阅读说明：本技术 中央空调冷站群控节能控制方法、装置、设备及存储介质 (Central air-conditioning cold station group control energy-saving control method, device, equipment and storage medium ) 是由 胡佳 杨瑞 谭江浩 董海雷 李申 于 2021-08-31 设计创作，主要内容包括：本发明公开了一种中央空调冷站群控节能控制方法、装置、设备及存储介质,其中,所述方法包括：获取中央空调的总能耗预测模型,所述总能耗预测模型用于表征中央空调的总能耗与中央空调冷站设备运行参数之间的关系；获取中央空调冷站的负荷预测模型,以确定预测时刻中央空调冷站的预测负荷；将所述预测负荷发送至中央空调冷站设备,得到中央空调冷站设备的约束条件；根据所述总能耗预测模型和所述约束条件,基于预设优化算法确定中央空调冷站设备的最优运行参数,以使中央空调的总能耗满足优化条件；并使中央空调冷站设备根据最优运行参数运行。上述方法通过考虑中央空调冷站负荷和能耗得到设备的最佳运行参数,以确保中央空调高效节能运行。(The invention discloses a method, a device, equipment and a storage medium for controlling the energy conservation of a central air-conditioning cold station group control, wherein the method comprises the following steps: acquiring a total energy consumption prediction model of the central air conditioner, wherein the total energy consumption prediction model is used for representing the relation between the total energy consumption of the central air conditioner and the operation parameters of the central air conditioner cold station equipment; acquiring a load prediction model of the central air-conditioning cold station to determine the predicted load of the central air-conditioning cold station at the prediction moment; sending the predicted load to central air-conditioning cold station equipment to obtain a constraint condition of the central air-conditioning cold station equipment; determining the optimal operation parameters of the central air-conditioning cold station equipment based on a preset optimization algorithm according to the total energy consumption prediction model and the constraint conditions so that the total energy consumption of the central air-conditioning meets the optimization conditions; and the central air-conditioning cold station equipment is operated according to the optimal operation parameters. The method obtains the optimal operation parameters of the equipment by considering the load and the energy consumption of the cold station of the central air conditioner so as to ensure the efficient and energy-saving operation of the central air conditioner.)

1. A group control energy-saving control method for a central air-conditioning cold station is characterized by comprising the following steps:

acquiring a total energy consumption prediction model of a central air conditioner, wherein the total energy consumption prediction model is used for representing the relation between the total energy consumption of the central air conditioner and the operation parameters of the central air conditioner cold station equipment;

acquiring a load prediction model of a central air-conditioning cold station to determine the predicted load of the central air-conditioning cold station at the prediction moment;

sending the predicted load to the central air-conditioning cold station equipment to obtain a constraint condition of the central air-conditioning cold station equipment;

determining the optimal operation parameters of the central air-conditioning cold station equipment based on a preset optimization algorithm according to the total energy consumption prediction model and the constraint conditions so that the total energy consumption of the central air conditioner meets the optimization conditions; and enabling the central air-conditioning cold station equipment to operate according to the optimal operation parameters.

2. The method for controlling energy conservation of a central air-conditioning cold station group control system as claimed in claim 1, wherein the obtaining of the total energy consumption prediction model of the central air-conditioning system is specifically as follows:

acquiring historical data of the central air-conditioning cold station equipment, wherein the central air-conditioning cold station equipment comprises a water chilling unit, a chilled water pump, a cooling water pump and a cooling tower;

establishing an initial prediction submodel of each device of the central air-conditioning cold station;

performing model training on the initial prediction submodel by using the historical data to generate an energy consumption prediction model corresponding to each device of the central air-conditioning cold station;

and determining a total energy consumption prediction model of the central air conditioner based on the energy consumption prediction models corresponding to the devices of the central air conditioner cold station.

3. The method as claimed in claim 2, wherein the historical data of the central air-conditioning cold station equipment comprises:

the water inlet temperature of cooling water, the water supply temperature of chilled water and the load of the water chilling unit;

the chilled water pump rotation speed ratio and the chilled water pump flow rate of the chilled water pump;

a cooling water pump flow ratio of the cooling water pump;

the cooling water flow of the cooling tower, the water inlet temperature of the cooling tower and the air volume ratio of the fan of the cooling tower.

4. The method according to claim 1, wherein the obtaining of the load prediction model of the central air-conditioning cold station is performed to determine a predicted load of the central air-conditioning cold station at a predicted time, and specifically includes:

acquiring indoor and outdoor meteorological information at a predicted moment;

and determining the predicted load of the central air-conditioning cold station at the predicted moment according to the load prediction model and the indoor and outdoor meteorological information.

5. The method for controlling energy conservation of a central air-conditioning cold station group control system as claimed in claim 1, wherein the optimization condition of meeting the total energy consumption of the central air-conditioning system comprises:

the total energy consumption satisfies a minimization condition; or the like, or, alternatively,

the total energy consumption is less than a preset energy consumption threshold.

6. The utility model provides a central air conditioning cold station crowd accuse energy-saving control device which characterized in that includes:

the energy consumption prediction module is used for obtaining a total energy consumption prediction model of the central air conditioner, and the total energy consumption prediction model is used for representing the relation between the total energy consumption of the central air conditioner and the operation parameters of the central air conditioner cold station equipment;

the load prediction module is used for acquiring a load prediction model of the central air-conditioning cold station so as to determine the predicted load of the central air-conditioning cold station at the prediction moment;

the constraint determining module is used for sending the predicted load to the central air-conditioning cold station equipment to obtain a constraint condition of the central air-conditioning cold station equipment;

the optimization control module is used for determining the optimal operation parameters of the central air-conditioning cold station equipment based on a preset optimization algorithm according to the total energy consumption prediction model and the constraint conditions so as to enable the total energy consumption of the central air conditioner to meet the optimization conditions; and enabling the central air-conditioning cold station equipment to operate according to the optimal operation parameters.

7. The device as claimed in claim 6, wherein the load prediction module is specifically configured to:

acquiring indoor and outdoor meteorological information at a predicted moment;

and determining the predicted load of the central air-conditioning cold station at the predicted moment according to the load prediction model and the indoor and outdoor meteorological information.

8. The device for controlling the energy conservation of the cluster control of the cold stations of the central air conditioners as claimed in claim 6, wherein the optimization condition of the total energy consumption of the central air conditioners comprises:

the total energy consumption satisfies a minimization condition; or the like, or, alternatively,

the total energy consumption is less than a preset energy consumption threshold.

9. An electronic device, comprising a plurality of processors, a memory, and a computer program stored in the memory and operable on the processors, wherein the processors implement the method for controlling energy conservation for a cluster of central air-conditioning cold stations according to any one of claims 1 to 5 when executing the computer program.

10. A computer storage medium, characterized in that the storage medium has computer instructions for executing the method for controlling energy conservation of a central air-conditioning cold station cluster as claimed in any one of claims 1 to 5.

Technical Field

The invention relates to the technical field of central air conditioner operation control, in particular to a central air conditioner cold station group control energy-saving control method, device, equipment and storage medium.

Background

The control of the cold station of the central air conditioner refers to the centralized optimization control of all devices (such as a refrigerator, a freezing pump, a cooling tower fan and the like) in the cold station of the central air conditioning system.

The conventional method for controlling the cold station of the central air-conditioning system mainly adjusts some equipment in a water pump, a unit or a cooling tower, and does not consider the coupling among the operating parameters of the equipment, so that the energy consumption of the central air-conditioning system cannot be effectively reduced.

Disclosure of Invention

In order to overcome the technical problems in the prior art, the invention provides a method, a device, equipment and a storage medium for controlling the group control energy conservation of a central air-conditioning cold station.

In a first aspect, the present invention provides a group control energy-saving control method for a central air-conditioning cold station, including:

acquiring a total energy consumption prediction model of a central air conditioner, wherein the total energy consumption prediction model is used for representing the relation between the total energy consumption of the central air conditioner and the operation parameters of the central air conditioner cold station equipment;

acquiring a load prediction model of a central air-conditioning cold station to determine the predicted load of the central air-conditioning cold station at the prediction moment;

sending the predicted load to the central air-conditioning cold station equipment to obtain a constraint condition of the central air-conditioning cold station equipment;

determining the optimal operation parameters of the central air-conditioning cold station equipment based on a preset optimization algorithm according to the total energy consumption prediction model and the constraint conditions so that the total energy consumption of the central air conditioner meets the optimization conditions; and enabling the central air-conditioning cold station equipment to operate according to the optimal operation parameters.

Optionally, the obtaining of the total energy consumption prediction model of the central air conditioner specifically includes:

acquiring historical data of the central air-conditioning cold station equipment, wherein the central air-conditioning cold station equipment comprises a water chilling unit, a chilled water pump, a cooling water pump and a cooling tower;

establishing an initial prediction submodel of each device of the central air-conditioning cold station;

performing model training on the initial prediction submodel by using the historical data to generate an energy consumption prediction model corresponding to each device of the central air-conditioning cold station;

and determining a total energy consumption prediction model of the central air conditioner based on the energy consumption prediction models corresponding to the devices of the central air conditioner cold station.

Optionally, the historical data of the central air-conditioning cold station device includes:

the water inlet temperature of cooling water, the water supply temperature of chilled water and the load of the water chilling unit;

the chilled water pump rotation speed ratio and the chilled water pump flow rate of the chilled water pump;

a cooling water pump flow ratio of the cooling water pump;

the cooling water flow of the cooling tower, the water inlet temperature of the cooling tower and the air volume ratio of the fan of the cooling tower.

Optionally, the obtaining a load prediction model of the central air-conditioning cold station to determine a predicted load of the central air-conditioning cold station at a predicted time specifically includes:

acquiring indoor and outdoor meteorological information at a predicted moment;

and determining the predicted load of the central air-conditioning cold station at the predicted moment according to the load prediction model and the indoor and outdoor meteorological information.

Optionally, the central air conditioner's total energy consumption satisfying the optimization condition includes:

the total energy consumption satisfies a minimization condition; or the like, or, alternatively,

the total energy consumption is less than a preset energy consumption threshold.

In a second aspect, the present invention provides a group control energy-saving control device for a central air-conditioning cold station, comprising:

the energy consumption prediction module is used for obtaining a total energy consumption prediction model of the central air conditioner, and the total energy consumption prediction model is used for representing the relation between the total energy consumption of the central air conditioner and the operation parameters of the central air conditioner cold station equipment;

the load prediction module is used for acquiring a load prediction model of the central air-conditioning cold station so as to determine the predicted load of the central air-conditioning cold station at the prediction moment;

the constraint determining module is used for sending the predicted load to the central air-conditioning cold station equipment to obtain a constraint condition of the central air-conditioning cold station equipment;

the optimization control module is used for determining the optimal operation parameters of the central air-conditioning cold station equipment based on a preset optimization algorithm according to the total energy consumption prediction model and the constraint conditions so as to enable the total energy consumption of the central air conditioner to meet the optimization conditions; and enabling the central air-conditioning cold station equipment to operate according to the optimal operation parameters.

Optionally, the load prediction module is specifically configured to:

acquiring indoor and outdoor meteorological information at a predicted moment;

and determining the predicted load of the central air-conditioning cold station at the predicted moment according to the load prediction model and the indoor and outdoor meteorological information.

Optionally, the central air conditioner's total energy consumption satisfying the optimization condition includes:

the total energy consumption satisfies a minimization condition; or the like, or, alternatively,

the total energy consumption is less than a preset energy consumption threshold.

In a third aspect, the present invention provides an electronic device, which includes a plurality of processors, a memory, and a computer program stored in the memory and executable on the processors, wherein the processors implement the method for controlling energy conservation in a cluster of central air-conditioning cold stations according to the first aspect when executing the computer program.

In a fourth aspect, the present invention provides a computer storage medium, where the computer storage medium has computer instructions for executing the method for controlling energy conservation in a central air-conditioning cold station cluster.

Compared with the prior art, the invention has the beneficial effects that:

according to the central air-conditioning cold station group control energy-saving control method, device, equipment and storage medium, the influence of the cold load of the central air-conditioning cold station on energy consumption is comprehensively considered, the energy consumption prediction model and the load prediction model are correspondingly established, the equipment operation parameters which enable the central air-conditioning to have the lowest energy consumption under the load constraint condition are found through the preset optimization algorithm, the central air-conditioning cold station is ensured to operate efficiently, and the optimal energy-saving state is achieved.

Drawings

In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a schematic flow chart of a group control energy-saving control method for a central air-conditioning cold station according to an embodiment of the present invention;

fig. 2 is a schematic structural diagram of a group control energy-saving control device for a central air-conditioning cold station according to an embodiment of the present invention;

fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It should be understood that the step numbers used herein are for convenience of description only and are not intended as limitations on the order in which the steps are performed.

It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

The terms "comprises" and "comprising" indicate the presence of the described 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.

The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.

As shown in fig. 1, an embodiment of the present invention provides a group control energy-saving control method for a central air-conditioning cold station, including the following steps:

s1: and acquiring a total energy consumption prediction model of the central air conditioner, wherein the total energy consumption prediction model is used for representing the relation between the total energy consumption of the central air conditioner and the operation parameters of the central air conditioner cold station equipment.

For large buildings, the energy consumption of the central air-conditioning system can account for 40% of the total energy consumption of the building, the central air-conditioning cold station is used as the core of the central air-conditioning system and is mainly used for providing chilled water of the central air conditioner for the building, and the energy consumption of the central air-conditioning cold station accounts for about 60% of the energy consumption of the whole central air-conditioning equipment, so that the research on the energy-saving optimized operation control strategy of the central air-conditioning cold station has important practical significance, and for the existing central air-conditioning equipment, the optimized control of the central air-conditioning cold station directly influences the energy efficiency of the whole central air-conditioning system under the condition of not changing the original equipment.

The central air-conditioning cold station generally comprises four types of equipment, namely a water chilling unit (chiller), a cooling water pump, a chilled water pump and a cooling tower, wherein the energy consumption of the central air-conditioning cold station is mainly generated by the four types of equipment, the energy consumption of other equipment is changed due to the change of the operation condition of each piece of equipment, and the four types of equipment are mutually coupled to influence the total energy consumption of the central air conditioner.

For the evaluation of the running efficiency of the central air-conditioning cold station, the total cooling capacity provided in a certain period of time is generally divided by the total energy consumption, so that the running parameters of four types of equipment are optimized and controlled by comprehensively considering two factors of the cooling load capacity and the total energy consumption of the central air-conditioning cold station, and the high-efficiency running of the central air-conditioning cold station is realized.

S2: and acquiring a load prediction model of the central air-conditioning cold station to determine the predicted load of the central air-conditioning cold station at the prediction moment.

The load of the central air conditioner is, specifically, a cooling load amount, which is heat that must be taken away from the interior of the building by the central air conditioner in order to maintain the indoor temperature required for the building, or a cooling amount that needs to be supplied to the building at a certain time.

The load prediction model can be used for predicting the load condition of a building at a certain future time, the load of the building is usually related to indoor and outdoor meteorological information, a building structure and equipment and personnel in the building, under the general condition, the building structure is usually fixed and unchanged, and the equipment and the personnel in the building have certain rules and can be circulated, so that the main factors influencing the temperature change of the building are indoor and outdoor meteorological parameters, particularly including indoor and outdoor temperature and indoor and outdoor humidity, and therefore, when the cold load of a central air-conditioning cold station at a certain target time/time period is predicted, calculation needs to be carried out by combining the indoor and outdoor meteorological parameters.

S3: and sending the predicted load to the central air-conditioning cold station equipment to obtain the constraint condition of the central air-conditioning cold station equipment.

Specifically, load values obtained through prediction are respectively sent to the water chilling unit, the cooling water pump, the chilled water pump and the cooling tower, allowable operation ranges of corresponding operation parameters of the water chilling unit, the cooling water pump, the chilled water pump and the cooling tower are obtained through calculation, constraint conditions of four types of equipment of the central air conditioner are further determined, the cold station of the central air conditioner is ensured to operate stably, and the target refrigerating capacity is achieved.

S4: determining the optimal operation parameters of the central air-conditioning cold station equipment based on a preset optimization algorithm according to the total energy consumption prediction model and the constraint conditions so that the total energy consumption of the central air conditioner meets the optimization conditions; and enabling the central air-conditioning cold station equipment to operate according to the optimal operation parameters.

And selecting different operation parameter combinations of the central air-conditioning cold station equipment to correspondingly obtain the total energy consumption of the central air-conditioning under the constraint condition of the central air-conditioning cold station equipment based on the predicted load, and when the total energy consumption meets the optimization condition, obtaining the operation parameter combination meeting the constraint condition as the optimal energy efficiency operation parameter of the central air-conditioning, and enabling the central air-conditioning cold station equipment to operate according to the optimal energy efficiency operation parameter.

Specifically, the central air conditioner satisfying the optimization condition for total energy consumption includes: the total energy consumption meets the minimization condition; or the total energy consumption is less than a preset energy consumption threshold.

In one embodiment, the method for obtaining the total energy consumption prediction model of the central air conditioner specifically comprises the following steps: acquiring historical data of the central air-conditioning cold station equipment, wherein the central air-conditioning cold station equipment comprises a water chilling unit, a chilled water pump, a cooling water pump and a cooling tower; establishing an initial prediction submodel of each device of the central air-conditioning cold station; performing model training on the initial prediction submodel by using the historical data to generate an energy consumption prediction model corresponding to each device of the central air-conditioning cold station; and determining a total energy consumption prediction model of the central air conditioner based on the energy consumption prediction models corresponding to the devices of the central air conditioner cold station.

In this embodiment, an initial prediction sub-model may be established through a machine learning algorithm or a deep learning algorithm, and this process usually requires a large amount of data for training, so that in this embodiment, historical data of the central air-conditioning cold station device is extracted from the database to train the initial prediction sub-models of four types of devices, specifically, the historical data recorded in the database includes a chiller unit operation parameter, a chilled water pump operation parameter, a cooling tower operation parameter, a meteorological parameter, power data of each device, and cooling capacity or cooling capacity table data of the central air-conditioning cold station; the data listed above is only an example, and does not represent that the database in this embodiment includes only the data, the amount of data extracted in this embodiment should be as large and detailed as possible, and at least one complete cooling period is included as much as possible, the more data used for training, the better the training effect, meanwhile, this embodiment does not specifically limit the data included in the database, and the application depends on the need of establishing a prediction model.

Specifically, the operation parameters of the water chilling unit comprise the temperature of cooling water inlet and outlet water, the temperature of chilled water supply, the load of the water chilling unit and the like; the operation parameters of the chilled water pump comprise the rotating speed ratio of the chilled water pump, the flow rate of the chilled water pump and the like; the operation parameters of the cooling water pump comprise the flow ratio of the cooling water pump; the operation parameters of the cooling tower comprise the heat discharge quantity of the cooling tower, the air flow, the cooling water flow, the water inlet temperature of the cooling tower, the air quantity ratio of a fan of the cooling tower and the like.

In this embodiment, historical data corresponding to the water chilling unit, the cooling water pump, the chilled water pump and the cooling tower in the database are respectively used as training data to train the initial prediction submodels corresponding to the four types of equipment, so as to construct four independent energy consumption prediction submodels.

For example, historical data such as output load, power and cooling water inlet and outlet temperature of a water chilling unit are extracted from a database, the power of the water chilling unit is used as output, corresponding parameters of other historical data are set as input layers, and corresponding training samples and labels are established; and then setting proper hidden layer number, neuron number, activation function and learning rate, and calculating an initial prediction submodel of the water chilling unit to obtain an energy consumption prediction submodel of the water chilling unit, wherein the energy consumption prediction submodel is used for representing the relation between the energy consumption of the water chilling unit and the operation parameters of water chilling unit equipment.

It should be noted that the specific training and construction processes of the energy consumption prediction submodels corresponding to the cooling water pump, the chilled water pump, and the cooling tower are consistent with the energy consumption prediction submodel of the water chilling unit, and are not described herein again.

Based on the energy consumption prediction submodels of the four types of equipment, a total energy consumption prediction model of the central air conditioner can be determined; specifically, the total energy consumption prediction model of the central air conditioner can be obtained by weighted summation of energy consumption prediction submodels of four types of equipment, namely: respectively setting weight coefficients of energy consumption prediction submodels of the four types of equipment, multiplying output results of the energy consumption prediction submodels by the corresponding weight coefficients, and summing up to obtain a total energy consumption prediction model of the central air conditioner; in some embodiments, the weight coefficient of the energy consumption prediction submodel of the four types of devices may be directly set to 1: 1: 1: 1, or setting the weight coefficient according to the control requirement.

In one embodiment, the load prediction model of the central air-conditioning cold station is obtained to determine the predicted load of the central air-conditioning cold station at the prediction time, specifically: acquiring indoor and outdoor meteorological information at a predicted moment; and determining the predicted load of the central air-conditioning cold station at the predicted moment according to the load prediction model and the indoor and outdoor meteorological information.

It can be understood that the historical data of the central air-conditioning cold station in the data also comprises historical information corresponding to historical loads, including indoor and outdoor meteorological information, building structure information, building internal heating equipment information, personnel information and the like; specifically, an initial load prediction model can be established by using an artificial intelligence algorithm, historical cold load data in a database is used as a label, other data such as indoor and outdoor meteorological information are used as input training samples, network model parameters such as the number of hidden layers, the number of neurons and the learning rate are set, then the initial load prediction model training is carried out, and the load prediction model based on the neural network is established.

The artificial intelligent Network used by the load prediction model can be a Long short-term memory (LSTM) Network, a convolutional Neural Network or a Gated Recurrent Neural Network (GRN) or other deep Neural Network models so as to improve the prediction accuracy of the cold load value at the target time.

And after the predicted load at the target prediction time is obtained based on the load prediction model, the predicted load is respectively transmitted to four types of equipment of the central air-conditioning cold station, and the corresponding water chilling unit constraint condition, cooling water pump constraint condition, chilled water pump constraint condition and cooling tower constraint condition are obtained.

The constraint condition of the equipment is set to limit the control adjustment range of the equipment operation parameters and ensure the stable operation of the central air-conditioning cold station equipment; for example, the temperature of chilled water is limited by the refrigerating capacity of a central air conditioner, the temperature value cannot be too low to prevent the evaporator from frost cracking, but the temperature is too high to meet the requirement of the cooling load of the central air conditioner, and on the other hand, the range of the temperature of cooling water also needs to ensure that the condensing pressure is within the safety standard of pressure vessel equipment; for another example, the flow rate of the chilled water pump is required to be not too low so as to prevent the evaporator from frost cracking, and the flow rate of the cooling water pump is not too low so as to avoid influencing the heat exchange performance of the condenser, thereby influencing the operation energy efficiency of the chilled water pump or the cooling water pump; on the other hand, the flow rates of the chilled water pump and the cooling water pump are also limited by the performance of the apparatus.

In one embodiment, after energy consumption values of four types of equipment under different operation parameters are calculated according to energy consumption prediction submodels of a water chilling unit, a cooling water pump, a chilled water pump and a cooling tower respectively, an equipment operation parameter which enables the total energy consumption of the central air conditioner to be the lowest under four constraint conditions based on a water chilling unit constraint condition, a cooling water pump constraint condition, a chilled water pump constraint condition and a cooling tower constraint condition is found through a preset optimization algorithm, wherein the total energy consumption of the central air conditioner is obtained through calculation of the energy consumption values of the four types of equipment.

Specifically, the preset optimization algorithm comprises a genetic algorithm, an ant colony algorithm or a particle swarm algorithm; the genetic algorithm is an optimized search technology developed on the basis of the principle that the excellent and bad deaths exist in the biological evolution process.

Illustratively, when a genetic algorithm is used for parameter optimization, the individual fitness value of the population can be calculated by using energy consumption prediction submodels of a water chilling unit, a cooling water pump, a chilled water pump and a cooling tower and corresponding constraint conditions, and whether the population meets the optimization standard or not is judged; if the optimal solution accords with the optimization standard, outputting the optimal individual and the optimal solution represented by the optimal individual, and finishing the optimization calculation; otherwise, executing selection, crossing and variation operations in the genetic algorithm to generate a new generation of population, calculating the individual fitness value of the new generation of population, and judging whether the optimization standard is satisfied or not; and circulating the steps until the optimization standard is met, and outputting the optimal individual and the optimal solution represented by the optimal individual.

In one embodiment, after obtaining the optimal operation parameters of the central air-conditioning cold station equipment at the target prediction time, the sensor can be used for obtaining the operation parameter information corresponding to the target prediction time, the actual central air-conditioning load at the target prediction time and the actual operation output parameters of the central air-conditioning cold station equipment are obtained through calculation according to the operation parameter information, and then the predicted central air-conditioning load, the predicted optimal operation parameters, the actual central air-conditioning load and the actual operation output parameters are uploaded to the database to be stored, so that a correction basis is provided for next optimization control, and the accuracy of the central air-conditioning operation control is further improved.

The central air-conditioning cold station group control energy-saving control method provided by the embodiment of the invention correspondingly establishes the energy consumption prediction model and the load prediction model by comprehensively considering the influence of the cold load of the central air-conditioning cold station and the energy consumption of equipment, finds the optimal equipment operation parameter which enables the total energy consumption of the central air-conditioning to be lowest under the load constraint condition through the optimization algorithm, and performs the group control energy-saving control of the central air-conditioning by using the optimal operation parameter to enable the energy consumption to be lowest, and simultaneously ensures the efficient operation of the central air-conditioning cold station to achieve the optimal energy-saving effect.

As shown in fig. 2, an embodiment of the present invention provides a central air-conditioning cold station group control energy-saving control apparatus, which includes an energy consumption prediction module 101, a load prediction module 102, a constraint determination module 103, and an optimization control module 104.

The energy consumption prediction module 101 is configured to obtain a total energy consumption prediction model of the central air conditioner, where the total energy consumption prediction model is used to represent a relationship between total energy consumption of the central air conditioner and an operation parameter of the central air conditioner cold station device.

The load prediction module 102 is configured to obtain a load prediction model of the central air-conditioning cold station to determine a predicted load of the central air-conditioning cold station at a prediction time.

The constraint determining module 103 is configured to send the predicted load to the central air-conditioning cold station device to obtain a constraint condition of the central air-conditioning cold station device.

The optimization control module 104 is configured to determine an optimal operation parameter of the central air-conditioning cold station device based on a preset optimization algorithm according to the total energy consumption prediction model and the constraint condition, so that the total energy consumption of the central air conditioner satisfies the optimization condition; and enabling the central air-conditioning cold station equipment to operate according to the optimal operation parameters.

In an embodiment, the energy consumption prediction module 101 is further specifically configured to: respectively taking corresponding historical data of a water chilling unit, a cooling water pump, a freezing water pump and a cooling tower in a database as training data, training initial prediction submodels corresponding to the four types of equipment, and establishing four independent energy consumption prediction submodels; and determining a total energy consumption prediction model of the central air conditioner based on the four energy consumption prediction submodels.

In one embodiment, the load prediction module 102 is further specifically configured to: historical cold load data in a database is used as a label, other data such as indoor and outdoor meteorological information and the like are used as input training samples, network model parameters such as the number of hidden layers, the number of neurons, the learning rate and the like are set, then model training is carried out, and a load prediction model is established.

It should be noted that the central air-conditioning cold station group control energy-saving control device portion provided in the embodiment of the present invention corresponds to the central air-conditioning cold station group control energy-saving control method portion in the embodiment of the present invention, and the description of the central air-conditioning cold station group control energy-saving control device portion specifically refers to the central air-conditioning cold station group control energy-saving control method portion, and is not repeated herein.

As shown in fig. 3, an electronic apparatus 800 according to an embodiment of the present invention includes a processor 801 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)802 or a program loaded from a storage section 808 into a Random Access Memory (RAM) 803; the processor 801 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like; the processor 801 may also include onboard memory for caching purposes. The processor 801 may comprise a single processing unit or a plurality of processing units for performing the different actions of the method flows according to embodiments of the present invention.

In the RAM803, various programs and data necessary for the operation of the system 800 are stored; the processor 801, the ROM802, and the RAM803 are connected to each other by a bus 804; the processor 801 performs various operations of the method flow according to the embodiment of the present invention by executing programs in the ROM802 and/or the RAM 803. Note that the programs may also be stored in one or more memories other than the ROM802 and RAM 803; the processor 801 may also perform various operations of method flows according to embodiments of the present invention by executing programs stored in the one or more memories.

According to an embodiment of the present invention, electronic device 800 may also include an input/output (I/O) interface 805, input/output (I/O) interface 805 also connected to bus 804; electronic device 800 may also include one or more of the following components connected to I/O interface 805: an input portion 806 including a keyboard, a mouse, and the like; an output section 807 including a signal such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 808 including a hard disk and the like; and a communication section 809 including a network interface card such as a LAN card, a modem, or the like. The communication section 809 performs communication processing via a network such as the internet. A drive 810 is also connected to the I/O interface 805 as necessary. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 810 as necessary, so that a computer program read out therefrom is mounted on the storage section 808 as necessary.

According to an embodiment of the invention, the method flow of the embodiment of the invention may be implemented as a computer software program. For example, embodiments of the invention include a computer program product comprising a computer program embodied on a computer-readable storage medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 809, and/or installed from the removable medium 811; the computer program, when executed by the processor 801, performs the above-described functions defined in the system of the embodiment of the present invention. The above described systems, devices, apparatuses, modules, units, etc. may be implemented by computer program modules according to embodiments of the present invention.

The present invention also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the present invention.

According to an embodiment of the present invention, the computer readable storage medium may be a non-volatile computer readable storage medium. Examples may include, but are not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

For example, according to embodiments of the present invention, a computer-readable storage medium may include the ROM802 and/or the RAM803 described above and/or one or more memories other than the ROM802 and the RAM 803.

While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.