CN117804030A

CN117804030A - Cloud edge cooperation-based air conditioner cold station operation optimization method and device and electronic equipment

Info

Publication number: CN117804030A
Application number: CN202311615886.6A
Authority: CN
Inventors: 张震; 张林超; 国杰; 张鑫; 王祥军
Original assignee: Xinao Shuneng Technology Co Ltd
Current assignee: Xinao Shuneng Technology Co Ltd
Priority date: 2023-11-29
Filing date: 2023-11-29
Publication date: 2024-04-02

Abstract

The application is suitable for the technical field of operation tuning, and provides an air conditioner cold station operation tuning method, an air conditioner cold station operation tuning device and electronic equipment based on cloud edge cooperation, wherein the air conditioner cold station operation tuning method comprises the following steps: determining a valley price turning moment based on the current moment and the peak valley electricity price, and determining the valley price turning moment as a shutdown moment of a host in the air conditioner cold station; predicting the cold storage time length of a host machine based on the current indoor and outdoor temperatures and the current working condition data of an air conditioner cold station; subtracting the cold accumulation time length from the shutdown time to obtain a cold accumulation starting time; predicting the exceeding time of the room temperature based on a preset exceeding room temperature threshold value, the current indoor and outdoor temperatures and the current working condition data of an air conditioner cold station; adding the time of shutdown with the time of exceeding the standard of the room temperature to obtain the time of startup; and generating an operation optimization strategy based on the cold accumulation starting time, the shutdown time and the startup time. The method and the device can realize that the output load of the air conditioner cold station is adjusted according to real-time requirements, the comprehensive energy efficiency and the cost of the air conditioner cold station can be optimal, and more energy sources are prevented from being wasted.

Description

Cloud edge cooperation-based air conditioner cold station operation optimization method and device and electronic equipment

Technical Field

The application belongs to the technical field of operation tuning, and particularly relates to an air conditioner cold station operation tuning method and device based on cloud edge cooperation and electronic equipment.

Background

In the traditional air-conditioning cold station operation control process, the method is limited by the professional level of operators and the working state of multiple functions, the equipment in the air-conditioning cold station cannot be finely managed, the starting and stopping time of the equipment in the air-conditioning cold station is fixed, the operation combination mode of the equipment is fixed, the set parameters are basically not regulated, and because the load of the air-conditioning cold station is in different time periods in one day, the different days in one quarter are greatly different, the output load of the air-conditioning cold station cannot be regulated according to real-time requirements, the comprehensive energy efficiency and the cost of the air-conditioning cold station cannot be optimized, and great waste is caused.

Disclosure of Invention

In order to solve the problem that the output load of the air conditioner cold station cannot be adjusted according to real-time requirements, the comprehensive energy efficiency and the cost of the air conditioner cold station cannot be optimized, and large energy waste is caused, the embodiment of the application provides an air conditioner cold station operation optimizing method and device based on cloud edge cooperation, and electronic equipment.

The application is realized by the following technical scheme:

In a first aspect, an embodiment of the present application provides an air conditioner cold station operation optimization method based on cloud edge coordination, including:

acquiring current indoor and outdoor temperatures and current working condition data of an air conditioner cold station;

determining the latest valley price turning moment in the future based on the current moment and the peak valley price, and determining the valley price turning moment as the shutdown moment of a host in the air conditioner cold station, wherein the valley price turning moment is the moment when the electricity price is changed from low to high;

predicting the cold accumulation duration of the host machine based on the current indoor and outdoor temperatures and current working condition data of an air conditioner cold station;

subtracting the cold accumulation duration from the shutdown time to obtain a cold accumulation starting time of the host;

predicting the exceeding time of the room temperature based on a preset exceeding room temperature threshold value, the current indoor and outdoor temperatures and the current working condition data of the air conditioner cold station;

adding the room temperature exceeding time to the shutdown time to obtain the startup time of the host;

generating an operation optimizing strategy of the air conditioner cold station based on the cold accumulation starting time, the shutdown time and the startup time; the operation optimizing strategy comprises the steps of controlling the host to start cold accumulation at the cold accumulation starting time, controlling the host to be turned off at the turning-off time, and controlling the host to be turned on again at the turning-on time.

In some embodiments, the air conditioning cold station includes a cold water main, a fan coil unit FCU, and a cryopump; the current working condition data comprise the chilled water inlet and outlet temperature of the host and the chilled water flow of the chilled pump;

predicting the cold storage duration of the host based on the current indoor and outdoor temperatures and current working condition data of an air conditioner cold station, including:

inputting the current moment, the current day type, the current indoor and outdoor temperatures and current working condition data of the FCU into a cold load prediction model to predict the cold load required by the next moment;

calculating the supply load of the host machine at the next moment based on the temperature of the chilled water inlet and outlet of the host machine and the flow of chilled water;

judging whether the supply load is greater than the cooling load;

if yes, updating the chilled water inlet temperature of the host based on the supply load and the cold load, updating the current moment, and jumping to input the current moment, the current day type, the current indoor and outdoor temperatures and current working condition data of the FCU into a cold load prediction model to obtain the required cold load;

otherwise, the difference value of the final moment minus the initial moment is determined as the cold accumulation duration; the initial time is the initial current time, and the final time is the current time after the last update.

In some embodiments, the updating the chilled water inlet temperature of the host based on the supply load and the cooling load comprises:

updating the chilled water inlet temperature of the host based on the following formula:

wherein,for the updated chilled water inlet temperature of the host, T ₂ To update the chilled water inlet temperature of the host before Q ₃ For the supply load, Q ₁ For the cold load, N is the chilled water volume.

In some embodiments, the air conditioning cold station further comprises a cooling pump, and the current working condition data further comprises cooling water flow of the cooling pump, load rate of the host machine, cooling water inlet temperature, start number of FCU, set temperature and fan gear;

before said determining whether said supply load is greater than said cooling load, further comprising:

inputting the current moment, the current day type, the current indoor and outdoor temperatures and the working condition data of the current FCU into a host electricity load prediction model to predict the electricity load of the host at the next moment;

inputting the load rate, the chilled water flow, the cooling water flow, the chilled water inlet temperature and the cooling water inlet temperature into a host computer COP model, and predicting the energy efficiency ratio COP of the host computer at the next moment;

After the determining whether the supply load is greater than the cooling load, if so, before updating the current time, further comprising:

calculating an energy consumption of the host based on the supply load and the COP;

after the operation tuning strategy of the air conditioner cold station is generated, the method further comprises the following steps:

judging whether an operation optimization strategy of the air conditioner cold station is effective or not based on the room temperature exceeding time length, the cold storage time length, the electricity load and the energy consumption of the host;

and if the operation optimization strategy of the air-conditioning cold station is effective, issuing the operation optimization strategy of the air-conditioning cold station to the air-conditioning cold station through an edge gateway.

In some embodiments, the determining whether the operation tuning strategy of the air conditioner cold station is valid based on the room temperature exceeding time period and the cold storage time period, the electricity load and the energy consumption of the host machine includes:

calculating additional charge of cold accumulation based on electricity price, cold accumulation duration of the host, electricity load and energy consumption;

calculating a saving cost based on the room temperature exceeding time, the electricity load of the host and the electricity price;

dividing the cold accumulation extra cost by the saving cost to obtain a saving rate;

judging whether the section rate is larger than a preset section rate;

If yes, judging that the operation optimization strategy of the air conditioner cold station is effective;

otherwise, the operation optimizing strategy of the air conditioner cold station is invalid.

In some embodiments, the calculating the cold storage additional charge based on the electricity price and the cold storage duration, the electricity load, and the energy consumption of the host includes:

calculating the cold storage extra cost based on the following formula:

wherein cost is the extra charge of cold accumulation, T ₁ For the cold accumulation start time, T ₂ For the shutdown time, P _t For the energy consumption of the host at the time t, Q _2,t The power utilization load of the host at the time t is provided, and the price is electricity price;

the calculating the saving cost based on the room temperature exceeding time length, the electricity load of the host machine and the electricity price comprises the following steps:

the savings cost is calculated based on the following formula:

wherein, save is the cost, T ₃ And the starting time is the starting time.

In some embodiments, the air conditioning cold station comprises a cold water main, a cryopump, and a fan coil unit FCU; the current working condition data comprise chilled water supply temperature of the host machine and chilled water flow of the chilled pump; the number of FCU openings;

based on a preset exceeding room temperature threshold, current indoor and outdoor temperatures and current working condition data of the air conditioner cold station, predicting the room temperature exceeding time length comprises the following steps:

And inputting the preset exceeding room temperature threshold, the running duration of chilled water accumulation, the average indoor and outdoor temperature of the host machine from starting up to the current moment, the current indoor and outdoor temperature, the starting number of FCUs, the chilled water flow rate of the host machine and the chilled water supply temperature into a trained room temperature exceeding model, and predicting the room temperature exceeding duration.

In a second aspect, an embodiment of the present application provides an air conditioner cold station operation tuning device based on cloud edge coordination, including:

the acquisition module is used for acquiring the current indoor and outdoor temperature and the current working condition data of the air conditioner cold station;

the first determining module is used for determining the latest valley price turning moment in the future based on the current moment and the peak valley price, determining the valley price turning moment as the shutdown moment of the host in the air conditioner cold station, wherein the valley price turning moment is the moment when the electricity price is turned from low to high;

the first calculation module is used for predicting the cold accumulation duration of the host machine based on the current indoor and outdoor temperature and the current working condition data of the air conditioner cold station;

the second determining module is used for subtracting the cold accumulation duration from the shutdown time to obtain a cold accumulation starting time of the host;

The second calculation module is used for predicting the exceeding time of the room temperature based on a preset exceeding room temperature threshold value, the current indoor and outdoor temperatures and the current working condition data of the air conditioner cold station;

the third determining module is used for adding the room temperature exceeding time to the shutdown time to obtain the startup time of the host;

the generating module is used for generating an operation optimizing strategy of the air conditioner cold station based on the cold accumulation starting time, the shutdown time and the startup time; the operation optimizing strategy comprises the steps of controlling the host to start cold accumulation at the cold accumulation starting time, controlling the host to be turned off at the turning-off time, and controlling the host to be turned on again at the turning-on time.

In a third aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor implements the cloud edge collaboration-based air conditioning cold station running optimization method according to any one of the first aspect when executing the computer program.

In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, where a computer program is stored, where the computer program is executed by a processor to implement the cloud edge collaboration-based air conditioning cold station operation tuning method according to any one of the first aspect.

In a fifth aspect, an embodiment of the present application provides a computer program product, where the computer program product when executed on a terminal device causes the terminal device to execute the cloud edge collaboration-based air conditioning cold station operation tuning method in any one of the first aspects.

It will be appreciated that the advantages of the second to fifth aspects may be found in the relevant description of the first aspect, and are not described here again.

Compared with the related art, the embodiment of the application has the beneficial effects that: according to the embodiment of the application, the current indoor and outdoor temperatures and the current working condition data of the air conditioner cold station are obtained, so that an operation optimization strategy of the air conditioner cold station is generated according to the real-time data; determining the latest valley price turning moment in the future based on the current moment and the peak valley electricity price, determining the valley price turning moment as the shutdown moment of a host in an air conditioner cold station, and storing cold for chilled water in a period with lower electricity price; predicting the cold storage time length of a host machine based on the current indoor and outdoor temperatures and the current working condition data of an air conditioner cold station; subtracting the cold accumulation duration from the shutdown time to obtain a host cold accumulation starting time; predicting the exceeding time of the room temperature based on a preset exceeding room temperature threshold value, the current indoor and outdoor temperatures and the current working condition data of an air conditioner cold station; adding the room temperature exceeding time to the shutdown time to obtain the startup time of the host machine so as to determine the time when the chilled water cold accumulation is exhausted and the room temperature exceeds the standard, and restarting the host machine; and generating an operation optimizing strategy of the air conditioner cold station based on the cold accumulation starting time, the shutdown time and the startup time. The method and the device can realize that the output load of the air conditioner cold station is adjusted according to real-time requirements, the comprehensive energy efficiency and the cost of the air conditioner cold station can be optimal, and more energy sources are prevented from being wasted.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments or the description of the related art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort to a person of ordinary skill in the art.

Fig. 1 is a schematic diagram of an air conditioning cold station operation tuning system based on cloud edge coordination according to an embodiment of the present application;

FIG. 2 is a schematic flow chart of an air conditioning cold station operation optimizing method based on cloud edge coordination according to an embodiment of the present application;

FIG. 3 is a schematic flow chart of a dynamic simulation of a user-side water loop thermodynamic system provided in an embodiment of the present application;

fig. 4 is a schematic structural diagram of an air conditioning cold station operation optimizing device based on cloud edge coordination according to an embodiment of the present application;

fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It should also be understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.

As used in this specification and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".

In addition, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.

Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.

In the traditional cold station operation control process, the method is limited by the professional level and the working state of multiple functions of an operator, the equipment in the air conditioner cold station cannot be finely managed, the starting and stopping time of the equipment in the cold station is fixed, the operation combination mode of the equipment is fixed, the set parameters are basically not adjusted, the load of the cold station is greatly different in different days in one quarter in different time periods of the day, the output load of the refrigeration guiding station cannot be adjusted according to real-time requirements, the comprehensive energy efficiency and the cost of the cold station cannot be optimal, and great waste is caused. And because the load of the heating and ventilation system is influenced by various factors such as outdoor weather conditions, building occupancy rate, room functions, people flow density and the like, and thermal inertia exists, a local group control system of a cold station and on-site operators cannot make real-time and correct regulation and control instructions, and the energy consumption is high.

The traditional solution is to deploy a set of group control system locally, and the main functions are as follows:

(1) Host computer intelligent control

The host computer is started by one key, and has the function of timing on-off.

And comprehensively analyzing the running economy of the refrigerating machine room according to the parameters such as the load requirement of the terminal air conditioner, the temperature and humidity acquisition information of different indoor areas, the efficiency of the host, and the like, and automatically adjusting the number of the opened hosts in the most economical mode.

(2) Linkage control

According to the start-stop condition of the host, the related water pump, the cooling tower fan and the electric valve are linked to execute related actions, the number and frequency of operation are automatically adjusted, and the linkage and protection are achieved.

(3) Air conditioner circulating pump control

According to the parameters of the load demand of the terminal air conditioner, the pressure difference, the temperature difference and the like of the pipe network, the number and the operating frequency of the water pump are automatically adjusted, and the energy-saving economic operation and the terminal load demand are met. The pressure difference is used as a pump set frequency conversion target, and the minimum frequency of the water pump operation can be set.

(4) Cooling water circulation pump control

The cooling water pump is controlled in a variable frequency and energy-saving manner according to different operation conditions such as host load requirements, condensation temperature and the like. The temperature difference is used as a pump set frequency conversion target, and the minimum frequency of the water pump operation can be set.

(5) Energy-saving control of cooling tower

And (3) air path waterway balance control: the variable frequency control of the cooling tower fan avoids the phenomenon of air mixing and water mixing among the tower groups, fully utilizes the packing area of the cooling tower when the total flow of the cooling tower groups changes within the range of 30% -100% of design flow, realizes that the water outlet temperature of the cooling tower approaches to the wet bulb temperature to be less than or equal to 4 ℃, ensures the supply of the lowest cooling water supply temperature of a host, improves the operation efficiency of the host, and enables the lowest temperature of the cooling water tower to be set.

Although the traditional solution can realize part of automatic control function, the setting parameters are manually set, the setting parameters are fixed and random, the setting parameters cannot be dynamically adjusted according to the load change of a user, and the system cannot operate at the optimal energy efficiency level, so that a large amount of energy sources are wasted.

Based on the above problems, the embodiment of the application provides an air conditioner cold station operation tuning method based on cloud-edge cooperation, by the edge-cloud cooperation operation tuning technology, the operation data of a user and the working condition data of the air conditioner cold station are uploaded through an edge gateway, a personalized parameter tuning strategy is formed at the cloud, and parameter setting is performed on a local group control system or main equipment at high frequency, so that the system always operates in an optimal state, and the cost is reduced and the efficiency is improved.

For example, the embodiment of the application can be applied to an air conditioning cold station operation tuning system based on cloud edge coordination as shown in fig. 1. In the system, data of main equipment in the cold station is accessed through an edge gateway, a parameter adjusting strategy is formed through expert experience and a built-in algorithm model, the parameter adjusting strategy is issued to the edge gateway, and then the parameter adjusting strategy is issued to the main equipment of the cold station through the edge gateway, so that real-time adjustment of parameters is realized. On the premise of meeting the temperature and humidity requirements of the tail end of the air conditioner, the purpose of cooling as required and efficiently cooling is achieved through the start-stop time management of the host in the air conditioner cooling station.

The cloud-edge-collaboration-based air conditioner cold station operation optimizing method is described in detail below with reference to fig. 1.

Fig. 2 is a schematic flowchart of an air conditioning cold station operation tuning method based on cloud edge coordination according to an embodiment of the present application, and referring to fig. 2, the detailed description of the air conditioning cold station operation tuning method based on cloud edge coordination is as follows:

in S201, current indoor and outdoor temperatures and current operating mode data of an air conditioning cold station are acquired.

The air conditioning cold station comprises a cold water host, a fan coil unit FCU, a refrigerating pump, a cooling tower and other devices.

The current working condition data comprise the temperature of a chilled water inlet and outlet of a host, the temperature of a cooling water inlet, the load rate, the starting number of FCUs, the set temperature and the fan gear (such as a high gear, a middle gear and a low gear), the chilled water flow of a chilled pump and the cooling water flow of a cooling pump.

In the embodiment of the application, data such as chilled water inlet and outlet temperature, cooling water inlet temperature, load factor, starting quantity of FCU, set temperature of FCU, fan gear of FCU, chilled water flow and cooling water flow are obtained through an edge gateway.

In S202, based on the current time and the peak-to-valley electricity price, the valley price turning time of the latest time in the future is determined, and the valley price turning time is determined as the shutdown time of the host in the air conditioner cold station, and the valley price turning time is the time when the electricity price is turned from low to high.

In the embodiment of the application, the time when the latest power price in the future is changed from low to high is determined according to the power price of each period and the current time, and is called a valley price turning time, and all devices in the energy stations except the freezing pump are turned off at the valley price turning time, namely, a host machine, a cooling pump and a cooling tower are required to be turned off at the moment.

In S203, the cold accumulation period of the host is predicted based on the current indoor and outdoor temperatures and the current operating mode data of the air conditioning cold station.

In the embodiment, the cold storage duration of the host is predicted through dynamic simulation of a user side water loop thermodynamic system.

In some embodiments of the present application, when predicting the cold accumulation period of the host, the prediction may be performed by:

step 1: and inputting the current moment, the current day type, the current indoor and outdoor temperatures and the current working condition data of the FCU into a cold load prediction model to predict the cold load required by the next moment.

Specifically, the current moment, the current day type, the current indoor and outdoor temperatures, the starting quantity of the FCU, the set temperature of the FCU and the fan gear of the FCU are input into a trained cold load prediction model, and the required cold load is predicted.

Step 2: and calculating the supply load of the host machine at the next moment based on the chilled water inlet and outlet temperature and the chilled water flow rate of the host machine.

In particular according toThe supply load of the host is calculated.

Wherein Q is ₃ To supply the load, V is the chilled water flow, T ₂ For chilled water inlet temperature, T ₁ Is the chilled water outlet temperature.

Step 5: it is determined whether the supply load is greater than the cooling load.

It should be appreciated that during the main machine cold storage phase, the chilled water temperature continues to drop, and as the main machine side outlet temperature difference continues to drop, the main machine side supply load continues to drop, and when the main machine side supply load is lower than the end air conditioning load (i.e., the cold load), the cold storage equilibrium state is considered to be reached, and therefore, by comparing the supply load with the cold load, it is determined whether the cold storage equilibrium state is reached. When the supply load is larger than the cold load, the cold storage balance state is not reached; when the supply load is less than or equal to the cold load, a cold storage equilibrium state is reached.

Step 6: if yes, updating the chilled water inlet temperature of the host machine based on the supply load and the cooling load, updating the current moment, and jumping to the step 1.

Specifically, when the cold accumulation balance state is not reached, the chilled water inlet temperature and the current time are required to be updated, the updated chilled water inlet temperature is replaced by the chilled water inlet temperature before updating, the updated current time is replaced by the current time before updating, and then the process jumps to the step 1 to continue to execute all the steps.

Step 7: otherwise, the difference value of the final moment minus the initial moment is determined as the cold accumulation duration.

Specifically, the cold storage equilibrium state is reached at this time, and the cold storage duration can be obtained by subtracting the initial time from the final time.

The current time is the whole time, for example, 8 hours, 11 hours, or the like. The current day type includes a working day and a rest day, and the determination of the working day and the rest day may be determined with reference to a calendar. The initial time is the initial current time, namely the whole point time. The final time is the current time after the last update.

Optionally, the chilled water inlet temperature of the host is updated based on the following formula:

wherein,to update the chilled water inlet temperature of the host, T ₂ To update the chilled water inlet temperature of the host before the update, Q ₁ For the cold load, N is the chilled water volume.

Optionally, the current time is updated based on the following formula:

t ^* ＝t+60

wherein t is ^* For the updated current time, t is the current time before updating. It should be noted that 60 in the formula for updating the current time represents 60 seconds, that is, 1 minute is taken as a simulation step length, and the refrigeration of the air conditioner cold station is simulated.

In some embodiments of the present application, before the step 5, the method further includes:

step 3: and inputting the current moment, the current day type, the current indoor and outdoor temperatures and the working condition data of the current FCU into a host electricity load prediction model to predict the electricity load of the host at the next moment.

Specifically, the current moment, the current day type, the current indoor and outdoor temperatures, the starting quantity of the FCU, the set temperature of the FCU and the fan gear of the FCU are input into a trained main machine electricity load prediction model, and electricity loads of the main machine are predicted.

Step 4: and inputting the load rate, the chilled water flow, the cooling water flow, the chilled water inlet temperature and the cooling water inlet temperature into a host computer COP model, and predicting the energy efficiency ratio COP of the host computer at the next moment.

Meanwhile, before updating the current time in step 6, the method further includes:

Based on the supply load and COP, the energy consumption of the host is calculated.

Specifically, byAnd calculating the energy consumption of the host.

Wherein P is the energy consumption of the host, and COP is the host COP.

In S204, the cold accumulation start time of the host is obtained by subtracting the cold accumulation time period from the shutdown time.

In S205, based on the preset exceeding room temperature threshold, the current indoor and outdoor temperatures, and the current working condition data of the air conditioning cold station, the room temperature exceeding duration is predicted.

In some embodiments of the present application, when the room temperature exceeding time period is predicted, a preset exceeding room temperature threshold value, a running time period of the chilled water accumulation, an average indoor and outdoor temperature from starting up to a current time of the host, a current indoor and outdoor temperature, an FCU starting number, a chilled water flow rate of the host and a chilled water supply temperature of the chilled water may be input into a trained room temperature exceeding model, and the room temperature exceeding time period is predicted.

The preset exceeding room temperature threshold is a preset room temperature exceeding threshold, for example, 1 ℃.

In S206, the power-on time of the host is obtained by adding the time of power-off to the time of exceeding the standard of the room temperature.

In S207, an operation tuning strategy of the air conditioner cold station is generated based on the cold accumulation start time, the shutdown time, and the startup time.

The operation optimizing strategy comprises the steps of controlling the host to start cold accumulation at the cold accumulation starting time, controlling the host to be turned off at the turning-off time, and controlling the host to be turned on again at the turning-on time.

Specifically, when the control host machine is shut down at the shutdown time, the cooling pump and the cooling tower are also shut down. The chilled water is stored to a temperature set value by utilizing lower electricity price between the cold storage starting time and the shutdown time; and the refrigerating capacity in the chilled water is utilized to bear the air conditioning load between the shutdown time and the startup time, the chilled water cold accumulation capacity is exhausted at the startup time, the room temperature exceeds the standard, and the station-side air conditioning system (i.e. the host) needs to be restarted.

In some embodiments of the present application, after generating the operation tuning strategy of the air conditioning cold station, the method further includes: judging whether an operation optimization strategy of an air conditioner cold station is effective or not based on the exceeding room temperature duration, the cold accumulation duration of a host machine, the power load and the energy consumption; and if the operation optimization strategy is effective, issuing the operation optimization strategy of the air-conditioning cold station to the air-conditioning cold station through the edge gateway.

Optionally, when determining whether the operation optimization strategy of the air conditioner cold station is effective based on the room temperature exceeding time length, the cold storage time length, the electricity load and the energy consumption of the host, the method can determine by the following steps:

step A: and calculating the cold accumulation additional cost based on the electricity price, the cold accumulation duration of the host, the electricity load and the energy consumption.

And (B) step (B): and calculating the saving cost based on the time length of exceeding the standard of the room temperature, the power load and the power price of the host.

Step C: and dividing the extra charge of cold accumulation by the saving charge to obtain the saving rate.

Step D: judging whether the section rate is larger than a preset section rate.

Step E: if yes, judging that the operation optimization strategy of the air conditioner cold station is effective.

Step F: otherwise, the operation optimizing strategy of the air conditioner cold station is invalid.

And when the operation tuning strategy of the air conditioner cold station is judged to be effective, the effective operation tuning strategy is issued to the edge gateway. When the operation tuning strategy of the air-conditioning cold station is judged to be invalid, the operation tuning strategy of the air-conditioning cold station which is not optimal currently is represented, at the moment, the operation tuning strategy of the invalid air-conditioning cold station is not issued to the edge gateway, and information can be sent to the edge gateway, wherein the information indicates that the operation tuning strategy of the air-conditioning cold station which is not optimal currently.

Alternatively, the formula for calculating the extra charge for cold accumulation may be:

wherein cost is extra charge of cold accumulation, t ₁ Cold accumulation start time, t ₂ For the shutdown time, P _t For the energy consumption of the host at the time t, Q _2,t The price is the electricity price for the electricity load of the host at the time t.

The formula for calculating the cost savings may be:

wherein, save is cost, t ₃ Is the starting time.

It should be appreciated that the above-described cold load prediction model, host electrical load prediction model, host COP model, and room temperature superscalar model are all trained models in advance. The cold load prediction model is used to predict future cold loads. The power load of the host is predicted by the power load prediction model of the host so as to calculate t for the follow-up ₁ ～t ₂ Cold accumulation overhead and t in cold accumulation period ₂ ～t ₃ The savings in the shutdown period are prepared. The host COP model is obtained by modeling by taking a host COP as a modeling object, taking 5 influence factors as characteristics of a load factor of the host, inlet temperatures of chilled water and cooling water and flow rates of the chilled water and the cooling water, and using an AI model. The room temperature standard exceeding model is constructed by constructing an end-to-end model of the room temperature standard exceeding time of the room temperature after the system is shut down, and is used for evaluating the cold storage duration and the corresponding startup time.

In addition, the cold load prediction model, the main engine power consumption load prediction model, the main engine COP model and the room temperature standard exceeding model are all constructed according to the following procedures:

training set and test set division, machine learning model construction, super-parameter optimization, model fitting effect evaluation

In an embodiment of the present application, based on fig. 2 and fig. 3, the above air conditioning cold station operation tuning method based on cloud edge coordination may be: firstly, acquiring current indoor and outdoor temperature and current working condition data of an air conditioner cold station through an edge gateway, and determining the shutdown time of a host machine based on the current time and peak-valley electricity price. Then, based on the cold load prediction model, the cold load Q is predicted ₁ The method comprises the steps of carrying out a first treatment on the surface of the Based on a host electricity load prediction model, predicting the electricity load Q of the host _2,t The method comprises the steps of carrying out a first treatment on the surface of the Predicting COP based on the host COP model; calculating the supply load Q of the host ₃ . Thereafter, the supply load Q is determined ₃ Whether or not it is greater than the cooling load Q ₁ If yes, updating the chilled water inlet temperature T of the host ₂ And calculating the energy consumption P of the host, and updating the current time t. Then based on the updated chilled water inlet temperature of the hostAnd updated current time t ^* Re-predicting Q ₁ 、Q _2,t COP and Q ₃ Then judge Q again ₃ Whether or not it is greater than Q ₁ If at this time Q ₃ Is less than or equal to Q ₁ Determining the cold accumulation time length according to the difference between the final time and the initial time, and combining the shutdown time t ₂ The cold accumulation starting time t can be determined ₁ . Based on a room temperature standard exceeding model, predicting the room temperature standard exceeding time length, and at the shutdown time t ₂ On the basis of adding the room temperature exceeding time length, the starting time t can be determined ₃ . So far, the operation tuning strategy of the air conditioner cold station is generated. To ensure that the operation optimization strategy of the air conditioner cold station is effective, calculating t ₁ ～t ₂ Cold accumulation overhead and t in cold accumulation period ₂ ～t ₃ And (3) saving the cost in the shutdown time period, comparing the ratio of the extra cold accumulation cost to the saved cost with a preset section rate, further judging whether the generated operation tuning strategy of the air conditioner cold station is effective, and if so, issuing the operation tuning strategy of the air conditioner cold station to the edge gateway.

According to the embodiment, remote regulation and control of equipment in the air conditioner cold station are realized through a digital means, an AI optimization model (namely a cold load prediction model, a host power load prediction model, a host COP model and a room temperature standard exceeding model) is established according to equipment characteristics and load characteristics of different users, and the equipment in the air conditioner can be regulated and controlled at high frequency, so that the aim of efficiently cooling according to needs is fulfilled, and the effect of improving efficiency and reducing cost is realized.

It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic of each process, and should not limit the implementation process of the embodiment of the present application in any way.

Corresponding to the cloud-edge-collaboration-based air-conditioning cold station operation tuning method described in the above embodiments, fig. 4 shows a block diagram of the cloud-edge-collaboration-based air-conditioning cold station operation tuning device provided in the embodiment of the present application, and for convenience of explanation, only the portions relevant to the embodiments of the present application are shown.

Referring to fig. 4, the cloud edge collaboration-based air conditioning cold station operation tuning device in the embodiment of the present application may include an obtaining module 401, a first determining module 402, a first calculating module 403, a second determining module 404, a second calculating module 405, a third determining module 406, and a generating module 407.

The acquiring module 401 is configured to acquire current indoor and outdoor temperatures and current working condition data of an air conditioner cold station.

The first determining module 402 is configured to determine, based on the current time and the peak-to-valley electricity price, a valley price turning time that is the latest in the future, and determine the valley price turning time as a shutdown time of a host in the air conditioner cold station, where the valley price turning time is a time when the electricity price is changed from low to high.

The first calculating module 403 is configured to predict a cold storage duration of the host based on the current indoor and outdoor temperatures and current working condition data of the air conditioner cold station.

The second determining module 404 is configured to subtract the cold accumulation duration from the shutdown time to obtain a cold accumulation start time of the host.

The second calculation module 405 is configured to predict a time period when the room temperature exceeds the standard based on a preset exceeding room temperature threshold, a current indoor and outdoor temperature, and current working condition data of the air conditioner cold station.

And a third determining module 406, configured to add the time of shutdown to the time of exceeding the standard of the room temperature to obtain the time of startup of the host.

A generating module 407, configured to generate an operation tuning policy of the air conditioner cold station based on the cold storage start time, the shutdown time and the startup time; the operation optimizing strategy comprises the steps of controlling the host to start cold accumulation at the cold accumulation starting time, controlling the host to be turned off at the turning-off time, and controlling the host to be turned back on at the turning-on time.

Optionally, the air conditioning cold station comprises a cold water host, a fan coil unit FCU and a freezing pump; the current working condition data comprise the chilled water inlet and outlet temperature of the host machine and the chilled water flow of the chilled pump.

The first calculation module 403 is specifically configured to input the current time, the current day type, the current indoor and outdoor temperatures, and the current working condition data of the FCU into a cold load prediction model, and predict a cold load required at the next time; calculating the supply load of the host machine at the next moment based on the temperature of the chilled water inlet and outlet of the host machine and the flow of the chilled water; judging whether the supply load is larger than the cooling load; if yes, updating the chilled water inlet temperature of the host based on the supply load and the cold load, updating the current moment, and jumping to input the current moment, the current day type, the current indoor and outdoor temperatures and the current working condition data of the FCU into a cold load prediction model to obtain the required cold load; otherwise, subtracting the difference value of the initial moment from the final moment to determine the cold accumulation duration; the initial time is the initial current time, and the final time is the current time after the last update.

Optionally, the first calculation module 403 is specifically configured to update the chilled water inlet temperature of the host based on the following formula:

/>

Wherein,to update the chilled water inlet temperature of the host, T ₂ To update the chilled water inlet temperature of the host before the update, Q ₃ To supply the load, Q ₁ For the cold load, N is the chilled water volume.

Optionally, the air conditioning cold station further comprises a cooling pump, and the current working condition data further comprises cooling water flow of the cooling pump, load rate of the host, cooling water inlet temperature, starting number of FCU, set temperature and fan gear.

The first calculation module 403 is specifically configured to input the current time, the current day type, the current indoor and outdoor temperatures, and the current FCU operating mode data into the host power consumption load prediction model, and predict the power consumption load of the host at the next time; and inputting the load rate, the chilled water flow, the cooling water flow, the chilled water inlet temperature and the cooling water inlet temperature into a host computer COP model, and predicting the energy efficiency ratio COP of the host computer at the next moment.

The first calculation module 403 is specifically configured to calculate the energy consumption of the host based on the supply load and COP.

The generating module 407 is specifically configured to determine whether an operation optimization strategy of the air conditioner cold station is effective based on the room temperature exceeding time period, the cold storage time period of the host, the power load and the energy consumption; and if the operation optimization strategy is effective, issuing the operation optimization strategy of the air-conditioning cold station to the air-conditioning cold station through the edge gateway.

Optionally, the generating module 407 is specifically configured to calculate the extra charge for cold accumulation based on the electricity price and the cold accumulation duration, the electricity load and the energy consumption of the host; calculating the saving cost based on the exceeding time of the room temperature, the electricity load of the host and the electricity price; dividing the extra charge of cold accumulation by the saving charge to obtain a saving rate; judging whether the section rate is greater than a preset section rate; if yes, judging that the operation optimization strategy of the air conditioner cold station is effective; otherwise, the operation optimizing strategy of the air conditioner cold station is invalid.

Optionally, the generating module 407 is specifically configured to calculate the cold accumulation additional cost based on the following formula:

wherein cost is extra charge of cold accumulation, T ₁ To cool storage start time, T ₂ For the shutdown time, P _t For the energy consumption of the host at the time t, Q _2,t The price is the electricity price for the electricity load of the host at the time t.

The generating module 407 is specifically configured to calculate the savings cost based on the following formula:

wherein, save cost, T ₃ Is the starting time.

Optionally, the air conditioning cold station comprises a cold water main machine, a refrigerating pump and a fan coil unit FCU; the current working condition data comprise chilled water supply temperature of the host machine and chilled water flow of the chilled pump; number of FCU open.

The second calculation module 405 is specifically configured to input a preset standard exceeding room temperature threshold, a running duration of running total of chilled water, an average indoor and outdoor temperature of the host from startup to a current moment, a current indoor and outdoor temperature, an FCU opening number, a chilled water flow rate of the host, and a chilled water supply temperature into a trained room temperature standard exceeding model, and predict a room temperature standard exceeding duration.

It should be noted that, because the content of information interaction and execution process between the above devices/units is based on the same concept as the method embodiment of the present application, specific functions and technical effects thereof may be referred to in the method embodiment section, and will not be described herein again.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.

The embodiment of the application also provides an electronic device, referring to fig. 5, the electronic device 500 may include: at least one processor 510, a memory 520, and a computer program stored in the memory 520 and executable on the at least one processor 510, the processor 510, when executing the computer program, implementing the steps of any of the various method embodiments described above, such as S201 to S207 in the embodiment shown in fig. 2. Alternatively, the processor 510 may perform the functions of the modules/units of the apparatus embodiments described above, such as the functions of the modules 401 to 407 shown in fig. 4, when executing the computer program.

By way of example, a computer program may be partitioned into one or more modules/units that are stored in memory 520 and executed by processor 510 to complete the present application. The one or more modules/units may be a series of computer program segments capable of performing particular functions for describing the execution of the computer program in the electronic device 500.

It will be appreciated by those skilled in the art that fig. 5 is merely an example of an electronic device and is not meant to be limiting, and may include more or fewer components than shown, or may combine certain components, or different components, such as input-output devices, network access devices, buses, etc.

The processor 510 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 520 may be an internal memory unit of the electronic device, or may be an external memory device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card), or the like. The memory 520 is used to store the computer program and other programs and data required by the electronic device. The memory 520 may also be used to temporarily store data that has been output or is to be output.

The bus may be an industry standard architecture (Industry Standard Architecture, ISA) bus, an external device interconnect (Peripheral Component, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, the buses in the drawings of the present application are not limited to only one bus or one type of bus.

The cloud-edge-collaboration-based air conditioner cold station operation optimization method can be applied to electronic equipment such as computers, wearable equipment, vehicle-mounted equipment, tablet computers, notebook computers, netbooks, personal digital assistants (personal digital assistant, PDAs), augmented reality (augmented reality, AR)/Virtual Reality (VR) equipment, mobile phones and the like, and the specific types of the electronic equipment are not limited.

The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the steps in each embodiment of the air conditioner cold station operation optimizing method based on cloud edge cooperation when being executed by a processor.

The embodiment of the application provides a computer program product, which enables the mobile terminal to realize the steps in each embodiment of the air conditioner cold station operation optimizing method based on cloud edge cooperation when being executed when the computer program product runs on the mobile terminal.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application implements all or part of the flow of the method of the above embodiments, and may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a camera device/electronic apparatus, a recording medium, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a U-disk, removable hard disk, magnetic or optical disk, etc.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other manners. For example, the apparatus/network device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.

Claims

1. An air conditioner cold station operation optimizing method based on cloud edge cooperation is characterized by comprising the following steps:

2. The method of claim 1, wherein the air conditioning cold station comprises a cold water main, a fan coil unit FCU, and a cryopump; the current working condition data comprise the chilled water inlet and outlet temperature of the host and the chilled water flow of the chilled pump;

judging whether the supply load is greater than the cooling load;

3. The method of claim 2, wherein said updating the chilled water inlet temperature of the host based on the supply load and the cooling load comprises:

4. The method of claim 2, wherein the air conditioning cold station further comprises a cooling pump, the current operating condition data further comprises cooling water flow rate of the cooling pump, load factor of the host machine and cooling water inlet temperature, and the number of FCUs opened, set temperature and fan gear;

5. The method of claim 4, wherein the determining whether the operation tuning strategy of the air conditioning cold station is valid based on the room temperature exceeding time period and the cold storage time period, the power load, and the energy consumption of the host machine comprises:

judging whether the section rate is larger than a preset section rate;

6. The method of claim 5, wherein calculating the cold storage premium based on the electricity price and the cold storage time period, the electricity load, and the energy consumption of the host machine comprises:

calculating the cold storage extra cost based on the following formula:

wherein cost is the extra charge for cold accumulation，T ₁ For the cold accumulation start time, T ₂ For the shutdown time, P _t For the energy consumption of the host at the time t, Q _2,t The power utilization load of the host at the time t is provided, and the price is electricity price;

the savings cost is calculated based on the following formula:

wherein, save is the cost, T ₃ And the starting time is the starting time.

7. The method of claim 1, wherein the air conditioning cold station comprises a cold water main, a cryopump, and a fan coil unit FCU; the current working condition data comprise chilled water supply temperature of the host machine and chilled water flow of the chilled pump; the number of FCU openings;

8. Air conditioner cold station operation optimizing device based on cloud limit is cooperated, its characterized in that includes:

9. An electronic device comprising a memory and a processor, the memory having stored therein a computer program executable on the processor, wherein the processor implements the method of any of claims 1 to 7 when the computer program is executed.

10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the method according to any one of claims 1 to 7.