Prediction Method, Prediction Device and Readable Storage Medium for Building Air Conditioning Load

Abstract

The present invention provides a method and apparatus for predicting building air-conditioning load and a readable storage medium. Among them, the method for predicting building air-conditioning load includes: obtaining building information of a target building; determining the building thermal performance composition of the target building according to the building information of the target building; obtaining a preset module component corresponding to the building thermal performance composition of the target building and a component function of the preset module component from a pre-established building thermal performance composition module library; establishing a building model equation of the target building according to the component function of the preset module component; and solving the building model equation to obtain the air-conditioning load of the target building. Through the technical solution of the present invention, the combination of module components can be realized according to the building information of different buildings, so as to quickly and accurately predict the cooling and heating loads of the building air-conditioning system, thereby providing a basis for adjusting the operation mode of the air-conditioning system, and achieving building energy conservation to the greatest extent.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular, to a method for predicting building air-conditioning load, a device for predicting building air-conditioning load, and a readable storage medium. Background Art

[0002] The function of a heating, ventilation, and air conditioning (HVAC) system is to maintain the indoor temperature and humidity to meet specific requirements under the influence of indoor and outdoor cold and heat source interference factors, and the cooling and heating load is a key parameter for the design, selection, and operation control of the HVAC system. Calculating the cooling and heating load of a building's HVAC system quickly and accurately can make the process of design selection, operation, and maintenance control of the HVAC system more energy-efficient.

[0003] Currently, the main methods for calculating or predicting the cooling and heating load of an HVAC system are as follows:

[0004] (1) Cooling and heating load index estimation method. As shown in Figure 1 , determine the building type (such as office, hospital, or school building) and room type (such as office, lounge, or classroom) according to the building drawings, query the unit area cooling and heating load estimation index of the air-conditioning area according to the building type and room type, and then multiply it by the building area of the area to obtain the building cooling and heating load. Since this method requires setting a large number of empirical coefficients and safety coefficients, the calculated result is too conservative, the load is too large, the equipment capacity selected based on this method is too large, which is bound to result in low operating efficiency and thus energy waste during operation.

[0005] (2) White box model calculation method. As shown in Figure 2 , on the basis of obtaining detailed building drawings and the meteorological conditions of the region where the building is located, construct a building model room by room and floor by floor according to the materials and detailed dimensions of the maintenance structure, and at the same time input the thermal disturbance conditions of indoor equipment and personnel and the corresponding operation schedule. After constructing a fine building physical model, then use a self-built heat transfer and mass transfer model and solution method or an existing building load calculation software to solve, and finally obtain the building cooling and heating load. This method has very strict requirements for users, not only requires strong professional knowledge, but also the modeling process takes a lot of time.

[0006] (3) Black box model calculation method. As shown in Figure 3As shown in the figure, data is collected for existing buildings that require load forecasting or load calculation. The collected data includes basic building information (envelope structure, orientation, number of floors, floor height, etc.), personnel and equipment information, regional meteorological information, and air conditioning system information for back-calculating building loads. On the premise of fully collecting data, methods of data science or statistics are used to extract characteristic parameters affecting building cooling and heating loads, and then a black-box model for predicting and calculating building cooling and heating loads is constructed, trained, and verified, so as to obtain the building cooling and heating loads under given external parameter conditions. However, the black-box model cannot reflect the thermal physical properties of the building. Therefore, when the calculation time is too long or the influencing parameters inside and outside the building change too much, the characteristics of the model are likely to change, resulting in a deviation in the prediction results. Moreover, it is difficult to perform high-precision operations and the generality is low in the case of lack of data for new buildings. Summary of the Invention

[0007] The present invention aims to solve at least one of the technical problems existing in the prior art or related technologies.

[0008] To this end, one aspect of the present invention provides a method for predicting building air conditioning loads.

[0009] Another aspect of the present invention provides a device for predicting building air conditioning loads.

[0010] Still another aspect of the present invention provides a readable storage medium.

[0011] In view of this, according to one aspect of the present invention, a method for predicting building air conditioning loads is provided, including: obtaining building information of a target building; determining the building thermal composition of the target building according to the building information of the target building; obtaining preset module components corresponding to the building thermal composition of the target building and component functions of the preset module components from a pre-established building thermal composition module library; establishing a building model equation of the target building according to the component functions of the preset module components; and solving the building model equation to obtain the air conditioning load of the target building.

[0012] In this technical solution, a plurality of preset module components are stored in a pre-established building thermal composition module library, and the preset module components have their own component functions, and the building thermal composition of the target building has a certain correspondence with the preset module components. After obtaining the building thermal composition of the target building, the corresponding preset module components can be determined, and the building thermal resistance and heat capacity network diagram of the target building can be drawn through the preset module components. Furthermore, after the component functions of each preset module component are combined, the building model equation of the target building is obtained, and the air-conditioning load of the target building is obtained after solving the building model equation. Through the technical solution of the present invention, the combination of module components can be realized according to the building information of different buildings, thereby realizing the rapid and accurate prediction of the cold and hot loads of the building air-conditioning system, thereby providing the adjustment basis of the air-conditioning system operation mode, and realizing building energy saving to the greatest extent.

[0013] According to another aspect of the present invention, a device for predicting building air conditioning load is proposed, comprising: a memory storing programs or instructions; and a processor, which implements the above-mentioned building air conditioning load prediction method when executing the program or instructions.

[0014] The device for predicting building air-conditioning load provided by the present invention implements the steps of the above-mentioned method for predicting building air-conditioning load when the program or instruction is executed by the processor. Therefore, the device for predicting building air-conditioning load includes all the beneficial effects of the above-mentioned method for predicting building air-conditioning load.

[0015] According to another aspect of the present invention, a readable storage medium is provided, on which a program or instruction is stored. When the program or instruction is executed by a processor, the method for predicting the building air conditioning load as described above is implemented.

[0016] The readable storage medium provided by the present invention implements the steps of the above-mentioned building air-conditioning load prediction method when the program or instruction is executed by the processor, so the readable storage medium includes all the beneficial effects of the above-mentioned building air-conditioning load prediction method.

[0017] Additional aspects and advantages of the present invention will become apparent from the following description or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0019] Figure 1 A schematic diagram of a method for estimating a cooling and heating load index in the related art is shown;

[0020] Figure 2 A schematic diagram of a method for calculating cooling and heating load indicators using a white box model in the related art is shown;

[0021] Figure 3 Shows the schematic diagram of the principle of the method for calculating the cooling and heating load indexes using a black box model in the related art;

[0022] Figure 4 Shows one of the schematic flowcharts of the method for predicting the building air-conditioning load according to an embodiment of the present invention;

[0023] Figure 5 Shows one of the schematic structural diagrams of the building thermal composition according to an embodiment of the present invention;

[0024] Figure 6 Shows the second schematic structural diagram of the building thermal composition according to an embodiment of the present invention;

[0025] Figure 7 Shows the third schematic structural diagram of the building thermal composition according to an embodiment of the present invention;

[0026] Figure 8 Shows the fourth schematic structural diagram of the building thermal composition according to an embodiment of the present invention;

[0027] Figure 9 Shows the fifth schematic structural diagram of the building thermal composition according to an embodiment of the present invention;

[0028] Figure 10 Shows the second schematic flowchart of the method for predicting the building air-conditioning load according to an embodiment of the present invention;

[0029] Figure 11 Shows the schematic flowchart of the method for calculating the building cooling and heating loads based on a thermal resistance-capacitance network modular gray box model according to an embodiment of the present invention;

[0030] Figure 12 Shows the building thermal resistance-capacitance network diagram according to an embodiment of the present invention;

[0031] Figure 13 Shows the schematic diagram of obtaining the building thermal resistance-capacitance network by combining building thermal modules according to an embodiment of the present invention;

[0032] Figure 14 Shows the schematic block diagram of the device for predicting the building air-conditioning load according to an embodiment of the present invention. Detailed implementation manners

[0033] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0034] It should be noted that all directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention are only used to explain the relative positional relationship and movement conditions between components in a certain specific posture (as shown in the attached drawings). If this specific posture changes, the directional indications will also change accordingly.

[0035] In addition, in the present invention, descriptions such as "first" and "second" are only for descriptive purposes and should not be construed as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0036] In the present invention, unless otherwise clearly specified and limited, terms such as "connection" and "fixation" should be understood in a broad sense. For example, "fixation" can be a fixed connection, a detachable connection, or integrated; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components or the interaction relationship between two components, unless otherwise clearly limited. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0037] In addition, the technical solutions between various embodiments of the present invention can be combined with each other, but it must be based on the ability of those of ordinary skill in the art to implement. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the protection scope required by the present invention.

[0038] An embodiment of the first aspect of the present invention provides a method for predicting the building air-conditioning load, which is described in detail as follows. Figures 4 to 14 This will be described in detail below.

[0039] Embodiment 1 Figure 4 FIG. 1 shows one of the flow diagrams of the method for predicting the building air-conditioning load according to the embodiment of the present invention. Among them, the prediction method includes:

[0040] Step 402: Obtain the building information of the target building;

[0041] Step 404: Determine the building thermal performance composition of the target building according to the building information of the target building;

[0042] Step 406: Obtain the preset module components corresponding to the building thermal performance composition of the target building and the component functions of the preset module components from the pre-established building thermal performance composition module library.

[0043] Step 408, establishing a building model equation of the target building according to the component functions of the preset module components;

[0044] Step 410, solving the building model equation to obtain the air conditioning load of the target building.

[0045] In this embodiment, a plurality of preset module components are stored in a pre-established building thermal composition module library, and the preset module components have their own component functions, and the building thermal composition of the target building has a certain correspondence with the preset module components. After obtaining the building thermal composition of the target building, the corresponding preset module components can be determined, and the building thermal resistance and heat capacity network diagram of the target building can be drawn through the preset module components. Furthermore, after the component functions of each preset module component are combined, the building model equation of the target building is obtained, and the air-conditioning load of the target building is obtained after solving the building model equation. Through the embodiments of the present invention, the combination of module components can be realized according to the building information of different buildings, thereby realizing the rapid and accurate prediction of the cold and hot loads of the building air-conditioning system, thereby providing the adjustment basis of the air-conditioning system operation mode, and realizing building energy saving to the greatest extent.

[0046] It should be noted that after solving the building model equation, in addition to being able to obtain the air-conditioning load of the target building, various temperature parameters of the target building can also be obtained, such as the wall temperature, indoor object temperature, roof temperature, etc. of the target building, thereby achieving the purpose of quickly and accurately predicting the building temperature.

[0047] In the above embodiment, the building model equation of the target building is established according to the component function of the preset module component, which specifically includes: obtaining the target outdoor meteorological parameters; and establishing the building model equation according to the component function of the preset module component and the target outdoor meteorological parameters.

[0048] In this embodiment, the building model equation is determined by the component functions of the target outdoor meteorological parameters and the preset module components, that is, the embodiment of the present invention only needs to reasonably combine the preset module components corresponding to the building thermal components involved in the target building, and then combine the target outdoor meteorological parameters to accurately obtain the building model equation of the target building. Furthermore, by solving the building model equation using a software algorithm, the cooling and heating loads of the building can be obtained. Since the target outdoor meteorological parameters are taken into account, the calculated cooling and heating loads can be made more accurate.

[0049] In the above embodiment, the building information of the target building includes at least one of the following: building materials, building structure, building function, and building type.

[0050] In this embodiment, the building information of the target building includes, but is not limited to, building materials, building structures, building functions, and building types. That is to say, the embodiments of the present invention fully consider the actual physical structure of the building for which the load needs to be predicted or calculated (for example, the shape structure of the building, the materials of the enclosure structure, etc.) and the functional types of the building (for example, schools, hospitals, offices, commercial buildings, etc.). Therefore, the calculated cooling and heating loads are more in line with the actual situation.

[0051] In the above embodiment, before obtaining the building information of the target building, it further includes: defining the corresponding relationship between the preset module components and the building thermodynamics composition.

[0052] In this embodiment, the building thermal resistance and capacitance network (that is, the building thermodynamics physical model) is a highly coupled non-linear model. In order to achieve decoupling and distinguish the heat flow transfer caused by different factors, its form is similar to that of an electric circuit. An electric circuit is composed of different components such as power sources, resistors, and capacitors, and different components interact with each other to form an electric current in the entire circuit and keep flowing. Therefore, the embodiments of the present invention perform a modular split on the building thermodynamics composition of the entire building, and compare different building thermodynamics compositions to different circuit elements (that is, preset module components), making the display form of the building thermodynamics composition of the building simpler and improving the speed of constructing the building thermal resistance and capacitance network diagram and the building model equation.

[0053] In the above embodiment, the preset module components include a thermal resistance module, a heat capacitance module, and a heat source module. Defining the corresponding relationship between the preset module components and the building thermodynamics composition specifically includes: analogizing the building thermal resistance and capacitance network to an electric circuit, and analogizing the thermal resistance module to the resistor in the electric circuit, the heat capacitance module to the capacitor in the electric circuit, and the heat flow released or absorbed by the heat source module to the electric current in the electric circuit, where the building thermal resistance and capacitance network includes multiple building thermodynamics compositions; defining the building thermodynamics composition with heat transfer ability and heat storage ability as a series circuit of the thermal resistance module and the heat capacitance module; defining the building thermodynamics composition with heat transfer ability as the thermal resistance module; defining the building thermodynamics composition with heat storage ability as the heat capacitance module; defining the building thermodynamics composition with heat transfer ability, heat storage ability, and building heat source as a series circuit of the thermal resistance module, the heat source module, and the heat capacitance module; defining the building thermodynamics composition with heat storage ability and building heat source as a series circuit of the heat source module and the heat capacitance module.

[0054] In this embodiment, based on the similarity between "electric potential - current" and "thermal potential - heat flow" in physics, the thermal resistance of the building envelope structure that has the ability to transfer heat and hinders the heat flow transfer between the inside and outside of the building is analogized to the resistance in an electric circuit, the objects in the building that have a large heat capacity and thus have a certain heat storage and heat accumulation ability (such as walls, roofs, furniture, indoor air, etc.) are analogized to the capacitance in an electric circuit, and the temperature difference between the outdoor temperature and the indoor temperature in the building is analogized to the potential difference in an electric circuit. Furthermore, a thermal resistance - capacitance network for heat flow transfer between the outside and the inside of the building can be constructed.

[0055] Specifically, as Figure 5 shown, the building thermal engineering components that are dominated by heat transfer ability and heat storage ability (such as walls, roofs, etc.) are defined as a series circuit of a thermal resistance module R and a heat capacitance module C.

[0056] As Figure 6 shown, the building thermal engineering components that are dominated by heat transfer ability (such as lightweight walls, windows, etc.) are defined as the thermal resistance module R.

[0057] As Figure 7 shown, the building thermal engineering components that are dominated by heat storage ability (such as indoor air, etc.) are defined as the heat capacitance module C.

[0058] As Figure 8 shown, the building thermal engineering components that are dominated by heat transfer ability, heat storage ability, and have an internal heat source (such as indoor heat dissipation equipment, etc.) are defined as a series circuit of a thermal resistance module R, a heat source module Q, and a heat capacitance module C.

[0059] As Figure 9 shown, the building thermal engineering components that are dominated by heat storage ability and have an internal heat source (such as indoor occupants, etc.) are defined as a series circuit of a heat source module Q and a heat capacitance module C.

[0060] Embodiment 2, Figure 10 shows the second flow schematic diagram of the method for predicting the building air - conditioning load according to the embodiment of the present invention. Among them, the prediction method includes:

[0061] Step 1002, define the corresponding relationship between the preset module components and the building thermal engineering components;

[0062] Step 1004, obtain the building information of the sample building;

[0063] Step 1006, establish a building thermal engineering component module library according to the building information of the sample building and the corresponding relationship;

[0064] Step 1008, obtain the building information of the target building;

[0065] Step 1010, determine the building thermal engineering components of the target building according to the building information of the target building;

[0066] Step 1012, obtain a preset module component corresponding to the building thermal performance composition of the target building and the component function of the preset module component from a pre-established building thermal performance composition module library.

[0067] Step 1014, establish a building model equation for the target building according to the component function of the preset module component.

[0068] Step 1016, solve the building model equation to obtain the air-conditioning load of the target building.

[0069] In this embodiment, different building thermal performance compositions are first compared to different circuit elements (i.e., preset module components), and then a building thermal performance composition module library is established based on the building information of the sample building and the corresponding relationship. In actual application, determine the building thermal performance composition according to the information such as building materials, building structure, building function, and building type of the target building for which the air-conditioning load needs to be predicted, and then correspondingly obtain the preset module components from the building thermal performance composition module library, form the building thermal resistance and heat capacity network diagram of the target building, and establish a building model equation for the target building according to the component function of the preset module component. After solving, the air-conditioning load of the target building is obtained. Through the embodiments of the present invention, it is possible to realize the combination of module components based on the building thermal performance composition module library, and then realize the rapid and accurate prediction of the cooling and heating loads of the building air-conditioning system, thereby giving the adjustment basis for the operation mode of the air-conditioning system, and being able to achieve building energy conservation to the greatest extent.

[0070] In the above embodiment, a building thermal performance composition module library is established according to the building information and the corresponding relationship of the sample building, which specifically includes: determining the building thermal performance composition of the sample building according to the first building information of the sample building; determining the preset module component corresponding to the building thermal performance composition of the sample building according to the corresponding relationship; determining the first thermal performance composition function according to the building thermal performance composition of the sample building, and determining the second thermal performance composition function and the thermal performance composition change information of the sample building according to the first building information and the second building information of the sample building; determining the function correction coefficient according to the first thermal performance composition function and the second thermal performance composition function; correcting the component function of the preset module component according to the function correction coefficient; and establishing a building thermal performance composition module library according to the preset module component, the corrected component function, and the thermal performance composition change information.

[0071] Among them, the first building information includes at least one of the following: building materials, building structure, building function, building type; the second building information includes at least one of the following: indoor equipment heat source usage information, outdoor meteorological parameters, air-conditioning operation energy consumption, indoor environmental parameters.

[0072] In this embodiment, when the building thermal engineering is modularized, the thermal resistance-capacitance network of the target building can be constructed by combination, so as to calculate the air-conditioning load of the building. Specifically, the thermal resistance and heat capacity of each component of the thermal resistance-capacitance network can be calculated according to the materials and dimensions of the building, and the heat source components in the building in the thermal resistance-capacitance network can be determined according to the functions and types of the building. However, in actual engineering, the exterior walls and roofs of buildings are often not flat single-sided structures. The external facades may be built in concave, convex or curved forms according to the functions, types and shapes of the buildings. Structures such as cold bridges and heat bridges that enhance or reduce the heat transfer effect will be formed between the walls of the building; in addition, the usage conditions of the personnel and equipment inside the building will also have different modes according to the different functional types of the building. These two factors above will cause great deviations in the prediction of the building air-conditioning load if the thermal resistance, heat capacity and heat source components of the building are simply constructed according to the knowledge of heat transfer or thermodynamics.

[0073] Therefore, in order to reduce the deviations caused by the above reasons, the embodiment of the present invention will adopt a method combining data and physical models to construct a building thermal engineering component library, and summarize and sort out the thermal resistance and heat capacity characteristics of common forms of building structures for quick calling during actual use. The specific method is as follows:

[0074] Search for and collect multiple sample buildings with typical structures (wherein, the multiple sample buildings with typical structures must cover common exterior facades, roof structures and different functional types, such as schools, hospitals, offices, commercial buildings, etc.), and obtain the first building information and the second building information of the sample buildings. Further, according to the first building information of the sample buildings, determine the building thermal engineering composition of the sample buildings, and determine the preset module components corresponding to the building thermal engineering composition. Then, determine the first thermal engineering composition function according to the building thermal engineering composition, and determine the second thermal engineering composition function and the thermal engineering composition change information of the sample buildings according to the first building information and the second building information. Since the first thermal engineering composition function is simply determined according to the first building information, there may be some differences between the first thermal engineering composition function and the second thermal engineering composition function. And because the second building information is considered, the second thermal engineering composition function is more accurate than the first thermal engineering composition function. Compare the first thermal engineering composition function with the second thermal engineering composition function to obtain a function correction coefficient, and then use the function correction coefficient to correct the component function of the preset module components.

[0075] Finally, store the preset module components, the corrected component functions, the function correction coefficients, and the changes in the thermal composition information in the database to obtain a building thermal composition module library. In the embodiment of the present invention, on the one hand, the component functions of the preset module components are corrected to make the component functions more accurate and more in line with the actual building, and a building thermal composition module library is constructed by combining data with physical models, so as to ensure the accuracy of air-conditioning compliance calculation; on the other hand, considering information such as indoor equipment heat source usage information, outdoor meteorological parameters, air-conditioning operation energy consumption, and indoor environmental parameters, it is avoided that the thermal resistance, heat capacity, and heat source components of the building are simply constructed based on heat transfer or thermodynamics knowledge, which will cause a large deviation in the prediction of building air-conditioning loads.

[0076] In any of the above embodiments, the function correction coefficient includes a thermal resistance function correction coefficient and / or a heat capacity function correction coefficient.

[0077] In this embodiment, the function correction coefficient includes a thermal resistance function correction coefficient and a heat capacity function correction coefficient. The thermal resistance value of the thermal resistance component in the preset module component is corrected by the thermal resistance function correction coefficient, and the heat capacity value of the heat capacity component in the preset module component is corrected by the heat capacity function correction coefficient to ensure that the component function is more accurate and more in line with the actual building.

[0078] In any of the above embodiments, the changes in the thermal composition information include changes in heat capacity and / or changes in heat sources.

[0079] In this embodiment, the change in heat capacity information refers to the law of change of the characteristics of the heat capacity component compared with indoor personnel or other indoor objects over time, and the heat source change function refers to the law of change of the indoor internal heat source or the outdoor solar radiation heat source over time. In the embodiment of the present invention, by considering the above two laws, the accuracy of air-conditioning load prediction is improved.

[0080] It should be noted that in any of the above embodiments, according to the first building information and the second building information, the second thermal composition function and the changes in the thermal composition information of the sample building are determined, specifically including: constructing a thermal resistance and heat capacity network of the sample building according to the thermal structure characteristics of the sample building, and taking the air-conditioning energy consumption of the building as the evaluation criterion under the conditions of the indoor design conditions of the building (i.e., indoor environmental parameters) and outdoor meteorological parameters, to determine the thermal resistance value, heat capacity value, indoor heat source, and the change of the heat source during equipment operation in the sample building, where the thermal resistance value, heat capacity value, and indoor heat source are the second thermal composition function, that is, the change of the heat source during equipment operation is the change in the thermal composition information.

[0081] In summary, first, compared with the cold and heat load index estimation methods in the related art, the embodiments of the present invention start from the actual physical structure of the building, fully considering the actual physical structure of the building for which the load needs to be predicted or calculated (for example, shape structure, enclosure structure material, etc.), the functional type of the building (for example, internal heat source type, heat capacity change law), and the outdoor meteorological parameters of the region where it is located. Therefore, the load results obtained by using the prediction method of the embodiments of the invention will be more consistent with the actual load.

[0082] Second, compared with the white box model calculation method in the related art, on the basis of constructing a complete building thermal engineering component library in the early stage, the embodiments of the present invention greatly streamline the time required for building the model each time. The user only needs to make a reasonable combination according to the building thermal engineering components involved in the target building, and then solve the system of equations according to the generalized method (which can be realized by software algorithms), and the air conditioning load of the building can be obtained quickly.

[0083] Third, compared with the black box model calculation method in the related art, although a large amount of data also needs to be processed in the early stage of building the library in the embodiments of the present invention, however, based on the concept of modularization, the embodiments of the present invention have stronger universality. Once the modules in the building thermal engineering component library are constructed, different types of building thermal resistance and heat capacity networks can be built; on the other hand, compared with the black box model method, the various factors affecting the load results in the embodiments of the present invention have been decoupled, and its essence still conforms to the laws of the building thermal physics model, and there will be no phenomenon that the characteristic variables change and the calculation results deviate when the time is too long or the time span changes, as in the black box model.

[0084] Embodiment 3, in a specific embodiment, the present invention proposes a method for calculating the cold and heat load of a building based on a modular gray box model of a thermal resistance and heat capacity network. Specifically, through physics knowledge, the heat flow network of the building is compared to an electric circuit in the electrical field, and the heat resistance components, heat capacity components, and heat source components in the heat flow network are modularized, and the data science method is used to determine the characteristics of each component. Furthermore, the combination of modules can be realized according to the structural form and function of different buildings, so as to accurately predict the cold and heat load of the building's heating, ventilation and air conditioning system at a small time cost. The flow chart of the method for calculating the cold and heat load of a building based on a modular gray box model of a thermal resistance and heat capacity network is as Figure 11 shown, and specifically includes:

[0085] Step 1100, modular splitting of building thermal engineering components;

[0086] Step 1102, obtaining the building envelope structure, building envelope materials, indoor equipment heat source usage, regional location meteorological parameters, indoor air conditioning operation energy consumption, etc. of a typical building (i.e., the above-mentioned sample building);

[0087] Step 1104, generate a thermal resistance and heat capacity network diagram of a typical building;

[0088] Step 1106, determine the thermal resistance values, heat capacity values, and indoor heat sources of the first type based on the building envelope structure and building envelope materials;

[0089] Step 1108, specify the initial values of the thermal resistance components, the initial values of the heat capacity components, the indoor heat sources, and the heat source change rules, and establish a building thermal resistance and heat capacity network for the typical building;

[0090] Step 1110, solve and calculate the building thermal resistance and heat capacity network of the typical building to obtain the thermal resistance values, heat capacity values, indoor heat sources, and heat source change rules of the second type;

[0091] Step 1112, based on the thermal resistance values, heat capacity values, and indoor heat sources of the first type, and the thermal resistance values, heat capacity values, and indoor heat sources of the second type, calculate the parameter differences within the building thermal resistance and heat capacity network of the typical building and the energy consumption differences of the HVAC system;

[0092] Step 1114, determine whether the differences converge. If they converge, proceed to Step 1116; otherwise, proceed to Step 1118;

[0093] Step 1116, calibration completed, output the building thermal engineering component library composed of thermal engineering module components;

[0094] Step 1118, use the multi-parameter nonlinear regression method to re-determine the thermal resistance components, heat capacity components, indoor heat sources, and heat source change rules, and return to Step 1108;

[0095] Step 1120, obtain the shape and functions of the target building;

[0096] Step 1122, obtain the meteorological parameters of the region where the target building is located;

[0097] Step 1124, call the building thermal engineering component library to construct a thermal resistance and heat capacity network diagram of the target building;

[0098] Step 1126, construct a first-order mathematical equation set for the target building;

[0099] Step 1128, solve the first-order mathematical equation set to obtain the cooling and heating loads of the building HVAC system.

[0100] In this embodiment, first, the thermal resistance and heat capacity network of the building is compared to an electric circuit in the electrical field. Imitating the idea of an electric circuit, the entire building thermal engineering composition is modularly split. According to the dominant position of the heat transfer capacity, heat storage capacity, and the heat source situation, the modular components of the thermal resistance and heat capacity network are divided into five categories.

[0101] Then, build a module library, that is, use a combination of data and physical models to build a building thermal engineering module library, summarize and organize the thermal resistance and thermal capacity characteristics of common forms of building structures, so that they can be quickly called in actual use. On the basis of the already built building thermal engineering module library, according to the characteristics of the building appearance and building functions of the target building, select appropriate modules from the building thermal engineering module library for splicing to build the thermal gray box model of the entire building, that is, build the thermal resistance and thermal capacity network diagram of the target building. Based on the thermal resistance and thermal capacity network diagram of the target building, after combining the equations of all modules, establish the first-order mathematical equation group of the target building, and standardize it for easy solution.

[0102] Finally, a general solution method is used to solve the standardized first-order mathematical equations. That is, based on the premise of energy conservation inside the building, the cooling and heating loads of the building's HVAC system are calculated according to Kirchhoff's principle in electricity, and finally the thermal parameters inside the building and the cooling and heating loads of the building's HVAC system are obtained.

[0103] In the embodiment of the present invention, a reasonable model and an efficient method are used to calculate the cooling and heating loads of the building HVAC system, which greatly reduces the time for system design, selection and solving control feedback variables, improves the matching degree between the system and the building, and improves the energy utilization efficiency.

[0104] Specifically, the building cooling and heating load calculation method based on the thermal resistance and heat capacitance network modular gray box model is mainly divided into two parts: comparing the building thermal resistance and heat capacitance network to a circuit module and calculating the building cooling and heating load. The following describes these two parts in detail:

[0105] In the first part, based on the thermal resistance and heat capacitance network, the building thermal resistance and heat capacitance network is compared to a circuit module

[0106] Based on the similarity between "electric potential-current" and "thermal potential-heat flow" in physics, the thermal resistance of the building's enclosure structure, which has heat transfer capability and hinders the heat flow transfer inside and outside the building, is compared to the resistor in the circuit, and the objects in the building with large heat capacity and thus certain heat storage and heat accumulation capabilities (such as walls, roofs, furniture, indoor air, etc.) are compared to the capacitor in the circuit, and the temperature difference between the outdoor temperature and the indoor temperature in the building is compared to the potential difference in the circuit, thereby building a thermal resistance and heat capacitance network for outdoor and indoor heat flow transfer. The thermal resistance and heat capacitance network diagram of the building is as follows Figure 12 As shown, the thermal resistance and heat capacity network of the building includes: outdoor air temperature T out , Indoor air temperature T in , wall temperature T wall , Indoor object temperature T imass , roof temperature T roof 、Roof thermal resistance R Roof, Window thermal resistance R win , Wall thermal resistance R wall , Indoor object thermal resistance R imass , Indoor object heat capacity C imass , Indoor air heat capacity C in , Wall heat capacity C w , Roof heat capacity C roof , Indoor ventilation heat gain / loss Q venti , Indoor heat source heat gain / loss Q IHL , HVAC system cooling / heating load Q AC , Solar radiation heat Q sol , Ceiling thermal resistance R cell .

[0107] Part Two, Calculation of Building Cooling and Heating Loads Based on the Modular Grey-Box Model of Thermal Resistance and Heat Capacity Network

[0108] (1) Modular Split of Building Thermal Performance Components

[0109] An electrical circuit is composed of different components such as power sources, resistors, and capacitors. Different components interact with each other, thus forming an electric current that continuously circulates throughout the circuit. The building thermal resistance and heat capacity network is a highly coupled non-linear model. In order to achieve decoupling and distinguish the heat flow transfer caused by different factors, the embodiments of the present invention perform a modular split on the thermal performance components of the entire building, splitting them into different thermal performance modular components (i.e., the above-mentioned preset modular components). Specifically, the Figure 12 building thermal performance components with different physical meanings and morphological characteristics are split. For example, the building thermal performance components dominated by heat transfer capacity and heat storage capacity are split into a series circuit of a thermal resistance module R and a heat capacity module C as shown in Figure 5 ; the building thermal performance components dominated by heat transfer capacity are split into a thermal resistance module R as shown in Figure 6 ; the building thermal performance components dominated by heat storage capacity are split into a heat capacity module C as shown in Figure 7 ; the building thermal performance components dominated by heat transfer capacity, heat storage capacity, and having an internal heat source are split into a series circuit of a thermal resistance module R, a heat source module Q, and a heat capacity module C as shown in Figure 8 ; the building thermal performance components dominated by heat storage capacity and having an internal heat source are split into a series circuit of a heat source module Q and a heat capacity module C as shown in Figure 9 .

[0110] (2) Construction of the Building Thermal Performance Component Library

[0111] As described above, when the building thermal engineering is modularized, the thermal resistance and heat capacity network of the target building can be constructed by combination, so as to calculate the cooling and heating loads of the building. The thermal resistance and heat capacity of each component of the thermal resistance and heat capacity network can be calculated according to the materials and dimensions of the building, and the heat sources in the building can be determined according to the functions and types of the building. However, in actual engineering, the exterior walls and roofs of buildings are often not flat single-sided structures. The external facade may be built in concave, convex or curved forms according to the functions, types and shapes of the building. Structures that enhance or reduce the heat transfer effect, such as cold bridges and heat bridges, will be formed between the walls of the building. In addition, the usage of personnel and equipment inside the building will also have different modes according to the different functional types of the building. These two factors will lead to a large deviation in the prediction of the building air-conditioning load if the thermal resistance, heat capacity and heat source components of the building are simply constructed based on heat transfer or thermodynamics knowledge.

[0112] Therefore, in order to reduce the deviation caused by the above reasons, the embodiments of the present invention will adopt a method combining data and physical models to construct a building thermal engineering component library, summarize and sort out the thermal resistance and heat capacity characteristics of common building structures for quick call during actual use. The steps for generating the building thermal engineering component library are as Figure 11 shown in steps 1102 to 1118 in

[0113] Specifically, find and collect multiple typical buildings with typical structures (where multiple typical buildings with typical structures must cover common exterior facades, roof structures and different functional types, such as schools, hospitals, offices, commercial buildings, etc.), and obtain the building envelope structure materials, indoor equipment heat source usage, regional location meteorological parameters, indoor air-conditioning operation energy consumption, etc. of the typical building.

[0114] According to the thermal engineering structure characteristics of such typical buildings, construct the thermal resistance and heat capacity network diagram of the typical building. Under the conditions of indoor environmental parameters and outdoor meteorological parameters of the building, taking the air-conditioning energy consumption of the building as the evaluation criterion, determine the second-class thermal resistance values, heat capacity values, and indoor heat sources, and summarize the variation laws (i.e., heat source variation laws) of the building thermal engineering components compared by indoor personnel and equipment under typical building types.

[0115] Finally, compare the determined second-class thermal resistance values, heat capacity values, and indoor heat sources with the first-class thermal resistance values, heat capacity values, and indoor heat sources determined solely based on building materials and structural forms, determine the correction function relationships of the thermal resistance and heat capacity of the typical envelope structures of typical buildings, and organize these clear relationships and laws into the building thermal engineering component library for use.

[0116] According to the method for generating the building thermal engineering component library, the thermal resistance components, heat capacity components, and heat source components (i.e., the heat source change law) of various types of envelope structures can be written in the forms of formulas (1) to (4).

[0117] For the thermal resistance component:

[0118]

[0119] In formula (1), R s refers to the corrected thermal resistance of a certain type of envelope structure, is the sum of the thermal resistances of the layers of materials that make up this type of envelope structure, n is the number of envelope structures, and ζ shape is the thermal resistance correction coefficient fitted based on the typical structure of the envelope structure.

[0120] For the heat capacity component:

[0121] C s = λ shape × C c (2)

[0122] C s = C I (t) (3)

[0123] In formulas (2) and (3), C s refers to the corrected heat capacity of the type of envelope structure, C c is the heat capacity of the layers of materials that make up this type of envelope structure, λ shape is the heat capacity correction coefficient fitted based on the typical structure of the envelope structure, and C I (t) refers to the law of change with time of the characteristics of the heat capacity component compared to indoor occupants or other indoor objects. And C s in formula (2) refers to the heat capacity that does not change with time, such as the heat capacity formed by the envelope structure, and C s in formula (3) refers to the heat capacity that changes with time, such as the heat capacity formed by indoor occupants or other indoor objects.

[0124] For the heat source component:

[0125] Q s = Q(t) (4)

[0126] In formula (4), Q s refers to the law of change with time of the internal heat source indoors or the solar radiation heat source outdoors, and t refers to time.

[0127] (3) Combination of the building thermal engineering grey box model modules

[0128] Such as Figure 13As shown, according to the actual envelope structure, function, and type characteristics of the target building, appropriate modules (for example, envelope structure modules or internal heat source modules) can be selected from the building thermal component module library for splicing to form a thermal gray box model of the entire building (i.e., the thermal resistance and heat capacity network diagram of the building).

[0129] (4) General solution method for building cooling and heating loads

[0130] According to the structural characteristics of the target building, the thermal resistance and heat capacity network diagram of the target building is constructed by calling modules from the building thermal engineering module library. For each module, the general formula of its calculation model can be written as:

[0131]

[0132] Where t is time, T is temperature, n is the number of modules, C s is the heat capacity, R s,j is the thermal resistance, Q s,k It refers to the law of change of indoor internal heat source or outdoor solar radiation heat source over time.

[0133] After all the module equations are combined, the first-order equations are solved using a generalized method to obtain the various temperature parameters inside the building and the required heating and cooling loads of the building HVAC system. Since there are already mature solutions for solving the first-order equations, they will not be repeated here.

[0134] The second embodiment of the present invention provides a device 1400 for predicting building air conditioning load, such as Figure 14 As shown, the prediction device 1400 for building air conditioning load includes:

[0135] Memory 1402, storing programs or instructions;

[0136] Processor 1404, when executing a program or instruction, implements the method for predicting the building air conditioning load of any of the above embodiments.

[0137] The memory 1402 and the processor 1404 may be connected via a bus or other means. The processor 1404 may include one or more processing units, and the processor 1404 may be a central processing unit (CPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other chips.

[0138] For the prediction device of building air-conditioning load provided by the present invention, when the program or instruction is executed by a processor, it implements the steps of the prediction method of building air-conditioning load in any of the above embodiments. Therefore, this computer-readable storage medium includes all the beneficial effects of the prediction method of building air-conditioning load in any of the above embodiments.

[0139] An embodiment of the third aspect of the present invention provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, it implements the prediction method of building air-conditioning load in any of the above embodiments.

[0140] Among them, the computer-readable storage medium includes a read-only memory (ROM), a random access memory (RAM), a magnetic disk, an optical disc, etc.

[0141] For the readable storage medium provided by the present invention, when the program or instruction is executed by a processor, it implements the steps of the prediction method of building air-conditioning load in any of the above embodiments. Therefore, this readable storage medium includes all the beneficial effects of the prediction method of building air-conditioning load in any of the above embodiments.

[0142] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for predicting building air conditioning load, characterized in that, Including: Obtaining the building information of the target building; Determining the building thermal performance composition of the target building according to the building information of the target building; Obtaining the preset module components corresponding to the building thermal performance composition of the target building and the component functions of the preset module components from a pre-established building thermal performance composition module library; Establishing a building model equation of the target building according to the component functions of the preset module components; Solving the building model equation to obtain the air-conditioning load of the target building; Before obtaining the building information of the target building, the prediction method of the building air-conditioning load further includes: Defining the corresponding relationship between the preset module components and the building thermal performance composition; Obtaining the building information of the sample building; Establishing the building thermal performance composition module library according to the building information of the sample building and the corresponding relationship; The establishing the building thermal performance composition module library according to the building information of the sample building and the corresponding relationship specifically includes: Determining the building thermal performance composition of the sample building according to the first building information of the sample building; Determining the preset module components corresponding to the building thermal performance composition of the sample building according to the corresponding relationship; Determining a first thermal performance composition function according to the building thermal performance composition of the sample building, and determining a second thermal performance composition function and thermal performance composition change information of the sample building according to the first building information and the second building information of the sample building; Determining a function correction coefficient according to the first thermal performance composition function and the second thermal performance composition function; Correcting the component functions of the preset module components according to the function correction coefficient; Establishing a building thermal performance composition module library according to the preset module components, the corrected component functions and the thermal performance composition change information.

2. The prediction method of building air-conditioning load according to claim 1, characterized in that The establishing the building model equation of the target building according to the component functions of the preset module components specifically includes: Obtaining target outdoor meteorological parameters; Establishing the building model equation according to the component functions of the preset module components and the target outdoor meteorological parameters.

3. The prediction method of the building air-conditioning load according to claim 1, characterized in that The building information of the target building includes at least one of the following: building materials, building structure, building function, building type.

4. The prediction method of building air-conditioning load according to any one of claims 1 to 3, characterized in that The preset module components include a thermal resistance module, a heat capacity module and a heat source module. The defining the corresponding relationship between the preset module components and the building thermal performance composition specifically includes: Analogizing the building thermal resistance-capacitance network to an electric circuit, and analogizing the thermal resistance module to the resistor in the electric circuit, the heat capacity module to the capacitor in the electric circuit, and the heat flow released or absorbed by the heat source module to the current in the electric circuit, wherein the building thermal resistance-capacitance network includes a plurality of the building thermal performance compositions; Defining the building thermal performance composition with heat transfer ability and heat storage ability as a series circuit of the thermal resistance module and the heat capacity module; Defining the building thermal performance composition with heat transfer ability as the thermal resistance module; Defining the building thermal performance composition with heat storage ability as the heat capacity module; Define the building thermal engineering composition with heat transfer capacity, heat storage capacity, and building heat source as a series circuit of the thermal resistance module, the heat source module, and the heat capacity module; Define the building thermal engineering composition with heat storage capacity and building heat source as a series circuit of the heat source module and the heat capacity module.

5. The method for predicting building air-conditioning load according to any one of claims 1 to 3, characterized in that The first building information includes at least one of the following: building materials, building structure, building function, building type; The second building information includes at least one of the following: indoor equipment heat source usage information, outdoor meteorological parameters, air-conditioning operation energy consumption, indoor environmental parameters.

6. The method for predicting building air-conditioning load according to any one of claims 1 to 3, characterized in that The function correction coefficient includes a thermal resistance function correction coefficient and / or a heat capacity function correction coefficient.

7. The method for predicting building air-conditioning load according to any one of claims 1 to 3, characterized in that The thermal engineering composition change information includes heat capacity change information and / or heat source change information.

8. A prediction device for building air conditioning load, characterized in that, Comprising: A memory storing programs or instructions; A processor, when the processor executes the programs or instructions, implements the method for predicting building air-conditioning load according to any one of claims 1 to 7.

9. A readable storage medium, on which a program or instructions are stored, characterized in that, When the programs or instructions are executed by the processor, the method for predicting building air-conditioning load according to any one of claims 1 to 7 is implemented.

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