device and the Internet of Things layer; the operating parameters of each device in the central air-conditioning system include the device ID that represents the spatial location of the device; the cloud database defines labels for the pre-processed data according to energy sub-items, equipment types, subsystems, spaces and instruments or other categories, and binds them to the device ID; the cloud database is equipped with a classified database table according to energy sub-items, equipment types, subsystems, spaces and instruments or other categories, and the pre-processed data is classified and stored according to the labels.

[0055] S4. Build an energy analysis platform that includes a preset evaluation model, map and model the space and subsystems, equipment, and instruments to which the central air-conditioning system belongs in the energy analysis platform, and configure parameters at the same time.

[0056] In this preferred embodiment, mapping and modeling the space and various subsystems, equipment, and instruments of the central air-conditioning system in the energy analysis platform specifically includes the following steps:

[0057] The mapping and modeling of the space and subsystems, equipment, and instruments of the central air-conditioning system in the energy analysis platform specifically includes the following steps:

[0058] S41. Create a space tree in the energy analysis platform, and enter the actual used space into the energy analysis platform according to the structure of area-building-floor-room;

[0059] S42. Enter the number and name of each instrument in the energy analysis platform, bind each instrument to its space and category, and match the unique identifier of the IoT layer with the operating parameters in the cloud database;

[0060] S43. Create various equipment models in the energy analysis platform according to the structure of equipment type-equipment name, and bind each equipment model to the space through the IoT layer address and unique identifier;

[0061] S44, configure the attributes of each device model, add the standard points of the model to each device model, including all parameters of different models and brands of devices;

[0062] S45. Each device model is matched with the operating parameters in the cloud database according to the unique identifier of the IoT layer. If the energy consumption of a device in the IoT layer is detected and provided by an instrument, the operating parameters of the corresponding instrument are bound to the device model of the device.

[0063] S5. In the energy analysis platform, the data divided in the cloud database is connected according to the evaluation model category. At the same time, the expert database is introduced to evaluate the operating status of the central air-conditioning system based on the empirical algorithm, and strategic reminders are issued for abnormal operating conditions.

[0064] Specifically, the evaluation categories include but are not limited to: equipment evaluation, indoor environment evaluation, cold and heat source and distribution evaluation, air conditioning evaluation, energy consumption evaluation, energy saving countermeasure verification, and efficiency evaluation. Further, step S5 is specifically implemented by the following method:

[0065] S51. In the energy analysis platform, the data divided in the cloud database is connected according to the evaluation category based on the unique identifier of the IoT layer;

[0066] S52, introducing an expert database, combining the real-time operating parameters obtained for each evaluation category and fitting them into an experience curve based on an experience algorithm;

[0067] S53, judging whether there are abnormal parameters based on the experience curve;

[0068] S54. According to the influencing factors of the abnormal parameters, the optimization strategy and the optimal values ​​of the parameters in the corresponding strategy are searched in the expert database and a reminder is given.

[0069] S6. Adjust the operating status of the equipment according to the strategy reminder, and optimize the evaluation model and strategy reminder in the expert database data according to the actual operating effect of the central air-conditioning system.

[0070] like Figure 2 The central air-conditioning system energy-saving evaluation and operation strategy optimization system shown is used to implement the above-mentioned central air-conditioning system energy-saving evaluation and operation strategy optimization method. The system is constructed based on the central air-conditioning system combined with the Internet of Things technology, and is characterized in that the central air-conditioning system energy-saving evaluation and operation strategy optimization system includes sensors for real-time monitoring and collecting operating parameters of various equipment in the central air-conditioning system, an Internet of Things cloud platform connected to various equipment and sensor networks, a cloud database for processing, analyzing and storing the operating parameters collected by the sensors, an energy analysis platform, and a device control module for remotely regulating various equipment;

[0071] The energy analysis platform includes but is not limited to:

[0072] The system modeling module maps the central air-conditioning system to the energy analysis platform, and connects the operating parameters of the space and various subsystems, equipment, and instruments of the central air-conditioning system with the IoT cloud platform;

[0073] The expert database can be expanded and acquired by web crawler technology, data API interface or data set, including but not limited to the equipment manufacturer's technical documents, operation manuals, historical fault reports, maintenance records, and information obtained through expert interviews and training materials;

[0074] Evaluation models, including but not limited to equipment evaluation models, indoor environment evaluation models, cold and heat source and transmission and distribution evaluation models, air conditioning evaluation models, energy consumption evaluation models, energy-saving countermeasures verification models, and efficiency evaluation models;

[0075] The visualization module visualizes the central air-conditioning system model and the evaluation model in the form of graphics, images, and charts.

[0076] The following is a further explanation of the equipment evaluation, indoor environment evaluation, cold and heat source and distribution evaluation, air conditioning evaluation, energy consumption evaluation, energy-saving countermeasure verification, efficiency evaluation and corresponding strategy reminders described in the present invention through specific embodiments.

[0077] 1. Equipment evaluation and strategy reminder:

[0078] Based on steps S1-S4, the device evaluation model is connected to the divided device operation parameters in the cloud database according to the unique identifier of the IoT layer;

[0079] Introduce an expert database to fit the equipment rated operating parameters and equipment operating parameters into an experience curve based on an experience algorithm;

[0080] Determine whether any device has abnormal operating parameters based on the experience curve; if no device has abnormal operating parameters, keep the original settings and continue to operate; if a device has abnormal operating parameters, determine the application strategy based on the influencing factors and find the optimal value of the corresponding operating parameters in the corresponding strategy.

[0081] 2. Environmental assessment and strategic reminders:

[0082] Based on steps S1-S4, the environmental assessment model is connected to the cloud database according to the unique identifier of the IoT layer, and the environmental parameters such as PM2.5, carbon dioxide, temperature, humidity, etc. divided by space are confirmed, and the terminal equipment described in the space is confirmed;

[0083] Introduce an expert database to fit the empirical parameters and actual environmental parameters into an empirical curve based on an empirical algorithm;

[0084] Determine whether there are spaces with abnormal environmental parameters based on the experience curve; if there are no spaces with abnormal operating parameters, the terminal devices belonging to the space will maintain the original settings and continue to operate; if a space has abnormal environmental parameters, determine the combination strategy of the application and the terminal devices belonging to the space based on the influencing factors, and find the optimal values ​​of the operating parameters of the terminal device group in the corresponding combination strategy.

[0085] 3. Air conditioning evaluation and strategy reminder:

[0086] Based on steps S1-S4, the air conditioning evaluation model is connected to the cloud database according to the unique identifier of the IoT layer and the five parameters of fresh air enthalpy, return air enthalpy, supply air enthalpy, fresh air ratio, and CO2 concentration calculated according to the equipment type;

[0087] Introduce the expert database, combine the empirical parameters with the fresh air enthalpy value, return air enthalpy value, and fresh air ratio, and combine the empirical parameters with the fresh air enthalpy value, supply air enthalpy value, and fresh air ratio to fit into an empirical curve based on the empirical algorithm;

[0088] Based on the experience curve, according to the preset indicator judgment logic (such as fresh air enthalpy value < return air enthalpy value, and fresh air ratio <30%, fresh air enthalpy value >6.38kJ / kg), determine whether there is any equipment with abnormal parameters; if no equipment has abnormal operating parameters, keep the original settings and continue to operate; if a certain equipment has abnormal operating parameters, determine the application strategy based on the influencing factors and find the optimal value of the operating parameters of the corresponding equipment in the corresponding strategy, such as the adjustment optimization strategy of the outlet / supply air and fresh air valve opening.

[0089] In specific operation, the enthalpy value and fresh air ratio are calculated by the following formula:

[0090] I=1.01t+(2500+1.84t)d

[0091] Fresh air ratio = Fresh air ratio: Fresh air volume / (Fresh air volume + Return air volume)*100%

[0092] Where: I is the enthalpy of moist air; t is the air temperature, unit is ℃; d is the moisture content of air, unit is g / kg dry air; constant 1.01 is the average specific heat of dry air at constant pressure, unit is Kj / (kg·K); constant 1.84 is the average specific heat of water vapor at constant pressure, unit is Kj / kg.

[0093] 4. Cold and heat source and distribution assessment and strategy reminder:

[0094] Based on steps S1-S4, the cold and heat source and distribution evaluation model is connected to the operating parameters of the refrigeration host, cooling pump group, freezing pump group, and cooling tower equipment divided by equipment type in the cloud database according to the unique identifier of the IoT layer;

[0095] Introducing an expert database, the rated parameters of each device and the parameters used for analysis are combined with real-time parameters to fit into an experience curve based on an experience algorithm;

[0096] Specific:

[0097] Parameters used in the analysis of the refrigeration host model: chiller COP, annual load, enthalpy value; analysis is performed by fitting the efficiency trend of a single chiller, overall efficiency distribution, load generation and occurrence duration into an empirical curve based on an empirical algorithm;

[0098] Parameters used in the chilled water pump model analysis: chilled water main flow, chilled water main pressure; by fitting the chilled water pump group pressure and flow into an empirical curve based on an empirical algorithm, the real-time data of the chilled water distribution system is analyzed;

[0099] Parameters used in the cooling pump model analysis: cooling water main flow, cooling water main pressure; by fitting the pressure and flow of the cooling pump group into an empirical curve based on an empirical algorithm, the real-time data of the cooling water distribution system is analyzed;

[0100] The parameters used in the cooling tower model analysis are: cooling water main supply temperature, outdoor wet bulb temperature, and operation time of each cooling tower. The empirical curve is fitted based on the empirical algorithm to analyze the cooling tower group.

[0101] According to the above experience curve, determine whether there are any parameters in the real-time data with abnormal operating parameters; if there are no parameters with abnormal operating parameters, keep the original settings and continue to run; if a parameter has abnormal operating parameters, determine the application strategy based on the influencing factors and find the optimal value of the corresponding operating parameter in the corresponding strategy.

[0102] Specifically, the seasonal energy efficiency ratio of the cold source, the seasonal energy consumption ratio of the chiller, the chilled water transfer coefficient, and the cooling water transfer coefficient are calculated by the following formulas:

[0103]

[0104] In the formula: SEER -sys represents the seasonal energy efficiency ratio of the cooling source system; Q s Indicates the total power supply of the cold source system during the cooling season, in kW·h; ∑N si It indicates the cumulative power consumption of each power-consuming equipment in the cooling source system during the cooling season, in kW·h; ECR indicates the seasonal energy consumption ratio of the chiller; N sc Indicates the total power supply of the chilled water system during the cooling season, in kW·h; WTF chw represents the chilled water transport coefficient; Q s Indicates the total energy consumption of the chilled water pump during the cooling season, in kW·h; WTF cw Indicates the cooling water transport coefficient; Q cw Indicates the heat delivered by cooling water during the cooling season, in kW·h; N cw It indicates the total energy consumption of the cooling water pump during the cooling season, in kW·h.

[0105] 5. Energy consumption assessment and strategy reminder:

[0106] Based on steps S1-S4, the energy consumption assessment model is connected to the energy sub-items, subsystems, and equipment type operating parameters in the cloud database according to the unique identifier of the IoT layer;

[0107] Introduce expert database, combine empirical parameters with subsystem operating parameters to fit empirical curves based on empirical algorithms;

[0108] Determine whether any energy sub-item has abnormal operating data based on the experience curve; if no energy sub-item has abnormal operating data, keep the original settings and continue to operate; if a sub-item or sub-item has abnormal operating parameters, check the equipment or instrument with abnormal data based on the influencing factors, determine the applied strategy and find the optimal value of the equipment or instrument operating parameters in the corresponding strategy; specifically, the abnormal equipment or instrument is checked in the following way: In the first stage, the energy consumption of the created subsystem items is split according to time, and the first-level energy consumption sub-items with abnormal energy consumption are analyzed according to the mean, year-on-year and month-on-month energy consumption; in the second stage, the first-level sub-items with abnormal energy consumption are split according to the space and its bound equipment or meters to determine the specific space and equipment / meters with energy consumption; in the third stage, the specific abnormal equipment is checked.

[0109] 6. Energy-saving countermeasures verification analysis and strategy reminders:

[0110] Based on steps S1-S4, the energy-saving countermeasure verification analysis model is connected to the electricity, water and gas energy consumption parameters divided by sub-item types in the cloud database according to the unique identifier of the IoT layer;

[0111] By introducing an expert database, the energy consumption during the benchmark period is combined with specific electricity, water and gas energy consumption parameters to fit into an experience curve based on an empirical algorithm, and the optimal value of each energy consumption can be obtained;

[0112] Determine whether there are abnormal energy consumption parameters based on the experience curve; if there are no abnormal operating parameters, the operation and maintenance strategy or parameter adjustment effect is excellent; if there are abnormal operating parameters, determine the application strategy based on the influencing factors and find the optimal value of the corresponding operating parameter in the corresponding strategy.

[0113] Furthermore, based on the phased energy consumption parameters of electricity, water and gas, we can obtain electricity distribution, electricity trend, scatter plot of electricity and enthalpy, energy consumption distribution of gas and water, and energy consumption trend of gas and water, and further analyze the energy consumption of the central air-conditioning system at a certain stage.

[0114] 7. Efficiency evaluation and strategy reminder:

[0115] Based on steps S1-S4, the efficiency evaluation model is connected to the cloud database according to the unique identifier of the IoT layer, and the energy consumption index divided by device type, the four major indicators of the cold source system (cold source seasonal energy efficiency ratio, chiller seasonal energy consumption ratio, chilled water transfer coefficient, cooling water transfer coefficient) and the parameters of the efficiency of the chiller, cooling tower and cooling pump.

[0116] Introducing the expert database, the empirical parameters in the same stage are combined with the parameters of the stage to fit into the empirical curve based on the empirical algorithm;

[0117] Efficiency evaluation is carried out based on the experience curve to provide a basis for the next stage of operation and maintenance and equipment strategy, and then preset the optimization strategy for the next stage;

[0118] After the strategy is adjusted, the feasibility of the strategy is verified through the energy-saving verification module. If the strategy adjustment effect is poor, the strategy is adjusted and verified again.

[0119] The above-described embodiments are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for energy-saving evaluation and operation strategy optimization of a central air-conditioning system, characterized in that: The following steps are involved: S1. Build an IoT platform based on the central air-conditioning system, and use sensors or gateways to monitor and collect the operating parameters of various devices in the central air-conditioning system in real time; S2, the IoT platform formats and pre-processes the collected data and uploads it to the cloud database; S3, the cloud database categorizes and stores the pre-processed data according to energy sub-items, equipment types, subsystems, spaces and instruments or other categories; S4. Build an energy analysis platform with a preset evaluation model, map and model the space and various subsystems, equipment, and instruments of the central air-conditioning system in the energy analysis platform, and configure parameters at the same time; S5. In the energy analysis platform, the data divided in the cloud database is connected according to the evaluation model category. At the same time, the expert database is introduced to evaluate the operating status of the central air-conditioning system based on the experience algorithm, and strategic reminders are issued for abnormal operating status; S6. Adjust the operating status of the equipment according to the strategy reminder, and optimize the information and strategy reminders in the expert database data according to the actual operating results of the central air-conditioning system.

2. According to claim 1, a central air conditioning system energy-saving evaluation and operation strategy optimization method is characterized in that: The method of preprocessing the collected data in step S2 includes but is not limited to mean interpolation, most likely data prediction and box plot method; The mean interpolation can fill in the missing of continuous data according to the mean of data of several previous and next time periods; The most likely data prediction can predict and fill in missing values ​​for scattered point distribution type data; The box plot method can delete or correct abnormal data outside the upper and lower limits according to the data distribution.

3. According to claim 1, a central air conditioning system energy-saving evaluation and operation strategy optimization method is characterized in that: The operating parameters of each device in the central air conditioning system are transmitted to the cloud database via the MAC address, and the MAC address serves as a unique identifier for each device and the IoT layer; The operating parameters of each device in the central air conditioning system include the device ID that represents the spatial location of the device; The cloud database defines labels for the pre-processed data according to energy sub-items, equipment types, subsystems, spaces and instruments or other categories, and binds them to the equipment ID; The cloud database is provided with a classification database table according to energy sub-items, equipment types, subsystems, spaces and instruments or other categories, and the pre-processed data is classified and stored according to the labels.

4. According to claim 1, a central air conditioning system energy-saving evaluation and operation strategy optimization method is characterized in that: The step S4 of mapping and modeling the space and various subsystems, equipment, and instruments of the central air-conditioning system in the energy analysis platform specifically includes the following steps: S41. Create a space tree in the energy analysis platform, and enter the actual used space into the energy analysis platform according to the structure of area-building-floor-room; S42. Enter the number and name of each instrument in the energy analysis platform, bind each instrument to its space and category, and match the unique identifier of the IoT layer with the operating parameters in the cloud database; S43. Create various equipment models in the energy analysis platform according to the structure of equipment type-equipment name, and bind each equipment model to the space through the IoT layer address and unique identifier; S44, configure the attributes of each device model, add the standard points of the model to each device model, including all parameters of different models and brands of devices; S45. Each device model is matched with the operating parameters in the cloud database according to the unique identifier of the IoT layer. If the energy consumption of a device in the IoT layer is detected and provided by an instrument, the operating parameters of the corresponding instrument are bound to the device model of the device.

5. According to claim 1, a central air conditioning system energy-saving evaluation and operation strategy optimization method is characterized in that: The categories of the evaluation models include, but are not limited to: equipment evaluation, indoor environment evaluation, cold and heat source and distribution evaluation, air conditioning evaluation, energy consumption evaluation, energy-saving countermeasure verification, and efficiency evaluation.

6. According to claim 1, a central air conditioning system energy-saving evaluation and operation strategy optimization method is characterized in that: The step S5 is specifically implemented by the following method: S51. In the energy analysis platform, the data divided in the cloud database is connected according to the evaluation category based on the unique identifier of the IoT layer; S52, introducing an expert database, combining the real-time operating parameters obtained for each evaluation category and fitting them into an experience curve based on an experience algorithm; S53, judging whether there are abnormal parameters based on the experience curve; S54. According to the influencing factors of the abnormal parameters, the optimization strategy and the optimal values ​​of the parameters in the corresponding strategy are searched in the expert database and a reminder is given.

7. A central air conditioning system energy-saving evaluation and operation strategy optimization system, used to implement the central air conditioning system energy-saving evaluation and operation strategy optimization method described in any one of claims 1 to 6 above, the system is constructed based on the central air conditioning system combined with the Internet of Things technology, characterized in that: The central air conditioning system energy-saving evaluation and operation strategy optimization system includes: Sensors for real-time monitoring and collection of operating parameters of various devices in the central air-conditioning system, an IoT cloud platform connected to various devices and sensor networks, a cloud database for processing, analyzing and storing the operating parameters collected by the sensors, an energy analysis platform, and a device control module for remote control of various devices; The energy analysis platform includes but is not limited to: The system modeling module maps the central air-conditioning system to the energy analysis platform, and connects the operating parameters of the space and various subsystems, equipment, and instruments of the central air-conditioning system with the IoT cloud platform; The expert database can be expanded and acquired by web crawler technology, data API interface or data set, including but not limited to the equipment manufacturer's technical documents, operation manuals, historical fault reports, maintenance records, and information obtained through expert interviews and training materials; Evaluation models, including but not limited to equipment evaluation models, indoor environment evaluation models, cold and heat source and transmission and distribution evaluation models, air conditioning evaluation models, energy consumption evaluation models, energy-saving countermeasures verification models, and efficiency evaluation models; The visualization module visualizes the central air-conditioning system model and the evaluation model in the form of graphics, images, and charts.

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