Indoor radiation floor system multi-target regulation and control method and system based on load prediction

Abstract

The invention relates to the technical field of air conditioner control, and provides an indoor radiation floor system multi-target regulation and control method and system based on load prediction. The method comprises the following steps: based on indoor environment parameters, outdoor environment parameters, indoor personnel activity information and equipment parameters, adopting a cold and wet load prediction model to obtain a cold load and a wet load; based on the outdoor environment parameters, the cold load, the wet load and the air conditioning system operation information, an indoor environment and energy consumption prediction model is adopted, and the indoor temperature, the condensation temperature difference, the air system energy consumption and the water system energy consumption are obtained; the outdoor environment parameters, the cooling load, the wet load and the air conditioner system operation information serve as variables, target functions of the indoor temperature, the air conditioner PMV, the condensation temperature difference, the air system energy consumption and the water system energy consumption are built, corresponding weights are configured, and a total target function is calculated; and working parameters of the air conditioning system are set as constraint conditions, the total objective function is solved, and optimal regulation and control parameters are obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of air conditioning control, and in particular to a multi-objective control method and system for an indoor radiant floor system based on load prediction. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] At present, radiant floor air conditioning systems are favored by more and more users due to their comfort, stability and energy efficiency. Load forecasting is a key link in the on-demand control of air conditioning systems and is crucial to system operation control.

[0004] However, existing radiant floor air conditioning systems still have certain limitations in load forecasting and system control. Traditional load forecasting methods often rely on simple statistical models or empirical formulas. These methods have low prediction accuracy when dealing with complex indoor and outdoor environmental factors, especially in predicting cooling load and wet load. In terms of system control, existing radiant floor air conditioning systems mostly use fixed control logic. These methods are difficult to simultaneously meet multiple requirements such as indoor comfort, condensation risk control, and reducing system energy consumption. Summary of the invention

[0005] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a multi-objective control method and system for an indoor radiant floor system based on load prediction. The present invention can accurately control the indoor radiant floor air-conditioning system with stable control effect and can achieve the best energy-saving effect.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] A first aspect of the present invention provides a multi-objective control method for an indoor radiant floor system based on load prediction.

[0008] A multi-objective control method for an indoor radiant floor system based on load prediction, comprising:

[0009] Obtain indoor environmental parameters, outdoor environmental parameters, indoor personnel activity information, equipment parameters and air conditioning system operation information;

[0010] Based on indoor environmental parameters, outdoor environmental parameters, indoor personnel activity information and equipment parameters, the cold and wet load prediction model is used to obtain the cold load and wet load;

[0011] Based on outdoor environmental parameters, cooling load, wet load and air conditioning system operation information, the indoor environment and energy consumption prediction model is used to obtain indoor temperature, condensation temperature difference, wind system energy consumption and water system energy consumption;

[0012] Taking outdoor environmental parameters, cooling load, wet load and air conditioning system operation information as variables, objective functions of indoor temperature, air conditioning PMV, condensation temperature difference, wind system energy consumption and water system energy consumption are constructed respectively, and corresponding weights are configured to calculate the total objective function;

[0013] The operating parameters of the air-conditioning system are set as constraints, the overall objective function is solved, and the optimal control parameters are obtained.

[0014] Furthermore, the constraint conditions include air supply volume constraint, water supply pump state constraint and floor water supply temperature constraint.

[0015] Furthermore, the indoor environmental parameters include: indoor temperature, indoor humidity, floor surface temperature and condensation temperature difference, the outdoor environmental parameters include: outdoor temperature, outdoor humidity, solar radiation intensity and outdoor wind speed, the indoor personnel activity information includes: personnel moisture dissipation, human metabolic rate, clothing thermal resistance and personnel activity time, the equipment parameters include: lighting heat dissipation, equipment heat dissipation and lighting equipment start and stop time, the air-conditioning system operation information includes: fan air volume, radiant floor water supply pump switch, radiant panel water supply temperature, wind system energy consumption and water system energy consumption.

[0016] Furthermore, taking outdoor environmental parameters, cooling load, wet load and air conditioning system operation information as variables, objective functions of indoor temperature, air conditioning PMV, condensation temperature difference, wind system energy consumption and water system energy consumption are constructed respectively. The methods include:

[0017] Taking outdoor temperature, outdoor humidity, cooling load, wet load, fresh air fan air volume, radiant floor water supply temperature and radiant floor water supply pump status as variables, the indoor air temperature is set, and the indoor temperature objective function is constructed based on the difference between the indoor air temperature and the user-set value;

[0018] The air conditioning PMV objective function is constructed with outdoor temperature, outdoor humidity, cooling load, wet load, fresh air fan air volume, radiant floor water supply temperature and radiant floor water supply pump status as variables;

[0019] The condensation temperature difference objective function is constructed with outdoor temperature, outdoor humidity, cooling load, wet load, fresh air fan air volume, radiant floor water supply temperature and radiant floor water supply pump status as variables;

[0020] The objective function of wind system energy consumption is constructed with outdoor temperature, outdoor humidity, cooling load, wet load, fresh air fan air volume, radiant floor water supply temperature and radiant floor water supply pump status as variables;

[0021] The energy consumption objective function of the water system is constructed with outdoor temperature, outdoor humidity, cooling load, wet load, fresh air fan air volume, radiant floor water supply temperature and radiant floor water supply pump status as variables.

[0022] Furthermore, taking outdoor environmental parameters, cooling load, wet load and air conditioning system operation information as variables, objective functions of indoor temperature, air conditioning PMV, condensation temperature difference, wind system energy consumption and water system energy consumption are constructed respectively, and corresponding weights are configured to calculate the total objective function; the method includes:

[0023] Based on the indoor temperature objective function, the condensation temperature difference objective function, the wind system energy consumption objective function and the water system energy consumption objective function and the corresponding weights, the sum of products is calculated to obtain the total objective function.

[0024] Furthermore, taking outdoor environmental parameters, cooling load, wet load and air conditioning system operation information as variables, objective functions of indoor temperature, air conditioning PMV, condensation temperature difference, wind system energy consumption and water system energy consumption are constructed respectively, and corresponding weights are configured to calculate the total objective function; the method includes:

[0025] Based on the air conditioning PMV objective function, the condensation temperature difference objective function, the wind system energy consumption objective function and the water system energy consumption objective function and the corresponding weights, the sum of products is calculated to obtain the total objective function.

[0026] Furthermore, the overall objective function is solved to obtain the optimal control parameters, and the method includes:

[0027] An improved gray wolf optimization algorithm is used to randomly generate gray wolf individuals, which are input into the indoor environment and energy consumption prediction model to obtain indoor temperature, condensation temperature difference, wind system energy consumption, and water system energy consumption; the gray wolf individuals include: outdoor temperature, outdoor humidity, cooling load, wet load, fresh air fan air volume, radiant floor water supply temperature, and radiant floor water supply pump status;

[0028] Based on the total objective function, determine the fitness values ​​of individuals in the population, sort them, and select the three gray wolf individuals with the highest fitness. The fitness from high to low is α, β, δ wolf, and the rest are ω wolf;

[0029] The gray wolf tracks prey and updates the position of individual gray wolves. The objective function is used to calculate the fitness value of each gray wolf and rank them. At the same time, a certain proportion of gray wolf individuals with low fitness rankings are selected for Cauchy mutation. After the mutation, the fitness is recalculated and updated.

[0030] Determine whether the maximum number of iterations has been reached, and proceed to the next round of iteration or output the results.

[0031] A second aspect of the present invention provides a multi-objective control system for an indoor radiant floor system based on load prediction.

[0032] A multi-objective control system for indoor radiant floor system based on load prediction, comprising:

[0033] A data acquisition module is configured to: acquire indoor environmental parameters, outdoor environmental parameters, indoor personnel activity information, equipment parameters, and air conditioning system operation information;

[0034] A load prediction module is configured to: obtain a cooling load and a wet load by using a cooling and wet load prediction model based on indoor environmental parameters, outdoor environmental parameters, indoor personnel activity information and equipment parameters;

[0035] The environment and energy consumption prediction module is configured to: based on outdoor environmental parameters, cooling load, wet load and air conditioning system operation information, use the indoor environment and energy consumption prediction model to obtain indoor temperature, condensation temperature difference, wind system energy consumption, and water system energy consumption;

[0036] The objective function construction module is configured to: use outdoor environmental parameters, cooling load, wet load and air conditioning system operation information as variables, respectively construct objective functions of indoor temperature, air conditioning PMV, condensation temperature difference, wind system energy consumption and water system energy consumption, and configure corresponding weights to calculate the total objective function;

[0037] The solution module is configured to: set the operating parameters of the air-conditioning system as constraints, solve the overall objective function, and obtain the optimal control parameters.

[0038] A third aspect of the present invention provides a computer-readable storage medium.

[0039] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in the multi-objective control method for an indoor radiant floor system based on load prediction as described in the first aspect above.

[0040] A fourth aspect of the present invention provides a computer device.

[0041] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the multi-objective control method for an indoor radiant floor system based on load prediction as described in the first aspect above are implemented.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The present invention aims at the technical problems that the existing radiant floor air-conditioning system has fixed control logic, unstable control effect and cannot achieve the best energy-saving effect, and provides a multi-objective control method and system for an indoor radiant floor system based on load prediction, which obtains indoor environmental parameters, outdoor environmental parameters, indoor personnel activity information, equipment parameters and air-conditioning system operation information; based on the indoor environmental parameters, outdoor environmental parameters, indoor personnel activity information and equipment parameters, a cold and wet load prediction model is adopted to obtain the cold load and wet load; based on the outdoor environmental parameters, cold load, wet load and air-conditioning system operation information, an indoor environment and energy consumption prediction model is adopted to obtain the indoor temperature, condensation temperature difference, wind system energy consumption and water system energy consumption; with the outdoor environmental parameters, cold load, wet load and air-conditioning system operation information as variables, objective functions of indoor temperature, air-conditioning PMV, condensation temperature difference, wind system energy consumption and water system energy consumption are respectively constructed, and corresponding weights are configured to calculate the total objective function; the air-conditioning system working parameters are set as constraints, the total objective function is solved to obtain the optimal control parameters. The present invention uses a machine learning algorithm to predict indoor cold and wet loads, which can achieve more accurate load prediction, and uses a machine learning algorithm to predict the control effect. The two are combined to generate optimal control parameters, thereby maintaining the indoor thermal and humid environment in a comfortable range, ensuring that the indoor condensation temperature difference is in a safe range, and reducing system energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0045] Figure 1 is a flow chart of a multi-objective control method for an indoor radiant floor system based on load prediction shown in the present invention;

[0046] Figure 2 It is a specific flow chart of the multi-objective control method of the indoor radiant floor system based on load prediction shown in the present invention;

[0047] Figure 3 is a flow chart of load forecasting model training shown in the present invention;

[0048] Figure 4 It is a flow chart of indoor environment and energy consumption model training shown in the present invention;

[0049] Figure 5 The improved gray wolf optimization algorithm flow chart shown in the present invention;

[0050] Figure 6 It is a structural diagram of a multi-objective control system of an indoor radiant floor system based on load prediction shown in the present invention. DETAILED DESCRIPTION

[0051] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0052] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0053] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0054] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the methods and systems according to various embodiments of the present disclosure. It should be noted that each box in the flowchart or block diagram can represent a module, a program segment, or a part of a code, and the module, program segment, or a part of a code may include one or more executable instructions for implementing the logical functions specified in each embodiment. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the flowchart and / or block diagram, and the combination of boxes in the flowchart and / or block diagram can be implemented using a dedicated hardware-based system that performs a specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0055] Embodiment 1

[0056] like Figure 1As shown, this embodiment provides a multi-objective control method for an indoor radiant floor system based on load forecasting. This embodiment uses the method applied to a server as an example for illustration. It is understandable that the method can also be applied to a terminal, and can also be applied to a system including a terminal, a server, and a server, and is implemented through the interaction between the terminal and the server. The server can be an independent physical server, or a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communications, middleware services, domain name services, security services CDN, and big data and artificial intelligence platforms. The terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited thereto. The terminal and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in this application. In this embodiment, the method includes the following steps:

[0057] Obtain indoor environmental parameters, outdoor environmental parameters, indoor personnel activity information, equipment parameters and air conditioning system operation information;

[0058] Based on indoor environmental parameters, outdoor environmental parameters, indoor personnel activity information and equipment parameters, the cold and wet load prediction model is used to obtain the cold load and wet load;

[0059] Based on outdoor environmental parameters, cooling load, wet load and air conditioning system operation information, the indoor environment and energy consumption prediction model is used to obtain indoor temperature, condensation temperature difference, wind system energy consumption and water system energy consumption;

[0060] Taking outdoor environmental parameters, cooling load, wet load and air conditioning system operation information as variables, objective functions of indoor temperature, air conditioning PMV, condensation temperature difference, wind system energy consumption and water system energy consumption are constructed respectively, and corresponding weights are configured to calculate the total objective function;

[0061] The operating parameters of the air-conditioning system are set as constraints, the overall objective function is solved, and the optimal control parameters are obtained.

[0062] The present invention uses a machine learning algorithm to predict indoor cold and wet loads, which can achieve more accurate load prediction, and uses a machine learning algorithm to predict the control effect. The two are combined to generate optimal control parameters, thereby maintaining the indoor thermal and humid environment in a comfortable range, ensuring that the indoor condensation temperature difference is in a safe range, and reducing system energy consumption.

[0063] The present embodiment is described in detail below. Figure 2 As shown:

[0064] Step S1: Obtain historical parameters of indoor and outdoor environments, indoor personnel and equipment activity information, and air conditioning system operation history information.

[0065] Step S11: Collect indoor and outdoor environmental parameters through sensors, obtain indoor personnel activities and equipment start and stop conditions, and read the air conditioning system operation history information. Outdoor environmental parameters include: temperature, humidity, solar radiation intensity and wind speed; the indoor environmental parameters include: temperature, humidity, floor surface temperature and condensation temperature difference; the indoor personnel activity time and equipment switch start and stop time include: personnel moisture dissipation, light heat dissipation, equipment heat dissipation, human metabolic rate, clothing thermal resistance, personnel activity time and light equipment start and stop time; the air conditioning system operation history information includes: fan air volume, radiant floor water supply pump switch, radiant panel water supply temperature, wind system energy consumption and water system energy consumption.

[0066] In some embodiments, the collected parameter information is divided into historical data and real-time data. The historical data is used to train the model and perform verification, and the real-time data is used to calculate and optimize in real time during system operation to solve the optimal control parameters.

[0067] Step S12: Specifically, the amount of moisture dissipated by personnel, the amount of heat dissipated by lights, the amount of heat dissipated by equipment, the metabolic rate of the human body, the thermal resistance of clothing, the start and stop time of the lighting equipment, and the activity time of personnel cannot be obtained through sensors. The amount of moisture dissipated by personnel, the metabolic rate of the human body, and the thermal resistance of clothing are estimated in different grades based on experience, and the amount of heat dissipated by lights and the amount of heat dissipated by equipment are obtained in real time based on the actual operation schedule.

[0068] Step S121: The amount of moisture dissipated by the human body depends on the activity level and environmental conditions. Taking the average value of a single adult as an example:

[0069] Sitting (no activity): about 250-350g / h;

[0070] Light activity (such as office work): about 350-500g / h;

[0071] Moderate activity (such as walking): about 500-700g / h;

[0072] Heavy activity (such as running): about 700-1000g / h;

[0073] Step S122: The human metabolic rate refers to the energy metabolic rate per square meter of human body surface area, which depends on the activity level and environmental conditions. Taking the average value of a single adult as an example:

[0074] Sitting (light activity, such as reading): about 60-70W / m 2 ;

[0075] Light activity (such as standing work): about 70-80W / m 2 ;

[0076] Moderate activity (such as office work): about 80-100W / m2 ;

[0077] Heavy activity (such as heavy physical labor): about 100-120W / m 2 ;

[0078] Step S123: The thermal resistance of the clothing is a parameter used to reflect the thermal insulation performance of the clothing. It is inversely proportional to the thermal conductivity of the clothing and the unit is Clo:

[0079] Thin summer clothes (such as short-sleeved shirts, shorts): about 0.5-0.6Clo;

[0080] Spring and autumn clothes (such as long-sleeved shirts and long pants): about 0.7-1.0Clo;

[0081] Medium-thick spring and autumn clothing (such as sweaters, light jackets): about 1.1-1.5Clo;

[0082] Winter clothing (such as thick sweaters, coats or windbreakers): about 1.6-2.0Clo;

[0083] Thick winter clothing (such as thick coats, down jackets): about 2.1-2.5Clo;

[0084] Step S13: The PMV, condensation temperature difference, wind system energy consumption and water system energy consumption need to be further calculated.

[0085] The PMV refers to the ASHRAE 55-2017 standard formula:

[0086] PMV=0.303×e -0.036×M +0.028×(MW)+0.00000305×5733-0.00763×Pa+0.000149×Pa 2 +0.000438×M×(MW)+0.000119×M×Pa-0.000388×(MW)×Pa-0.000006×M×(MW)×Pa+0.0000000016×M×(MW)×(34-Ta)

[0087] Where: M represents metabolic rate, in megacalories per hour per square meter (met); W represents external workload, in megacalories per hour per square meter (met); Pa represents ambient air water vapor pressure, in Pascal (Pa); Ta represents indoor air temperature, in degrees Celsius (℃).

[0088] Step S131: The condensation temperature difference calculation formula is:

[0089] ΔT l =T d -T ld

[0090] Where, T d is the floor surface temperature, °C; T ld is the dew point temperature of indoor air, ℃; ΔT l is the condensation temperature difference, ℃.

[0091] Step S132: The wind system energy consumption calculation formula is:

[0092] h=C pa ×T+w(C pw ×T+R w ×Th ref )

[0093] ΔE x =V×ρ×(h1-h2)

[0094] Where h is specific enthalpy, kJ / kg; C pa is the specific heat of dry air, kJ / kg·K; T is the air temperature, ℃; w is the air moisture content, kg H2O / kg DryAir ; C pw is the specific heat capacity of water vapor, kJ / kg·K; R W is the gas constant of water vapor, kJ / kg·K. h ref is the reference enthalpy value, which is simplified to 0 here; V is the air volume of the air system, m 3 / h; h1 is the specific enthalpy of air at the fresh air fan inlet, kJ / kg; h2 is the specific enthalpy of air at the fresh air fan outlet, kJ / kg; ΔE x is the energy consumption of the wind system per hour, kW / h.

[0095] Step S133: The water system energy consumption calculation formula is:

[0096] ΔE s =Q×ρ×C×ΔT

[0097] Where ΔE s is the energy dissipated by the water system per hour, kW / h; Q is the water flow rate, m 3 / h; ρ is water density, 1000kg / m 3 ; C is the specific heat capacity of water, which is approximately 4.18 kJ / (kg·K); ΔT is the supply and return water temperature difference of the radiant floor, K.

[0098] Step S14: pre-process the acquired data to remove abnormal data, null value data, and duplicate data to obtain a sample data set.

[0099] like Figure 3As shown, step S2: the indoor and outdoor environment personnel parameters are sorted out, the data is processed, and a cold and wet load prediction model is established using machine learning.

[0100] Step S21: Select the required data and organize them into a data set. The input items are: outdoor temperature, outdoor humidity, indoor temperature, indoor humidity, outdoor wind speed, solar radiation intensity, personnel activities (including: personnel moisture dissipation, human metabolic rate, clothing thermal resistance and personnel activity time), lighting heat dissipation, equipment heat dissipation and lighting equipment start and stop time. The output items are cooling load and humidity load.

[0101] Step S22: Write a machine learning algorithm, normalize the data, divide it into a training set and a test set, and import the training set into the machine learning algorithm.

[0102] Step S23: training the cold and wet load prediction model, and adjusting the weight and offset of the cold and wet load prediction model through the optimization algorithm to make the prediction model more accurate.

[0103] Step S24: Input the test set into the cold and wet load prediction model to predict the cold and wet load, and use the root mean square error (RMSE), mean absolute error (MAE) and determination coefficient: R 2 Evaluate the model. If the model is trained based on the same data set, the smaller the RMSE and MAE values ​​are, the better the R 2 The closer it is to 1, the higher the model accuracy. The formulas for each indicator are:

[0104]

[0105] In the formula, y i is the actual value, is the predicted value, is the mean, and n is the sample size.

[0106] like Figure 4 As shown, step S3: the outdoor environment history, cold and wet load, and air conditioning system operation history data are sorted out, the data are processed, and a machine learning algorithm is used to establish an indoor environment and energy consumption prediction model.

[0107] Step S31: Select the required data and organize them into a data set. Input items: fresh air fan air volume, radiant floor water supply pump status, radiant floor water supply temperature, outdoor temperature, outdoor humidity, indoor cooling load and indoor wet load. Output items are indoor temperature, condensation temperature difference, wind system energy consumption and water system energy consumption.

[0108] Step S32: Write a machine learning algorithm, normalize the data, divide it into a training set and a test set, and import the training set into the machine learning algorithm.

[0109] In one embodiment, writing a machine learning algorithm, normalizing the data, dividing the data into a training set and a test set, and importing the training set into the machine learning algorithm includes the following steps:

[0110] S321. Read input items from historical data: fresh air fan air volume, radiant floor water supply pump status, radiant floor water supply temperature, outdoor temperature, outdoor humidity, indoor cooling load and indoor wet load. Output items are indoor temperature, condensation temperature difference, air system energy consumption and water system energy consumption.

[0111] S322. Write a BP neural network algorithm.

[0112] S323. Divide the data set, usually in a ratio of 70% training set and 30% test set, and normalize all input and output data to the [0,1] interval.

[0113] S324. Determine the neural network structure, the number of neurons in the input layer is 7, try to find the number of neuron nodes in the hidden layer, and the number of neurons in the output layer is 4.

[0114] S325. Input the hyperparameters of the model and start training.

[0115] S326. Complete indoor environment and energy consumption prediction model training.

[0116] Step S33: input the test set into the indoor environment and energy consumption prediction model, predict the indoor environment and energy consumption, and perform evaluation, which is specifically the same as the above step S23.

[0117] In some embodiments, the model participates in fitness calculations as part of a multi-objective optimization algorithm.

[0118] It should be noted that the above-mentioned machine learning algorithm can be based on neural network, support vector machine regression and other algorithms for prediction, and the embodiments of the present application are not limited to this.

[0119] Step S4: The load prediction model is applied to predict the cold and wet load, set the target adjustment temperature, determine the constraint conditions, and set the objective function of the multi-objective optimization. The specific process is as follows:

[0120] Step S41: Export the real-time data described in step S1, including: outdoor temperature, humidity, solar radiation intensity and wind speed; indoor temperature, humidity, floor surface temperature and condensation temperature difference; human sensible heat load, wet load, light heat dissipation, equipment heat dissipation, human metabolic rate, clothing thermal resistance, personnel activity status and lighting equipment start and stop status; input the real-time data into the cold and wet load prediction model to predict the real-time cold load and wet load in the room.

[0121] Step S42: extract the outdoor temperature and humidity in the real-time data, and combine them with the cooling load and the wet load to form an environmental parameter data set as part of the independent variable, which are x1, x2, x3, and x4 in sequence.

[0122] Step S43: Acquire the working parameters of the air conditioning system and set the optimization algorithm to generate individual constraints, including: the air supply volume range of the fresh air system, the state of the radiant floor water supply pump, and the radiant floor water supply temperature range.

[0123] Step S431: Specifically, the air supply volume constraint is as follows:

[0124]

[0125] In the formula, is the minimum air volume of the fresh air system, m 3 / h; is the maximum air volume of the fresh air system, m 3 / h; The air volume of the fresh air system to be regulated, m 3 / h.

[0126] Step S432: Specifically, the water supply pump state constraint is as follows:

[0127] 0≤K b ≤1

[0128] In the formula, 0 is off, 1 is on, K b Pump status.

[0129] Step S433: Specifically, the floor water supply temperature constraint is as follows:

[0130]

[0131] In the formula, is the minimum value of floor water supply temperature, ℃; is the maximum value of floor water supply temperature, ℃; The floor water supply pump temperature for regulation, ℃.

[0132] Specifically, the control parameters are used as part of the independent variables, namely the air volume of the fresh air fan, the radiant floor water supply temperature, and the radiant floor water supply pump state, which are x5, x6, and x7 respectively. Combined with the above-mentioned environmental parameter independent variables, they are used as the independent variables of the optimization algorithm.

[0133] Step S44: The objective function of the optimization algorithm is set. The present invention takes indoor thermal and humid environment, condensation risk, and energy consumption as optimization targets. Energy consumption is divided into wind system energy consumption and water system energy consumption, so the objective function is divided into four parts.

[0134] Step S441: Specifically, the indoor thermal and humidity environment index is PMV or indoor temperature, which is selected according to actual needs of the user.

[0135] Step S4411: Set the objective function of PMV to the minimum value of the absolute value of PMV, then the indoor thermal and humid environment objective function f1 is:

[0136] f1=min|P in (x1, x2, x3, x4, x5, x6, x7)|

[0137] Where P in is the indoor PMV value.

[0138] Step S4412: According to user needs, the indoor air temperature is set. The objective function of the indoor air temperature is set to the minimum value of the difference with the user set value. Then the indoor ambient temperature objective function f1 is:

[0139] f1=min|T in (x1, x2, x3, x4, x5, x6, x7)-T s |

[0140] Where, T in is the indoor temperature predicted by the above indoor environment and energy consumption prediction model, ℃; T s Indoor temperature value set by the user, ℃.

[0141] Step S442: Specifically, the smaller the condensation temperature difference, the greater the possibility of condensation; conversely, the greater the temperature difference, the smaller the possibility of condensation. In order to minimize the condensation risk, the objective function f2 of the condensation temperature difference is maximized to reduce the condensation risk.

[0142] f2=-min|T l (x1, x2, x3, x4, x5, x6, x7)|

[0143] Where, T l is the condensation temperature difference predicted by the above indoor environment and energy consumption prediction model, ℃.

[0144] Step S443: In this embodiment, the air system of the air conditioning system is mainly composed of a fresh air fan, so the enthalpy difference before and after the fresh air fan is used as the energy consumption of the air system; with low energy consumption as the goal, the energy consumption target function f3 of the air system can be obtained as:

[0145] f3=minQ f (x1, x2, x3, x4, x5, x6, x7)

[0146] Step S444: The water system of the air conditioning system is mainly based on the radiant floor, so the difference in energy carried by the floor supply and return water is used as the energy consumption of the water system. At the same time, the present invention aims at low energy consumption, and the energy consumption objective function f4 of the water system can be obtained as:

[0147] f4=minQ s (x1, x2, x3, x4, x5, x6, x7)

[0148] Step S445: To reduce the complexity of the algorithm and simplify the multi-objective optimization problem, the above indoor thermal and humid environment, condensation risk, and energy consumption function are weighted to form the thermal and humid, condensation, and energy consumption objective function f:

[0149] f=α1f1+α2f2+α3f3+α4f4

[0150] In the formula, the weights of each target α1, α2, α3, and α4 are adjusted according to the actual needs of the user.

[0151] Step S5: Apply the optimization algorithm to solve the target optimization model and calculate the optimal control parameters. In this embodiment, the improved gray wolf optimization algorithm is used, such as Figure 5 shown.

[0152] The steps to improve the gray wolf optimization algorithm are as follows:

[0153] S51: Set the maximum number of iterations and population size of the improved gray wolf optimization algorithm.

[0154] S52. Use Logistic mapping to generate the initial position of the population, and its expression is:

[0155] y n+1 = r·y n ·(1-y n )

[0156] Among them, y n Represents the position of the nth individual; r is a random number between [0, 1].

[0157] S53, using the above objective function f to calculate the fitness of the population, and sorting these fitness values. According to the sorting results, select the three gray wolf individuals with the highest fitness as the leadership α wolf, β wolf and δ wolf, and the rest as ω wolf, and record their positions and fitness, respectively: y α ,y β ,y δ and F α 、F β 、F δ ;

[0158] S54, the gray wolf performs prey tracking behavior and updates the individual position. In this step, the application updates the next iteration position of the ω wolf in the algorithm.

[0159] The mathematical model of gray wolves tracking prey is as follows:

[0160] y α (m) = y α (m)-A1·D α

[0161] y β (m) = y β (m)-A2·D β

[0162] y δ (m) = y δ (m)-A3·D δ

[0163]

[0164] +0.1(r1·(y α -y ω )+r2·(y β -y ω )+r2·(y δ -y ω ))

[0165] Among them, D α , D α , D α is the distance between wolf α, wolf β and wolf δ and the prey, y α ,y β ,y δ is the current position of α wolf, β wolf, δ wolf, y(m+1) is the position of ω wolf in the next iteration. r1, r2, r3 are random numbers between [0, 1]. Because the fitness of α wolf, β wolf and δ wolf decreases successively, their importance decreases successively. In the above formula, the weights of α wolf, β wolf and δ wolf decrease successively, and a certain degree of random disturbance is added at the same time.

[0166] In order to enhance the search performance of the algorithm, the present invention selects a multi-stage nonlinear convergence strategy, so that the algorithm can improve the exploration ability while ensuring efficiency during the iteration process, and thus the convergence factor a is updated to:

[0167]

[0168] Where: m is the current iteration number; M is the maximum iteration number; k1 and k2 are parameters that control the shape of the exponential function of e, and k3 is the exponent for adjusting the rate of decrease of a in the later stage of iteration, which can be adjusted according to actual needs.

[0169] S55. Use the objective function to calculate the fitness value and sort it. At the same time, select the gray wolf individuals with the lowest 5% fitness ranking and perform Cauchy mutation. After the mutation, recalculate the fitness and update the position and fitness of α, β, and δ wolves. The Cauchy mutation is:

[0170] y m+1 =y m +0.1×(r×Cauchy(0.5)+1)

[0171] Where r is a random number uniformly distributed in the range [0,1]; Cauchy(0.5) is a random number that obeys the Cauchy distribution.

[0172] S56, let the number of iterations m = m + 1, if m = M max The algorithm terminates, otherwise returns to step S54;

[0173] S57, output the current position and fitness of α as the optimal control parameters, and the optimization is completed.

[0174] Embodiment 2

[0175] like Figure 6 As shown, this embodiment provides a multi-objective control system for an indoor radiant floor system based on load prediction, including:

[0176] A data acquisition module is configured to: acquire indoor environmental parameters, outdoor environmental parameters, indoor personnel activity information, equipment parameters, and air conditioning system operation information;

[0177] A load prediction module is configured to: obtain a cooling load and a wet load by using a cooling and wet load prediction model based on indoor environmental parameters, outdoor environmental parameters, indoor personnel activity information and equipment parameters;

[0178] The environment and energy consumption prediction module is configured to: based on outdoor environmental parameters, cooling load, wet load and air conditioning system operation information, use the indoor environment and energy consumption prediction model to obtain indoor temperature, condensation temperature difference, wind system energy consumption and water system energy consumption;

[0179] The objective function construction module is configured to: use outdoor environmental parameters, cooling load, wet load and air conditioning system operation information as variables, respectively construct objective functions of indoor temperature, air conditioning PMV, condensation temperature difference, wind system energy consumption and water system energy consumption, and configure corresponding weights to calculate the total objective function;

[0180] The solution module is configured to: set the operating parameters of the air-conditioning system as constraints, solve the overall objective function, and obtain the optimal control parameters.

[0181] In some embodiments, the constraints include air supply volume constraints, water supply pump status constraints, and floor water supply temperature constraints.

[0182] In some embodiments, the indoor environmental parameters include: indoor temperature, indoor humidity, floor surface temperature and condensation temperature difference; the outdoor environmental parameters include: outdoor temperature, outdoor humidity, solar radiation intensity and outdoor wind speed; the indoor personnel activity information includes: personnel moisture dissipation, human metabolic rate, clothing thermal resistance and personnel activity time; the equipment parameters include: lighting heat dissipation, equipment heat dissipation and lighting equipment start and stop time; the air-conditioning system operation information includes: fan air volume, radiant floor water supply pump switch, radiant panel water supply temperature, wind system energy consumption and water system energy consumption.

[0183] In some embodiments, the objective function building module is further configured to:

[0184] Taking outdoor temperature, outdoor humidity, cooling load, wet load, fresh air fan air volume, radiant floor water supply temperature and radiant floor water supply pump status as variables, the indoor air temperature is set, and the indoor temperature objective function is constructed based on the difference between the indoor air temperature and the user-set value;

[0185] The air conditioning PMV objective function is constructed with outdoor temperature, outdoor humidity, cooling load, wet load, fresh air fan air volume, radiant floor water supply temperature and radiant floor water supply pump status as variables;

[0186] The condensation temperature difference objective function is constructed with outdoor temperature, outdoor humidity, cooling load, wet load, fresh air fan air volume, radiant floor water supply temperature and radiant floor water supply pump status as variables;

[0187] The objective function of wind system energy consumption is constructed with outdoor temperature, outdoor humidity, cooling load, wet load, fresh air fan air volume, radiant floor water supply temperature and radiant floor water supply pump status as variables;

[0188] The energy consumption objective function of the water system is constructed with outdoor temperature, outdoor humidity, cooling load, wet load, fresh air fan air volume, radiant floor water supply temperature and radiant floor water supply pump status as variables.

[0189] In some embodiments, the objective function construction module is further configured to calculate the sum of products based on the indoor temperature objective function, the condensation temperature difference objective function, the wind system energy consumption objective function and the water system energy consumption objective function and corresponding weights to obtain the total objective function.

[0190] In some embodiments, the objective function construction module is further configured to calculate the sum of products based on the air conditioning PMV objective function, condensation temperature difference objective function, wind system energy consumption objective function and water system energy consumption objective function and corresponding weights to obtain the total objective function.

[0191] In some embodiments, the solution module is further configured to:

[0192] An improved gray wolf optimization algorithm is used to randomly generate gray wolf individuals, which are input into the indoor environment and energy consumption prediction model to obtain indoor temperature, condensation temperature difference, wind system energy consumption, and water system energy consumption; the gray wolf individuals include: outdoor temperature, outdoor humidity, cooling load, wet load, fresh air fan air volume, radiant floor water supply temperature, and radiant floor water supply pump status;

[0193] Based on the total objective function, determine the fitness values ​​of individuals in the population, sort them, and select the three gray wolf individuals with the highest fitness. The fitness from high to low is α, β, δ wolf, and the rest are ω wolf;

[0194] The gray wolf tracks prey and updates the position of individual gray wolves. The objective function is used to calculate the fitness value of each gray wolf and rank them. At the same time, a certain proportion of gray wolf individuals with low fitness rankings are selected for Cauchy mutation. After the mutation, the fitness is recalculated and updated.

[0195] Determine whether the maximum number of iterations has been reached, and proceed to the next round of iteration or output the results.

[0196] Embodiment 3

[0197] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps in the multi-objective control method for an indoor radiant floor system based on load prediction as described in the first embodiment above are implemented.

[0198] Embodiment 4

[0199] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the multi-objective control method for an indoor radiant floor system based on load prediction as described in the first embodiment above are implemented.

[0200] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0201] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0202] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0203] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0204] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0205] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A multi-objective control method for indoor radiant floor system based on load prediction, characterized in that: include: Obtain indoor environmental parameters, outdoor environmental parameters, indoor personnel activity information, equipment parameters and air conditioning system operation information; Based on indoor environmental parameters, outdoor environmental parameters, indoor personnel activity information and equipment parameters, the cold and wet load prediction model is used to obtain the cold load and wet load; Based on outdoor environmental parameters, cooling load, wet load and air conditioning system operation information, the indoor environment and energy consumption prediction model is used to obtain indoor temperature, condensation temperature difference, wind system energy consumption and water system energy consumption; Taking outdoor environmental parameters, cooling load, wet load and air conditioning system operation information as variables, objective functions of indoor temperature, air conditioning PMV, condensation temperature difference, wind system energy consumption and water system energy consumption are constructed respectively, and corresponding weights are configured to calculate the total objective function; The operating parameters of the air-conditioning system are set as constraints, the overall objective function is solved, and the optimal control parameters are obtained.

2. The multi-objective control method for indoor radiant floor system based on load prediction according to claim 1 is characterized in that: The constraints include air supply volume constraint, water supply pump state constraint and floor water supply temperature constraint.

3. The multi-objective control method for indoor radiant floor system based on load prediction according to claim 1 is characterized in that: The indoor environmental parameters include: indoor temperature, indoor humidity, floor surface temperature and condensation temperature difference; the outdoor environmental parameters include: outdoor temperature, outdoor humidity, solar radiation intensity and outdoor wind speed; the indoor personnel activity information includes: personnel moisture dissipation, human metabolic rate, clothing thermal resistance and personnel activity time; the equipment parameters include: lighting heat dissipation, equipment heat dissipation and lighting equipment start and stop time; the air-conditioning system operation information includes: fan air volume, radiant floor water supply pump switch, radiant panel water supply temperature, wind system energy consumption and water system energy consumption.

4. The multi-objective control method for indoor radiant floor system based on load prediction according to claim 1 is characterized in that: Taking outdoor environmental parameters, cooling load, wet load and air conditioning system operation information as variables, objective functions of indoor temperature, air conditioning PMV, condensation temperature difference, wind system energy consumption and water system energy consumption are constructed respectively. The methods include: Taking outdoor temperature, outdoor humidity, cooling load, wet load, fresh air fan air volume, radiant floor water supply temperature and radiant floor water supply pump status as variables, the indoor air temperature is set, and the indoor temperature objective function is constructed based on the difference between the indoor air temperature and the user-set value; The air conditioning PMV objective function is constructed with outdoor temperature, outdoor humidity, cooling load, wet load, fresh air fan air volume, radiant floor water supply temperature and radiant floor water supply pump status as variables; The condensation temperature difference objective function is constructed with outdoor temperature, outdoor humidity, cooling load, wet load, fresh air fan air volume, radiant floor water supply temperature and radiant floor water supply pump status as variables; The objective function of wind system energy consumption is constructed with outdoor temperature, outdoor humidity, cooling load, wet load, fresh air fan air volume, radiant floor water supply temperature and radiant floor water supply pump status as variables; The energy consumption objective function of the water system is constructed with outdoor temperature, outdoor humidity, cooling load, wet load, fresh air fan air volume, radiant floor water supply temperature and radiant floor water supply pump status as variables.

5. The multi-objective control method for indoor radiant floor system based on load prediction according to claim 4 is characterized in that: Taking outdoor environmental parameters, cooling load, wet load and air conditioning system operation information as variables, objective functions of indoor temperature, air conditioning PMV, condensation temperature difference, wind system energy consumption and water system energy consumption are constructed respectively, and corresponding weights are configured to calculate the total objective function; the method includes: Based on the indoor temperature objective function, the condensation temperature difference objective function, the wind system energy consumption objective function and the water system energy consumption objective function and the corresponding weights, the sum of products is calculated to obtain the total objective function.

6. The multi-objective control method for indoor radiant floor system based on load prediction according to claim 4 is characterized in that: Taking outdoor environmental parameters, cooling load, wet load and air conditioning system operation information as variables, objective functions of indoor temperature, air conditioning PMV, condensation temperature difference, wind system energy consumption and water system energy consumption are constructed respectively, and corresponding weights are configured to calculate the total objective function; the method includes: Based on the air conditioning PMV objective function, the condensation temperature difference objective function, the wind system energy consumption objective function and the water system energy consumption objective function and the corresponding weights, the sum of products is calculated to obtain the total objective function.

7. The multi-objective control method for indoor radiant floor system based on load prediction according to claim 4 is characterized in that: The overall objective function is solved to obtain the optimal control parameters, and the method includes: An improved gray wolf optimization algorithm is used to randomly generate gray wolf individuals, which are input into the indoor environment and energy consumption prediction model to obtain indoor temperature, condensation temperature difference, wind system energy consumption, and water system energy consumption; the gray wolf individuals include: outdoor temperature, outdoor humidity, cooling load, wet load, fresh air fan air volume, radiant floor water supply temperature, and radiant floor water supply pump status; Based on the total objective function, determine the fitness values ​​of individuals in the population, sort them, and select the three gray wolf individuals with the highest fitness. The fitness from high to low is α, β, δ wolf, and the rest are ω wolf; The gray wolf tracks prey and updates the position of individual gray wolves. The objective function is used to calculate the fitness value of each gray wolf and rank them. At the same time, a certain proportion of gray wolf individuals with low fitness rankings are selected for Cauchy mutation. After the mutation, the fitness is recalculated and updated. Determine whether the maximum number of iterations has been reached, and proceed to the next round of iteration or output the results.

8. A multi-objective control system for indoor radiant floor systems based on load prediction, characterized in that: include: A data acquisition module is configured to: acquire indoor environmental parameters, outdoor environmental parameters, indoor personnel activity information, equipment parameters, and air conditioning system operation information; A load prediction module is configured to: obtain a cooling load and a wet load by using a cooling and wet load prediction model based on indoor environmental parameters, outdoor environmental parameters, indoor personnel activity information and equipment parameters; The environment and energy consumption prediction module is configured to: based on outdoor environmental parameters, cooling load, wet load and air conditioning system operation information, use the indoor environment and energy consumption prediction model to obtain indoor temperature, condensation temperature difference, wind system energy consumption, and water system energy consumption; The objective function construction module is configured to: use outdoor environmental parameters, cooling load, wet load and air conditioning system operation information as variables, respectively construct objective functions of indoor temperature, air conditioning PMV, condensation temperature difference, wind system energy consumption and water system energy consumption, and configure corresponding weights to calculate the total objective function; The solution module is configured to: set the operating parameters of the air-conditioning system as constraints, solve the overall objective function, and obtain the optimal control parameters.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the multi-objective control method for an indoor radiant floor system based on load prediction as described in any one of claims 1 to 7 are implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the multi-objective control method for an indoor radiant floor system based on load prediction as described in any one of claims 1 to 7 are implemented.

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