CN111237989A - Building ventilation air conditioner control method and device based on load prediction - Google Patents

Building ventilation air conditioner control method and device based on load prediction Download PDF

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CN111237989A
CN111237989A CN202010079947.1A CN202010079947A CN111237989A CN 111237989 A CN111237989 A CN 111237989A CN 202010079947 A CN202010079947 A CN 202010079947A CN 111237989 A CN111237989 A CN 111237989A
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energy consumption
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building
air
conditioning system
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CN111237989B (en
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赵常强
张雪庆
张敏
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Hisense TransTech Co Ltd
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
    • F24F11/46—Improving electric energy efficiency or saving
    • F24F11/47—Responding to energy costs
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2110/00—Control inputs relating to air properties
    • F24F2110/10—Temperature
    • F24F2110/12—Temperature of the outside air
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2120/00—Control inputs relating to users or occupants
    • F24F2120/10—Occupancy
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2140/00—Control inputs relating to system states
    • F24F2140/50—Load
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2140/00—Control inputs relating to system states
    • F24F2140/60—Energy consumption

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  • Combustion & Propulsion (AREA)
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  • Mathematical Physics (AREA)
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  • Air Conditioning Control Device (AREA)

Abstract

The embodiment of the invention provides a building ventilation air-conditioning control method and device based on load prediction, the method comprises the steps of obtaining current information data of a building, determining the current load of the building according to the current information data and a load prediction model of the building, training historical information data of the building to obtain the load prediction model, inputting multiple groups of variable combinations of an energy consumption model into an energy consumption model respectively to obtain total energy consumption corresponding to each group of variable combinations, determining the multiple groups of variable combinations of the energy consumption model according to the energy consumption model and the historical energy consumption data, determining the optimal operation parameters of each device in a building ventilation air-conditioning system according to the total energy consumption corresponding to each group of variable combinations and the current load of the building, controlling each device in the building ventilation air-conditioning system according to the optimal operation parameters of each device in the building ventilation air-conditioning system, and solving the problem that the load prediction of the heating ventilation air-conditioning system is inaccurate, Untimely temperature adjustment and energy waste, thereby realizing energy conservation and emission reduction.

Description

Building ventilation air conditioner control method and device based on load prediction
Technical Field
The embodiment of the invention relates to the field of prediction of time sequence data, in particular to a building ventilation air conditioner control method and device based on load prediction.
Background
In the heating ventilation air conditioning energy saving field, improving the energy saving rate is an important technology. At present, the energy saving rate is improved and meets the bottleneck, mainly because the load cannot be predicted in advance, the emergency regulation is delayed, the electric energy waste is serious, and the fuel consumption is higher just like the automobile is always started and stopped emergently.
With the rapid development of the heating, ventilation and air conditioning optimization control technology, a load prediction technology is gradually introduced into the heating, ventilation and air conditioning control system. However, the load prediction adopted by the existing heating, ventilating and air conditioning control technology cannot accurately predict the conditions such as periodicity, holidays and the like, the prediction accuracy rate is low, the usability of the heating, ventilating and air conditioning system introduced by the load prediction is poor, and further the temperature regulation of the heating, ventilating and air conditioning system is not timely and energy waste is caused.
In summary, a building ventilation air conditioner control method based on load prediction is needed to solve the problems of inaccurate load prediction, untimely temperature regulation and energy waste of the heating ventilation air conditioner.
Disclosure of Invention
The embodiment of the invention provides a building ventilation air-conditioning control method and device based on load prediction, which are used for solving the problems of inaccurate load prediction, untimely temperature regulation and energy waste of a heating ventilation air-conditioning.
In a first aspect, an embodiment of the present invention provides a method for controlling building ventilation air conditioners based on load prediction, including:
acquiring current information data of a building;
determining the current load of the building according to the current information data and the load prediction model of the building; the load prediction model is obtained by training historical information data of the building;
respectively inputting a plurality of groups of variable combinations of the energy consumption model into the energy consumption model to obtain total energy consumption corresponding to each group of variable combinations; the multi-group variable combination of the energy consumption model is determined according to the energy consumption model and historical energy consumption data;
determining the optimal operation parameters of each device in the building ventilation air-conditioning system according to the total energy consumption corresponding to each group of variable combination and the current load of the building;
and controlling each device in the building ventilation air-conditioning system according to the optimal operation parameter of each device in the building ventilation air-conditioning system.
According to the technical scheme, the current load of the building can be determined according to the current information data and the load prediction model of the building, multiple groups of variable combinations of the energy consumption model are respectively input into the energy consumption model to obtain the total energy consumption corresponding to each group of variable combinations, the optimal operation parameters of each device in the building ventilation and air conditioning system can be determined according to the total energy consumption corresponding to each group of variable combinations and the current load of the building, and each device in the building ventilation and air conditioning system is controlled according to the optimal operation parameters of each device in the building ventilation and air conditioning system. Because each device in the building ventilation air-conditioning system can be regulated and controlled according to the optimal operation parameter of each device in the building ventilation air-conditioning system, each device is in the optimal operation state under the condition that the current load of the building is met, the problems of inaccurate load prediction, untimely temperature regulation and energy waste of the heating ventilation air-conditioning system are solved, the room temperature is accurately controlled, the human body comfort level of the building is improved, and the energy-saving effect is achieved.
Optionally, the information data of the building includes a time period, an outdoor temperature, power consumption and a human flow;
training the historical information data of the building to obtain the load prediction model, wherein the training comprises the following steps:
inputting the time period, the outdoor temperature, the power consumption and the pedestrian volume in the historical information data of the building into a preset trend model for training to obtain a trained trend model;
inputting the time period, the outdoor temperature, the power consumption and the pedestrian flow in the historical information data of the building into a preset periodic model for training to obtain a trained periodic model;
inputting the time period, the outdoor temperature, the power consumption and the human flow in the historical information data of the building into a preset holiday model for training to obtain a trained holiday model;
and fitting the trained trend model, the periodic model and the holiday model to determine the load prediction model.
According to the technical scheme, the load prediction model is determined by training the preset trend model, the preset periodic model and the preset holiday model and fitting the trained trend model, periodic model and holiday model. The time sequence is divided into a trend item, a period item, a holiday item and the like by load prediction, each part is trained by using historical information data of the building to establish a corresponding model, each model is fitted to establish a uniform load prediction model, and the load prediction model comprehensively considers the trend, the periodicity and the influence of holidays of the data and can improve the accuracy of building load prediction.
Optionally, the energy consumption model of the building ventilation air-conditioning system comprises a water chilling unit energy consumption model, a cooling tower energy consumption model and an air-conditioning box energy consumption model;
determining a plurality of variable combinations of the energy consumption model according to the energy consumption model and historical energy consumption data, including:
inputting the refrigerating capacity and the air supply processing capacity in the historical energy consumption data of the air-conditioning box into the energy consumption model of the air-conditioning box to obtain the chilled water quantity of the air-conditioning box; the refrigerating capacity and the air supply processing capacity are determined according to an air supply quantity detection value, an air supply humidity detection value and a set value of the air conditioning box;
inputting the refrigerating capacity in the historical energy consumption data of the air conditioning box and the supply water temperature and the intake water temperature of the chilled water in the historical energy consumption data of the water chilling unit into the energy consumption model of the water chilling unit to obtain the total unit energy consumption of the water chilling unit;
inputting the condensation heat load, the cooling water inlet temperature and the cooling water inlet and outlet temperature difference in the historical energy consumption data of the water chilling unit into the cooling tower energy consumption model to obtain the fan air volume of the cooling tower and determine the cooling water volume of the cooling tower; the condensation heat load is determined according to the total energy consumption of the unit and the refrigerating capacity;
and combining the chilled water supply temperature, the chilled water quantity, the cooling water inlet temperature, the cooling water quantity and the fan air quantity to determine the multi-group variable combination of the energy consumption model of the building ventilation air-conditioning system.
According to the technical scheme, historical energy consumption data of the building ventilation air-conditioning system are respectively input into the energy consumption models corresponding to the historical energy consumption data, variables corresponding to the models can be obtained, the variables corresponding to the models are combined to determine the multi-group variable combination of the energy consumption models of the building ventilation air-conditioning system, and data support can be provided for determining the variable combination with the lowest energy consumption, so that the effect of overall optimization energy-saving control is achieved.
Optionally, the determining the optimal operating parameters of each device in the building ventilation and air conditioning system according to the total energy consumption corresponding to each group of variable combinations and the current load of the building includes:
inputting each variable combination of the multiple variable combinations into an energy consumption model of the building ventilation air-conditioning system according to the current load of the building to obtain total energy consumption corresponding to each variable combination;
comparing the total energy consumption corresponding to each group of variable combination to determine the variable combination corresponding to the lowest total energy consumption;
and determining the variable combination corresponding to the lowest total energy consumption as the optimal operation parameter of each device in the building ventilation and air conditioning system.
According to the technical scheme, the variable combination corresponding to the lowest total energy consumption, namely the optimal operation parameters of each device in the building ventilation and air conditioning system, is determined by comparing the total energy consumption corresponding to each group of variable combination. Due to the adjustment hysteresis of the air-conditioning water system, the load prediction and the real-time feedback are combined, the guide value of the optimized operation parameter of the building air-conditioning system is given in advance based on the current load, and the feedback of the change of the building load is combined, so that the building air-conditioning system can be in the optimal operation state under the condition of meeting the current load requirement, and the optimal energy-saving effect is achieved.
Optionally, the controlling, according to the optimal operating parameter of each device in the building ventilation and air-conditioning system, each device in the building ventilation and air-conditioning system includes:
and updating the operation parameters in the control page of each device in the building ventilation air-conditioning system to the corresponding optimal operation parameters of each device in the building ventilation air-conditioning system, so that each device in the building ventilation air-conditioning system is regulated to be in an optimal operation state.
According to the technical scheme, the operation parameters in the control pages of the devices in the building ventilation air-conditioning system are correspondingly updated according to the optimal operation parameters of the devices in the building ventilation air-conditioning system, so that the building air-conditioning system can be in the optimal operation state under the condition of meeting the current load requirement, the room temperature can be accurately controlled, the human body comfort level of a building is improved, and the optimal energy-saving effect is achieved.
In a second aspect, an embodiment of the present invention further provides a building ventilation air-conditioning control device based on load prediction, including: the acquisition unit is used for acquiring current information data of the building;
the processing unit is used for determining the current load of the building according to the current information data of the building and a load prediction model; the load prediction model is obtained by training historical information data of the building; respectively inputting a plurality of groups of variable combinations of the energy consumption model into the energy consumption model to obtain total energy consumption corresponding to each group of variable combinations; the multi-group variable combination of the energy consumption model is determined according to the energy consumption model and historical energy consumption data; determining the optimal operation parameters of each device in the building ventilation air-conditioning system according to the total energy consumption corresponding to each group of variable combination and the current load of the building; and controlling each device in the building ventilation air-conditioning system according to the optimal operation parameter of each device in the building ventilation air-conditioning system.
Optionally, the information data of the building includes a time period, an outdoor temperature, power consumption and a human flow;
the processing unit is specifically configured to:
inputting the time period, the outdoor temperature, the power consumption and the pedestrian volume in the historical information data of the building into a preset trend model for training to obtain a trained trend model;
inputting the time period, the outdoor temperature, the power consumption and the pedestrian flow in the historical information data of the building into a preset periodic model for training to obtain a trained periodic model;
inputting the time period, the outdoor temperature, the power consumption and the human flow in the historical information data of the building into a preset holiday model for training to obtain a trained holiday model;
and fitting the trained trend model, the periodic model and the holiday model to determine the load prediction model.
Optionally, the energy consumption model of the building ventilation air-conditioning system comprises a water chilling unit energy consumption model, a cooling tower energy consumption model and an air-conditioning box energy consumption model;
the processing unit is specifically configured to:
inputting the refrigerating capacity and the air supply processing capacity in the historical energy consumption data of the air-conditioning box into the energy consumption model of the air-conditioning box to obtain the chilled water quantity of the air-conditioning box; the refrigerating capacity and the air supply processing capacity are determined according to an air supply quantity detection value, an air supply humidity detection value and a set value of the air conditioning box;
inputting the refrigerating capacity in the historical energy consumption data of the air conditioning box and the supply water temperature and the intake water temperature of the chilled water in the historical energy consumption data of the water chilling unit into the energy consumption model of the water chilling unit to obtain the total unit energy consumption of the water chilling unit;
inputting the condensation heat load, the cooling water inlet temperature and the cooling water inlet and outlet temperature difference in the historical energy consumption data of the water chilling unit into the cooling tower energy consumption model to obtain the fan air volume of the cooling tower and determine the cooling water volume of the cooling tower; the condensation heat load is determined according to the total energy consumption of the unit and the refrigerating capacity;
and combining the chilled water supply temperature, the chilled water quantity, the cooling water inlet temperature, the cooling water quantity and the fan air quantity to determine the multi-group variable combination of the energy consumption model of the building ventilation air-conditioning system.
Optionally, the processing unit is specifically configured to:
inputting each variable combination of the multiple variable combinations into an energy consumption model of the building ventilation air-conditioning system according to the current load of the building to obtain total energy consumption corresponding to each variable combination;
comparing the total energy consumption corresponding to each group of variable combination to determine the variable combination corresponding to the lowest total energy consumption;
and determining the variable combination corresponding to the lowest total energy consumption as the optimal operation parameter of each device in the building ventilation and air conditioning system.
Optionally, the processing unit is specifically configured to:
and updating the operation parameters in the control page of each device in the building ventilation air-conditioning system to the corresponding optimal operation parameters of each device in the building ventilation air-conditioning system, so that each device in the building ventilation air-conditioning system is regulated to be in an optimal operation state.
In a third aspect, an embodiment of the present invention provides a computing device, including:
a memory for storing program instructions;
and the processor is used for calling the program instructions stored in the memory and executing the building ventilation air-conditioning control method based on load prediction according to the obtained program.
In a fourth aspect, embodiments of the present invention provide a computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to execute a method for controlling ventilation and air conditioning of a building based on load prediction.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced 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 to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of a system architecture according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a method for controlling a building ventilation air conditioner based on load prediction according to an embodiment of the present invention;
FIG. 3 is a flow chart of a method for predicting building load according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a change point of a trend model in building load prediction according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a building ventilation and air conditioning control device based on load prediction according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, 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.
Fig. 1 is a system architecture provided in an embodiment of the present invention. As shown in fig. 1, the system architecture may include an acquisition module 100, a storage module 101, a prediction module 102, a decision module 103, a control module 104, and a human-machine interaction module 105.
The acquisition module 100 is configured to acquire temperature, humidity, and weather data of each area of the building, and feed back actual temperatures acquired in real time to the decision module 103, where the decision module 103 adjusts the actual temperatures in combination with current actual temperatures when generating control parameters.
The storage module 101 is used for storing various types of data acquired by the acquisition module 100.
The prediction module 102 is configured to establish a load prediction model according to the historical data stored in the storage module 101, and perform load prediction based on current data. The prediction module 102 may continuously read historical data from the storage module 101 to perform optimization training on the prediction model.
The decision module 103 is configured to generate control parameters of each device based on the current load predicted by the prediction module 102 and an energy consumption model of the whole building ventilation and air conditioning system, and in combination with the actual temperature acquired in real time by the acquisition module 100, and then transmit the operation parameters of each device to the control module 104.
The control module 104 is used for receiving the control parameters of the decision module 103 and controlling each device in the building ventilation air-conditioning system.
The human-computer interaction module 105 is used for displaying data such as outdoor temperature, outdoor humidity, current control parameters and power consumption.
It should be noted that the structure shown in fig. 1 is only an example, and the embodiment of the present invention is not limited thereto.
Based on the above description, fig. 2 exemplarily shows a flow of a load prediction-based building ventilation and air conditioning control method according to an embodiment of the present invention, which may be performed by a load prediction-based building ventilation and air conditioning control apparatus.
As shown in fig. 2, the specific steps of the process include:
step 201, obtaining the current information data of the building.
In an embodiment of the present invention, the current information data of the building may include a time period, an outdoor temperature, a power consumption amount, and a human traffic amount. The current information data of the building can be acquired by the acquisition module 100 and stored in the storage module 101.
Step 202, determining the current load of the building according to the current information data and the load prediction model of the building.
In the embodiment of the invention, the load prediction model is obtained by training the historical information data of the building. The load prediction is to decompose the time sequence, divide the time sequence into a trend item, a period item, a holiday item and other parts, train each part by using the historical information data of the building to establish a corresponding model, and fit each model to establish a unified load prediction model. Specifically, inputting time periods, outdoor temperature, power consumption and pedestrian flow in historical information data of the building into a preset trend model for training to obtain a trained trend model; inputting time periods, outdoor temperature, power consumption and pedestrian flow in historical information data of the building into a preset periodic model for training to obtain a trained periodic model; inputting time periods, outdoor temperature, power consumption and pedestrian flow in historical information data of the building into a preset holiday model for training to obtain a trained holiday model; and fitting the trained trend model, periodic model and holiday model to determine a load prediction model, wherein the load prediction model comprehensively considers the trend and periodicity of the data and the influence of holidays.
For example, the load prediction of the building is based on a time series model Prophet, which is suitable for data with obvious intrinsic regularity, especially for data with obvious trend, periodicity and influence of holidays. A fitting based on time series ranking and machine learning. Training of the data model is performed on the basis of historical data, and prediction of building load can be well achieved. The historical data is divided into input variables (key factors affecting the load) and output variables (building load). Based on analysis and calculation, the building lighting load and the infiltration wind load are basically in a stable state, the number of main persons influencing the load and outdoor weather meteorological parameters are used, the input variables of the Prophet are dry-wet bulb temperature, the predicted time interval and the human flow, and the output variable is the building load. Specifically, as shown in fig. 3. The analysis method divides the time series into several parts through the decomposition of the time series, and generally speaking, in the time series data prediction, besides a season term, a trend term and a residual term, the time series has the effect of holidays. Therefore, in the algorithm, the above four terms are considered simultaneously, and the following formula is formed:
y(t)＝g(t)+s(t)+h(t)+ε(t)………………………………………………(1)
wherein g (t) represents a trend term which represents the variation trend of the time series on the non-periodic top; s (t) represents a period term, alternatively referred to as a seasonal term, typically in units of weeks or years; h (t) represents a holiday term which represents whether holidays exist on the same day; ε (t) represents the error term or residue term, which the algorithm finally adds up to obtain the predicted value of the time series by fitting them.
a) Trend model g (t):
there are two functions in the trend model, one is a logistic regression based function and the other is a piecewise linear function. The invention uses logistic regression functions to build trend models. The functional model of the logistic regression is as follows:
f(x)＝C/(1+e-k(x-m))………………………………………………(2)
where C is called the maximum asymptotic value of the curve, k is the rate of increase of the curve, and m is the midpoint of the curve, but in practice these three parameters may not all be constant but are likely to change with time, so in this model of Prophet, these three parameters are all replaced by functions that change with time, i.e. C (t), k (t), and m (t).
In addition, the trend does not change all the time, and changes at some specific time or potential periodic curve, such as t of fig. 41，t2Are two change points of the time series.
b) Period model s (t):
the time series usually shows seasonal variation with seasonal variation of day, week, month, year, etc., also called periodic variation, and in the periodic model, the periodicity of the time series is simulated using fourier series. The specific formula is as follows:
Figure BDA0002379934680000101
where L represents a period, a sequence of years, N is 10, and N is 3 when a period is a period, β is a normal distribution, and the larger the standard deviation σ, the more significant the effect of season, and the smaller the standard deviation, the less significant the effect of season.
c) Festival and holiday model h (t):
besides weekends, a plurality of holidays also exist, and the influence degree of each holiday on the time series is different, so that different holidays can be regarded as mutually independent models, and different functions can be set for different holidays to represent the influence range of the holidays. The specific formula is as follows:
Figure BDA0002379934680000102
wherein D isiRepresenting a period of time before and after the holiday, M representing the holidays of M sections, and a parameter kiThe range of influence of holidays is a normal function. v represents the magnitude of the influence of holidays on the model, and a larger value means a larger influence on the model, and a smaller value means a smaller influence on the model. The normal function is formulated as follows:
Figure BDA0002379934680000103
and 203, respectively inputting the multiple groups of variable combinations of the energy consumption model into the energy consumption model to obtain the total energy consumption corresponding to each group of variable combinations.
In the embodiment of the invention, an energy consumption model of a building ventilation air-conditioning system comprises a water chilling unit energy consumption model, a water pump energy consumption model, a cooling tower energy consumption model and an air conditioning box energy consumption model, refrigerating capacity and air supply processing capacity in historical energy consumption data of an air conditioning box are input into the air conditioning box energy consumption model to obtain the chilled water quantity of the air conditioning box, the refrigerating capacity and the air supply processing capacity are determined according to an air supply detection value, an air supply humidity detection value and a set value of the air conditioning box, refrigerating capacity in the historical energy consumption data of the air conditioning box, chilled water supply temperature and cooling water inlet temperature in the historical energy consumption data of the water chilling unit are input into the water chilling unit energy consumption model to obtain total unit energy consumption of the water chilling unit, condensing heat load, cooling water inlet temperature and cooling water inlet and outlet temperature difference in the historical energy consumption data of the water chilling unit are input into the cooling tower energy consumption model, and determining the cooling water quantity of the cooling tower, determining the condensation heat load according to the total energy consumption and the refrigerating capacity of the unit, combining the chilled water supply temperature, the chilled water quantity, the cooling water inlet temperature, the cooling water quantity and the fan air quantity, and determining the multi-group variable combination of the energy consumption model of the building ventilation air-conditioning system.
Specifically, the energy consumption model of the building ventilation air-conditioning system is determined according to the following energy consumption models:
1) optimizing the energy consumption model and operation of the water chilling unit:
a plurality of factors influence the energy consumption of a water chilling unit, and the ASHARE application handbook indicates that the energy consumption of the water chilling unit is related to the condensing temperature, the evaporating temperature and the load of the unit. The condensation humidity in the water chiller is mainly influenced by the inlet water temperature of cooling water, the evaporation temperature is related to the supply water temperature of chilled water, and accordingly, an energy consumption model of the water chiller about the supply water temperature of the chilled water, the inlet water temperature of the cooling water and the unit load is provided, and the expression of the energy consumption model is as follows:
COP＝β0+β1*Qe+β2*Tldg+β3*Tlqj+β4*Qe 2+β5*Qe*Tldg+β6*Qe*Tlqj+β7*Tldg*Tlqj……………………………………………………………(6)
wherein, β0：β7For the coefficient of the energy consumption model, TlqjFor the inlet water temperature of the cooling water, TldgSupply of water temperature, Q, to chilled watereFor the load of the water chilling unit, the energy consumption model of the water chilling unit can be identified by combining the actual operation data of the unit, and the energy consumption model coefficient is obtained.
Firstly, parameter identification is carried out on the water chilling unit energy consumption model by using actual operation parameters to obtain unknown model coefficients, and the water chilling unit energy consumption model under partial load is established. Then an optimization algorithm based on the penalty function method. The load distribution can be carried out when the water chilling units with the same rated refrigerating capacity run in a combined mode, and the number of the started water chilling units and the born refrigerating load can be obtained when the load rates are in different ranges. In a system with a plurality of water chilling units running in parallel, the most common load distribution control method is an equal chilled water supply water temperature method, namely for a system with the same rated refrigerating capacity of the water chilling units, the load is evenly distributed among the units running; for systems with different unit rated refrigerating capacities, each running water chilling unit bears the refrigerating capacity of the system according to the proportion of the rated refrigerating capacity to the total unit customized refrigerating capacity, and the control method is considered to be close to optimal.
2) Optimizing the energy consumption model and operation of the water pump:
establishing a water pump performance model expression of n water pumps running in parallel as follows:
H＝a1*Q2+a2*Q+a3…………………………………………………………………(7)
Figure BDA0002379934680000121
the multiple water pumps are connected in parallel, the lift of the water pumps is unchanged, the flow is added, and the working conditions and the pipe network characteristic curve of the air-conditioning water system are designed. Obtaining the dividing points of each speed regulating range, finding out the number n of the water pumps connected in parallel under a certain flow through the dividing points to obtain the flow and the lift of a single water pump, obtaining an equivalent pipe network characteristic curve according to the flow and the lift, and finally calculating the value Q of the equivalent pipe network characteristic curvexUnder the flow, the total power consumption of the parallel connection of the n water pumps is as follows:
Figure BDA0002379934680000122
firstly, the relation between the operation lift and the flow of the water pump and the relation between the efficiency and the flow of the water pump are identified according to the actual operation parameters of the water pump. And (4) obtaining a pipe network characteristic curve according to the design working condition of the water system, namely the design flow and the design lift. Combining the principle that a plurality of water pumps run in parallel to enable the lift to be unchanged and the flow rate to be superposed, respectively connecting the characteristic curve of the pipe network with the H-Q equation of the running of 1, 2 and 3 … water pumps to obtain the demarcation points of each speed regulation range when different water pumps are connected in parallel, judging the number of the water pumps needing to be started at the moment according to the fact that the actual flow rate belongs to the flow rate range formed by the demarcation points, and further obtaining the flow rate needed to be born by a single water pump. And the equivalent pipe network characteristic curve passing through the operation point can be obtained by calculating the lift (the lift is calculated according to a pipe network characteristic curve formula of the total flow) under the flow and combining the flow born by a single water pump. And obtaining similar working condition points (Q, H) of the operating point by combining the operating point with an H-Q curve equation of a single water pump, and substituting the flow of the similar working condition points into a water pump efficiency formula of the single water pump to obtain the water pump efficiency of the operating point. And taking the ratio of the flow rate of the operation point to the flow rate of the similar working condition point as a speed change ratio, and calculating the efficiency of the water pump operation motor and the efficiency of the frequency converter through the speed change ratio. And calculating the operation energy consumption of a single water pump at the operation point by combining a water pump energy consumption calculation formula, and multiplying the energy consumption by the number of the single water pump to obtain the total energy consumption of the multiple water pumps in parallel operation.
3) Optimizing a cooling tower heat exchange energy consumption model and operation:
the fan air volume of the cooling tower can be obtained by knowing the water inlet temperature of the cooling tower, the ambient wet bulb temperature, the cooling water mass flow and the heat dissipation capacity of the cooling tower. At the moment, the limitation of the number of cooling towers is considered, although a plurality of cooling towers are connected in parallel and run synchronously, the number of the cooling towers in parallel is limited, and the low-frequency fan frequency converter cannot achieve the energy-saving effect because the power consumption of the fan frequency converter accounts for a large proportion of the total power consumption (the power consumption of the fan plus the power consumption of the frequency converter). According to the literature, when the frequency is in a range of 30Hz-50Hz (high-efficiency operation frequency range), the frequency conversion energy-saving effect is obvious, so that when the air volume of a single cooling tower is calculated, the number of the cooling towers at the moment is considered, and the frequency corresponding to the air volume of the cooling tower under the heat dissipation capacity is ensured to be in the high-efficiency operation frequency range, and can be judged according to the following formula:
Figure BDA0002379934680000131
wherein m isedRated air quantity f of cooling tower fanedIs the rated frequency of the cooling tower fan, faThe corresponding frequency under the air quantity. The air volume calculation formula is as follows:
Figure BDA0002379934680000132
wherein m isc1Flow rate of a single cooling tower, d1，d2，d3Is a parameter to be identified; t iswbAmbient wet bulb temperature at this time; t iscoThe water inlet temperature of the cooling tower.
Further, the calculation formula of the energy consumption model of the single cooling tower fan is as follows:
P＝c1*Fa 2+c2*Fa+c3…………………………………………………………(12)
wherein, Fa＝3600maRho (air volume flow m)3/h)。
4) Energy consumption and heat exchange model of air conditioning box:
the air-conditioning box is a key device for realizing heat exchange between an air system and a water system, and sensible heat and latent heat of inlet air of the air-conditioning box are taken away by chilled water, so that calculation is necessary for the heat exchange process of the air-conditioning box.
The calculation formula of the heat exchange model of the air-conditioning box is as follows:
Figure BDA0002379934680000141
wherein Q ist，Qt，edRespectively providing cooling capacity under the actual working condition and the standard working condition of the air conditioning box; g, GedRespectively the chilled water amount under the actual working condition and the standard working condition of the air conditioning box; v, VedRespectively representing the air volume of the air conditioning box under the actual working condition and the standard working condition;ts1，ts1，edthe air inlet wet bulb temperatures under the actual working condition and the standard working condition of the air conditioning box are respectively set; t is tw1，tw1，edThe temperatures of the chilled water inlet under the actual working condition and the standard working condition of the air conditioning box are respectively set; and a and b are heat exchange model coefficients which need to be identified according to actual parameters. The heat exchange model can be used for calculating the required chilled water quantity under the conditions of known cold load, air supply quantity and air supply state.
A polynomial relation between a success rate and an air supply quantity is considered for a model of an air conditioner box fan:
P＝c1*Va 3+c2*Va 2+c3*Va+c4……………………………………………………(14)
wherein, VaIs the amount of the fan air, c1：c4The model coefficient of the energy consumption of the fan needs to be identified according to actual operation parameters.
5) And energy-saving optimization for the building ventilation air-conditioning system:
step 1: the refrigerating capacity required by the air conditioning box and the air supply handling capacity required by the air conditioning box can be obtained by combining the air supply quantity detection value, the air supply temperature and humidity detection value and the set value of the air conditioning box, and the value of the refrigerating capacity and the air supply handling capacity is input into the air conditioning box heat exchange model under the condition of the known chilled water supply temperature to calculate the amount of chilled water required at the moment.
Step 2: the method comprises the steps of inputting the cold quantity required by an air conditioning box (namely the refrigerating capacity required by a cold machine) and the water supply temperature of chilled water, and the water inlet temperature of the chilled water into an energy consumption model of the water chilling units, analyzing by using a penalty function method to obtain the load rate, the number of opened water chilling units and the total energy consumption of the units required to be born by the two water chilling units at the moment, and obtaining the return water temperature of the chilled water by combining the quantity of the chilled water.
Step 3: the condensation heat load required to be born by the cooling tower can be obtained according to the total energy consumption and the refrigerating capacity of the unit, the condensation heat load, the water inlet temperature of cooling water and the water inlet temperature difference of the cooling water are input into a heat exchange model of the cooling tower, and the air volume of a fan of the cooling tower can be obtained by combining the outdoor dry-wet-bulb temperature at the moment. And simultaneously, the water quantity of the cooling water is calculated according to the water inlet temperature of the cooling water and the temperature difference between the inlet water and the outlet water.
Step 4: based on the air volume and the water volume and the energy consumption of the water chilling unit, the total energy consumption value of the central air conditioning system can be obtained, and by combining the modes of the supply water temperature of the variable chilled water, the intake water temperature of the variable chilled water and the variable water flow, a plurality of groups of chilled water supply water temperatures, water flows, intake water temperatures of the cooling water, water flows and air volume of the cooling tower can be input into an energy consumption model for calculation, and by combining the currently acquired actual temperature, the group of variable combinations which can enable the system energy consumption to be the lowest is selected as an optimized parameter to be output. The method is characterized in that variable flow and variable water temperature coordinated optimization are realized aiming at the global optimization of an energy consumption model of the building ventilation air-conditioning system, the parameters in the building ventilation air-conditioning system are mutually influenced, the controllable variables of the building ventilation air-conditioning system are globally optimized, and the multivariable global optimization energy-saving control of the variable flow, the variable temperature and the variable air volume is comprehensively considered, so that the globally optimal energy-saving effect is achieved.
And 204, determining the optimal operation parameters of each device in the building ventilation and air conditioning system according to the total energy consumption corresponding to each group of variable combination and the current load of the building.
In the embodiment of the invention, each group of variable combinations of a plurality of groups of variable combinations is input into an energy consumption model of the building ventilation air-conditioning system according to the current load of the building to obtain the total energy consumption corresponding to each group of variable combinations, the total energy consumption corresponding to each group of variable combinations is compared to determine the variable combination corresponding to the lowest total energy consumption, and the variable combination corresponding to the lowest total energy consumption is determined as the optimal operation parameter of each device in the building ventilation air-conditioning system. Specifically, a genetic algorithm is used for establishing an energy consumption model of the central air-conditioning system based on historical data, and the adjustable variable is analyzed to obtain a controllable variable combination (such as a combination of chilled water supply temperature, chilled water flow, cooling water inlet temperature, cooling water flow and fan air volume). When the load is satisfied, each group of variable combination is input into the energy consumption model, so that a system energy consumption value can be obtained. However, each variable has its own reasonable variation range, and if the reasonable variation range of each variable is divided into 5 parts, 3125 variable combinations are formed for the 5 variables, wherein any one variable combination corresponds to an energy consumption value of a system. The genetic algorithm can be used for finding out the combination of the set of variables which can ensure that the energy consumption of the system is the lowest on the premise of meeting the load so as to control each device in the building ventilation and air conditioning system.
And step 205, controlling each device in the building ventilation air-conditioning system according to the optimal operation parameter of each device in the building ventilation air-conditioning system.
In the embodiment of the invention, the operation parameters in the control page of each device in the building ventilation and air-conditioning system are updated to the optimal operation parameters of each device in the corresponding building ventilation and air-conditioning system, so that each device in the building ventilation and air-conditioning system is regulated to the optimal operation state.
The embodiment shows that the current load of the building can be determined by obtaining the current information data of the building and according to the current information data and the load prediction model of the building, multiple groups of variable combinations of the energy consumption model are respectively input into the energy consumption model to obtain the total energy consumption corresponding to each group of variable combinations, the optimal operation parameters of each device in the building ventilation and air conditioning system can be determined according to the total energy consumption corresponding to each group of variable combinations and the current load of the building, and each device in the building ventilation and air conditioning system is controlled according to the optimal operation parameters of each device in the building ventilation and air conditioning system. The load prediction model is established by decomposing the time sequence, holiday information can be artificially set, accurate load prediction is realized, and optimal control is performed by combining the global energy consumption model, so that each device is in an optimal operation state under the condition of meeting the current load of the building, the problems of inaccurate load prediction, untimely temperature adjustment and energy waste of the heating ventilation air conditioner are solved, the room temperature is accurately controlled, the human body comfort level of the building is improved, the purposes of saving energy and reducing emission of building operators are realized, the application cost is reduced, and green energy-saving buildings are built.
Based on the same technical concept, fig. 5 exemplarily shows a load prediction-based building ventilation and air conditioning control device according to an embodiment of the present invention, which can execute the flow of a load prediction-based building ventilation and air conditioning control method.
As shown in fig. 5, the apparatus includes:
an obtaining unit 501, configured to obtain current information data of a building;
a processing unit 502, configured to determine a current load of the building according to the current information data of the building and a load prediction model; the load prediction model is obtained by training historical information data of the building; respectively inputting a plurality of groups of variable combinations of the energy consumption model into the energy consumption model to obtain total energy consumption corresponding to each group of variable combinations; the multi-group variable combination of the energy consumption model is determined according to the energy consumption model and historical energy consumption data; determining the optimal operation parameters of each device in the building ventilation air-conditioning system according to the total energy consumption corresponding to each group of variable combination and the current load of the building; and controlling each device in the building ventilation air-conditioning system according to the optimal operation parameter of each device in the building ventilation air-conditioning system.
Optionally, the information data of the building includes a time period, an outdoor temperature, power consumption and a human flow;
the processing unit 502 is specifically configured to:
inputting the time period, the outdoor temperature, the power consumption and the pedestrian volume in the historical information data of the building into a preset trend model for training to obtain a trained trend model;
inputting the time period, the outdoor temperature, the power consumption and the pedestrian flow in the historical information data of the building into a preset periodic model for training to obtain a trained periodic model;
inputting the time period, the outdoor temperature, the power consumption and the human flow in the historical information data of the building into a preset holiday model for training to obtain a trained holiday model;
and fitting the trained trend model, the periodic model and the holiday model to determine the load prediction model.
Optionally, the energy consumption model of the building ventilation air-conditioning system comprises a water chilling unit energy consumption model, a cooling tower energy consumption model and an air-conditioning box energy consumption model;
the processing unit 502 is specifically configured to:
inputting the refrigerating capacity and the air supply processing capacity in the historical energy consumption data of the air-conditioning box into the energy consumption model of the air-conditioning box to obtain the chilled water quantity of the air-conditioning box; the refrigerating capacity and the air supply processing capacity are determined according to an air supply quantity detection value, an air supply humidity detection value and a set value of the air conditioning box;
inputting the refrigerating capacity in the historical energy consumption data of the air conditioning box and the supply water temperature and the intake water temperature of the chilled water in the historical energy consumption data of the water chilling unit into the energy consumption model of the water chilling unit to obtain the total unit energy consumption of the water chilling unit;
inputting the condensation heat load, the cooling water inlet temperature and the cooling water inlet and outlet temperature difference in the historical energy consumption data of the water chilling unit into the cooling tower energy consumption model to obtain the fan air volume of the cooling tower and determine the cooling water volume of the cooling tower; the condensation heat load is determined according to the total energy consumption of the unit and the refrigerating capacity;
and combining the chilled water supply temperature, the chilled water quantity, the cooling water inlet temperature, the cooling water quantity and the fan air quantity to determine the multi-group variable combination of the energy consumption model of the building ventilation air-conditioning system.
Optionally, the processing unit 502 is specifically configured to:
inputting each variable combination of the multiple variable combinations into an energy consumption model of the building ventilation air-conditioning system according to the current load of the building to obtain total energy consumption corresponding to each variable combination;
comparing the total energy consumption corresponding to each group of variable combination to determine the variable combination corresponding to the lowest total energy consumption;
and determining the variable combination corresponding to the lowest total energy consumption as the optimal operation parameter of each device in the building ventilation and air conditioning system.
Optionally, the processing unit 502 is specifically configured to:
and updating the operation parameters in the control page of each device in the building ventilation air-conditioning system to the corresponding optimal operation parameters of each device in the building ventilation air-conditioning system, so that each device in the building ventilation air-conditioning system is regulated to be in an optimal operation state.
Based on the same technical concept, an embodiment of the present invention provides a computing device, including:
a memory for storing program instructions;
and the processor is used for calling the program instructions stored in the memory and executing the building ventilation air-conditioning control method based on load prediction according to the obtained program.
Based on the same technical concept, embodiments of the present invention provide a computer-readable storage medium storing computer-executable instructions for causing a computer to perform a method for controlling ventilation and air conditioning for a building based on load prediction.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present application and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (10)

1. A building ventilation air conditioner control method based on load prediction is characterized by comprising the following steps:
acquiring current information data of a building;
determining the current load of the building according to the current information data and the load prediction model of the building; the load prediction model is obtained by training historical information data of the building;
respectively inputting a plurality of groups of variable combinations of the energy consumption model into the energy consumption model to obtain total energy consumption corresponding to each group of variable combinations; the multi-group variable combination of the energy consumption model is determined according to the energy consumption model and historical energy consumption data;
determining the optimal operation parameters of each device in the building ventilation air-conditioning system according to the total energy consumption corresponding to each group of variable combination and the current load of the building;
and controlling each device in the building ventilation air-conditioning system according to the optimal operation parameter of each device in the building ventilation air-conditioning system.
2. The method of claim 1, wherein the information data for the building includes time periods, outdoor temperatures, power consumption, and traffic;
training the historical information data of the building to obtain the load prediction model, wherein the training comprises the following steps:
inputting the time period, the outdoor temperature, the power consumption and the pedestrian volume in the historical information data of the building into a preset trend model for training to obtain a trained trend model;
inputting the time period, the outdoor temperature, the power consumption and the pedestrian flow in the historical information data of the building into a preset periodic model for training to obtain a trained periodic model;
inputting the time period, the outdoor temperature, the power consumption and the human flow in the historical information data of the building into a preset holiday model for training to obtain a trained holiday model;
and fitting the trained trend model, the periodic model and the holiday model to determine the load prediction model.
3. The method of claim 1, wherein the energy consumption models for the building ventilation air conditioning system include a chiller energy consumption model, a cooling tower energy consumption model, and an air conditioning cabinet energy consumption model;
determining a plurality of variable combinations of the energy consumption model according to the energy consumption model and historical energy consumption data, including:
inputting the refrigerating capacity and the air supply processing capacity in the historical energy consumption data of the air-conditioning box into the energy consumption model of the air-conditioning box to obtain the chilled water quantity of the air-conditioning box; the refrigerating capacity and the air supply processing capacity are determined according to an air supply quantity detection value, an air supply humidity detection value and a set value of the air conditioning box;
inputting the refrigerating capacity in the historical energy consumption data of the air conditioning box and the supply water temperature and the intake water temperature of the chilled water in the historical energy consumption data of the water chilling unit into the energy consumption model of the water chilling unit to obtain the total unit energy consumption of the water chilling unit;
inputting the condensation heat load, the cooling water inlet temperature and the cooling water inlet and outlet temperature difference in the historical energy consumption data of the water chilling unit into the cooling tower energy consumption model to obtain the fan air volume of the cooling tower and determine the cooling water volume of the cooling tower; the condensation heat load is determined according to the total energy consumption of the unit and the refrigerating capacity;
and combining the chilled water supply temperature, the chilled water quantity, the cooling water inlet temperature, the cooling water quantity and the fan air quantity to determine the multi-group variable combination of the energy consumption model of the building ventilation air-conditioning system.
4. The method of claim 1, wherein said determining optimal operating parameters for each device in said building ventilation and air conditioning system based on said total energy consumption and said current load of said building for each set of variable combinations comprises:
inputting each variable combination of the multiple variable combinations into an energy consumption model of the building ventilation air-conditioning system according to the current load of the building to obtain total energy consumption corresponding to each variable combination;
comparing the total energy consumption corresponding to each group of variable combination to determine the variable combination corresponding to the lowest total energy consumption;
and determining the variable combination corresponding to the lowest total energy consumption as the optimal operation parameter of each device in the building ventilation and air conditioning system.
5. The method of any one of claims 1 to 4, wherein said controlling each device in the building ventilation air conditioning system based on optimal operating parameters of each device in the building ventilation air conditioning system comprises:
and updating the operation parameters in the control page of each device in the building ventilation air-conditioning system to the corresponding optimal operation parameters of each device in the building ventilation air-conditioning system, so that each device in the building ventilation air-conditioning system is regulated to be in an optimal operation state.
6. A building ventilation air conditioner control device based on load prediction is characterized by comprising:
the acquisition unit is used for acquiring current information data of the building;
the processing unit is used for determining the current load of the building according to the current information data of the building and a load prediction model; the load prediction model is obtained by training historical information data of the building; respectively inputting a plurality of groups of variable combinations of the energy consumption model into the energy consumption model to obtain total energy consumption corresponding to each group of variable combinations; the multi-group variable combination of the energy consumption model is determined according to the energy consumption model and historical energy consumption data; determining the optimal operation parameters of each device in the building ventilation air-conditioning system according to the total energy consumption corresponding to each group of variable combination and the current load of the building; and controlling each device in the building ventilation air-conditioning system according to the optimal operation parameter of each device in the building ventilation air-conditioning system.
7. The apparatus of claim 6, wherein the information data for the building includes time periods, outdoor temperatures, power consumption, and traffic;
the processing unit is specifically configured to:
inputting the time period, the outdoor temperature, the power consumption and the pedestrian volume in the historical information data of the building into a preset trend model for training to obtain a trained trend model;
inputting the time period, the outdoor temperature, the power consumption and the pedestrian flow in the historical information data of the building into a preset periodic model for training to obtain a trained periodic model;
inputting the time period, the outdoor temperature, the power consumption and the human flow in the historical information data of the building into a preset holiday model for training to obtain a trained holiday model;
and fitting the trained trend model, the periodic model and the holiday model to determine the load prediction model.
8. The apparatus of claim 6, wherein the energy consumption model of the building ventilation air conditioning system comprises a chiller energy consumption model, a cooling tower energy consumption model, and an air conditioning cabinet energy consumption model;
the processing unit is specifically configured to:
inputting the refrigerating capacity and the air supply processing capacity in the historical energy consumption data of the air-conditioning box into the energy consumption model of the air-conditioning box to obtain the chilled water quantity of the air-conditioning box; the refrigerating capacity and the air supply processing capacity are determined according to an air supply quantity detection value, an air supply humidity detection value and a set value of the air conditioning box;
inputting the refrigerating capacity in the historical energy consumption data of the air conditioning box and the supply water temperature and the intake water temperature of the chilled water in the historical energy consumption data of the water chilling unit into the energy consumption model of the water chilling unit to obtain the total unit energy consumption of the water chilling unit;
inputting the condensation heat load, the cooling water inlet temperature and the cooling water inlet and outlet temperature difference in the historical energy consumption data of the water chilling unit into the cooling tower energy consumption model to obtain the fan air volume of the cooling tower and determine the cooling water volume of the cooling tower; the condensation heat load is determined according to the total energy consumption of the unit and the refrigerating capacity;
and combining the chilled water supply temperature, the chilled water quantity, the cooling water inlet temperature, the cooling water quantity and the fan air quantity to determine the multi-group variable combination of the energy consumption model of the building ventilation air-conditioning system.
9. A computing device, comprising:
a memory for storing program instructions;
a processor for calling program instructions stored in said memory to execute the method of any one of claims 1 to 5 in accordance with the obtained program.
10. A computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform the method of any one of claims 1 to 5.
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