Energy-saving optimization method for building cold source system

Abstract

An energy-saving optimization method for a building cold source system comprises the following steps: S1, acquiring a topological graph of a target building cold source system, analyzing a system structure, and collecting historical operation data of the system; s2, constructing a cold source system energy consumption model, and performing parameter identification on the cold source system energy consumption model to improve the precision; s3, energy consumption is calculated through the cold source system energy consumption model by means of the cold load and the system operation parameters, and the optimal operation state of the system is searched for with the minimum system energy consumption as the target through an LECALO algorithm; and S4, outputting the minimum energy consumption and the optimal operation state of the building cold source system, and issuing the minimum energy consumption and the optimal operation state to the building cold source system software control platform. According to the method, the operation efficiency of the building cold source system can be remarkably improved, and energy consumption and carbon emission are effectively reduced.

Description

Technical Field

[0001] The invention relates to the technical field of optimization control of building cold source systems, and in particular to an energy-saving optimization method for building cold source systems. Background Art

[0002] With the rapid development of social economy, energy demand and carbon emissions have increased year by year, leading to increasingly serious energy shortages and environmental pollution problems worldwide. According to the "2022 China Building Energy Consumption and Carbon Emissions Research Report", in 2020, the total energy consumption of China's buildings throughout their life cycle reached 2.27 billion tons of standard coal equivalent, accounting for 45.5% of the country's total energy consumption, and the corresponding total carbon emissions were 5.08 billion tons of carbon dioxide, accounting for 50.9% of the country's total carbon emissions. Among the various components of building energy consumption, the cold source system is the largest energy consumption source, and its energy consumption accounts for about 40% to 60% of the total building energy consumption.

[0003] At present, most building cooling systems are operating at partial load, and the efficiency of the units is not high. The cooling load of buildings is affected by meteorological parameters, personnel flow, equipment heat source and other factors and is changing hourly. The energy consumption management of most buildings is mainly manual, and will not make adaptive adjustments according to the dynamic changes of cooling load. As a result, the equipment operation efficiency is low when the system is running at partial load, resulting in huge energy waste. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an energy-saving optimization method for a building cooling system, which can significantly improve the operating efficiency of the building cooling system, effectively reduce energy consumption and reduce carbon emissions.

[0005] The above technical objectives of the present invention are achieved through the following technical solutions:

[0006] A method for energy-saving optimization of a building cooling system comprises the following steps:

[0007] S1, obtain the topology of the target building cooling system, analyze the system structure, and collect the system historical operation data;

[0008] S2, constructing the energy consumption model of the cold source system and performing parameter identification on the energy consumption model of the cold source system to improve the accuracy;

[0009] S3, calculates energy consumption using cooling load and system operating parameters through the cold source system energy consumption model, and finds the optimal operating state of the system with the goal of minimizing system energy consumption through the LECALO algorithm;

[0010] S4, outputs the system minimum energy consumption and optimal operating status, and sends it to the building cooling system software control platform.

[0011] Furthermore, in step S1, the equipment in the target building cold source system topology diagram includes a chiller, a cooling tower, a chilled water pump and a cooling water pump.

[0012] Furthermore, the system operation history data includes at any time point during the system operation:

[0013] The chilled water outlet temperature, chilled water return temperature, cooling water outlet temperature, cooling water return temperature, chilled water flow rate, cooling water flow rate and chiller operating power of each chiller;

[0014] The flow rate of each cooling tower, the inlet water temperature of the cooling tower, the outlet water temperature of the cooling tower, the number of fans turned on, the power of the fans and the air volume of the fans;

[0015] The flow rate of each chilled water pump, the operating frequency of the chilled water pump, and the operating power of the chilled water pump;

[0016] The flow rate of each cooling water pump, the operating frequency of the cooling water pump, and the operating power of the cooling water pump;

[0017] Building outdoor environment temperature and humidity;

[0018] Indoor ambient temperature and humidity of the building.

[0019] Furthermore, the energy consumption model of the cold source system in step S2 includes a chiller model, a chilled water pump and a cooling water pump model, and a cooling tower model;

[0020] The energy consumption model of the cold source system is established as follows:

[0021] F obj =P system

[0022] =P chiller +P chilled_water_pump +P cooling_water_pump +P cooling_tower

[0023] =F(Q load ,t,hu,statet,t w,e,L ,δt w,e,L ,t w,c,L ,δt w,c )

[0024] Among them, P chiller is the energy consumption of the chiller, P chilled_water_pump is the energy consumption of the chilled water pump, P cooling_water_pump is the energy consumption of cooling water pump, P cooling_tower is the energy consumption of cooling tower;

[0025] Cooling load Q load, temperature t, humidity hu are the actual on-site environmental parameters, statet represents the device status information, t w,e,L Indicates the chilled water outlet temperature, δt w,e,L Chilled water temperature difference, t w,c,L Indicates the cooling water outlet temperature, δt w,c Cooling water temperature difference.

[0026] Furthermore, the refrigeration capacity of the chiller model under different operating conditions is expressed as:

[0027] Chiller model prediction energy efficiency ratio COP = a1 + b1 (PLR) + c1 (PLR) 2

[0028] Among them, a1, b1, c1 are the coefficients corresponding to the polynomial, and PLR represents the partial load ratio of the chiller;

[0029] Full load of chiller Q met =m chw ·Cp chw ·(T chw,in -T chw,out )

[0030] Among them, T chw,in Represents the inlet water temperature of the chilled water pump, T chw,out Represents the outlet water temperature of the chilled water pump, Cp chw Represents the specific heat capacity of the fluid entering the chilled water pump, m chw Represents the flow rate of chilled water in the chiller;

[0031] The energy Q released by the chiller to the surrounding environment rejectedt =m cw ·Cp cw ·(T cw,in -T cw,out )

[0032] Among them, T cw,out Represents the cooling water outlet temperature; Cp cw Represents the specific heat capacity of the fluid entering the cooling water pump; m cw Represents the flow rate of cooling water in the chiller, from which the actual power of the chiller can be deduced as:

[0033] The actual power of the chiller is P = Q met -Q rejected

[0034] Where P represents the actual power consumed by the chiller; the actual energy efficiency ratio COP of the chiller can be expressed as:

[0035]

[0036] Chiller load Q load =m chw ·Cp chw ·(T chw,in -T chw,set )

[0037] Among them, T chw,set Represents the set temperature of the chilled water pump;

[0038] Capacity represents the capacity of the chiller under current conditions, and the partial load ratio at this time is expressed by the formula:

[0039]

[0040] The coefficients corresponding to the polynomial are obtained through data fitting, and each working condition corresponds to a specific coefficient.

[0041] Furthermore, in the chilled water pump and cooling water pump models, the relationship between the flow rate, head, power and rated state of the water pump is expressed as:

[0042]

[0043] Q0, H0, P0, f0 represent the rated flow, rated head, rated power, and rated frequency respectively; Q, H, P, and f represent the current flow, current head, current power, and current frequency respectively;

[0044] Let the frequency ratio of the water pump f / f0, that is, the speed ratio of the water pump, be n. The flow-head curve relationship during variable frequency operation is expressed as:

[0045] H=a·Q 2 +b·Q0·n+c·n 2

[0046] System pipe network impedance S a =S c +S d +S e , where Sc is the impedance of the cold source equipment, Sd is the impedance of the cold source pipeline, and Se is the impedance of the terminal pipeline;

[0047] Calculate the head H=S of the current variable frequency water pump working point a Q 2 , thereby calculating the current water pump speed ratio n:

[0048]

[0049] The energy consumption of the cooling water pump or the chilled water pump is:

[0050] P=P0·n 3 .

[0051] Furthermore, in the cooling tower model,

[0052] Cooling tower heat dissipation Q = εm a (h a,o -h a,i )

[0053] Where ε is the heat transfer efficiency of the cooling tower, m a is the mass of dry air entering the heat exchanger, h a,o is the enthalpy of wet air at the cooling tower outlet, h a,i is the enthalpy of moist air at the cooling tower inlet; the enthalpy of moist air based on the wet bulb temperature t is fitted using the following formula:

[0054] The enthalpy of moist air h = a + b·t + c·t 2

[0055] Calculate heat transfer efficiency NTU is the number of heat exchange units in the equipment parameters;

[0056] The cooling tower outlet water temperature is:

[0057]

[0058] Among them, m in is the water inlet flow rate, T in is the cooling tower inlet water temperature, c is the specific heat capacity of water;

[0059] The current power of the fixed frequency cooling tower is P = N·P rated , where P rated is the rated power of the fan, and N is the number of running fans;

[0060] When the outlet water temperature of the cooling tower is lower than the set value for a period of time, the cooling tower fan is turned off and the number of fans running is N-1. Conversely, when the outlet water temperature of the cooling tower exceeds the set value for a period of time, if the cooling tower fan is in the off state, the cooling tower fan is turned on and the number of fans running is N+1; if all fans are fully turned on and the outlet water temperature of the cooling tower still cannot reach the set value, the user will receive a notification that the water temperature cannot reach the set value.

[0061] Furthermore, in step S3, the LECALO algorithm uses the startup status and operating parameters of the equipment in the building cooling system as optimization variables, and minimizes the system energy consumption as the optimization goal.

[0062] The present invention has the following beneficial effects:

[0063] 1. The present invention adopts a mechanism modeling method based on parameter identification to construct an energy consumption model of the cold source system. Only a small amount of historical data is needed to obtain a high-precision energy consumption model, which is applicable to a variety of practical scenarios.

[0064] 2. The present invention constructs an optimization model for the entire system. The optimization variables include multiple dimensions such as equipment operating status, chilled water outlet temperature, cooling water outlet temperature and temperature difference. The optimization result is the actual optimal operating status of the system.

[0065] 3. The LECALO algorithm proposed by the present invention is based on the ALO algorithm, which improves the global exploration and local development capabilities of the algorithm through mechanisms such as levy flight, adaptive elite guidance and Cauchy mutation.

[0066] 4. The LECALO algorithm proposed in the present invention ensures the search efficiency of the solution space by maintaining the diversity of the population. The algorithm has a simple structure and a small number of parameters, which meets the real-time and stability requirements in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 is a flow chart of the steps of the present invention;

[0068] Figure 2 A schematic diagram of the topological structure of the target area cold source system in an embodiment of the present invention;

[0069] Figure 3 A comparison chart of the predicted energy consumption and the actual energy consumption of the energy consumption model constructed for the present invention;

[0070] Figure 4 A COP-PLR characteristic curve diagram of a chiller model constructed for the present invention;

[0071] Figure 5 A flow-power characteristic curve diagram of a chilled water pump and a cooling water pump model is constructed for the present invention;

[0072] Figure 6 The algorithm flow chart of LECALO proposed by the present invention;

[0073] Figure 7 This is a comparison chart of the energy-saving optimization effects of the LECALO algorithm proposed in the present invention and other algorithms. DETAILED DESCRIPTION

[0074] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0075] A method for optimizing energy saving of building cooling system, such as Figure 1 As shown, it mainly includes the following steps:

[0076] S1, obtain the target building cold source system topology map, analyze the system structure, and collect the system historical operation data. The equipment in the target building cold source system topology map includes chillers, cooling towers, chilled water pumps, and cooling water pumps. Figure 2The topological structure diagram of the target area cold source system in this example is shown, which includes 1 centrifugal chiller, 2 screw chillers, 4 cooling water pumps, 5 refrigerated water pumps and 4 cooling towers. In this embodiment, the system historical operation data of an office building in Hangzhou from April to October were collected. The system operation history data includes: the chilled water outlet temperature, chilled water return temperature, cooling water outlet temperature, cooling water return temperature, chilled water flow, cooling water flow and chiller operating power of each chiller at any time point during the system operation; the flow of each cooling tower, the inlet temperature of the cooling tower, the outlet temperature of the cooling tower, the number of fans turned on, the power of the fan and the air volume of the fan; the flow of each refrigerated water pump, the operating frequency of the refrigerated water pump, and the operating power of the refrigerated water pump; the flow of each cooling water pump, the operating frequency of the cooling water pump, and the operating power of the cooling water pump; the outdoor ambient temperature and humidity of the building; the indoor ambient temperature and humidity of the building.

[0077] S2, construct the energy consumption model of the cold source system, and identify the parameters of the energy consumption model of the cold source system through the actual cold source system data to improve the accuracy. Figure 3 As shown, the actual energy consumption of the cold source system under various working conditions is compared with the energy consumption predicted by the cold source system energy consumption model. The average deviation of the system is 4.56%, indicating that the cold source system energy consumption model provided by this method can accurately reflect the operating status of the system.

[0078] The energy consumption model of the cold source system includes the chiller model, the chilled water pump and cooling water pump model, and the cooling tower model;

[0079] For the chiller model, its refrigeration capacity under different operating conditions can be expressed as follows:

[0080] Chiller model prediction energy efficiency ratio COP = a1 + b1 (PLR) + c1 (PLR) 2

[0081] Among them, a1, b1, c1 are the coefficients corresponding to the polynomial, and PLR represents the partial load ratio of the chiller;

[0082] Full load of chiller Q met =m chw ·Cp chw ·(T chw,in -T chw,out )

[0083] Among them, T chw,in Represents the inlet water temperature of the chilled water pump, T chw,out Represents the outlet water temperature of the chilled water pump, Cp chw Represents the specific heat capacity of the fluid entering the chilled water pump, m chw Represents the flow rate of chilled water in the chiller;

[0084] The energy Q released by the chiller to the surrounding environment rejectedt =m cw ·Cp cw ·(T cw,in -T cw,out )

[0085] Among them, T cw,out Represents the cooling water outlet temperature; Cp cw Represents the specific heat capacity of the fluid entering the cooling water pump; m cw Represents the flow rate of cooling water in the chiller, from which the actual power of the chiller can be deduced as:

[0086] The actual power of the chiller is P = Q met -Q rejected

[0087] Where P represents the actual power consumed by the chiller; the actual energy efficiency ratio COP of the chiller can be expressed as:

[0088] Chiller actual energy efficiency ratio

[0089] Chiller load Q load =m chw ·Cp chw ·(T chw,in -T chw,set )

[0090] Among them, T chw,set Represents the set temperature of the chilled water pump;

[0091] Capacity represents the capacity of the chiller under current conditions, and the partial load ratio at this time is expressed by the formula:

[0092]

[0093] The coefficients corresponding to the polynomial are obtained through data fitting, and each operating condition corresponds to a specific coefficient. On this basis, further corrections can be made to obtain a more accurate model. Under the conditions of 7°C chilled water outlet temperature and 30°C cooling water outlet temperature, the COP curve of the chiller model predicted by the model data is compared with the actual COP of the chiller. Figure 4 As shown, the three comparison contents in the figure are the characteristic curves of three types of chillers, including centrifugal chillers, large screw chillers and small screw chillers.

[0094] For the chilled water pump and cooling water pump models, when the chilled water pump and cooling water pump are operated with variable frequency, the relationship between their flow rate, head, power and rated state is as follows:

[0095]

[0096] Among them, Q0, H0, P0, f0 represent the rated flow, rated head, rated power, and rated frequency respectively; Q, H, P, and f represent the current flow, current head, current power, and current frequency respectively;

[0097] Let the frequency ratio of the water pump f / f0, that is, the speed ratio of the water pump, be n. The flow-head curve relationship during variable frequency operation is expressed as:

[0098] H=a·Q 2 +b·Q0·n+c·n 2

[0099] At the same time, the operation of the water pump is also related to the pipe network impedance. The concept of pipe network impedance is similar to resistance, which comprehensively reflects the local resistance and along-the-way resistance of the water pipes in the cold source system. The pipe network impedance in the system can be divided into three parts, namely, the cold source equipment impedance brought by the chiller and the water pump, the cold source pipeline impedance, and the terminal pipeline impedance.

[0100] System pipe network impedance S a =S c +S d +S e , where Sc is the impedance of the cold source equipment, Sd is the impedance of the cold source pipeline, and Se is the impedance of the terminal pipeline;

[0101] Therefore, it is necessary to first substitute the target flow into the pipe network characteristic curve equation to calculate the head of the current variable frequency water pump working point:

[0102] H=S a Q 2

[0103] Thus the current water pump speed ratio n is calculated:

[0104]

[0105] The energy consumption of the cooling water pump or the freezing water pump is: P = P0·n 3 .

[0106] The flow-power characteristic curve of the chilled water pump and cooling water pump model obtained by data fitting is as follows: Figure 5 There are four different types of variable frequency chilled water pumps and cooling water pumps, that is, there are four characteristic curves.

[0107] For the cooling tower model, the cooling tower uses water as the circulating coolant, absorbs heat from the cooling water, and releases it to the atmosphere through heat exchange with the air and spray water. The heat dissipation formula of the cooling tower is as follows:

[0108] Cooling tower heat dissipation Q = εm a(h a,o -h a,i )

[0109] Where ε is the heat transfer efficiency of the cooling tower, m a is the mass of dry air entering the heat exchanger, h a,o is the enthalpy of wet air at the cooling tower outlet, h a,i is the enthalpy of the moist air at the cooling tower inlet; the enthalpy of the moist air based on the wet bulb temperature t is fitted using the following formula:

[0110] The enthalpy of moist air h = a + b·t + c·t 2

[0111] By querying the number of heat exchange units NTU in the equipment parameters, the heat exchange efficiency ε can be calculated according to the following formula

[0112]

[0113] Therefore, the cooling tower outlet water temperature T w,out According to the water flow rate m in Calculate, where T in is the cooling tower inlet water temperature:

[0114]

[0115] In the above formula, c is the specific heat capacity of water. For a fixed frequency cooling tower, the current power P is mainly determined by the rated power P of the fan. rated and the number of running fans N, which can be calculated as:

[0116] P=N·P rated

[0117] When the outlet water temperature of the cooling tower is lower than the set value for a period of time, the cooling tower fan is turned off and the number of fans running is N-1. Conversely, when the outlet water temperature of the cooling tower exceeds the set value for a period of time, if the cooling tower fan is in the off state, the cooling tower fan is turned on and the number of fans running is N+1; if all fans are fully turned on and the outlet water temperature of the cooling tower still cannot reach the set value, the user will receive a notification that the water temperature cannot reach the set value.

[0118] Based on the chiller model, chilled water pump and cooling water pump model, and cooling tower model provided above, the energy consumption model of the entire building cooling system can be established as follows:

[0119] F obj =P system

[0120] =P chiller +P chilled_water_pump +P cooling_water_pump +P cooling_tower

[0121] =F(Q load ,t,hu,statet,t w,e,L ,δt w,e,L ,t w,c,L ,δt w,c )

[0122] Among them, P chiller is the energy consumption of the chiller, P chilled_water_pump is the energy consumption of the chilled water pump, P cooling_water_pump is the energy consumption of cooling water pump, P cooling_tower is the energy consumption of cooling tower;

[0123] Cooling load Q load , temperature t, humidity hu are the actual on-site environmental parameters, statet represents the device status information, t w,e,L Indicates the chilled water outlet temperature, δt w,e,L Chilled water temperature difference, t w,c,L Indicates the cooling water outlet temperature, δt w,c Cooling water temperature difference.

[0124] After reading the environment parameter Q load , t, hu, the chilled water flow, power consumption, and chiller evaporation temperature t are calculated using the current chilled water inlet temperature and the set chilled water output temperature. e . According to the current cooling water flow, the cooling water flow, energy consumption, and cooling tower energy consumption are calculated. By iteratively calculating the evaporation temperature calculated from the circulating cold water flow, the condensation temperature is determined and the power consumption of the chiller is obtained. Finally, the power consumption of the entire building cold source system and the current parameter settings are obtained. Based on these variables, according to the calculation of P above chiller , P chilled_water_pump and P cooling_water_pump Since the cooling tower is operated at industrial frequency, P can be directly calculated through the opening information in statet and the rated power cooling_tower The purpose of this application is to optimize the energy saving of the cold source system, so by finding t in the search space w,e,L ,δt w,e,L ,t w,c,L ,δt w,c ,statet, to minimize the energy consumption of the system P system That is, the optimization goal.

[0125] S3, calculates energy consumption using the cooling load and system operating parameters through the cold source system energy consumption model. The system operating parameters are set as the system operating data at a certain moment. The LECALO algorithm is used to find the optimal system operating state with the goal of minimizing system energy consumption. That is, the LECALO algorithm improved based on the ant lion algorithm takes the equipment startup state and operating parameter settings as optimization variables, and minimizes system energy consumption as the optimization goal. The flowchart of the LECALO algorithm is shown in the attached figure. Figure 6 As shown, the specific process is as follows:

[0126] (1) First, initialize the number of ants N and dimension D, where dimension D represents the operating parameters of the cold source system at a certain moment. In this embodiment, dimension D in the LECALO algorithm includes the chilled water outlet temperature, chilled water return temperature, cooling water outlet temperature, cooling water return temperature, chilled water flow, cooling water flow and chiller operating power of four chillers; the flow, cooling tower inlet temperature, cooling tower outlet temperature, number of fans turned on, fan power and fan air volume of four cooling towers; the flow, operating frequency and operating power of five chilled water pumps; the flow, operating frequency and operating power of four cooling water pumps.

[0127] Then randomly initialize the ant position in the search space, assuming that X i is the position of ant i. Each position of ant i represents a corresponding solution. Its position is a vector that conforms to the above D dimension, that is, a running state of the corresponding system. U and L are the upper and lower boundaries of the search space respectively. The initial position of the ant can be expressed as:

[0128] X i =L+rand(UL)

[0129] The position information is stored in the ant position matrix M ant In the example, the fitness of the ant is calculated according to the objective function and stored as M oa The fitness of the ant represents the system energy consumption corresponding to the above solution. The D-dimensional vector represented by this position is a system operation state. By inputting this vector into the system energy model, the corresponding system energy consumption, that is, the fitness of the ant, can be calculated. In this application, minimizing the system energy consumption is used as the optimization goal, that is, to find the individual with the lowest fitness.

[0130] The initialization of the ant lion position is also randomly generated and stored as M antlion , the fitness matrix is ​​saved as M oal , select the antlion with the highest fitness as the elite antlion R e and save the location.

[0131] (2) Random walk of ants.ant Each ant in M antlion Select an antlion, and the ant will randomly walk around the antlion and the elite antlion at the same time. The random walk here is equivalent to traversing various operating states of the cold source system. Levy Flight is a random walk strategy that includes frequent short-distance walks and occasional long-distance walks. It ensures that the population searches the nearby area in detail while having a certain degree of mutation. Alternating between the two methods can fully traverse the solution domain, which helps to improve the global search ability of the algorithm. Levy flight obeys Levy distribution, and its probability density function is as follows:

[0132]

[0133] Where: 0<α≤2 controls the sharpness of the Levy distribution graph, and γ>0 controls the scale of the distribution. When α=2, the Levy distribution is equivalent to the Gaussian distribution, and when α=1, the Levy distribution is equivalent to the Cauchy distribution.

[0134] For general α values, it is difficult to analyze the probability density function of Levy distribution, so the simulation method is usually used to generate Levy distributed random numbers. The Levy flight trajectory that fully traverses the set solution domain is:

[0135]

[0136] Where: i = 1, 2, 3…, cumsum(S i ) is the accumulation of the previous i Levy flight positions.

[0137]

[0138] According to the calculated Levy flight trajectory, the Levy flight position is mapped to the solution domain (u b ,l b ). To ensure that the direction of the ant’s random walk is globally optimal, the random walk around the common ant lion Random walk with elite antlions Taking the average, the final position of the ant is for:

[0139]

[0140] (3) Antlion predation. The random walk of ants will be affected by the antlion trap. In order to mathematically model this hypothesis, the following equation is proposed:

[0141]

[0142] where ct is the minimum value of all variables at the tth iteration, d t represents the vector containing the maximum values ​​of all variables at iteration t, is the minimum value of all variables of the i-th ant, are all the variables of the ith ant, and shows the position of the jth ant selected in the ith iteration. The above shows that the ants walk randomly in a hypersphere defined by vectors c and d, which surrounds a selected antlion. It is assumed that the antlion is able to build traps proportional to its fitness and that the ants need to move randomly. However, once the antlions realize that there is an ant in the trap, they shoot sand outwards from the center of the pit. This behavior slides off the trapped ants that try to escape. In order to mathematically model this behavior, the radius of the ant's random walk hypersphere is adaptively reduced. The following formula is proposed in this regard:

[0143]

[0144] Where I is a ratio, t is the current iteration number, T is the maximum iteration number, and cw is a constant defined based on the current iteration (when t>0.1T, w=2, when t>0.5T, w=3, when t>0.75T, w=4, when t>0.9T, w=5, when t>0.9T, w=6). Basically, the constant w can adjust the accuracy level of the development.

[0145] The final stage of hunting is when an ant reaches the bottom of the pit and is caught by the jaws of the antlion. After this stage, the antlion pulls the ant into the sand and eats its body. To model this process, it is assumed that prey capture occurs when the ant becomes healthier (enters the sand) than its corresponding antlion. The antlion is then required to update its position to the latest position of the hunted ant in order to increase its chances of capturing new prey. The following formula is proposed in this regard:

[0146]

[0147] From the above formula, we can see that only when an ant lion preys on an ant with higher fitness than itself can it update to a position with higher fitness. If there is no ant with higher fitness within the range of its trap, it is likely that the ant lion has fallen into a local optimal position. In this case, if the ant lion continues to attract ants to develop in the area at the original position, it will weaken the diversity and optimization ability of the population. The Cauchy mutation operator can greatly improve the global exploration ability, convergence accuracy and stability of the algorithm. The Cauchy distribution is as follows:

[0148]

[0149] Therefore, a dynamic Cauchy mutation strategy is proposed based on the Cauchy mutation operator, and its equation is as follows:

[0150] X′=X+η·Cauchy(0,1)

[0151] Where X′ is the updated position of the initial position X; Cauchy(0,1) is the standard Cauchy random distribution at t=1; parameter η is a constant used to control the mutation intensity of the Cauchy distribution. This strategy sorts N ant lions by fitness and selects S ant lions with poor fitness according to the ratio β. Since the number of mutant individuals has different effects on the algorithm performance at different stages of optimization, the ratio coefficient β is dynamically adjusted at different stages of optimization.

[0152] S=β·N

[0153]

[0154] The comparison of the effect of using LECALO algorithm and other optimization algorithms for energy-saving optimization of cold source system is shown in the attached figure. Figure 7 As shown, other algorithms include ALO, ARO, BRO, DMOA, EVO, GSKA, HBO, WaOA, and WHO. It can be seen that under the three working conditions of high, medium, and low loads, the LEKALO algorithm performs best in convergence speed and optimization effect, taking into account both the accuracy and speed of optimization.

[0155] For the ALO algorithm, see Seyedali Mirjalili. “The ant lion optimizer”. In: Advances in engineering software 83 (2015), pp. 80–98.

[0156] For the ARO algorithm, see Liying Wang et al. "Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems". In: Engineering Applications of Artificial Intelligence114 (2022), p.105082.

[0157] For the BRO algorithm, see Taymaz Rahkar Farshi. "Battle royale optimization algorithm". In: Neural Computing and Applications 33.4 (2021), pp. 1139–1157.

[0158] For the DMOA algorithm, see Jeffrey O Agushaka, Absalom E Ezugwu, and Laith Abualigah. “Dwarf mongoose optimization algorithm”. In: Computer methods in applied mechanics and engineering 391 (2022), p. 114570.

[0159] For the EVO algorithm, see Mahdi Azizi et al. "Energy valley optimizer: a novel metaheuristic algorithm for global and engineering optimization". In: Scientific Reports 13.1 (2023), p. 226.

[0160] For the GSKA algorithm, see Ali Wagdy Mohamed, AnasAHadi, and Ali Khater Mohamed. "Gaining-sharing knowledge based algorithm for solving optimization problems: novel nature-inspired algorithm". In: International Journal of Machine Learning and Cybernetics 11.7 (2020), pp. 1501–1529.

[0161] For the HBO algorithm, see Qamar Askari, Mehreen Saeed, and Irfan Younas. "Heap-based optimizer inspired by corporate rank hierarchy for global optimization". In: Expert Systems with Applications 161 (2020), p. 113702.

[0162] For the WaOA algorithm, see Pavel Trojovsk`y and Mohammad Dehghani. "Walrus optimization algorithm: a new bio-inspired metaheuristic algorithm". In: (2022).

[0163] For the WHO algorithm, see D Amali and M Dinakaran. "Wildebeest herd optimization: a new global optimization algorithm inspired by wildebeestherding behavior". In: Journal of Intelligent & Fuzzy Systems 37.6 (2019), pp. 8063–8076.

[0164] S4: When the algorithm termination condition is met, the currently recorded minimum energy consumption and optimal operating state are output, and the information is sent to the cold source system platform in the format of a data packet, and the system is controlled and optimized based on the algorithm optimization result to achieve energy-saving optimization of the cold source system. The cold source system platform refers to the software management platform of the cold source system, which controls the specific equipment in the system. The platform does not involve the implementation of the method steps of this application, so it is not repeated here.

[0165] In general, the present invention adopts a mechanism modeling method based on parameter identification to construct an energy consumption model of a cold source system. Only a small amount of historical data is needed to obtain a high-precision energy consumption model, which is applicable to a variety of practical scenarios. In addition, the present invention constructs an optimization model for the building cold source system as a whole. The optimization variables include multiple dimensions such as the operating status of the equipment, the outlet water temperature and temperature difference on the freezing side and the cooling side, and the optimization result is the actual optimal operating state of the system. At the same time, the LECALO algorithm proposed by the present invention based on the ALO algorithm improves the global exploration and local development capabilities of the algorithm through mechanisms such as levy flight, adaptive elite guidance, and Cauchy mutation. The LECALO algorithm proposed by the present invention ensures the search efficiency of the solution space by maintaining the diversity of the population. The algorithm has a simple structure and a small number of parameters, which meets the real-time and stability requirements in practical applications.

[0166] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A method for energy-saving optimization of a building cold source system, characterized in that: The following steps are involved: S1, obtain the topology of the target building cooling system, analyze the system structure, and collect the system historical operation data; S2, constructing the energy consumption model of the cold source system and performing parameter identification on the energy consumption model of the cold source system to improve the accuracy; S3, calculate the energy consumption by using the cooling load and system operating parameters through the energy consumption model of the cold source system, formulate the LECALO algorithm based on the improved ant lion algorithm, and use the LECALO algorithm to find the optimal operating state of the system with the goal of minimizing the system energy consumption; In this algorithm: it includes the random walk of ants and the predation of ant lions; Initialize the position of the ants, define the position of each ant at a certain moment as one of the operating states of the system, define the dimension of the ant as the system operating parameters at a certain moment, that is, the system operating parameters under a certain operating state, define the number of ants as the number at each moment, that is, the number of each operating state, and define the fitness of the ant as the corresponding system energy consumption at a certain moment, that is, the corresponding system energy consumption under a certain operating state; Initialize the position of the ant lion, and define the ant lion with the highest fitness as the elite ant lion, and the rest as ordinary ant lions; the ants randomly walk around the ordinary ant lions and the elite ant lions. The random walk of the ants is to traverse various operating states of the cold source system. The Levy flight strategy is added to the random walk of the ants to improve the global search ability; the random walk of the ants around the ordinary ant lion and the random walk of the ants around the elite ant lion are averaged to optimize the direction of the ant random walk and determine the optimal position of the ants; the ant lion preys on the ants and updates the position of the ant lion. Dynamic Cauchy mutation is performed during the predation of the ant lion to update the fitness of the ant lion, and the ant lion with the highest fitness is selected as the new elite ant lion; after multiple iterations of the above steps, the final elite ant lion position is determined, and the ants at this position before being preyed are the optimal operating state of the system, and the ant fitness here is the minimum energy consumption of the system; S4 outputs the minimum energy consumption and optimal operating status of the building cooling system and sends it to the building cooling system software control platform.

2. The energy-saving optimization method for a building cooling system according to claim 1, characterized in that: In step S1, the equipment in the target building cold source system topology diagram includes a chiller, a cooling tower, a chilled water pump and a cooling water pump.

3. The energy-saving optimization method for a building cooling system according to claim 2 is characterized in that: The system operation history data includes the following at any time point during system operation: The chilled water outlet temperature, chilled water return temperature, cooling water outlet temperature, cooling water return temperature, chilled water flow rate, cooling water flow rate and chiller operating power of each chiller; The flow rate of each cooling tower, the inlet water temperature of the cooling tower, the outlet water temperature of the cooling tower, the number of fans turned on, the power of the fans and the air volume of the fans; The flow rate of each chilled water pump, the operating frequency of the chilled water pump, and the operating power of the chilled water pump; The flow rate of each cooling water pump, the operating frequency of the cooling water pump, and the operating power of the cooling water pump; Building outdoor environment temperature and humidity; Indoor ambient temperature and humidity of the building.

4. The energy-saving optimization method for a building cooling system according to claim 2, characterized in that: The energy consumption model of the cold source system in step S2 includes a chiller model, a chilled water pump and a cooling water pump model, and a cooling tower model; The energy consumption model of the cold source system is established as follows: F obj =P system =P chiller +P chilled_water_pump +P cooling_water_pump +P cooling_tower =F(Q load ,t,hu,statet,t w,e,L ,δt w,e,L ,t w,c,L ,δt w,c ) Among them, P chiller is the energy consumption of the chiller, P chilled_water_pump is the energy consumption of the chilled water pump, P cooling_water_pump is the energy consumption of cooling water pump, P cooling_tower is the energy consumption of cooling tower; Cooling load Q load , temperature t, humidity hu are the actual on-site environmental parameters, statet represents the device status information, t w,e,L Indicates the chilled water outlet temperature, δt w,e,L Chilled water temperature difference, t w,c,L Indicates the cooling water outlet temperature, δt w,c Cooling water temperature difference.

5. The energy-saving optimization method for a building cooling system according to claim 4 is characterized in that: The refrigeration capacity of the chiller model under different operating conditions is expressed as: Chiller model prediction energy efficiency ratio COP = a1 + b1 (PLR) + c1 (PLR) 2 Among them, a1, b1, c1 are the coefficients corresponding to the polynomial, and PLR represents the partial load ratio of the chiller; Full load of chiller Q met =m chw ·Cp chw ·(T chw,in -T chw,out ) Among them, T chw,in Represents the inlet water temperature of the chilled water pump, T chw,out Represents the outlet water temperature of the chilled water pump, Cp chw Represents the specific heat capacity of the fluid entering the chilled water pump, m chw Represents the flow rate of chilled water in the chiller; The energy Q released by the chiller to the surrounding environment rejectedt =m cw ·Cp cw ·(T cw,in -T cw,out ) Among them, T cw,out Represents the cooling water outlet temperature; Cp cw Represents the specific heat capacity of the fluid entering the cooling water pump; m cw Represents the flow rate of cooling water in the chiller, from which the actual power of the chiller can be deduced as: The actual power of the chiller is P = Q met -Q rejected Where P represents the actual power consumed by the chiller; the actual energy efficiency ratio COP of the chiller can be expressed as: Chiller actual energy efficiency ratio Chiller load Q load =m chw ·Cp chw ·(T chw,in -T chw,set ) Among them, T chw,set Represents the set temperature of the chilled water pump; Capacity represents the capacity of the chiller under current conditions, and the partial load ratio at this time is expressed by the formula: The coefficients corresponding to the polynomial are obtained through data fitting, and each working condition corresponds to a specific coefficient.

6. The energy-saving optimization method for a building cooling system according to claim 4, characterized in that: For the chilled water pump and cooling water pump models, when the pump is operated with variable frequency, the relationship between its flow rate, head, power and rated state is expressed as: Q0, H0, P0, f0 represent the rated flow, rated head, rated power, and rated frequency respectively; Q, H, P, and f represent the current flow, current head, current power, and current frequency respectively; Let the frequency ratio of the water pump f / f0, that is, the speed ratio of the water pump, be n. The flow-head curve relationship during variable frequency operation is expressed as: H=a·Q 2 +b·Q0·n+c·n 2 System pipe network impedance S a =S c +S d +S e , where Sc is the impedance of the cold source equipment, Sd is the impedance of the cold source pipeline, and Se is the impedance of the terminal pipeline; Calculate the head H=S of the current variable frequency water pump working point a Q 2 , thereby calculating the current water pump speed ratio n: The energy consumption of the cooling water pump or the chilled water pump is: P=P0·n 3 。 7. The energy-saving optimization method for a building cooling system according to claim 4 is characterized in that: In the cooling tower model, Cooling tower heat dissipation Q = εm a (h a,o -h a,i ) Where ε is the heat transfer efficiency of the cooling tower, m a is the mass of dry air entering the heat exchanger, h a,o is the enthalpy of wet air at the cooling tower outlet, h a,i is the enthalpy of moist air at the cooling tower inlet; the enthalpy of moist air based on the wet bulb temperature t is fitted using the following formula: The enthalpy of moist air h = a + b·t + c·t 2 Then calculate the heat transfer efficiency ε, Among them, NTU is the number of heat exchange units in the equipment parameters; The cooling tower outlet water temperature is: Among them, m in is the water inlet flow rate, T in is the cooling tower inlet water temperature, c is the specific heat capacity of water; The current power of the fixed frequency cooling tower is P = N·P rated , where P rated is the rated power of the fan, and N is the number of running fans; When the outlet water temperature of the cooling tower is lower than the set value for a period of time, the cooling tower fan is turned off and the number of fans running is N-1. Conversely, when the outlet water temperature of the cooling tower exceeds the set value for a period of time, if the cooling tower fan is in the off state, the cooling tower fan is turned on and the number of fans running is N+1; if all fans are fully turned on and the outlet water temperature of the cooling tower still cannot reach the set value, the user will receive a notification that the water temperature cannot reach the set value.

8. The energy-saving optimization method for a building cooling system according to claim 1, characterized in that: In step S3, the LECALO algorithm uses the startup status and operating parameters of the equipment in the building cooling system as optimization variables, and minimizes the system energy consumption as the optimization goal.

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