A smart energy management system for energy saving in public buildings

Abstract

The present invention relates to the technical field of energy management, and discloses a smart energy management system for energy conservation in public buildings, including: constructing a fine-grained public building HVAC control system model and a public building cost model; constructing a hybrid optimization objective function based on the public building HVAC control system model and the public building cost model, and converting the hybrid optimization objective function into a single time slot decision optimization problem; constructing a virtual queue for wind speed distribution in all areas of the public building; optimizing and solving the single time slot decision optimization problem based on the virtual queue to obtain the public building HVAC system model parameters, and controlling the regional temperature according to the parameters. The present invention realizes energy conservation control of public buildings under the premise of ensuring high user comfort, optimizes the decision problem based on the virtual queue method, and when the virtual queue is stable, the air volume control boxes in different areas are fairly scheduled.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and in particular to an intelligent energy management system for energy saving in public buildings. Background Art

[0002] The energy consumption of HVAC systems accounts for about 40% of the energy consumption of public buildings. The key to reducing the energy consumption of public buildings is to reduce the energy consumption of HVAC systems, but reducing the energy consumption of HVAC systems will sacrifice the thermal comfort of users, resulting in a poor user experience. Therefore, it is very necessary to consider the integrated analysis of building energy consumption and thermal comfort. Although the Internet of Things technology can promote the integrated balance of energy consumption and thermal comfort in public buildings, due to the existence of multi-source uncertain system parameters in practice and the difficulty in obtaining a building thermodynamic model that is both accurate enough and easy to control, it is still a great challenge to accurately and effectively analyze the relationship between energy and thermal comfort in public buildings. In response to this problem, this patent proposes an intelligent energy management system and method for energy saving in public buildings, which aims to solve the integrated analysis of energy and thermal comfort of intelligent public buildings under uncertain environments. Summary of the invention

[0003] In view of this, the present invention provides an intelligent energy management method for energy saving in public buildings, the purpose of which is to (1) construct a cost model based on user comfort and the electricity cost of the air-conditioning system in the public building, and obtain energy-saving control parameters of the public building that ensure high user comfort by solving the cost model; (2) construct a fine-grained public building HVAC control system model considering multiple parameters of the air-conditioning system to improve the accuracy of air-conditioning system control; (3) optimize the decision problem based on the virtual queue method. When the virtual queue is stable, it means that the wind speed of the air supply fan allocated to different areas in the public building is fair, and the air volume control boxes in different areas are fairly scheduled.

[0004] To achieve the above object, the present invention provides a smart energy management method for energy saving in public buildings, comprising the following steps:

[0005] S1: Divide the public building into N areas and build a fine-grained public building HVAC control system model. The temperature inside each area is controlled by the HVAC control system.

[0006] S2: construct a utility building cost model, wherein the utility building cost includes user experience cost, fan power consumption cost and heat exchange coil power consumption cost;

[0007] S3: Construct a hybrid optimization objective function based on the public building HVAC control system model and the public building cost model, and transform the hybrid optimization objective function into a single time slot decision optimization problem;

[0008] S4: construct a virtual queue of wind speed distribution for all zones in the public building;

[0009] S5: Based on the virtual queue, the single time slot decision optimization problem is optimized and solved to obtain the model parameters of the HVAC system of the public building, and the regional temperature is controlled according to the parameters to achieve energy saving in the public building.

[0010] As a further improvement method of the present invention:

[0011] Optionally, in step S1, the public building is divided into N areas, including:

[0012] Divide the public building into N areas, separate each area with doors or walls, and deploy temperature and humidity sensors in each area;

[0013] In a specific embodiment of the present invention, the areas divided by the public building include enclosed rooms and halls, etc.;

[0014] Each divided area has an air volume control box, which has an air valve and a blower built in it, wherein the air valve is used to adjust the air supply rate of the blower and return the air in the area to the control unit;

[0015] All air volume control boxes and control units in public buildings are used as public building HVAC control systems, and the internal temperature of each divided area is controlled by the public building HVAC control system. The control unit includes a frequency conversion controller, a heat exchange coil, a chilled water pump, a ventilation air valve and an air supply control machine, wherein the frequency conversion controller is used to control the operating frequency of the heat exchange coil and the air supply control machine, the ventilation air valve is used to mix the external fresh air with the air in the area, and the chilled water in the heat exchange coil is used to cool the mixed air. The cooled air is transported to the corresponding air volume control box based on the air supply control machine, the chilled water pump is used to store chilled water and transport chilled water to the heat exchange coil, and temperature and humidity sensors are deployed on the ventilation air valve.

[0016] Optionally, constructing a fine-grained public building HVAC control system model in step S1 includes:

[0017] A fine-grained public building HVAC control system model is constructed. The public building HVAC control system model is:

[0018]

[0019]

[0020]

[0021] in:

[0022] S i,t represents the temperature of the ith area of ​​the divided public building at time t, S 0,t Indicates the temperature of the mixed air in the ventilation damper at time t;

[0023] C represents the specific heat of air at constant pressure;

[0024] s i,t represents the air supply temperature of the fan in the ith area of ​​the public building at time t, v i,t Indicates the air supply rate of the blower;

[0025] represents the humidity of the ith area of ​​the public building at time t, represents the humidity of the mixed air at time t, and the temperature and humidity of the mixed air are sensed by the temperature and humidity sensor deployed by the ventilation valve;

[0026] P represents the saturation pressure of water vapor, which is set to 2300Pa;

[0027] B represents atmospheric pressure;

[0028] q i,t It represents the number of electrical equipment used in the ith area of ​​a public building at time t, where the electrical equipment is equipment that uses the electrical energy of the public building.

[0029] In the embodiment of the present invention, except for the air supply rate, all other parameters in the model can be obtained in real time. The control center sends the air supply rate parameters to the air volume control box to control the air supply rate in the air volume control box.

[0030] Optionally, constructing a public building cost model in step S2 includes:

[0031] A public building cost model is constructed, wherein the public building cost includes user experience cost, fan power consumption cost and heat exchange coil power consumption cost, and the public building cost model is:

[0032]

[0033] in:

[0034] f t represents the electricity price during time t;

[0035] ξ represents the coefficient of performance of the heat exchange coil, and η represents the efficiency of the heat exchange coil;

[0036] It represents the suitable temperature of the ith zone at time t set by the user;

[0037] Cost trepresents the utility building cost at time t;

[0038] Said represents the user experience cost, represents the power consumption cost of the blower, Represents the power consumption cost of the heat exchange coil.

[0039] Optionally, in step S3, a hybrid optimization objective function is constructed based on the public building HVAC control system and the public building cost model, including:

[0040] Based on the public building HVAC control system and the public building cost model, a hybrid optimization objective function is constructed. The hybrid optimization objective function F is:

[0041]

[0042]

[0043] The optimization goal is to minimize the cost of public buildings;

[0044] The constraints of the hybrid optimization objective function are:

[0045] S min ≤S i,t ≤S max

[0046] v min ≤v i,t ≤v max

[0047] in:

[0048] S min represents the lower limit of the regional temperature, S max Indicates the upper temperature limit of the region;

[0049] v min Indicates the lower limit of the wind speed of the fan, v max Indicates the upper limit of the fan's wind speed.

[0050] Optionally, in step S3, the hybrid optimization objective function is converted into a single time slot decision optimization problem, including:

[0051] The hybrid optimization objective function is converted into a single time slot decision optimization problem. The conversion process is as follows:

[0052] S31: Determine that the unit time slot length is τ;

[0053] S32: constructing a public building HVAC control system model and a public building cost model for a unit time slot respectively, wherein the public building HVAC control system model for a unit time slot is:

[0054]

[0055]

[0056]

[0057] in:

[0058] k represents the time slot subscript, indicating the kth time slot, K represents the total number of time slots, and the time slot length of each time slot is τ;

[0059] The utility building cost model per time slot is:

[0060]

[0061] in:

[0062] f k Indicates the electricity price within a unit time slot;

[0063] S33: Based on the single time slot model, determine the single time slot decision optimization problem:

[0064]

[0065] in:

[0066] E(·) represents the calculated expectation, sup represents the calculated supremum, and the decision variable is the wind speed of the ith area of ​​the public building in the kth time slot.

[0067] Optionally, the virtual queue for wind speed allocation in all regions in step S4 includes:

[0068] Construct a virtual queue for wind speed distribution in all areas of a public building. For any i-th area, the construction result of the virtual dispatch queue is:

[0069] The virtual queue result of the public building area i in the kth time slot is:

[0070] Q i,k =S i,k +σ i

[0071] σ i =min{S i,L -S i,L-1},L≤i-1

[0072] The virtual queue result at the k+1th time slot is:

[0073]

[0074] Then the virtual queue result of the public building in the ith area is:

[0075] {Q i,1 ,Q i,2 ,…,Q i,k ,…,Q i,K}

[0076] in:

[0077] K represents the total number of time slots.

[0078] Optionally, in step S5, optimizing and solving the single time slot decision optimization problem based on the virtual queue to obtain the model parameters of the air conditioning and heating ventilation and air conditioning system of the public building, and controlling the regional temperature according to the parameters, including:

[0079] The single time slot decision optimization problem is optimized and solved based on the virtual queue to obtain the model parameters of the public building air conditioning HVAC system. The model parameters of the public building air conditioning HVAC system to be solved are the air supply rate of the HVAC in any area of ​​the public building. The parameter solution process is as follows:

[0080] S51: Objective function for constructing a virtual queue:

[0081]

[0082] in:

[0083] g k represents the objective function of the kth time slot;

[0084] Then the offset result ε of the objective function is k for:

[0085] ε k =E(g k+1 -g k |Q k )

[0086] in:

[0087] Q k =(Q 1,k ,Q 2,k ,…,Q N,k );

[0088] S52: Calculate the difference of the objective function of adjacent time slots:

[0089]

[0090] in:

[0091]

[0092] but

[0093] S53: If This indicates that the virtual queue is stable, and the single-slot decision optimization problem is transformed into the following problem F':

[0094]

[0095]

[0096] F' to v i,k The first-order partial derivative of Taking 0, we get

[0097] Then the air supply rate of the fan in any i-th area of ​​the public building at the k-th time slot is for:

[0098]

[0099] Will As the public building air conditioning HVAC system model parameters, the control center sends the public building air conditioning HVAC system model parameters to the air volume control box to control the air supply rate in the air volume control box.

[0100] In order to solve the above problems, the present invention provides a smart energy management system for energy saving in public buildings, characterized in that the system comprises:

[0101] Model building module, used to build fine-grained public building HVAC control system model and public building cost model;

[0102] A data processing device is used to construct a hybrid optimization objective function based on a public building HVAC control system model and a public building cost model, and convert the hybrid optimization objective function into a single time slot decision optimization problem to construct a virtual queue for wind speed distribution in all areas of the public building;

[0103] The energy-saving management module is used to optimize and solve the single-time-slot decision optimization problem based on the virtual queue, obtain the model parameters of the air-conditioning and HVAC system of the public building, and control the regional temperature according to the parameters.

[0104] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0105] a memory storing at least one instruction; and

[0106] The processor executes the instructions stored in the memory to implement the above-mentioned smart energy management method for energy saving in public buildings.

[0107] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned smart energy management method for energy saving in public buildings.

[0108] Compared with the prior art, the present invention proposes a smart energy management method for energy saving in public buildings, which has the following advantages:

[0109] First, this scheme converts the public building energy-saving problem into a hybrid optimization objective function by constructing a fine-grained public building HVAC control system model, which is:

[0110]

[0111]

[0112]

[0113] Where: S i,t represents the temperature of the ith area of ​​the divided public building at time t, S 0,t represents the temperature of the mixed air in the ventilation damper at time t; C represents the constant pressure specific heat of the air; s i,t represents the air supply temperature of the fan in the ith area of ​​the public building at time t, v i,t Indicates the air supply rate of the blower; represents the humidity of the ith area of ​​the public building at time t, represents the humidity of the mixed air at time t, and the temperature and humidity of the mixed air are sensed by the temperature and humidity sensor deployed by the ventilation valve; P represents the saturation pressure of water vapor, which is set to 2300Pa; B represents the atmospheric pressure; q i,t It represents the number of electrical equipment used in the ith area of ​​a public building at time t. And a public building cost model is constructed, wherein the public building cost includes user experience cost, fan power consumption cost and heat exchange coil power consumption cost, and the public building cost model is:

[0114]

[0115] Where: f t represents the electricity price within time t; ξ represents the performance coefficient of the heat exchange coil, and η represents the efficiency of the heat exchange coil; Cost represents the suitable temperature of the ith area at time t set by the user; t represents the public construction cost at time t; represents the user experience cost, represents the power consumption cost of the blower, Represents the power consumption cost of the heat exchange coil. Based on the public building HVAC control system and the public building cost model, a hybrid optimization objective function is constructed, and the hybrid optimization objective function F is:

[0116]

[0117]

[0118] The optimization goal is to minimize the cost of public buildings; the constraints of the mixed optimization objective function are:

[0119] S min ≤S i,t ≤S max

[0120] v min ≤v i,t ≤v max

[0121] Where: S min represents the lower limit of the regional temperature, S max Indicates the upper limit of regional temperature; v min Indicates the lower limit of the wind speed of the fan, v max Indicates the upper limit of the wind speed of the fan. This solution considers various parameters of the air-conditioning system to build a fine-grained public building HVAC control system model to improve the accuracy of air-conditioning system control, and builds a cost model based on user comfort and the electricity cost of the air-conditioning system in public buildings. By solving the cost model, energy-saving control parameters for public buildings that ensure high user comfort are obtained.

[0122] At the same time, this scheme proposes a solution process based on virtual queues. By constructing a virtual queue for wind speed distribution in all areas of a public building, for any i-th area, the construction result of its virtual dispatch queue is: The virtual queue result of the i-th area of ​​a public building in the k-th time slot is:

[0123] Q i,k =S i,k +σ i

[0124] σ i =min{S i,L -S i,L-1},L≤i-1

[0125] The virtual queue result at the k+1th time slot is:

[0126]

[0127] Then the virtual queue result of the public building in the ith area is:

[0128] {Q i,1 ,Q i,2 ,…,Q i,k ,…,Q i,K}

[0129] Where: K represents the total number of time slots. Based on the virtual queue, the single time slot decision optimization problem is optimized and solved to obtain the model parameters of the air conditioning HVAC system of the public building. The model parameters of the air conditioning HVAC system of the public building to be solved are the air supply rate of the HVAC in any area of ​​the public building. The parameter solution process is: Construct the objective function of the virtual queue:

[0130]

[0131] Where: g k represents the objective function of the kth time slot; then the offset result of the objective function ε k for:

[0132] ε k =E(g k+1 -g k |Q k )

[0133] Where: Q k =(Q 1,k ,Q 2,k ,…,Q N,k );Calculate the difference of the objective function of adjacent time slots:

[0134]

[0135] in: but like This means that the virtual queue is stable and converts the single-slot decision optimization problem into the following problem F':

[0136]

[0137]

[0138] F' to v i,k The first-order partial derivative of Taking 0, we get Then the air supply rate of the fan in any i-th area of ​​the public building at the k-th time slot is for:

[0139]

[0140] Will As the model parameters of the HVAC system of the public building, the control center sends the model parameters of the HVAC system of the public building to the air volume control box to control the air supply rate in the air volume control box. This scheme optimizes the decision problem based on the virtual queue method. When the virtual queue is stable, it means that the wind speed of the air supply fan allocated to different areas in the public building is fair, and the air volume control boxes in different areas are fairly scheduled. BRIEF DESCRIPTION OF THE DRAWINGS

[0141] Figure 1 A schematic diagram of a flow chart of a smart energy management method for energy saving in public buildings provided by one embodiment of the present invention;

[0142] Figure 2 A functional module diagram of a smart energy management system for energy saving in public buildings provided by an embodiment of the present invention;

[0143] Figure 3 A schematic diagram of the structure of an electronic device for implementing a smart energy management method for energy saving in public buildings provided in one embodiment of the present invention.

[0144] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0145] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0146] The embodiment of the present application provides a smart energy management method for energy conservation in public buildings. The execution subject of the smart energy management method for energy conservation in public buildings includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the smart energy management method for energy conservation in public buildings can be executed by software or hardware installed in a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0147] Embodiment 1:

[0148] S1: Divide the public building into N areas and build a fine-grained public building HVAC control system model. The temperature inside each area is controlled by the HVAC control system.

[0149] In step S1, the public building is divided into N areas, including:

[0150] Divide the public building into N areas, separate each area with doors or walls, and deploy temperature and humidity sensors in each area;

[0151] In a specific embodiment of the present invention, the areas divided by the public building include enclosed rooms and halls, etc.;

[0152] Each divided area has an air volume control box, which has an air valve and a blower built in it, wherein the air valve is used to adjust the air supply rate of the blower and return the air in the area to the control unit;

[0153] All air volume control boxes and control units in public buildings are used as public building HVAC control systems, and the internal temperature of each divided area is controlled by the public building HVAC control system. The control unit includes a frequency conversion controller, a heat exchange coil, a chilled water pump, a ventilation air valve and an air supply control machine, wherein the frequency conversion controller is used to control the operating frequency of the heat exchange coil and the air supply control machine, the ventilation air valve is used to mix the external fresh air with the air in the area, and the chilled water in the heat exchange coil is used to cool the mixed air. The cooled air is transported to the corresponding air volume control box based on the air supply control machine, the chilled water pump is used to store chilled water and transport chilled water to the heat exchange coil, and temperature and humidity sensors are deployed on the ventilation air valve.

[0154] In the step S1, a fine-grained public building HVAC control system model is constructed, including:

[0155] A fine-grained public building HVAC control system model is constructed. The public building HVAC control system model is:

[0156]

[0157]

[0158]

[0159] in:

[0160] S i,t represents the temperature of the ith area of ​​the divided public building at time t, S 0,t Indicates the temperature of the mixed air in the ventilation damper at time t;

[0161] C represents the specific heat of air at constant pressure;

[0162] s i,t represents the air supply temperature of the fan in the ith area of ​​the public building at time t, v i,t Indicates the air supply rate of the blower;

[0163] represents the humidity of the ith area of ​​the public building at time t, represents the humidity of the mixed air at time t, and the temperature and humidity of the mixed air are sensed by the temperature and humidity sensor deployed by the ventilation valve;

[0164] P represents the saturation pressure of water vapor, which is set to 2300Pa;

[0165] B represents atmospheric pressure;

[0166] q i,t It represents the number of electrical equipment used in the ith area of ​​a public building at time t, where the electrical equipment is equipment that uses the electrical energy of the public building.

[0167] In the embodiment of the present invention, except for the air supply rate, all other parameters in the model can be obtained in real time. The control center sends the air supply rate parameters to the air volume control box to control the air supply rate in the air volume control box.

[0168] S2: Construct a utility building cost model, wherein the utility building cost includes user experience cost, fan power consumption cost, and heat exchange coil power consumption cost.

[0169] The public building cost model is constructed in step S2, including:

[0170] A public building cost model is constructed, wherein the public building cost includes user experience cost, fan power consumption cost and heat exchange coil power consumption cost, and the public building cost model is:

[0171]

[0172] in:

[0173] f t represents the electricity price during time t;

[0174] ξ represents the coefficient of performance of the heat exchange coil, and η represents the efficiency of the heat exchange coil;

[0175] It represents the suitable temperature of the ith zone at time t set by the user;

[0176] Cost t represents the utility building cost at time t;

[0177] Said represents the user experience cost, represents the power consumption cost of the blower, Represents the power consumption cost of the heat exchange coil.

[0178] S3: A hybrid optimization objective function is constructed based on the public building HVAC control system model and the public building cost model, and the hybrid optimization objective function is transformed into a single time-slot decision optimization problem.

[0179] In the step S3, the hybrid optimization objective function is converted into a single time slot decision optimization problem, including:

[0180] The hybrid optimization objective function is converted into a single time slot decision optimization problem. The conversion process is as follows:

[0181] S31: Determine that the unit time slot length is τ;

[0182] S32: constructing a public building HVAC control system model and a public building cost model for a unit time slot respectively, wherein the public building HVAC control system model for a unit time slot is:

[0183]

[0184]

[0185]

[0186] in:

[0187] k represents the time slot subscript, indicating the kth time slot, K represents the total number of time slots, and the time slot length of each time slot is τ;

[0188] The utility building cost model per time slot is:

[0189]

[0190] in:

[0191] f k Indicates the electricity price within a unit time slot;

[0192] S33: Based on the single time slot model, determine the single time slot decision optimization problem:

[0193]

[0194] in:

[0195] E(·) represents the calculated expectation, sup represents the calculated supremum, and the decision variable is the wind speed of the ith area of ​​the public building in the kth time slot.

[0196] S4: Construct a virtual queue of wind speed distribution for all zones in a public building.

[0197] The virtual queues for wind speed allocation in all regions in step S4 include:

[0198] Construct a virtual queue for wind speed distribution in all areas of a public building. For any i-th area, the construction result of the virtual dispatch queue is:

[0199] The virtual queue result of the public building area i in the kth time slot is:

[0200] Q i,k =S i,k +σ i

[0201] σ i =min{S i,L -S i,L-1},L≤i-1

[0202] The virtual queue result at the k+1th time slot is:

[0203]

[0204] Then the virtual queue result of the public building in the ith area is:

[0205] {Q i,1 ,Q i,2 ,…,Q i,k ,…,Q i,K}

[0206] in:

[0207] K represents the total number of time slots.

[0208] S5: Based on the virtual queue, the single time slot decision optimization problem is optimized and solved to obtain the model parameters of the HVAC system of the public building, and the regional temperature is controlled according to the parameters to achieve energy saving in the public building.

[0209] In the step S5, the single time slot decision optimization problem is optimized and solved based on the virtual queue to obtain the model parameters of the air conditioning HVAC system of the public building, and the regional temperature is controlled according to the parameters, including:

[0210] The single time slot decision optimization problem is optimized and solved based on the virtual queue to obtain the model parameters of the public building air conditioning HVAC system. The model parameters of the public building air conditioning HVAC system to be solved are the air supply rate of the HVAC in any area of ​​the public building. The parameter solution process is as follows:

[0211] S51: Objective function for constructing a virtual queue:

[0212]

[0213] in:

[0214] g k represents the objective function of the kth time slot;

[0215] Then the offset result ε of the objective function is k for:

[0216] ε k =E(g k+1 -g k |Q k )

[0217] in:

[0218] Q k =(Q 1,k ,Q 2,k ,…,Q N,k );

[0219] S52: Calculate the difference of the objective function of adjacent time slots:

[0220]

[0221] in:

[0222]

[0223] but

[0224] S53: If This indicates that the virtual queue is stable, and the single-slot decision optimization problem is transformed into the following problem F':

[0225]

[0226]

[0227] F' to v i,k The first-order partial derivative of Taking 0, we get

[0228] Then the air supply rate of the fan in any i-th area of ​​the public building at the k-th time slot is for:

[0229]

[0230] Will As the public building air conditioning HVAC system model parameters, the control center sends the public building air conditioning HVAC system model parameters to the air volume control box to control the air supply rate in the air volume control box.

[0231] Embodiment 2:

[0232] like Figure 2 , which is a functional module diagram of a smart energy management system for energy saving in public buildings provided by an embodiment of the present invention, which can implement the smart energy management system in Example 1.

[0233] The smart energy management system 100 for energy saving in public buildings of the present invention can be installed in an electronic device. According to the functions to be implemented, the smart energy management system for energy saving in public buildings can include a model building module 101, a data processing device 102 and an energy saving management module 103. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, which are stored in the memory of the electronic device.

[0234] The model building module 101 is used to build a fine-grained public building HVAC control system model and a public building cost model;

[0235] The data processing device 102 is used to construct a hybrid optimization objective function based on the public building HVAC control system model and the public building cost model, and convert the hybrid optimization objective function into a single time slot decision optimization problem to construct a virtual queue for wind speed distribution in all areas of the public building;

[0236] The energy-saving management module 103 is used to optimize and solve the single-time slot decision optimization problem based on the virtual queue, obtain the model parameters of the air-conditioning and heating ventilation and air-conditioning system of the public building, and control the regional temperature according to the parameters.

[0237] In detail, each module in the smart energy management system 100 for energy saving in public buildings described in the embodiment of the present invention is used in the same manner as described above. Figure 1 The same technical means are used as the smart energy management method for energy saving in public buildings described in the specification and can produce the same technical effects, so they will not be repeated here.

[0238] Embodiment 3:

[0239] like Figure 3 , which is a schematic diagram of the structure of an electronic device for implementing a smart energy management method for energy saving in public buildings provided by an embodiment of the present invention.

[0240] The electronic device 1 may include a processor 10 , a memory 11 , and a bus, and may also include a computer program stored in the memory 11 and executable on the processor 10 , such as a program 12 .

[0241] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. The memory 11 may be an internal storage unit of the electronic device 1 in some embodiments, such as a mobile hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 may also include both an internal storage unit of the electronic device 1 and an external storage device. The memory 11 may be used not only to store application software and various types of data installed in the electronic device 1, such as the code of the program 12, etc., but also to temporarily store data that has been output or is to be output.

[0242] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules (such as programs 12 for implementing smart energy management) stored in the memory 11, and calls data stored in the memory 11, so as to execute various functions of the electronic device 1 and process data.

[0243] The bus may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0244] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0245] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, etc. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0246] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0247] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0248] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0249] The program 12 stored in the memory 11 in the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0250] The public building is divided into N areas, and a fine-grained public building HVAC control system model is constructed. The temperature inside each area is controlled by the HVAC control system.

[0251] Constructing a utility building cost model;

[0252] Based on the public building HVAC control system model and the public building cost model, a hybrid optimization objective function is constructed and converted into a single time slot decision optimization problem.

[0253] Construct a virtual queue of wind speed distribution for all zones in a public building;

[0254] Based on the virtual queue, the single time slot decision optimization problem is optimized and solved to obtain the model parameters of the HVAC system of the public building. The regional temperature is controlled according to the parameters to achieve energy saving in the public building.

[0255] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0256] It should be noted that the serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments. And the terms "including", "comprising" or any other variants thereof in this article are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "including a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0257] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0258] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A smart energy management method for energy saving in public buildings, characterized in that: The method comprises: S1: Divide the public building into N areas and build a fine-grained public building HVAC control system model. The temperature inside each area is controlled by the HVAC control system. S2: construct a utility building cost model, wherein the utility building cost includes user experience cost, fan power consumption cost and heat exchange coil power consumption cost; S3: Construct a hybrid optimization objective function based on the public building HVAC control system model and the public building cost model, and transform the hybrid optimization objective function into a single time slot decision optimization problem; S4: construct a virtual queue of wind speed distribution for all zones in the public building; S5: Optimizing and solving the single time slot decision optimization problem based on the virtual queue to obtain the model parameters of the air conditioning and heating ventilation and air conditioning system of the public building, and controlling the regional temperature according to the parameters, wherein the optimization solution process includes: The single time slot decision optimization problem is optimized and solved based on the virtual queue to obtain the model parameters of the public building air conditioning HVAC system. The model parameters of the public building air conditioning HVAC system to be solved are the air supply rate of the HVAC in any area of ​​the public building. The parameter solution process is as follows: S51: Objective function for constructing a virtual queue: in: g k represents the objective function of the kth time slot; Then the offset result ε of the objective function is k for: ε k =E(g k+1 -g k |Q k ) in: Q k =(Q 1,k ,Q 2,k ,…,Q N,k ); S52: Calculate the difference of the objective function of adjacent time slots: in: but S53: If This indicates that the virtual queue is stable, and the single-slot decision optimization problem is transformed into the following problem F′: Reverse F′ to v i，k The first-order partial derivative of Taking 0, we get Then the air supply rate v of the fan in any i-th area of ​​the public building at the k-th time slot is i,k * for: v i，k * =max(v min ,min(v max ,v i，k ′)) v i,k * As the public building air conditioning HVAC system model parameters, the control center sends the public building air conditioning HVAC system model parameters to the air volume control box to control the air supply rate in the air volume control box.

2. A smart energy management method for energy saving in public buildings as claimed in claim 1, characterized in that: In step S1, the public building is divided into N areas, including: Divide the public building into N areas, separate each area with doors or walls, and deploy temperature and humidity sensors in each area; Each divided area has an air volume control box, which has an air valve and a blower built in it, wherein the air valve is used to adjust the air supply rate of the blower and return the air in the area to the control unit; All air volume control boxes and control units in public buildings are used as public building HVAC control systems, and the internal temperature of each divided area is controlled by the public building HVAC control system. The control unit includes a frequency conversion controller, a heat exchange coil, a chilled water pump, a ventilation air valve and an air supply control machine, wherein the frequency conversion controller is used to control the operating frequency of the heat exchange coil and the air supply control machine, the ventilation air valve is used to mix the external fresh air with the air in the area, and the chilled water in the heat exchange coil is used to cool the mixed air. The cooled air is transported to the corresponding air volume control box based on the air supply control machine, the chilled water pump is used to store chilled water and transport chilled water to the heat exchange coil, and temperature and humidity sensors are deployed on the ventilation air valve.

3. A smart energy management method for energy saving in public buildings as claimed in claim 2, characterized in that: In the step S1, a fine-grained public building HVAC control system model is constructed, including: A fine-grained public building HVAC control system model is constructed. The public building HVAC control system model is: in: S i,t represents the temperature of the ith area of ​​the divided public building at time t, S 0,t Indicates the temperature of the mixed air in the ventilation damper at time t; C represents the specific heat of air at constant pressure; s i,t represents the air supply temperature of the fan in the ith area of ​​the public building at time t, v i,t Indicates the air supply rate of the blower; represents the humidity of the ith area of ​​the public building at time t, represents the humidity of the mixed air at time t, and the temperature and humidity of the mixed air are sensed by the temperature and humidity sensor deployed by the ventilation valve; P represents the saturation pressure of water vapor, which is set to 2300Pa; B represents atmospheric pressure; q i,t It represents the number of electrical equipment used in the ith area of ​​a public building at time t, where the electrical equipment is equipment that uses the electrical energy of the public building.

4. The intelligent energy management method for energy saving in public buildings according to claim 1, characterized in that: The public building cost model is constructed in step S2, including: A public building cost model is constructed, wherein the public building cost includes user experience cost, fan power consumption cost and heat exchange coil power consumption cost, and the public building cost model is: in: f t represents the electricity price during time t; ξ represents the coefficient of performance of the heat exchange coil, and η represents the efficiency of the heat exchange coil; It represents the suitable temperature of the ith zone at time t set by the user; Cost t represents the utility building cost at time t; Said represents the user experience cost, represents the power consumption cost of the blower, Represents the power consumption cost of the heat exchange coil.

5. A smart energy management method for energy saving in public buildings as claimed in claim 4, characterized in that: In the step S3, a hybrid optimization objective function is constructed based on the public building HVAC control system and the public building cost model, including: Based on the public building HVAC control system and the public building cost model, a hybrid optimization objective function is constructed. The hybrid optimization objective function F is: The optimization goal is to minimize the cost of public buildings; The constraints of the hybrid optimization objective function are: S min ≤S i,t ≤S max in min ≤in i,t ≤in max in: S min represents the lower limit of the regional temperature, S max Indicates the upper temperature limit of the region; v min Indicates the lower limit of the wind speed of the fan, v max Indicates the upper limit of the fan's wind speed.

6. A smart energy management method for energy saving in public buildings as claimed in claim 5, characterized in that: In the step S3, the hybrid optimization objective function is converted into a single time slot decision optimization problem, including: The hybrid optimization objective function is converted into a single time slot decision optimization problem. The conversion process is as follows: S31: Determine that the unit time slot length is τ; S32: constructing a public building HVAC control system model and a public building cost model for a unit time slot respectively, wherein the public building HVAC control system model for a unit time slot is: in: k represents the time slot subscript, indicating the kth time slot, K represents the total number of time slots, and the time slot length of each time slot is τ; The utility building cost model per time slot is: in: f k Indicates the electricity price within a unit time slot; S33: Based on the single time slot model, determine the single time slot decision optimization problem: in: E(·) represents the calculated expectation, sup represents the calculated supremum, and the decision variable is the wind speed of the ith area of ​​the public building in the kth time slot.

7. The intelligent energy management method for energy saving in public buildings according to claim 1, characterized in that: The virtual queues for wind speed allocation in all regions in step S4 include: Construct a virtual queue for wind speed distribution in all areas of a public building. For any i-th area, the construction result of the virtual dispatch queue is: The virtual queue result of the public building area i in the kth time slot is: Q i,k =S i,k +σ i s i =min{S i,L -S i,L-1 },L≤i-1 The virtual queue result at the k+1th time slot is: Then the virtual queue result of the public building in the i-th area is: {Q i,1 ,Q i,2 ,…,Q i,k ,…,Q i,K } in: K represents the total number of time slots.

8. A smart energy management system for energy saving in public buildings, characterized in that: The system comprises: Model building module, used to build fine-grained public building HVAC control system model and public building cost model; A data processing device is used to construct a hybrid optimization objective function based on a public building HVAC control system model and a public building cost model, and convert the hybrid optimization objective function into a single time slot decision optimization problem to construct a virtual queue for wind speed distribution in all areas of the public building; The energy-saving management module is used to optimize and solve the single-time slot decision optimization problem based on the virtual queue, obtain the model parameters of the air-conditioning and heating ventilation and air-conditioning system of the public building, and control the regional temperature according to the parameters to realize a smart energy management method for energy saving in public buildings as described in claims 1-7.

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