CN116951672A

CN116951672A - Central air conditioner energy efficiency analysis method, device and readable storage medium

Info

Publication number: CN116951672A
Application number: CN202311213440.0A
Authority: CN
Inventors: 李金妮; 曾巍; 戎月坤; 麦桢杰
Original assignee: Shenzhen Zhongzheng Information Technology Co ltd
Current assignee: Shenzhen Zhongzheng Information Technology Co ltd
Priority date: 2023-09-20
Filing date: 2023-09-20
Publication date: 2023-10-27
Anticipated expiration: 2043-09-20
Also published as: CN116951672B

Abstract

The application discloses a method, equipment and readable storage medium for analyzing energy efficiency of a central air conditioner, relating to the field of air conditioning control systems, wherein the method comprises the following steps: according to the equipment operation data and the position information of the air conditioning equipment, determining energy efficiency information data containing building space structure information; according to the environmental difference value of weather data and internal environment data corresponding to the equipment operation data, determining influence parameters of the weather data on the air conditioning equipment in terms of altitude and installation positions respectively; and determining an energy efficiency analysis result of the air conditioning equipment according to the influence parameters, the energy efficiency information data and the corresponding environment temperature of the air conditioning equipment. The technical problems that the energy efficiency analysis result of the building central air conditioner in the related technology is weak in anti-interference performance and the law of the energy consumption change of the central air conditioner system cannot be accurately reflected are solved, and the technical effects of accurately analyzing the energy efficiency of the central air conditioner and improving the energy efficiency management efficiency are achieved.

Description

Central air conditioner energy efficiency analysis method, device and readable storage medium

Technical Field

The present application relates to the field of air conditioning control systems, and more particularly, to a method and apparatus for analyzing energy efficiency of a central air conditioner, and a readable storage medium.

Background

The common large-scale central air conditioner uses water as medium to transmit, the terminal air conditioning system adopts a fan coil, and exchanges energy between the terminal of a user and an energy center to realize concentrated cooling or heating.

In the related art, a central air conditioner basically realizes the adjustment of the energy supply amount of the air conditioner by the automatic control of a refrigerating host, and generally takes the temperature of the water supply or return of chilled water as the basis of the load change of terminal equipment. In the actual running of the central air conditioner, the load of the whole system is continuously changed because each area of the building air conditioner is influenced by various factors of the outside and the inside.

Therefore, when the energy efficiency of the air conditioning system is analyzed and the cold energy is adjusted, the related analysis method has hysteresis on aging and weak anti-interference performance, so that the law of energy consumption change of the central air conditioning system cannot be accurately reflected.

Disclosure of Invention

The embodiment of the application solves the technical problems that the energy efficiency analysis result of the building central air conditioner in the related technology is weak in anti-interference performance and cannot accurately reflect the law of the energy consumption change of a central air conditioner system by providing the energy efficiency analysis method, the equipment and the readable storage medium of the central air conditioner, and achieves the technical effects of establishing an energy efficiency analysis model of the central air conditioner with strong anti-interference performance and high accuracy and improving the energy efficiency management efficiency.

The embodiment of the application provides a central air conditioner energy efficiency analysis method, which comprises the following steps of:

according to the equipment operation data and the position information of the air conditioning equipment, determining energy efficiency information data containing building space structure information;

according to the environmental difference value of weather data and internal environment data corresponding to the equipment operation data, determining influence parameters of the weather data on the air conditioning equipment in terms of altitude and installation positions respectively;

and determining an energy efficiency analysis result of the air conditioning equipment according to the influence parameters, the energy efficiency information data and the corresponding environment temperature of the air conditioning equipment.

Optionally, the step of determining the energy efficiency information data including the building space structure information according to the equipment operation data and the position information of the air conditioning equipment includes:

determining an adaptive interface of the air conditioning equipment according to the equipment identifier of the air conditioning equipment;

acquiring the equipment operation data containing time information according to the adaptive interface;

establishing a three-dimensional topological network according to the position information, and taking the air conditioning equipment as a network node of the three-dimensional topological network;

And associating the equipment operation data with the network node to generate the energy efficiency information data with a space structure and a time sequence.

Optionally, the step of determining the influence parameters of the weather data on the air conditioning equipment in terms of altitude and installation position according to the environmental difference value of the weather data and the internal environmental data corresponding to the equipment operation data includes:

determining the weather data corresponding to each piece of position information based on the local weather category and the acquired external environment data;

determining the environmental difference value between the internal environmental data corresponding to the position information and the weather data;

establishing a statistical model of the environmental difference value and the equipment operation data by taking the weather category and the position information as influence factors;

and determining the influence parameters according to the statistical model.

Optionally, the step of establishing a statistical model of the environmental difference value and the device operation data with the weather category and the location information as influence factors includes:

according to the same weather category, the corresponding environmental difference value and the equipment operation data, determining a position influence factor corresponding to the position information by adopting a statistical method;

According to the same position information, the corresponding environmental difference value and the corresponding equipment operation data, and determining weather influence factors corresponding to the weather categories by adopting a statistical method;

determining the mapping relation of each environment difference value to the equipment operation data;

and establishing the statistical model according to the position influence factor, the weather influence factor and the mapping relation.

Optionally, the step of determining the energy efficiency analysis result of the air conditioning equipment according to the influence parameter, the energy efficiency information data and the corresponding environmental temperature of the air conditioning equipment includes:

determining weather weights, altitude weights and installation position weights in an energy efficiency analysis model based on the influence parameters;

determining a set temperature corresponding to the position information according to the energy efficiency information data;

and determining the energy efficiency analysis result according to the set temperature, the environment temperature, the occupied space information and the equipment operation data corresponding to the position information and combining the weather weight, the altitude weight and the installation position weight.

Optionally, after the step of determining the energy efficiency analysis result of the air conditioning equipment according to the influence parameter, the energy efficiency information data and the corresponding environmental temperature of the air conditioning equipment, the method further includes:

Determining equipment optimization parameters according to the energy efficiency analysis result;

determining equipment setting parameters according to the predicted weather data and the equipment optimizing parameters;

and updating an energy efficiency analysis model according to the equipment operation data corresponding to the equipment setting parameters.

Optionally, the step of determining the device setting parameters according to the predicted weather data and the device optimizing parameters includes:

acquiring the predicted weather data containing time information;

determining operation data of the air conditioning equipment based on an energy efficiency analysis model according to the predicted weather data and the equipment type of the air conditioning equipment as input parameters;

and determining a temperature set point, a wind speed and an operation mode of the air conditioning equipment according to the operation data and the equipment optimization parameters.

Optionally, the step of updating the energy efficiency analysis model according to the equipment operation data corresponding to the equipment setting parameters includes:

simulating the simulated operation data of the air conditioning equipment and the corresponding simulated environment temperature according to the equipment setting parameters;

determining simulation accuracy of the energy efficiency analysis model according to the equipment operation data, the simulation operation data and the simulation environment temperature;

And carrying out iterative optimization on the energy efficiency analysis model according to the simulation accuracy.

In addition, the application also provides a central air conditioner energy efficiency analysis device, which comprises a memory, a processor and a central air conditioner energy efficiency analysis program which is stored on the memory and can run on the processor, wherein the steps of the central air conditioner energy efficiency analysis method are realized when the processor executes the central air conditioner energy efficiency analysis program.

In addition, the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a central air conditioner energy efficiency analysis program, and the central air conditioner energy efficiency analysis program realizes the steps of the central air conditioner energy efficiency analysis method when being executed by a processor.

One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:

according to the equipment operation data and the position information of the air conditioning equipment, the energy efficiency information data containing the building space structure information is determined; according to the environmental difference value of weather data and internal environment data corresponding to the equipment operation data, determining influence parameters of the weather data on the air conditioning equipment in terms of altitude and installation positions respectively; and determining an energy efficiency analysis result of the air conditioning equipment according to the influence parameters, the energy efficiency information data and the environment temperature corresponding to the air conditioning equipment, so that the energy efficiency information data can be well fitted with a real environment, and the anti-interference performance is high. Therefore, the technical problems that the energy efficiency analysis result of the building central air conditioner in the related technology is weak in anti-interference performance and the law of the energy consumption change of the central air conditioner system cannot be accurately reflected are effectively solved, and the technical effects that the energy efficiency information data of the central air conditioner is dynamically updated by fitting the real environment and the accuracy of the analysis result is improved are further achieved.

Drawings

FIG. 1 is a schematic flow chart of a method for analyzing energy efficiency of a central air conditioner according to an embodiment of the present application;

fig. 2 is a schematic flow chart of refinement of step S120 in the third embodiment of the method for analyzing energy efficiency of a central air conditioner according to the present application;

fig. 3 is a schematic flow chart of refinement in step S330 in the third embodiment of the method for analyzing energy efficiency of a central air conditioner according to the present application;

fig. 4 is a schematic flow chart of refinement of step S430 in the fourth embodiment of the method for analyzing energy efficiency of a central air conditioner according to the present application;

fig. 5 is a schematic diagram of a hardware structure related to an embodiment of the central air conditioning energy efficiency analysis device of the present application.

Detailed Description

In the related art, when analyzing the energy efficiency of a central air conditioner in a building, operation data of a smart meter is generally acquired to determine the power consumption of the central air conditioner, and then energy efficiency data is determined according to the cooling capacity and the power consumption released by the central air conditioner. In a further scheme, a charging output port of the central air conditioner is connected to the central control, and the energy efficiency calculation and the cold energy allocation of the central air conditioner are realized by monitoring the cold energy required by each terminal air conditioner. However, in the method, only the operation data in the air conditioning system are considered, so that the analysis method has no universality, weak anti-interference performance and large fluctuation of analysis results. The embodiment of the application adopts the main technical scheme that: establishing a three-dimensional structure according to the installation position of the air conditioning equipment, and generating energy efficiency information data based on operation data of each node associated equipment and internal environment data; and analyzing influence parameters of the weather data on each air conditioning equipment to update energy efficiency information data, and finally determining an energy efficiency analysis result of each air conditioning equipment and an overall energy efficiency result of a central air conditioning system of the building based on the environmental temperature. Therefore, the energy efficiency information data of the central air conditioner is dynamically updated according to the fitting reality environment, the accuracy of analysis results is improved, and the technical effects of improving the energy efficiency management efficiency of the central air conditioner and saving energy sources are achieved.

In order to better understand the above technical solution, exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the application to those skilled in the art.

Example 1

An embodiment of the present application provides a method for analyzing energy efficiency of a central air conditioner, referring to fig. 1, the method for analyzing energy efficiency of a central air conditioner includes:

step S110, energy efficiency information data containing building space structure information is determined according to the equipment operation data and the position information of the air conditioning equipment.

In the present embodiment, the air conditioning apparatus refers to each constituent device of the central air conditioning system, such as a cooling apparatus: including a chiller, cooling tower, or cooling coil, for providing a cooling medium. Air treatment device: including air handling units, fan coils, or air conditioning end units for handling and distributing cool and warm air and maintaining clean and comfortable indoor air. Air duct system: plumbing for delivering cold and hot air to individual rooms or areas. A chilled water circulation device and a cooling water circulation device for transporting a cooling medium. Control device: the system is used for monitoring and controlling parameters such as temperature, humidity, wind speed and the like of the air conditioning system, and adjusting and coordinating the operation of each module. Energy system: including an electrical power supply, thermal energy supply, or other energy supply system for providing the energy required by the air conditioning system. Monitoring and maintenance equipment: including sensors, meters, valves, etc. for monitoring and maintaining the air conditioning system. The position information refers to an installation position of the air conditioning equipment in the building, and may be coordinates in a three-dimensional coordinate system established by taking a point in the building as an origin of coordinates.

As an alternative implementation manner, equipment operation data of the air conditioning equipment are acquired through the adapting interface, the equipment operation data are associated with corresponding time information, and according to a pre-established three-dimensional space coordinate system, the coordinates of the installation position of the air conditioning equipment in the three-dimensional space coordinate system and the installation environment are used as position information. And determining corresponding topological nodes based on the position information, and associating the topological nodes with equipment operation data corresponding to the position information to generate energy efficiency information data.

As another alternative, after the location information is acquired, a matrix space is determined, and each layer is divided into n x n grids based on the number n of layers, thus obtaining n ³ Grid spaces corresponding to n in matrix space ³ And the elements are used for taking the corresponding equipment operation data as elements in the matrix according to the grid space where the position information is located, and the expression form of the energy efficiency information data is a matrix space.

And step S120, determining influence parameters of the weather data on the air conditioning equipment in terms of altitude and installation positions respectively according to the environmental difference values of the weather data and the internal environment data corresponding to the equipment operation data.

In the present embodiment, the weather data is classified into a weather type and external weather data. The weather types are the weather of the place where the building is located, such as cloudy, sunny, rainy, and also include the seasons of the place such as spring, summer, autumn, and winter. The external weather data are real-time environment data of the outside of the building, such as temperature, humidity, wind speed and the like, collected by the sensor. The influence degree of the weather data on the air conditioning equipment at different altitudes and different installation positions is considered, so that the influence parameters of the weather data on the air conditioning equipment are analyzed and used for updating the energy efficiency information data, so that the energy efficiency information data are more accurate. Different installation positions are distinguished according to the type of the place where the air conditioning equipment is installed. For example, the installation location may be a bathroom, an elevator room, an aisle, a public office area, a conference room, an office, etc.

As an alternative implementation manner, weather data at corresponding moments is determined according to time information of equipment operation data, and influence parameters of the weather data on energy efficiency of air conditioning equipment at different positions are analyzed based on time sequences.

And step S130, determining an energy efficiency analysis result of the air conditioning equipment according to the influence parameters, the energy efficiency information data and the corresponding environment temperature of the air conditioning equipment.

In this embodiment, relevant parameters in the energy efficiency information data are updated according to the influence parameters, so that the model is subjected to iterative optimization; and then combining the environmental temperature of the corresponding position of the air conditioning equipment, combining the set temperature of the air conditioning equipment, determining the energy efficiency analysis result of the air conditioning equipment through the energy efficiency information data, and further generating the energy efficiency analysis result of the integral central air conditioning system of the building.

Optionally, step S130 includes:

step S131, determining weather weights, altitude weights and installation position weights in the energy efficiency analysis model based on the influence parameters.

As an optional implementation manner, an analysis function is determined according to a preset weight value, an energy efficiency analysis model is established by combining the analysis function and the energy efficiency information data, and the weather weight, the altitude weight and the installation position weight in the energy efficiency analysis model corresponding to the time information are updated based on the time information corresponding to the influence parameter.

Step S132, determining the set temperature corresponding to the position information according to the energy efficiency information data.

As an alternative implementation manner, the set temperatures corresponding to different positions are determined according to the energy efficiency information data and the position information. For example, for a southern office building, the set temperature may need to be adjusted to a lower value to maintain a comfortable indoor temperature due to more sunlight.

And step S133, determining the energy efficiency analysis result according to the set temperature, the environment temperature, the occupied space information and the equipment operation data corresponding to the position information and combining the weather weight, the altitude weight and the installation position weight.

As an alternative embodiment, the energy efficiency analysis calculation is performed in combination with the location information, the set temperature, the ambient temperature, the occupied space information, and the equipment operation data, as well as the weather weight, the altitude weight, and the installation location weight determined previously. According to the calculation result, the energy efficiency analysis result of the office building, such as indexes of energy consumption level, energy utilization efficiency and the like, can be obtained.

Illustratively, a first ratio of the set temperature to the ambient temperature is determined, and a second ratio of the power consumption to the occupied space information in the equipment operation data is determined; and determining the energy efficiency analysis result according to the first ratio, the second ratio and the output cold energy corresponding to the equipment operation data by combining the weather weight, the altitude weight and the installation position weight.

For an example of the embodiment, equipment operation data and position information of the air conditioning equipment are acquired through an adaptive interface, and a matrix A of m rows and n columns is constructed, wherein m represents equipment serial numbers, and n represents time points. The element aij in the matrix a represents the energy efficiency value of the ith air conditioning unit at the jth point in time. Historical weather data including temperature, humidity, sunlight time, etc. is obtained. And then determining historical weather data corresponding to each device according to the device operation data. The historical weather data for each device is stored in a separate array, such as array T, H, S representing temperature, humidity, and sunlight time, respectively. And evaluating the influence parameters of the air conditioning equipment according to the historical weather data. For example, a regression model between temperature and energy efficiency may be established, and coefficients of energy efficiency for each device as a function of temperature may be calculated based on the regression model. The calculation results are stored in a matrix B, wherein each row represents an air conditioning unit and each column represents an influencing parameter. And multiplying each element in the matrix B with the corresponding element in the matrix A to obtain an updated energy efficiency information data matrix C. Specifically, element cji in matrix C represents the energy efficiency value of the ith air conditioning equipment at the jth point in time multiplied by the coefficient of the corresponding energy efficiency as a function of temperature. And acquiring the environment temperature t_i of each air conditioner, and then determining the energy efficiency analysis result of each air conditioner according to the updated energy efficiency information data matrix C and the environment temperature t_i. Specifically, the environmental temperature t_i may be multiplied by a corresponding element in the matrix C and summed to obtain the energy efficiency analysis result se_i for each device.

Based on the embodiment, the energy efficiency analysis result se_i of each air conditioning equipment can be obtained, and the influence of equipment operation data, position information and weather data is comprehensively considered by the result, so that the energy efficiency analysis result is more accurate and reliable. In addition, the model may analytically predict energy efficiency over a period of time in the future based on historical data.

The technical scheme provided by the embodiment of the application at least has the following technical effects or advantages:

because the energy efficiency information data comprising the building space structure is constructed according to the equipment operation data and the position information of the air conditioning equipment; determining weather data corresponding to the equipment operation data, and influencing parameters of the air conditioning equipment in the space structure; and updating the energy efficiency information data according to the influence parameters, and determining an energy efficiency analysis result of the air conditioning equipment by combining the corresponding environment temperature of the air conditioning equipment, so that the energy efficiency information data can be well fitted with a real environment, and the anti-interference performance is high. Therefore, the technical problems that the energy efficiency analysis result of the building central air conditioner in the related technology is weak in anti-interference performance and the law of the energy consumption change of the central air conditioner system cannot be accurately reflected are effectively solved, and the technical effects that the energy efficiency information data of the central air conditioner is dynamically updated by fitting the real environment and the accuracy of the analysis result is improved are further achieved.

Based on the first embodiment, a second embodiment of the present application provides a central air conditioner energy efficiency analysis method, and step S110 includes:

step S210, determining an adaptive interface of the air conditioning equipment according to the equipment identification of the air conditioning equipment.

As an alternative embodiment, the adapter interface information of the air conditioning device is obtained by means of a device identifier, such as a device model number, a device number, etc. The information may be from a technical manual provided by the manufacturer of the device, or by an automated system identifying the device identity, and then looking up the corresponding adapter interface information in a database based on the device identity.

Step S220, obtaining the device operation data including time information according to the adaptation interface.

As an alternative embodiment, the operating data includes, but is not limited to, an operating state of the air conditioning apparatus, such as on, off, cooling, heating, etc., and also includes an operating mode, an indoor temperature, an outdoor temperature, humidity, wind speed, etc. These data can be obtained from the air conditioning unit via the adapter interface, and at the same time, these data often also contain time information, for example the specific point in time or time period at which the data were obtained.

And step S230, a three-dimensional topological network is established according to the position information, and the air conditioning equipment is used as a network node of the three-dimensional topological network.

As an alternative embodiment, the location information may include, but is not limited to, an installation location of the air conditioner, room information, floor information, and the like. By means of the position information, a three-dimensional topological network can be constructed, and the air conditioning equipment is used as a node in the network.

Step S240, associating the device operation data with the network node, and generating the energy efficiency information data with a spatial structure and a time sequence.

As an alternative implementation manner, the acquired equipment operation data are associated with nodes in the constructed three-dimensional topological network, and energy efficiency information data with a spatial structure, namely, position information of air conditioning equipment in a three-dimensional space and time information of the equipment operation data are generated.

The embodiment provides a method for managing energy efficiency of air conditioning equipment, which constructs a three-dimensional topological network by acquiring an adaptive interface, operation data and position information of the air conditioning equipment, associates the equipment operation data with network nodes, generates energy efficiency information data with a space structure and a time sequence, can more comprehensively know the operation condition of the air conditioning equipment, and effectively manages and optimizes the energy efficiency of the air conditioning equipment.

Based on the first embodiment, a third embodiment of the present application provides a method for analyzing energy efficiency of a central air conditioner, referring to fig. 2, step S120 includes:

step S310, determining the weather data corresponding to each piece of location information based on the local weather category and the collected external environment data.

As an alternative embodiment, the sensor and the positioning device collect external environment data of the corresponding position of each position information. External environmental data includes, but is not limited to, temperature, humidity, illumination intensity, wind speed, and the like. The system determines weather data for each location based on the collected weather category and external environmental data. For example, the sensor detects that humidity is above a normal level and temperature is gradually decreasing, marking the weather category for that location as "rainy days". Integrating the acquired weather category and external environment data into weather data.

Step S320, determining the environmental difference value between the internal environmental data corresponding to the location information and the weather data.

As an alternative embodiment, the internal environmental data of each location is compared with the corresponding weather data to determine an environmental difference value. The internal environment data may include temperature, humidity, etc. when the air conditioning apparatus is operated. For example, if the temperature at which the device is operating is higher than the temperature of the weather data, the environmental difference value is the difference between the two. A difference value of each external environment data and the corresponding kind of internal environment data is determined.

And step S330, establishing a statistical model of the environmental difference value and the equipment operation data by taking the weather category and the position information as influence factors.

As an alternative implementation mode, the system uses weather category and position information as influencing factors, uses environment difference values and equipment operation data as response variables, and builds a statistical model. Regression analysis, machine learning, or other suitable data analysis methods are used in the process of building the statistical model.

Optionally, referring to fig. 3, step S330 includes:

step S331, determining a position influence factor corresponding to the position information by adopting a statistical method according to the same weather category, the corresponding environmental difference value and the equipment operation data;

step S332, determining weather influence factors corresponding to the weather categories by adopting a statistical method according to the same position information, the corresponding environmental difference values and the equipment operation data;

step S333, determining a mapping relationship between each environmental difference value and the device operation data;

step S334, establishing the statistical model according to the location impact factor, the weather impact factor and the mapping relation.

As an alternative embodiment, device operational data, location information, and weather categories are obtained. The equipment operation data can comprise the operation state, operation time, output cold energy, power consumption and the like of the equipment; the location information may include the floor on which the device is located, altitude, installation location, etc.; weather categories may include sunny days, cloudy days, rainy days, snowy days, and the like. And analyzing environmental difference values corresponding to each position information and difference values of equipment operation data corresponding to each weather category by adopting a statistical method according to the collected position information and weather categories. For example, according to historical data, the difference conditions of the equipment operation data in different positions and different weather can be analyzed by adopting methods such as analysis of variance and chi-square test, so as to obtain the environmental difference value and the difference value of the equipment operation data. And according to the collected equipment operation data and the environmental difference values, adopting a statistical method to analyze the mapping relation of each environmental difference value to the equipment operation data. For example, regression analysis or other methods may be used to analyze the linear or nonlinear relationship between the environmental difference values and the device operational data, thereby obtaining the mapping relationship.

And analyzing the position influence factors corresponding to each position information and the weather influence factors corresponding to each weather category by adopting a statistical method according to the collected position information and the weather category. For example, methods such as principal component analysis may be employed to analyze factors that influence location information and weather categories on equipment operation data. And establishing a statistical model of equipment operation data, position information and weather categories by adopting a statistical method according to the obtained mapping relation, the position influence factors and the weather influence factors. For example, a neural network, a support vector machine, and the like can be used to build a statistical model.

And predicting the running condition of the equipment under the given position information and weather category according to the established statistical model. For example, the expected operation state, operation time, setting parameters, and the like of the device may be calculated by substituting the current location information and weather category into the statistical model.

According to the embodiment, the mapping relation between the equipment operation data, the position information and the weather category is established, so that the operation condition of the equipment can be predicted more accurately, and the operation efficiency and the service life of the equipment can be improved.

And step S340, determining the influence parameters according to the statistical model.

As an alternative embodiment, the system may determine, based on the statistical model, parameters of the impact of the weather data on the operation of the device. For example, if the statistical model shows that the energy consumption of the device is 10% higher in "rainy days" than in other weather, then this 10% is the impact parameter of "rainy days" on the energy consumption of the device.

As another alternative implementation manner, the installation position is taken as a fixed quantity, first energy efficiency information data of air conditioning equipment with different altitudes are determined, weather data is taken as independent variables based on a statistical model, and first influence parameters of the different weather data on the first energy efficiency information data are determined; determining second energy efficiency information data of air conditioning equipment at different installation positions by taking the altitude as a fixed quantity, and determining second influence parameters of different weather data on the second energy efficiency information data by taking the weather data as independent variables based on a statistical model; and according to a preset weight value, adjusting the first influence parameter and the second influence parameter to be the influence parameter of the weather data on the altitude and the influence parameter of the weather data on the installation position.

In this embodiment, the weather data corresponding to each location information is determined based on the local weather category and the collected external environment data; establishing an environmental difference value between the internal environmental data and the weather data and a statistical model of the equipment operation data; and determining the influence parameters of the weather data on the position information according to the statistical model. The influence of weather types and position information on the energy consumption data of the air conditioning equipment is comprehensively considered, influence factors are respectively set so as to generate influence parameters of the weather data, and the accuracy of energy efficiency information data estimation and the overall anti-interference performance are improved. Thereby promoting the rationality of the whole cold energy distribution of building to the energy saving improves the air conditioner and uses experience.

Based on the first embodiment, the fourth embodiment of the present application provides a method for analyzing energy efficiency of a central air conditioner, and after step S130, the method further includes:

step S410, determining equipment optimization parameters according to the energy efficiency analysis result.

As an alternative implementation manner, an abnormal result in the energy efficiency analysis result is determined, the abnormal result is compared with a normal result in the historical data, and the equipment optimization parameters are determined according to the compared result and the energy efficiency analysis model.

Step S420, determining equipment setting parameters according to the predicted weather data and the equipment optimizing parameters.

As an optional implementation manner, determining the target air conditioning equipment corresponding to the equipment optimization parameter, determining the influence parameter of the weather data on the real-time target air conditioning equipment, and determining the equipment setting parameter corresponding to the target air conditioning equipment according to the influence parameter and the equipment optimization parameter.

Optionally, step S420 includes:

step S421, obtaining the predicted weather data containing time information.

Predicted weather data including time information is obtained by a weather forecast service or similar source. Such data typically includes temperature, humidity, rainfall, wind speed, and other relevant environmental parameters for a day or longer in the future.

Step S422, according to the predicted weather data and the equipment type of the air conditioning equipment as input parameters, determining the operation data of the air conditioning equipment based on an energy efficiency analysis model;

predicted weather data from weather forecast is obtained, along with device type from a device management system or similar source. The device type may include information on the device model number, year, manufacturer, energy efficiency rating, etc. Based on these data and the pre-collected energy efficiency information data, such as energy efficiency versus environmental parameters or tables, an energy efficiency analysis model or similar algorithm is used to determine the operational data of the air conditioning apparatus.

For example, environmental parameters related to the operation of the air conditioning apparatus, such as outdoor temperature and humidity, are extracted from the predicted weather data. And determining energy efficiency parameters of the air conditioning equipment according to the equipment type and the energy efficiency information data. For example, if the device is a high-efficiency air conditioner, the energy efficiency grade of the device is A, the corresponding energy efficiency information data can be queried according to the energy efficiency grade and the device model to obtain the energy efficiency parameters of the device. Based on an energy efficiency analysis model or similar algorithm, optimal operational data of the air conditioning device, including temperature set point, wind speed and operational mode, is calculated from the environmental parameters and the energy efficiency parameters. For example, if the outdoor temperature is expected to be 28 degrees celsius and the energy efficiency parameter of the air conditioning equipment is to consume 1 degree per hour, the optimal operation data may be to set the temperature to 26 degrees celsius, the wind speed to be medium speed, and the operation mode to be energy saving mode.

Step S423, determining a temperature set point, a wind speed and an operation mode of the air conditioning equipment according to the operation data and the equipment optimization parameters.

As an alternative embodiment, the temperature set point, the wind speed and the operation mode of the air conditioning equipment are determined according to the operation data and the equipment optimization parameters, and the temperature set point, the wind speed and the operation mode of the air conditioning equipment are further adjusted according to the calculated operation data and the preset equipment optimization parameters. If the device optimization parameters include noise reduction to improve user comfort, then a low speed may need to be selected over a medium speed in determining wind speed to reduce noise. Alternatively, if optimizing parameters includes increasing plant operating efficiency to conserve energy, then a slightly higher temperature may need to be selected in determining the temperature set point to reduce energy consumption.

In this way, the operating parameters of the air conditioning device may be dynamically adjusted to accommodate different environmental conditions and achieve higher energy efficiency and user comfort.

Step S430, updating an energy efficiency analysis model according to the equipment operation data corresponding to the equipment setting parameters.

As an alternative implementation manner, controlling the air conditioning equipment to perform work according to the equipment setting parameters, and acquiring equipment operation data corresponding to the equipment setting parameters; performing simulation work based on equipment setting parameters according to the energy efficiency analysis model, and determining simulation operation data; and iteratively updating the energy efficiency analysis model based on the equipment operation data and the simulation operation data to enable the energy efficiency analysis model to be more in line with the actual working condition of the building.

Optionally, referring to fig. 4, step S430 includes:

step S431, simulating the simulated operation data of the air conditioning equipment and the corresponding simulated environment temperature according to the equipment setting parameters;

step S432, determining simulation accuracy of the energy efficiency analysis model according to the equipment operation data, the simulation operation data and the simulation environment temperature;

and step S433, performing iterative optimization on the energy efficiency analysis model according to the simulation accuracy.

As an alternative embodiment, the collected plant operational data and actual ambient temperature are processed, including data cleansing, data conversion, data normalization, etc., so that the data is suitable for simulation and energy efficiency analysis. And simulating the running state of the equipment and the corresponding simulated environment temperature based on the equipment setting parameters and the actual environment temperature by using a preset energy efficiency analysis model. And comparing the equipment operation data with the simulation operation data, and calculating the simulation accuracy to evaluate the accuracy of the energy efficiency analysis model. Iterative optimization of the energy efficiency analysis model is performed according to simulation accuracy, for example, parameters in the model are adjusted, or new influencing factors are introduced.

Further, the steps are repeated, the energy efficiency analysis model is continuously optimized, and the simulation accuracy is improved, so that the model can accurately predict the energy efficiency performance of the equipment.

Optionally, besides weather data, information such as people flow, rental rate, working days and the like can be considered, so that the energy efficiency analysis model is assisted to be optimized.

As an optional implementation manner, when the energy efficiency analysis model is optimized according to the weather data of the associated time information, the traffic data, the space unit renting rate and the working day information corresponding to the time information are obtained, and the influence factors of the traffic data, the space unit renting rate and the working day information on the weather data are respectively established under the same weather data based on the weather data, the energy efficiency information data and the traffic data, the space unit renting rate and the working day information.

The weather weight is updated according to the influence factors of the traffic data, the space unit rental rate and the working day information on the weather data, the traffic data, the space unit rental rate and the working day information on the same day, so that the energy efficiency analysis model is closer to the real data. The technical effect of improving the accuracy of the energy efficiency analysis model is achieved.

In this embodiment, not only the influence of weather data on the energy efficiency analysis model is considered, but also the influence degree of weather data on the equipment operation data is indirectly influenced by considering the relationship among the traffic, the rate of renting and the working day and the equipment operation data, and the technical effects of improving the accuracy of the model and optimizing the analysis efficiency are realized by analyzing the potential association relationship in the data and reflecting the mutual influence factors among the related factors influencing the equipment operation data aiming at directly considering the influence of the traffic, the rate of renting and the working day on the equipment operation data.

Due to the adoption of the device setting parameters corresponding to the device optimizing parameters according to the energy efficiency analysis result and the predicted weather data; and then simulating the simulated operation data corresponding to the predicted weather data according to the equipment setting parameters, wherein the simulated operation data comprise power consumption, cooling capacity and the like. Comparing the acquired equipment operation data with the simulation operation data to determine the simulation accuracy of the energy efficiency analysis model; and carrying out iterative optimization on the energy efficiency analysis model according to the simulation accuracy. And further, the technical effects of combining local real-time weather changes and collected environmental data, dynamically updating the energy efficiency model and improving the accuracy of energy efficiency analysis are realized.

The application further provides a central air conditioner energy efficiency analysis device, and referring to fig. 5, fig. 5 is a schematic structural diagram of the central air conditioner energy efficiency analysis device of the hardware operation environment according to the embodiment of the application.

As shown in fig. 5, the central air conditioner energy efficiency analysis apparatus may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) Memory or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.

It will be appreciated by those skilled in the art that the structure shown in fig. 5 does not constitute a limitation of the central air conditioning energy efficiency analysis apparatus, and may include more or fewer components than shown, or may combine certain components, or may have a different arrangement of components.

Optionally, the memory 1005 is electrically connected to the processor 1001, and the processor 1001 may be configured to control operation of the memory 1005, and may also read data in the memory 1005 to implement central air conditioning energy efficiency analysis.

Alternatively, as shown in fig. 5, an operating system, a data storage module, a network communication module, a user interface module, and a central air conditioning energy efficiency analysis program may be included in the memory 1005 as one storage medium.

Optionally, in the central air conditioning energy efficiency analysis device shown in fig. 5, the network interface 1004 is mainly used for data communication with other devices; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the central air conditioning energy efficiency analysis apparatus of the present application may be provided in the central air conditioning energy efficiency analysis apparatus.

As shown in fig. 5, the central air conditioning energy efficiency analysis device invokes a central air conditioning energy efficiency analysis program stored in a memory 1005 through a processor 1001, and executes related step operations of the central air conditioning energy efficiency analysis method provided by the embodiment of the present application:

Optionally, the processor 1001 may call the central air conditioning energy efficiency analysis program stored in the memory 1005, and further perform the following operations:

and determining the influence parameters according to the statistical model.

Acquiring the predicted weather data containing time information;

In addition, the embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium is stored with a central air conditioner energy efficiency analysis program, and the central air conditioner energy efficiency analysis program realizes the relevant steps of any embodiment of the central air conditioner energy efficiency analysis method when being executed by a processor.

It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flowchart and/or block of the flowchart illustrations and/or block diagrams, and combinations of flowcharts and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.

While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.

It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims

1. The energy efficiency analysis method of the central air conditioner is characterized by comprising the following steps of:

2. The method of analyzing energy efficiency of a central air conditioner as set forth in claim 1, wherein the step of determining energy efficiency information data including spatial structure information of a building based on equipment operation data of the air conditioner and location information includes:

3. The method for analyzing energy efficiency of a central air conditioner according to claim 1, wherein the step of determining the influence parameters of the weather data on the air conditioner in terms of altitude and installation position, respectively, according to the environmental difference value of the weather data and the internal environmental data corresponding to the equipment operation data comprises:

and determining the influence parameters according to the statistical model.

4. The method for analyzing energy efficiency of a central air conditioner according to claim 3, wherein the step of establishing a statistical model of the environmental difference value and the equipment operation data using the weather category and the location information as influence factors comprises:

5. The method for analyzing energy efficiency of a central air conditioner according to claim 1, wherein the step of determining the energy efficiency analysis result of the air conditioner according to the influence parameter, the energy efficiency information data, and the corresponding ambient temperature of the air conditioner comprises:

6. The method for analyzing energy efficiency of a central air conditioner according to claim 1, wherein after the step of determining the energy efficiency analysis result of the air conditioner according to the influence parameter, the energy efficiency information data, and the corresponding ambient temperature of the air conditioner, the method further comprises:

7. The method of claim 6, wherein the step of determining the device setting parameters according to the predicted weather data and the device optimizing parameters comprises:

acquiring the predicted weather data containing time information;

8. The method of claim 6, wherein the step of updating the energy efficiency analysis model according to the device operation data corresponding to the device setting parameters comprises:

9. A central air conditioning energy efficiency analysis device, characterized by comprising a memory, a processor and a central air conditioning energy efficiency analysis program stored on the memory and operable on the processor, wherein the processor implements the steps of the central air conditioning energy efficiency analysis method according to any one of claims 1 to 8 when executing the central air conditioning energy efficiency analysis program.

10. A computer-readable storage medium, wherein a central air conditioning energy efficiency analysis program is stored on the computer-readable storage medium, and the central air conditioning energy efficiency analysis program, when executed by a processor, implements the steps of the central air conditioning energy efficiency analysis method according to any one of claims 1 to 8.