A variable flow active energy-saving intelligent control system for HVAC water system

Abstract

The present invention relates to the field of air conditioning energy-saving control technology, specifically a variable flow active energy-saving intelligent control system for a HVAC water system, including a flow optimization unit, a third modeling unit and a flow control unit, and also includes: a room temperature prediction unit, the room temperature prediction unit is used to predict the indoor temperature of a target building based on the characteristics of the target building to obtain the predicted indoor temperature of the target building, obtain the required indoor cooling load of the target building based on the predicted indoor temperature of the target building, and transmit the required indoor cooling load of the target building to the flow optimization unit. The present invention performs data modeling and temperature and flow prediction on the cooling tower, cooling coil and water pump of the HVAC water system in the target building through the room temperature prediction unit, the first modeling unit, the second modeling unit and the third modeling unit, so as to accurately estimate and optimize the required cooling load.

Description

Technical Field

[0001] The present invention relates to the technical field of air conditioning energy-saving control, and in particular to a variable flow active energy-saving intelligent control system for a heating, ventilation and air conditioning water system. Background Art

[0002] The HVAC water system is a system used to regulate the temperature and humidity in a building. It mainly uses water as a transmission medium to achieve heating, cooling and air conditioning. Its working principle involves the circulation of water, which is transported to various parts of the building through different equipment and piping systems to meet the needs of comfort and energy efficiency.

[0003] Traditional systems are often based on fixed flow and temperature control, and fail to fully consider the actual changes in the cooling and heating loads in the building. This may cause the system to operate at high energy consumption when the cooling demand is low, resulting in energy waste. Traditional systems usually do not have the ability to adjust in real time and cannot automatically optimize operating parameters according to actual environmental changes. This inflexible control method may not be able to fully adapt to changes in actual needs, thereby affecting system efficiency and comfort. Moreover, since traditional systems usually require regular manual adjustments and maintenance, the operating and maintenance costs are high. In addition, when the system fails or performance degrades, manual diagnosis and adjustment are often required, which increases maintenance workload and costs. Traditional systems are often designed and configured based on the worst case scenario to ensure that they can meet the needs under the most extreme conditions. This design method may cause the system to be in a non-optimal state during actual operation, resulting in equipment overload or resource waste. Summary of the invention

[0004] The purpose of the present invention is to address the problems existing in the background technology and to propose a variable flow active energy-saving intelligent control system for a HVAC water system.

[0005] The technical solution of the present invention is a variable flow active energy-saving intelligent control system for a HVAC water system, comprising a flow optimization unit, a third modeling unit and a flow control unit, and also comprising:

[0006] A room temperature prediction unit, the room temperature prediction unit is used to predict the indoor temperature of the target building based on the characteristics of the target building to obtain the predicted indoor temperature of the target building, obtain the required indoor cooling load of the target building based on the predicted indoor temperature of the target building, and transmit the required indoor cooling load of the target building to the flow optimization unit;

[0007] a first modeling unit, the first modeling unit being used to perform data modeling on a cooling tower of a HVAC water system in the target building to obtain a cooling tower model of the HVAC water system, obtain an outlet cooling water temperature of the cooling tower based on the cooling tower model of the HVAC water system, and transmit the outlet cooling water temperature of the cooling tower to a flow optimization unit;

[0008] The second modeling unit is used to perform data modeling on the cooling coil of the HVAC water system in the target building to obtain a cooling coil model of the HVAC water system, obtain the outlet condensate water temperature of the cooling coil based on the cooling coil model of the HVAC water system, and transmit the outlet condensate water temperature of the cooling coil to the flow optimization unit.

[0009] Preferably, the third modeling unit is used to perform data modeling on the water pump of the HVAC water system in the target building to obtain a water pump model of the HVAC water system, obtain the condensate flow of the HVAC water system based on the water pump model of the HVAC water system, and transmit the condensate flow of the HVAC water system to the flow optimization unit.

[0010] Preferably, the flow optimization unit receives the indoor cooling load required by the target building transmitted by the room temperature prediction unit, the outlet cooling water temperature of the cooling tower transmitted by the first modeling unit, the outlet condensing water temperature of the cooling coil transmitted by the second modeling unit, and the condensing water flow of the HVAC water system transmitted by the third modeling unit, and takes the indoor cooling load required by the target building as the maximum, the outlet cooling water temperature of the cooling tower, the outlet condensing water temperature of the cooling coil, and the condensing water flow of the HVAC water system as the minimum as the objective function, and takes the rated power of the HVAC water system as the constraint condition to construct a flow optimization model, and transmit the flow optimization model to the flow control unit.

[0011] Preferably, the flow control unit introduces the flow optimization model transmitted by the flow optimization unit, and solves the flow optimization model based on the optimization algorithm to obtain the flow control parameters of the HVAC water system, and optimizes and controls the HVAC water system based on the flow control parameters of the HVAC water system.

[0012] Preferably, predicting the indoor temperature of the target building based on the characteristics of the target building to obtain the predicted indoor temperature of the target building includes:

[0013] An energy balance model of the wall and indoor air of the target building is constructed based on the characteristics of the target building. The energy balance model is as follows:

[0014]

[0015] Among them, C i represents the indoor air heat capacity, C w is the wall heat capacity, T o Indicates the outdoor temperature, T i Indicates the indoor temperature, T w represents the wall temperature, f solar，i represents the indoor air volume flow rate, S represents the solar radiation heat gain coefficient, A win Represents the area of ​​the region, I solar Represents the solar radiation intensity, R win The thermal resistance of the window, R w，i Represents the heat transfer resistance between the wall and the indoor air, R w，o represents the heat transfer resistance between the wall and the outdoor air, α represents the absorptivity of the outer wall surface to solar radiation, A w Indicates the window area;

[0016] Based on the room temperature influencing factors of the target building, an indoor temperature model of the target building without HVAC is constructed. The indoor temperature model without HVAC is as follows:

[0017]

[0018] Among them, t a represents the indoor temperature without HVAC, Indicates the indoor thermal characteristic coefficient, u i represents the eigenvalue vector of the indoor space, q k represents the factors affecting room temperature, m represents the dimension of the factors affecting room temperature, and n represents the dimension of the spatial eigenvalue vector;

[0019] Based on the room temperature influencing factors of the target building, an indoor temperature model with HVAC of the target building is constructed. The indoor temperature model with HVAC is as follows:

[0020]

[0021] Among them, t b Indicates the indoor temperature with HVAC;

[0022] The indoor temperature model without HVAC and the indoor temperature model with HVAC are solved based on the energy balance model to obtain the indoor temperature without HVAC and the indoor temperature with HVAC of the target building.

[0023] Preferably, the required indoor cooling load of the target building is obtained based on the predicted indoor temperature of the target building. The required indoor cooling load of the target building is calculated as follows:

[0024]

[0025] Among them, q a Indicates the required indoor cooling load of the target building.

[0026] Preferably, the cooling coil model of the HVAC water system is as follows:

[0027]

[0028] Among them, t out represents the outlet condensate temperature of the cooling coil, t in Indicates the inlet condensing water temperature of the cooling coil, m a represents the total amount of air passing through the cooling coil, f represents the bypass air ratio, h air Indicates the enthalpy of inlet air, h ao represents the enthalpy of the unmixed outlet air, d ao Indicates the moisture content of the unmixed outlet air, d air Indicates the humidity content of the inlet air, h c Indicates the enthalpy of condensed water, m f represents the total flow of the cooling coil, C pf Indicates the specific heat of condensed water.

[0029] Preferably, the water pump model of the HVAC water system is as follows:

[0030]

[0031] Where q represents the flow rate of condensed water in the HVAC water system, P e represents the effective power of the variable frequency water pump, ρ represents the density of condensed water, g represents the acceleration of gravity, h c Indicates the enthalpy of condensed water.

[0032] Preferably, the cooling tower model of the HVAC water system is as follows:

[0033]

[0034] Among them, Q cell represents the heat transfer rate of the cooling tower of the HVAC water system, ε l represents the heat transfer efficiency on the air side, m l represents the dry air mass flow rate, h l，w，i It represents the specific enthalpy of wet air at the water inlet of the cooling tower, h l，i represents the specific enthalpy of moist air at the air inlet, m w，i represents the heat transfer rate of the cooling tower of the HVAC water system, C pw represents the specific heat capacity of cooling water at normal pressure, T w，iIndicates the cooling tower inlet water temperature, T ref Indicates the reference temperature of cooling water, m w，0 Indicates the cooling water outlet mass flow rate, T w，o Indicates the cooling tower outlet water temperature.

[0035] Preferably, solving the flow optimization model based on an optimization algorithm to obtain flow control parameters of the HVAC water system includes:

[0036] Set the initialization parameters of the optimization algorithm to initialize the population position. The population position initialization formula is as follows:

[0037]

[0038] Among them, x t and x t+1 represents the position of the individual at the t-th iteration and the t+1-th iteration, and p represents the preset parameter for controlling the nonlinear mapping;

[0039] Calculate the fitness value of the individual and calculate the elite individual position. The elite individual position calculation formula is as follows:

[0040]

[0041] in, represents the reverse solution of the j-th dimension individual at the t-th iteration, x i，j (t) represents the elite solution of the j-th dimension individual at the t-th iteration, k represents a random number in the interval [0, 1], u j (t) represents the maximum position of the individual at the tth iteration, l j (t) represents the minimum position of the individual at the tth iteration;

[0042] Marking a preset number of the individuals with the best fitness values ​​as leader individuals, and updating the positions of the remaining individuals in the population;

[0043] Calculate and sort the updated individual fitness values, obtain elite individuals, and update the elite individual positions;

[0044] The above steps are repeated until the optimal solution is found or the maximum number of iterations of the optimization algorithm is reached, thereby obtaining the flow control parameters of the HVAC water system.

[0045] Compared with the prior art, the above technical solution of the present invention has the following beneficial technical effects:

[0046] 1. The present invention performs data modeling and temperature and flow prediction on the cooling tower, cooling coil and water pump of the HVAC water system in the target building through the room temperature prediction unit, the first modeling unit, the second modeling unit and the third modeling unit, so as to accurately estimate and optimize the required cooling load, and optimize the flow based on these predicted data through the flow optimization unit, so as to reduce unnecessary energy consumption and improve the overall energy utilization efficiency of the system, and the flow optimization unit takes the cooling load of the target building, the outlet cooling water temperature of the cooling tower, the outlet condensate temperature of the cooling coil and the condensate flow as the optimization targets, and can adjust the outlet cooling water temperature of the cooling tower, the outlet condensate temperature of the cooling coil and the condensate flow to the optimal state under the premise of meeting the cooling demand of the building, thereby improving the operating efficiency and stability of the system.

[0047] 2. The present invention optimizes the flow optimization model in real time through the flow control unit, and can dynamically adjust the flow and other key parameters of the HVAC water system, so that it can be adjusted in real time according to actual needs and environmental changes. It can ensure that the indoor temperature in the target building is maintained within a comfortable range through accurate room temperature prediction and optimized cooling load control, thereby improving the comfort of the living or working environment in the building. In addition, through intelligent optimization and automatic control, the need for human intervention and manual adjustment is reduced, thereby reducing operation and maintenance costs, and reducing the risk of failure due to improper operation or insufficient maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A schematic diagram of the system architecture of an overall system in an embodiment of the present invention.

[0049] Figure numerals: 1. Room temperature prediction unit; 2. Flow optimization unit; 3. First modeling unit; 4. Second modeling unit; 5. Third modeling unit; 6. Flow control unit. DETAILED DESCRIPTION

[0050] Embodiment 1, as Figure 1 As shown, a variable flow active energy-saving intelligent control system for a HVAC water system proposed by the present invention includes a flow optimization unit 2, a third modeling unit 5 and a flow control unit 6, and also includes:

[0051] A room temperature prediction unit 1 is used to predict the indoor temperature of the target building based on the characteristics of the target building to obtain the predicted indoor temperature of the target building, obtain the required indoor cooling load of the target building based on the predicted indoor temperature of the target building, and transmit the required indoor cooling load of the target building to the flow optimization unit 2;

[0052] The first modeling unit 3 is used to perform data modeling on a cooling tower of a HVAC water system in a target building to obtain a cooling tower model of the HVAC water system, obtain an outlet cooling water temperature of the cooling tower based on the cooling tower model of the HVAC water system, and transmit the outlet cooling water temperature of the cooling tower to the flow optimization unit 2;

[0053] The second modeling unit 4 is used to perform data modeling on the cooling coil of the HVAC water system in the target building to obtain a cooling coil model of the HVAC water system, obtain the outlet condensate water temperature of the cooling coil based on the cooling coil model of the HVAC water system, and transmit the outlet condensate water temperature of the cooling coil to the flow optimization unit 2.

[0054] In the present invention, the cooling tower is mainly used to cool the hot water from inside the building. It releases heat into the atmosphere through the evaporative cooling process, thereby reducing the water temperature. The cooling tower is generally located outside the building and processes high-temperature cooling water; the cooling coil is located inside the building and is used to exchange heat between hot and cold water (usually from the cooling tower or other cooling systems) and indoor air, thereby regulating the indoor temperature. The cooling coil is usually installed in a fan coil unit or an air handling unit to transfer the low temperature of the cooling water to the air; the cooling water provided by the cooling tower is transported to the cooling coil, and the low temperature of the cooling water is transferred to the indoor air through the cooling coil to achieve a cooling effect. In short, the cooling tower is responsible for reducing the temperature of the cooling water, while the cooling coil is responsible for applying the low temperature of the cooling water to the indoor environment.

[0055] In an optional embodiment, the third modeling unit 5 is used to perform data modeling on the water pump of the HVAC water system in the target building to obtain a water pump model of the HVAC water system, obtain the condensate flow of the HVAC water system based on the water pump model of the HVAC water system, and transmit the condensate flow of the HVAC water system to the flow optimization unit 2.

[0056] It should be noted that water pumps are used to promote the flow of water in the heating, ventilation and air conditioning (HVAC) water system, ensuring that cooling water or hot water circulates in the system, thereby achieving effective heat exchange and system operation; water pumps transport hot water from inside the building to the cooling tower. In the cooling tower, the water pump causes the water to flow through the filler and radiator, allowing the water to come into contact with the air in the cooling tower, releasing heat and lowering the water temperature; the water cooled in the cooling tower is transported to the cooling coil by a water pump. The water pump ensures that the cooling water can flow stably through the cooling coil, exchange heat with the indoor air, and achieve a cooling effect.

[0057] In an optional embodiment, the flow optimization unit 2 receives the indoor cooling load required by the target building transmitted by the room temperature prediction unit 1, the outlet cooling water temperature of the cooling tower transmitted by the first modeling unit 3, the outlet condensing water temperature of the cooling coil transmitted by the second modeling unit 4, and the condensing water flow of the HVAC water system transmitted by the third modeling unit 5, and takes the indoor cooling load required by the target building as the maximum, the outlet cooling water temperature of the cooling tower, the outlet condensing water temperature of the cooling coil and the condensing water flow of the HVAC water system as the minimum as the objective function, and takes the rated power of the HVAC water system as the constraint condition to construct a flow optimization model, and transmit the flow optimization model to the flow control unit 6.

[0058] It should be noted that the goal of building a flow optimization model is to optimize the cooling water flow in the heating, ventilation and air conditioning (HVAC) water system to meet the cooling needs of the building while improving the system efficiency while maintaining the system power constraint; maximize the required indoor cooling load: ensure that the required cooling effect is achieved inside the building; outlet cooling water temperature of the cooling tower: a lower outlet temperature can improve cooling efficiency; outlet condensing water temperature of the cooling coil: a lower outlet temperature can enhance the cooling effect; minimize the condensing water flow: reducing the cooling water flow can save energy and reduce the system load.

[0059] In an optional embodiment, the flow control unit 6 introduces the flow optimization model transmitted by the flow optimization unit 2, and solves the flow optimization model based on the optimization algorithm to obtain the flow control parameters of the HVAC water system, and optimizes and controls the HVAC water system based on the flow control parameters of the HVAC water system.

[0060] Embodiment 2, a variable flow active energy-saving intelligent control system for a HVAC water system proposed by the present invention, compared with embodiment 1, this embodiment further includes: predicting the indoor temperature of a target building based on the characteristics of the target building to obtain the predicted indoor temperature of the target building, including:

[0061] Based on the characteristics of the target building, an energy balance model of the wall and indoor air of the target building is constructed. The energy balance model is as follows:

[0062]

[0063] Among them, C i is the indoor air heat capacity, C w is the wall heat capacity, T o Indicates the outdoor temperature, T i Indicates the indoor temperature, T w represents the wall temperature, f solar，i represents the indoor air volume flow rate, S represents the solar radiation heat gain coefficient, A win Represents the area of ​​the region, Isolar Represents the solar radiation intensity, R win The thermal resistance of the window, R w，i Represents the heat transfer resistance between the wall and the indoor air, R w，o represents the heat transfer resistance between the wall and the outdoor air, α represents the absorptivity of the outer wall surface to solar radiation, A w Indicates the window area;

[0064] Based on the influencing factors of the room temperature of the target building, the indoor temperature model of the target building without HVAC is constructed. The indoor temperature model without HVAC is as follows:

[0065]

[0066] Among them, t a represents the indoor temperature without HVAC, Indicates the indoor thermal characteristic coefficient, u i represents the eigenvalue vector of the indoor space, q k represents the factors affecting room temperature, m represents the dimension of the factors affecting room temperature, and n represents the dimension of the spatial eigenvalue vector;

[0067] Based on the influencing factors of the room temperature of the target building, the indoor temperature model of the target building with HVAC is constructed. The indoor temperature model with HVAC is as follows:

[0068]

[0069] Among them, t b Indicates the indoor temperature with HVAC;

[0070] Based on the energy balance model, the indoor temperature model without HVAC and the indoor temperature model with HVAC are solved to obtain the indoor temperature without HVAC and the indoor temperature with HVAC of the target building.

[0071] In an optional embodiment, the required indoor cooling load of the target building is obtained based on the predicted indoor temperature of the target building. The required indoor cooling load of the target building is calculated as follows:

[0072]

[0073] Among them, q a Indicates the required indoor cooling load of the target building.

[0074] In an alternative embodiment, the cooling coil model of the HVAC water system is as follows:

[0075]

[0076] Among them, t outrepresents the outlet condensate temperature of the cooling coil, t in Indicates the inlet condensing water temperature of the cooling coil, m a represents the total amount of air passing through the cooling coil, f represents the bypass air ratio, h air Indicates the enthalpy of inlet air, h ao represents the enthalpy of the unmixed outlet air, d ao Indicates the moisture content of the unmixed outlet air, d air Indicates the humidity content of the inlet air, h c Indicates the enthalpy of condensed water, m f represents the total flow of the cooling coil, C pf Indicates the specific heat of condensed water.

[0077] In an optional embodiment, the water pump model of the HVAC water system is as follows:

[0078]

[0079] Where q represents the flow rate of condensed water in the HVAC water system, P e represents the effective power of the variable frequency water pump, ρ represents the density of condensed water, g represents the acceleration of gravity, h c Indicates the enthalpy of condensed water.

[0080] In an alternative embodiment, the cooling tower model of the HVAC water system is as follows:

[0081]

[0082] Among them, Q cell represents the heat transfer rate of the cooling tower of the HVAC water system, ε l represents the heat transfer efficiency on the air side, m l represents the dry air mass flow rate, h l，w，i It represents the specific enthalpy of wet air at the water inlet of the cooling tower, h l，i represents the specific enthalpy of moist air at the air inlet, m w，i represents the heat transfer rate of the cooling tower of the HVAC water system, C pw represents the specific heat capacity of cooling water at normal pressure, T w，i Indicates the cooling tower inlet water temperature, T ref Indicates the reference temperature of cooling water, m w , 0 represents the cooling water outlet mass flow rate, T w , o represents the cooling tower outlet water temperature.

[0083] In an optional embodiment, the flow optimization model is solved based on an optimization algorithm to obtain flow control parameters of the HVAC water system, including:

[0084] Set the initialization parameters of the optimization algorithm and initialize the population position. The population position initialization formula is as follows:

[0085]

[0086] Among them, x t and x t+1 represents the position of the individual at the t-th iteration and the t+1-th iteration, and p represents the preset parameter for controlling the nonlinear mapping;

[0087] Calculate the fitness value of the individual and the position of the elite individual. The calculation formula for the position of the elite individual is as follows:

[0088]

[0089] in, represents the reverse solution of the j-th dimension individual at the t-th iteration, x i，j (t) represents the elite solution of the j-th dimension individual at the t-th iteration, k represents a random number in the interval [0, 1], u j (t) represents the maximum position of the individual at the tth iteration, l j (t) represents the minimum position of the individual at the tth iteration;

[0090] Mark a preset number of individuals with the best fitness values ​​as leader individuals, and update the positions of the remaining individuals in the population;

[0091] Calculate and sort the updated individual fitness values, obtain elite individuals, and update the elite individual positions;

[0092] Repeat the above steps until the optimal solution is found or the maximum number of iterations of the optimization algorithm is reached, thereby obtaining the flow control parameters of the HVAC water system.

[0093] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited thereto, and various changes can be made within the knowledge scope of technicians in the relevant technical field without departing from the purpose of the present invention.

Claims

1. A variable flow active energy-saving intelligent control system for a HVAC water system, comprising a flow optimization unit, a third modeling unit and a flow control unit, characterized in that: A room temperature prediction unit, the room temperature prediction unit is used to predict the indoor temperature of the target building based on the characteristics of the target building to obtain the predicted indoor temperature of the target building, obtain the required indoor cooling load of the target building based on the predicted indoor temperature of the target building, and transmit the required indoor cooling load of the target building to the flow optimization unit; a first modeling unit, the first modeling unit being used to perform data modeling on a cooling tower of a HVAC water system in the target building to obtain a cooling tower model of the HVAC water system, obtain an outlet cooling water temperature of the cooling tower based on the cooling tower model of the HVAC water system, and transmit the outlet cooling water temperature of the cooling tower to a flow optimization unit; a second modeling unit, the second modeling unit being used to perform data modeling on a cooling coil of a HVAC water system in the target building to obtain a cooling coil model of the HVAC water system, obtain an outlet condensed water temperature of the cooling coil based on the cooling coil model of the HVAC water system, and transmit the outlet condensed water temperature of the cooling coil to a flow optimization unit; Predicting the indoor temperature of the target building based on the characteristics of the target building to obtain the predicted indoor temperature of the target building includes: An energy balance model of the wall and indoor air of the target building is constructed based on the characteristics of the target building. The energy balance model is as follows: Among them, C i represents the indoor air heat capacity, C w is the wall heat capacity, T o Indicates the outdoor temperature, T i Indicates the indoor temperature, T w represents the wall temperature, f solar,i represents the indoor air volume flow rate, S represents the solar radiation heat gain coefficient, A win Represents the area of ​​the region, I solar Represents the solar radiation intensity, R win The thermal resistance of the window, R w,i Represents the heat transfer resistance between the wall and the indoor air, R w,o represents the heat transfer resistance between the wall and the outdoor air, α represents the absorptivity of the outer wall surface to solar radiation, A w Indicates the window area; Based on the room temperature influencing factors of the target building, an indoor temperature model of the target building without HVAC is constructed. The indoor temperature model without HVAC is as follows: Among them, t a represents the indoor temperature without HVAC, Indicates the indoor thermal characteristic coefficient, u i represents the eigenvalue vector of the indoor space, q k represents the factors affecting room temperature, m represents the dimension of the factors affecting room temperature, and n represents the dimension of the spatial eigenvalue vector; Based on the room temperature influencing factors of the target building, an indoor temperature model with HVAC of the target building is constructed. The indoor temperature model with HVAC is as follows: Among them, t b Indicates the indoor temperature with HVAC; The indoor temperature model without HVAC and the indoor temperature model with HVAC are solved based on the energy balance model to obtain the indoor temperature without HVAC and the indoor temperature with HVAC of the target building.

2. A variable flow active energy-saving intelligent control system for HVAC water system according to claim 1, characterized in that: The third modeling unit is used to perform data modeling on the water pump of the HVAC water system in the target building to obtain a water pump model of the HVAC water system, obtain the condensate flow of the HVAC water system based on the water pump model of the HVAC water system, and transmit the condensate flow of the HVAC water system to the flow optimization unit.

3. A variable flow active energy-saving intelligent control system for HVAC water system according to claim 2, characterized in that: The flow optimization unit receives the indoor cooling load required by the target building transmitted by the room temperature prediction unit, the outlet cooling water temperature of the cooling tower transmitted by the first modeling unit, the outlet condensing water temperature of the cooling coil transmitted by the second modeling unit, and the condensing water flow of the HVAC water system transmitted by the third modeling unit, and takes the indoor cooling load required by the target building as the maximum, the outlet cooling water temperature of the cooling tower, the outlet condensing water temperature of the cooling coil, and the condensing water flow of the HVAC water system as the minimum as the objective function, and takes the rated power of the HVAC water system as the constraint condition to construct a flow optimization model, and transmit the flow optimization model to the flow control unit.

4. A variable flow active energy-saving intelligent control system for HVAC water system according to claim 3, characterized in that: The flow control unit introduces the flow optimization model transmitted by the flow optimization unit, and solves the flow optimization model based on the optimization algorithm to obtain the flow control parameters of the HVAC water system, and optimizes and controls the HVAC water system based on the flow control parameters of the HVAC water system.

5. The variable flow active energy-saving intelligent control system for HVAC water system according to claim 1, characterized in that: The required indoor cooling load of the target building is obtained based on the predicted indoor temperature of the target building. The required indoor cooling load of the target building is calculated as follows: Among them, q a Indicates the required indoor cooling load of the target building.

6. A variable flow active energy-saving intelligent control system for HVAC water system according to claim 2, characterized in that: The cooling coil model of the HVAC water system is as follows: Among them, t out represents the outlet condensate temperature of the cooling coil, t in Indicates the inlet condensing water temperature of the cooling coil, m a represents the total amount of air passing through the cooling coil, f represents the bypass air ratio, h air Indicates the enthalpy of inlet air, h ao represents the enthalpy of the unmixed outlet air, d ao Indicates the moisture content of the unmixed outlet air, d air Indicates the humidity content of the inlet air, h c Indicates the enthalpy of condensed water, m f represents the total flow of the cooling coil, C pf Indicates the specific heat of condensed water.

7. A variable flow active energy-saving intelligent control system for HVAC water system according to claim 6, characterized in that: The water pump model of the HVAC water system is as follows: Where q represents the flow rate of condensed water in the HVAC water system, P e represents the effective power of the variable frequency water pump, ρ represents the density of condensed water, g represents the acceleration of gravity, h c Indicates the enthalpy of condensed water.

8. The variable flow active energy-saving intelligent control system for HVAC water system according to claim 7, characterized in that: The cooling tower model of the HVAC water system is as follows: Among them, Q cell represents the heat transfer rate of the cooling tower of the HVAC water system, ε l represents the heat transfer efficiency on the air side, m l represents the dry air mass flow rate, h l,w,i It represents the specific enthalpy of wet air at the water inlet of the cooling tower, h l,i represents the specific enthalpy of moist air at the air inlet, m w,i represents the heat transfer rate of the cooling tower of the HVAC water system, C pw represents the specific heat capacity of cooling water at normal pressure, T w,i Indicates the cooling tower inlet water temperature, T ref Indicates the reference temperature of cooling water, m w,0 Indicates the cooling water outlet mass flow rate, T w,o Indicates the cooling tower outlet water temperature.

9. The variable flow active energy-saving intelligent control system for HVAC water system according to claim 3, characterized in that: The flow optimization model is solved based on the optimization algorithm to obtain the flow control parameters of the HVAC water system, including: Set the initialization parameters of the optimization algorithm to initialize the population position. The population position initialization formula is as follows: Among them, x t and x t+1 represents the position of the individual at the t-th iteration and the t+1-th iteration, and p represents the preset parameter for controlling the nonlinear mapping; Calculate the fitness value of the individual and calculate the elite individual position. The elite individual position calculation formula is as follows: in, represents the reverse solution of the j-th dimension individual at the t-th iteration, x i,j (t) represents the elite solution of the j-th dimension individual at the t-th iteration, k represents a random number in the interval [0,1], u j (t) represents the maximum position of the individual at the tth iteration, l j (t) represents the minimum position of the individual at the tth iteration; Marking a preset number of the individuals with the best fitness values ​​as leader individuals, and updating the positions of the remaining individuals in the population; Calculate and sort the updated individual fitness values, obtain elite individuals, and update the elite individual positions; The above steps are repeated until the optimal solution is found or the maximum number of iterations of the optimization algorithm is reached, thereby obtaining the flow control parameters of the HVAC water system.

Images