Method and device for optimizing electricity consumption of air conditioning system, air conditioning system and computer readable storage medium

Abstract

The invention discloses a method for optimizing power consumption of an air conditioning system. The method comprises the steps that the electricity price of each time point in a future preset time period and the control parameter range of the air conditioning system at the first time point preset by a user are obtained; predicting a cooling capacity demand at each time point in a future preset time period; the total power of the air conditioning system in the future preset time period is calculated; and according to the electricity price, the control parameter range, the cooling capacity demand, the total power and a plurality of pre-established boundary conditions, target operation parameters of the air conditioning system at each time point are determined. According to the method, the cooling capacity requirement, the total power and the control parameter range, set by the user, of the first time point are combined, the multiple boundary conditions are matched for constraint, then the target operation parameters of the air conditioner system at each time point in the future preset time period can be more accurately determined, and therefore the effect of improving energy saving can be achieved. The invention further discloses a device for optimizing the electricity consumption of the air conditioning system, the air conditioning system and a computer readable storage medium.

Description

Technical Field

[0001] The present application relates to the technical field of electricity consumption in air-conditioning systems, for example, to a method and device for optimizing electricity consumption in air-conditioning systems, an air-conditioning system, and a computer-readable storage medium. Background Art

[0002] The power consumption of electrical equipment in large public buildings and office spaces is high, which will incur a lot of costs. Therefore, how to reduce the expenditure on electricity costs has become the main demand of users.

[0003] To this end, the relevant technology discloses a method for optimizing the temperature control load in a building for comprehensive comfort, including: constructing an optimization model for the temperature control load in a building for comprehensive comfort; wherein the temperature control load optimization model for the building takes the maximization of the comprehensive comfort as the goal, and takes the operating power constraint and the perceived comfort constraint of the temperature control load in the building as constraints, the comprehensive comfort is determined according to the perceived comfort and the economic comfort, the economic comfort is determined according to the electricity saving cost and economic compensation after the load in the building participates in electricity consumption regulation, and the perceived comfort is determined according to the indoor temperature of the indoor space in the building; the temperature control load optimization model in the building is solved based on the microhabitat particle swarm algorithm, and the optimal solution of the temperature control load optimization model in the building is used as the optimal power consumption of each temperature control load in each time period in a preset time period.

[0004] In the process of implementing the embodiments of the present disclosure, it is found that there are at least the following problems in the related art:

[0005] Although the relevant technology can improve the feasibility of temperature control loads in buildings participating in electricity consumption regulation and optimize the electricity consumption plan of each temperature control load, it belongs to a single group control / optimization strategy and has a poor energy-saving effect.

[0006] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present application, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0007] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical components or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

[0008] Embodiments of the present disclosure provide a method and device for optimizing power consumption of an air-conditioning system, an air-conditioning system, and a computer-readable storage medium to improve energy-saving effects.

[0009] In some embodiments, the method for optimizing electricity consumption of an air-conditioning system includes: obtaining the electricity price at each time point in a future preset time period, and the control parameter range for the air-conditioning system at the first time point preset by the user; predicting the cooling demand at each time point in the future preset time period; calculating the total power of the air-conditioning system in the future preset time period; and determining the target operating parameters of the air-conditioning system at each time point based on the electricity price, control parameter range, cooling demand, total power and multiple pre-established boundary conditions.

[0010] In some embodiments, the device for optimizing electricity consumption of an air-conditioning system includes: an acquisition module, configured to acquire the electricity price at each time point in a future preset time period, and the control parameter range for the air-conditioning system at the first time point preset by the user; a prediction module, configured to predict the cooling demand at each time point in the future preset time period; a calculation module, configured to calculate the total power of the air-conditioning system in the future preset time period; and a determination module, configured to determine the target operating parameters of the air-conditioning system at each time point based on the electricity price, control parameter range, cooling demand, total power and multiple pre-established boundary conditions.

[0011] In some embodiments, the device for optimizing power usage of an air conditioning system includes: a processor and a memory storing program instructions, and the processor is configured to execute the aforementioned method for optimizing power usage of an air conditioning system when running the program instructions.

[0012] In some embodiments, the air conditioning system includes: an air conditioning system body; and the aforementioned device for optimizing power consumption of the air conditioning system, which is installed in the air conditioning system body.

[0013] In some embodiments, the computer-readable storage medium stores program instructions, and when the program instructions are run, the aforementioned method for optimizing electricity consumption of an air-conditioning system is executed.

[0014] The method and device for optimizing the power consumption of an air conditioning system, the air conditioning system, and the computer-readable storage medium provided in the embodiments of the present disclosure can achieve the following technical effects:

[0015] The total power of the air conditioning system and the cooling demand at each time point in the future preset time period are predicted and calculated, and the cooling demand and total power are combined with the control parameter range of the first time point set by the user, and multiple boundary conditions are matched for constraints, so that the target operating parameters of the air conditioning system at each time point in the future preset time period can be more accurately determined. The accurate calculation of the operating parameters at each time point, when fed back to the electricity consumption, makes the electricity consumption more reasonable, and thus can achieve the effect of improving energy saving.

[0016] The above general description and the following description are exemplary and explanatory only and are not intended to limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] One or more embodiments are exemplarily described by corresponding drawings, which do not limit the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements, and the drawings do not constitute a scale limitation, and wherein:

[0018] Figure 1 is a schematic diagram of a method for optimizing power consumption of an air conditioning system provided by an embodiment of the present disclosure;

[0019] Figure 2 is a schematic diagram of another method for optimizing power consumption of an air conditioning system provided by an embodiment of the present disclosure;

[0020] Figure 3 is a schematic diagram of another method for optimizing power consumption of an air conditioning system provided by an embodiment of the present disclosure;

[0021] Figure 4 is a schematic diagram of another method for optimizing power consumption of an air conditioning system provided by an embodiment of the present disclosure;

[0022] Figure 5 is a schematic diagram of a device for optimizing power consumption of an air conditioning system provided by an embodiment of the present disclosure;

[0023] Figure 6 is a schematic diagram of another device for optimizing power consumption of an air conditioning system provided by an embodiment of the present disclosure;

[0024] Figure 7 It is a schematic diagram of an air conditioning system provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0025] In order to be able to understand the features and technical contents of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure is described in detail below in conjunction with the accompanying drawings. The attached drawings are for reference only and are not used to limit the embodiments of the present disclosure. In the following technical description, for the convenience of explanation, a full understanding of the disclosed embodiments is provided through multiple details. However, one or more embodiments can still be implemented without these details. In other cases, to simplify the drawings, well-known structures and devices can be simplified for display.

[0026] The terms "first", "second", etc. in the specification and claims of the embodiments of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged where appropriate, so that the embodiments of the embodiments of the present disclosure described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.

[0027] Unless otherwise stated, the term "plurality" means two or more.

[0028] In the embodiment of the present disclosure, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B indicates: A or B.

[0029] The term "and / or" is a description of the association relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B.

[0030] The term "correspondence" may refer to an association relationship or a binding relationship. The correspondence between A and B means that there is an association relationship or a binding relationship between A and B.

[0031] Combination Figure 1 As shown, the embodiment of the present disclosure provides a method for optimizing power consumption of an air conditioning system, comprising:

[0032] S101, the air conditioning system obtains the electricity price at each time point in a future preset time period, and the control parameter range of the air conditioning system at the first time point preset by the user.

[0033] S102, the air conditioning system predicts the cooling demand at each time point in a future preset time period.

[0034] S103: The air conditioning system calculates its total power in a future preset time period.

[0035] S104, the air conditioning system determines the target operating parameters of the air conditioning system at each time point according to the electricity price, the control parameter range, the cooling demand, the total power and a plurality of pre-established boundary conditions.

[0036] Get the electricity price at each time point in the future preset time period. The future preset time period is from 0:00 to 23:00 on the next day. Different time periods correspond to different electricity prices, which are usually called peak, valley and flat electricity prices. Here P(x) is used to represent the function of time-of-use electricity price. First, it is necessary to distinguish the supply voltage, because the electricity prices of different voltages will be slightly different. But for a specific project, the supply voltage is determined at the beginning. For projects with determined voltage, the form of the time-of-use electricity price P(x) is:

[0037]

[0038] Here x is the current hour. For example, if it is 8 o'clock in the morning, x is 8; if it is 4 o'clock in the afternoon, x is 16. h represents the set of hours. peak is the peak period, h plain For normal period, h foot It is the valley period. peak is the peak electricity price, P plainis the electricity price during normal hours, P foot The peak-valley-hour electricity price is 1.3553 yuan / kWh, 0.2894 yuan / kWh and 1.0000 yuan / kWh respectively.

[0039] Get the control parameter range of the air-conditioning system at the first time point preset by the user. Here, the future preset time period is from 0:00 to 23:00 of the next day, and each time point corresponds to an hour. Specifically: the first time point corresponds to 0:00 (0:00), the second time point corresponds to 1:00 (1:00), ..., the sixteenth time point corresponds to 15:00 (15:00), ..., the twenty-fourth time point corresponds to 23:00 (23:00). The user pre-sets the control parameter range for the air-conditioning system at the first time point. The control parameter range can be a temperature range, a wind speed range, and other parameter ranges. The specific control parameters can be determined by the user.

[0040] Predict the cooling demand at each time point in the future time period. Specifically: predict the cooling demand at 0 o'clock, 1 o'clock, ..., 15 o'clock, ..., 23 o'clock on the next day.

[0041] Calculate the total power of the air conditioning system in the future time period, that is, the total power of the air conditioning system from 0:00 to 23:00 the next day.

[0042] The air conditioning system has multiple pre-established boundary conditions stored in advance to constrain parameters such as cooling capacity, comfort, and the stability of the parameters.

[0043] The target operating parameters of the air-conditioning system at each time point in the future preset time period are determined by comprehensively considering the electricity price, cooling demand, and total power of the air-conditioning system at each time point in the future preset time period, combined with the user's setting of the control parameter range for the first time point, and multiple boundary conditions.

[0044] By using the method for optimizing the power consumption of the air conditioning system provided by the embodiment of the present disclosure, the total power of the air conditioning system and the cooling demand at each time point in the future preset time period are predicted and calculated, the cooling demand and the total power are combined with the control parameter range of the first time point set by the user, and multiple boundary conditions are matched for constraints, so that the target operating parameters of the air conditioning system at each time point in the future preset time period can be more accurately determined. The accurate calculation of the operating parameters at each time point is fed back to the power consumption, so that the power consumption is more reasonable, thereby achieving the effect of improving energy saving.

[0045] Combination Figure 2As shown, the embodiment of the present disclosure provides another method for optimizing the power consumption of an air conditioning system, comprising:

[0046] S101, the air conditioning system obtains the electricity price at each time point in a future preset time period, and the control parameter range of the air conditioning system at the first time point preset by the user.

[0047] S112, the air conditioning system obtains future time characteristics and future environment characteristics of a future preset time period.

[0048] S122, the air conditioning system predicts the cooling demand at each time point in a future preset time period according to the future time characteristics and future environmental characteristics.

[0049] S103: The air conditioning system calculates its total power in a future preset time period.

[0050] S104, the air conditioning system determines the target operating parameters of the air conditioning system at each time point according to the electricity price, the control parameter range, the cooling demand, the total power and a plurality of pre-established boundary conditions.

[0051] When presetting the cooling demand at each time point in the future time period, first obtain the future time characteristics and future environmental characteristics of the future preset time period. Among them, the future time characteristics can be obtained through the cloud calendar online, mainly including: the year, month, day, day of the next day, working day / rest day and other time-related characteristics. Future environmental characteristics refer to the temperature, humidity, rain and snow weather and other environment-related characteristics of the next day. Considering that the environment will not fluctuate too much in a short time, the ARIMA model (Autoregressive Integrated Moving Average Model) can be directly used to predict the future environmental characteristics. According to the future time characteristics and future environmental characteristics, predict the cooling demand at each time point in the future time period. In this way, the influence of time characteristics and environmental characteristics on the user's cooling demand is combined to predict the cooling demand at each time point in the future time period, which can make the prediction result more accurate. This is conducive to the subsequent accurate determination of the target operating parameters at each time point.

[0052] Optionally, in step S122, the air conditioning system predicts the cooling demand at each time point in a future preset time period according to the future time characteristics and the future environmental characteristics, including:

[0053] The air conditioning system searches the historical data for multiple groups of target historical data that are close to the future time characteristics and the future environmental characteristics and correspond to each future time point; wherein each group of target historical data includes a pair of corresponding historical time characteristics and historical environmental characteristics, and each group of target historical data corresponds to a historical cooling demand.

[0054] The air conditioning system calculates an average value of historical cooling demands corresponding to multiple sets of target historical data; wherein the multiple sets of target historical data correspond to the same future time point.

[0055] The air conditioning system uses the average value of historical cooling demand as the cooling demand at a corresponding future time point.

[0056] Every hour from 0 to 23 o'clock on the next day is a future time point.

[0057] The air conditioning system pre-stores multiple sets of historical data. The historical data can be data from the previous several months or the previous several years. Each set of historical data corresponds to a historical time point. Each set of historical data includes a pair of corresponding historical time features and historical environmental features. At the same time, each pair of historical time features and historical environmental features corresponds to a historical time point, and thus corresponds to a historical cooling demand.

[0058] The KNN (K-Nearest Neighbor) algorithm is used for prediction interpolation. Then, for each future time point, in the historical data, find a group of target historical data that is closest to the future time characteristics and future environmental characteristics in Euclidean distance. A>1, the specific value can be set by the user. Since each group of target historical data corresponds to the historical cooling demand at a historical time point, A historical cooling demands will be found for each future time point. Calculate the average value of the historical cooling demand corresponding to the same future time point, and use the average value as the cooling demand at that time point in the future time period. In this way, the cooling demand at each time point in the future time period can be predicted.

[0059] Combination Figure 3 As shown, the embodiment of the present disclosure provides another method for optimizing the power consumption of an air conditioning system, comprising:

[0060] S101, the air conditioning system obtains the electricity price at each time point in a future preset time period, and the control parameter range of the air conditioning system at the first time point preset by the user.

[0061] S102, the air conditioning system predicts the cooling demand at each time point in a future preset time period.

[0062] S113, the air conditioning system determines the power of the pre-selected structure according to a pre-established power model corresponding to the pre-selected structure of the air conditioning system.

[0063] S123, the air conditioning system uses the sum of the powers of the preselected structures as the total power of the air conditioning system in a future preset time period.

[0064] S104. The air conditioning system determines the target operating parameters at each time point of the air conditioning system according to the electricity price, control parameter range, cooling capacity demand, total power, and multiple pre-established boundary conditions.

[0065] The overall idea of this solution is to minimize the electricity cost for the day. Therefore, it is necessary to obtain the power consumption of the air conditioning system every hour. Power is directly related to the power consumption situation.

[0066] A pre-established power model is pre-stored in the air conditioning system:

[0067] The main engine power (W chiller ) model is related to the set outlet water temperature (T chws ), the cooling water outlet temperature (T cwr ), and the cooling capacity demand (Q req ). Then the main engine power model is: W chiller = f chiller (T chws , T cwr , Q req ).

[0068] The water pump power (W pump ) model is only related to the frequency (f req ). Then the water pump power model is: W pump = f pump (f req ).

[0069] The cooling tower power (W tower ) model is related to the number of cooling tower units in operation (n tower ), the frequency (f req ), the cooling water flow rate (q cw ), and the outdoor wet bulb temperature (T wetbulb ). Then the cooling tower power model is: W tower = f tower (n tower , f req , q cw , T wetbulb ).

[0070] The power (W SSAC ) model of the packaged air handling unit is related to the air velocity (V wind ), the internal temperature (T indoor ), the set temperature (T set ), and the fresh air enthalpy value (h amb ). Then the power model of the packaged air handling unit is: W SSAC = f SSAC (V wind , T indoor , T set , h amb ).

[0071] Optionally, the pre-selected structure of the air conditioning system includes: a main unit, a water pump, a cooling tower and a combined air conditioning unit. Based on the aforementioned power model, the total power expression of the air conditioning system is:

[0072]

[0073] Among them, n is the number of chillers; since there are both refrigeration pumps and cooling pumps, the number of pumps is 2n; k is the number of cooling towers; m is the number of combined air-conditioning units; among them, k and m are not related to the number of chillers, but are independent numbers related to the design of the air-conditioning system itself.

[0074] The total power of the air conditioning system in a preset time period in the future is calculated using the above power model and total power expression.

[0075] In addition, the air-conditioning system also pre-stores a pre-established terminal comfort model and cooling capacity calculation model.

[0076] Terminal comfort (R comf ) model and terminal humidity (H term ), temperature (T term ), total cooling demand (Q req ), the number of terminal openings (n tower )、Set temperature(T set ). The comfort calculation method here refers to the national standard GB / T18049. A custom weight is assigned to each item to calculate a comfort rating value. The range of the comfort rating value can be freely determined. Then the terminal comfort model is: R comf =f comf (H term ,T term ,Q req ,n tower ,T set ).

[0077] The calculation model of cooling capacity (Q) is relatively comprehensive and requires the chilled water outlet temperature (T chws ), cooling tower temperature (T cwr ), pump frequency (f pump ), the number of cooling machines turned on (n chiller ), external temperature (T out ), terminal temperature (T term ), wet bulb temperature (T wetbulb ) for mapping. This model can be built using models with strong expressive power, such as machine learning, deep learning models, etc., rather than simple mechanism formulas. The cooling capacity calculation model is: Q = f load (T chws ,T cwr,f pump ,n chuller ,T out ,T term ,T wetbulb ).

[0078] Optionally, the plurality of boundary conditions include: a cooling capacity boundary condition related to the user's cooling capacity demand, a comfort boundary condition related to the user's comfort, and a stability boundary condition related to the operating stability of the air-conditioning system.

[0079] First, for the air conditioning system, the supply of cooling capacity needs to be sufficient. The traditional group control algorithm is designed to meet the cooling demand at every moment. However, cooling capacity is a cumulative amount, and the traditional algorithm is prone to cause excess cooling capacity. Therefore, this embodiment does not recommend that the cooling capacity supply is sufficient at all times, but rather that the cumulative cooling capacity at each time point meets the predicted cooling demand based on the day. For example, for the cooling demand at 8 o'clock, it is required that the sum of the cooling capacity from 0 o'clock to 8 o'clock is greater than or equal to the sum of the cooling capacity from 0 o'clock to 8 o'clock predicted in S102. Therefore, the cooling capacity constraint condition includes the cooling capacity cumulative boundary condition set C 1 :

[0080] C 1 :

[0081] Among them, Q cal is the cooling demand at each time point calculated based on the cooling (Q) calculation model mentioned above; Q pred is the cooling demand at each time point predicted in S102; h is the time point, and its value is an integer between 0 and 23.

[0082] At the same time, in order to avoid excessive cooling during the day, the total cooling capacity of the day needs to be constrained. Therefore, the cooling capacity constraint also includes the total cooling capacity boundary condition C in the future preset time period 2 :

[0083] C 2 :

[0084] Among them, δ 1 is the first threshold. Optionally, δ 1 The value is 10%.

[0085] Secondly, the terminal comfort can also be restricted to a certain extent. Here, a comfort threshold needs to be preset, and the terminal comfort at each of the 24 time points in a day should not exceed the comfort threshold. Therefore, the comfort boundary condition set C 3 for:

[0086] C 3 :{R h -Rbase}

[0087] Among them, R h is the terminal comfort (R comf ) The comfort level at each time point in the future preset time period predicted by the model; R base is the second threshold.

[0088] Thirdly, considering the stability of the air conditioning system control, except for the control parameters at the first time point that only meet the simple range, the control parameters at the remaining 23 time points should all consider the range of change from the control parameters at the previous time point to ensure the stability of the air conditioning system operation. This range of change can be set by the user. Therefore, the stability boundary condition set C 4 for:

[0089] C 4 :

[0090] Among them, CP n is the operating parameter range of the air conditioning system at time point n; δ 2 is the third threshold; optionally, δ 2 The value is 10%.

[0091] When n is 1, CP n-1 is the operating parameter range of the air conditioning system at 0 o'clock, and is also the control parameter range of the air conditioning system at the first time point preset by the user. After the user enters the control parameter range of the first time point, the air conditioning system can use the relevant algorithm to calculate the control parameter range of the second time point, the third time point, and so on, and then substitute the two adjacent control parameter ranges into the stability boundary condition set C. 4 Calculation is performed in .

[0092] Of course, there are many conventional constraints that are limited by the actual system operation conditions. Since they are not related to the focus of this embodiment, they will not be described in detail and are respectively denoted as C 5 , C 6 , ..., C n Boundary condition set.

[0093] The boundary conditions are based on cumulative quantities rather than instantaneous quantities, which avoids the failure of the optimization control strategy due to frequent fluctuations in transient quantities.

[0094] Combination Figure 4 As shown, the embodiment of the present disclosure provides another method for optimizing the power consumption of an air conditioning system, comprising:

[0095] S101, the air conditioning system obtains the electricity price at each time point in a future preset time period, and the control parameter range of the air conditioning system at the first time point preset by the user.

[0096] S102, the air conditioning system predicts the cooling demand at each time point in a future preset time period.

[0097] S103: The air conditioning system calculates its total power in a future preset time period.

[0098] S114: The air conditioning system calculates a minimum target electricity cost according to the electricity price and the total power.

[0099] S124, the air conditioning system establishes a fitness function according to the minimum target electricity cost and multiple boundary conditions.

[0100] S134, the air conditioning system determines the target operating parameters of the air conditioning system at each time point according to the control parameter range, cooling demand and fitness function.

[0101] The target function is determined based on the electricity price and total power as Target function:

[0102]

[0103] Where j is the time point. The optimization goal is to minimize the Target function, and then calculate the minimum target electricity cost. At the same time, the Target function is constrained by the multiple boundary conditions mentioned above, and then the fitness function is established. The control parameter range of the air-conditioning system at the first time point preset by the user and the predicted cooling demand at each time point are used as the calculation basis, and the target operating parameters of the air-conditioning system at each time point are determined by the fitness function. In this way, the optimal result can be sought while meeting user needs and cooling demand.

[0104] Optionally, in S124, the air conditioning system establishes a fitness function according to the minimum target electricity cost and multiple boundary conditions, including:

[0105]

[0106] Among them, fitness is the fitness function; P(j) is the electricity price at time point j; W tot is the total power; C i is the boundary condition, m is the number of boundary conditions; is the penalty coefficient.

[0107] make Calculation boundary condition C i If the calculation result C i ≤0, then let C i =0; if the calculation result C i >0, then let C i =C i. Optionally,

[0108] Optionally, in S134, the air conditioning system determines the target operating parameters at each time point according to the control parameter range, the cooling demand and the fitness function, including:

[0109] The air conditioning system substitutes the control parameter range and cooling demand into the corresponding boundary conditions in the fitness function.

[0110] The air conditioning system uses an optimization algorithm to optimize the fitness function and obtain the target operating parameters at each time point.

[0111] Substitute the control parameter range of the air conditioning system at the first time point preset by the user into the stability boundary condition set C in the fitness function fitness 4 , substitute the predicted cooling demand at each time point into the cooling accumulation boundary condition set C in the fitness function fitness 1 and the total cooling capacity boundary condition C in the future preset time period 2 Substitute the terminal comfort calculated by the model into the comfort boundary condition set C in the fitness function fitness 3 In. The fitness function fitness is optimized by using an optimization algorithm to obtain the target operating parameters at each time point. The target operating parameters include: chilled water set temperature, cooling water set temperature, refrigeration pump frequency, cooling pump frequency, number of chillers, number of cooling towers, combined air conditioning unit set temperature and terminal set temperature. In this way, combined with the peak, valley and flat electricity prices, the target operating parameters at each time point obtained by optimization can not only meet the user's cooling demand at the corresponding time point, but also avoid excessive cooling, thereby achieving the purpose of improving energy saving effects. It can not only save electricity costs for users, but also relieve the pressure on the power supply side.

[0112] After the optimization calculation is completed, the calculation logic is exited, and the air conditioning system operates according to the corresponding target control parameters at each time point in the future preset time period. In this way, the background algorithm program of the air conditioning system can be prevented from running all the time, which is more in line with the actual situation of the HVAC system.

[0113] In addition, the control parameter range of the first time point is input by the user, and the target control parameter of the first time point obtained by the final optimization calculation is found within the control parameter range input by the user. In this way, the user is given a certain degree of freedom to make the target operating parameters match the user's needs.

[0114] The following is an example of the specific implementation process of the method for optimizing the power consumption of the air conditioning system provided by the embodiment of the present disclosure:

[0115] The air conditioning system has pre-stored host power (Wchiller )、Water pump power(W pump ), cooling tower power (W tower ), combined air conditioning unit power (W SSAC ), terminal comfort (R comf ), cooling capacity (Q), and C 1 , C 2 , C 3 , C 4 etc. boundary conditions.

[0116] Assuming that it is now August 1st, it is necessary to calculate the target operating parameters for 24 time points from 0:00 to 23:00 on August 2nd.

[0117] The air conditioning system receives the control parameter range entered by the user at 0:00 on August 2, and performs the following steps at 23:45 on August 1:

[0118] The air conditioning system obtains time characteristics such as the day of month, week, and working / rest day on August 2.

[0119] The air conditioning system uses the ARIMA model to obtain environmental characteristics such as temperature and humidity on August 2.

[0120] The air-conditioning system uses the KNN algorithm to search the historical database for multiple sets of target historical data that are closest to the time characteristics and environmental characteristics of August 2. Each set of target historical data includes a pair of corresponding historical time characteristics and historical environmental characteristics. Each set of target historical data corresponds to a historical cooling demand, and then multiple historical cooling demands corresponding to each time point are found.

[0121] The air conditioning system calculates the average value of multiple historical cooling demands corresponding to each time point, and uses the average value as the predicted cooling demand value at the corresponding time point.

[0122] Air conditioning system uses terminal comfort (R comf ) model to predict the comfort level at 24 time points on August 2.

[0123] The air conditioning system obtains the peak-valley and flat electricity prices P(x) on August 2.

[0124] The total power of the air-conditioning system is calculated based on the host power model, water pump power model, cooling tower power model, and combined air-conditioning unit power model.

[0125] Air conditioning system based on fitness function Substitute the predicted cooling demand values ​​at 24 time points into C 1 and C 2 In the above example, the predicted comfort level is substituted into C 3 In the example, substitute the control parameters at the first time point into C 4Among them,

[0126] The air-conditioning system uses an optimization algorithm and takes the above fitness function as the target to optimize the target operating parameters at 24 time points from 0:00 to 23:00 on August 2.

[0127] Combination Figure 5 As shown, the embodiment of the present disclosure provides a device 50 for optimizing the electricity consumption of an air-conditioning system, including: an acquisition module 51, a prediction module 52, a calculation module 53 and a determination module 54. The acquisition module 51 is configured to obtain the electricity price at each time point in a future preset time period, and the control parameter range for the air-conditioning system at the first time point preset by the user. The prediction module 52 is configured to predict the cooling demand at each time point in the future preset time period. The calculation module 53 is configured to calculate the total power of the air-conditioning system in the future preset time period. The determination module 54 is configured to determine the target operating parameters of the air-conditioning system at each time point according to the electricity price, the control parameter range, the cooling demand, the total power and a plurality of pre-established boundary conditions.

[0128] The device 50 for optimizing the power consumption of the air conditioning system provided by the embodiment of the present disclosure is used to predict and calculate the total power of the air conditioning system and the cooling demand at each time point in the future preset time period, and the cooling demand and the total power are combined with the control parameter range of the first time point set by the user, and multiple boundary conditions are matched for constraints, so that the target operating parameters of the air conditioning system at each time point in the future preset time period can be more accurately determined. The accurate calculation of the operating parameters at each time point is fed back to the power consumption, so that the power consumption is more reasonable, and the effect of improving energy saving can be achieved.

[0129] Combination Figure 6 As shown, the embodiment of the present disclosure provides a device 60 for optimizing the power consumption of an air conditioning system, including a processor 61 and a memory 62. Optionally, the device 60 may also include a communication interface 63 and a bus 64. The processor 61, the communication interface 63, and the memory 62 may communicate with each other through the bus 64. The communication interface 63 may be used for information transmission. The processor 61 may call the logic instructions in the memory 62 to execute the method for optimizing the power consumption of the air conditioning system of the above embodiment.

[0130] In addition, the logic instructions in the memory 62 described above may be implemented in the form of software functional units and when sold or used as independent products, may be stored in a computer-readable storage medium.

[0131] The memory 62 is a computer-readable storage medium that can be used to store software programs and computer executable programs, such as program instructions / modules corresponding to the method in the embodiment of the present disclosure. The processor 61 executes the function application and data processing by running the program instructions / modules stored in the memory 62, that is, the method for optimizing the power consumption of the air conditioning system in the above embodiment is implemented.

[0132] The memory 62 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory 62 may include a high-speed random access memory and may also include a non-volatile memory.

[0133] Combination Figure 7 As shown, an embodiment of the present disclosure provides an air conditioning system 70, including: an air conditioning system body, and the above-mentioned device 50 (60) for optimizing the power consumption of the air conditioning system. The device 50 (60) for optimizing the power consumption of the air conditioning system is installed in the air conditioning system body. The installation relationship described here is not limited to placement inside the air conditioning system body, but also includes installation connections with other components of the air conditioning system 70, including but not limited to physical connections, electrical connections or signal transmission connections. It can be understood by those skilled in the art that the device 50 (60) for optimizing the power consumption of the air conditioning system can be adapted to a feasible air conditioning system body, thereby realizing other feasible embodiments.

[0134] An embodiment of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to execute the above-mentioned method for optimizing electricity consumption of an air-conditioning system.

[0135] The technical solution of the embodiment of the present disclosure can be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiment of the present disclosure. The aforementioned storage medium may be a non-transient storage medium, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes.

[0136] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent only possible changes. Unless explicitly required, separate components and functions are optional, and the order of operation may vary. The parts and features of some embodiments may be included in or replace the parts and features of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and the claims, unless the context clearly indicates, the singular forms of "a", "an" and "the" are intended to include plural forms as well. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of listings containing one or more associated ones. In addition, when used in the present application, the term "comprise" and its variants "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof. In the absence of further restrictions, the elements defined by the sentence "comprising a ..." do not exclude the presence of other identical elements in the process, method or device comprising the elements. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can refer to the description of the method part.

[0137] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods for each specific application to implement the described functions, but such implementations should not be considered to exceed the scope of the embodiments of the present disclosure. The technicians may clearly understand that, for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above may refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0138] In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units can be only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to implement this embodiment. In addition, each functional unit in the embodiment of the present disclosure may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit.

[0139] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A method for optimizing power usage of an air conditioning system, It is characterized in that include: Obtain the electricity price at each time point in the future preset time period, and the control parameter range of the air conditioning system at the first time point preset by the user; Predict the cooling demand at each time point in the future preset time period; Calculate the total power of the air conditioning system in a preset time period in the future; Determine the target operating parameters of the air conditioning system at each time point based on electricity price, control parameter range, cooling demand, total power and multiple pre-established boundary conditions.

2. The method according to claim 1, It is characterized in that Predict the cooling demand at each time point in the future preset time period, including: Obtaining future time characteristics and future environment characteristics of a future preset time period; According to the future time characteristics and future environmental characteristics, the cooling demand at each time point in the future preset time period is predicted.

3. The method according to claim 2, It is characterized in that According to the future time characteristics and future environmental characteristics, the cooling demand at each time point in the future preset time period is predicted, including: Searching for multiple groups of target historical data that are close to future time characteristics and future environmental characteristics and correspond to each future time point in the historical data; wherein each group of target historical data includes a pair of corresponding historical time characteristics and historical environmental characteristics, and each group of target historical data corresponds to a historical cooling demand; Calculate the average value of the historical cooling demand corresponding to multiple sets of target historical data; wherein the multiple sets of target historical data correspond to the same future time point; The average value of historical cooling demand is used as the cooling demand at the corresponding future time point.

4. The method according to claim 1, It is characterized in that Calculate the total power of the air conditioning system in the future preset time period, including: Determining the power of the preselected structure according to a pre-established power model corresponding to the preselected structure of the air conditioning system; The sum of the powers of the preselected structures is taken as the total power of the air-conditioning system in a future preset time period.

5. The method according to claim 1, It is characterized in that Multiple boundary conditions, including: Cooling boundary conditions related to user cooling demand, comfort boundary conditions related to user comfort, and stability boundary conditions related to air-conditioning system operation stability.

6. The method according to claim 5, It is characterized in that Cold boundary conditions, including: Among them, C 1 is the set of boundary conditions for cooling accumulation; Q cal is the cooling demand at each time point calculated based on the model; Q pred is the predicted cooling demand at each time point; h is the time point, and its value is an integer between 0 and 23; Among them, C 2 is the total cooling capacity boundary condition in the future preset time period; δ 1 is the first threshold.

7. The method according to claim 5, It is characterized in that Comfort boundary conditions, including: C 3 :{R h -R base } Among them, C 3 is the set of comfort boundary conditions; R h The predicted comfort level at each time point in the future preset time period; R base is the second threshold.

8. The method according to claim 5, It is characterized in that Stability boundary conditions, including: Among them, C 4 is the set of stability boundary conditions; CP n is the operating parameter range of the air conditioning system at time point n; δ 2 is the third threshold; Among them, when n is 1, CP n-1 is the operating parameter of the air-conditioning system at 0 o'clock, and also the control parameter range of the air-conditioning system at the first time point preset by the user.

9. The method according to any one of claims 1 to 8, It is characterized in that Determine the target operating parameters of the air conditioning system at each time point based on electricity prices, control parameter ranges, cooling demand, total power, and multiple pre-established boundary conditions, including: Calculate the minimum target electricity cost based on the electricity price and total power; Establish a fitness function based on the minimum target electricity cost and multiple boundary conditions; According to the control parameter range, cooling demand and fitness function, the target operating parameters of the air-conditioning system at each time point are determined.

10. The method according to claim 9, It is characterized in that The fitness function is established according to the minimum target electricity cost and multiple boundary conditions, including: Among them, fitness is the fitness function; P(j) is the electricity price at time point j; W tot is the total power; C i is the boundary condition, m is the number of boundary conditions; is the penalty coefficient.

11. The method according to claim 9, It is characterized in that According to the control parameter range, cooling demand and fitness function, determine the target operating parameters at each time point, including: Substitute the control parameter range and cooling demand into the corresponding boundary conditions in the fitness function; The optimization algorithm is used to optimize the fitness function and obtain the target operating parameters at each time point.

12. A device for optimizing the power consumption of an air conditioning system, It is characterized in that include: An acquisition module is configured to acquire the electricity price at each time point in a future preset time period and a control parameter range for the air conditioning system at a first time point preset by a user; A prediction module, configured to predict the cooling demand at each time point in a future preset time period; A calculation module, configured to calculate the total power of the air conditioning system in a future preset time period; The determination module is configured to determine the target operating parameters of the air conditioning system at each time point according to the electricity price, the control parameter range, the cooling demand, the total power and a plurality of pre-established boundary conditions.

13. A device for optimizing power consumption of an air conditioning system, comprising a processor and a memory storing program instructions, It is characterized in that The processor is configured to execute the method for optimizing electricity consumption of an air conditioning system according to any one of claims 1 to 11 when running the program instructions.

14. An air conditioning system, It is characterized in that include: Air conditioning system body; The device for optimizing electricity consumption of an air-conditioning system as described in claim 12 or 13 is installed on the air-conditioning system body.

15. A computer-readable storage medium storing program instructions, It is characterized in that When the program instructions are executed, the computer is used to execute the method for optimizing electricity consumption of an air-conditioning system according to any one of claims 1 to 11.

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