Methods and devices for predicting air conditioning load data
By using a building load standard database and scaling factor in air conditioning load data prediction, the problems of large workload and low accuracy in air conditioning load data prediction are solved, and efficient and accurate air conditioning load prediction is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- GREE ELECTRIC APPLIANCE INC OF ZHUHAI
- Filing Date
- 2022-12-08
- Publication Date
- 2026-05-26
AI Technical Summary
In existing technologies, predicting air conditioning load data involves a large workload and has limited accuracy, which makes the planning of central air conditioning systems difficult.
Based on a pre-established building load standard database, initial load data is queried by matching basic parameters, and the initial load data is scaled using a scaling factor to obtain load forecast data.
It reduces the amount of calculation, improves the accuracy and efficiency of air conditioning load forecasting, and is applicable to a variety of building engineering projects, saving time and effort.
Smart Images
Figure CN115828602B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of air conditioning system planning technology, specifically to a method and apparatus for predicting air conditioning load data. Background Technology
[0002] With rapid economic development and rising living standards, people's demands for thermal comfort are also increasing. As air conditioning technology has advanced, central air conditioning systems are now widely used in various types of buildings.
[0003] The construction cycle of large-scale buildings is generally long. In the early stages of project development, the lack of detailed engineering data, such as heating and cooling loads, necessary for designing central air conditioning systems poses significant challenges to the planning process. Furthermore, modern large-scale buildings often have complex building envelopes to simultaneously meet practicality and aesthetic requirements. Using building load simulation software for calculations would be extremely labor-intensive, potentially impacting project progress. In addition, the diverse types of buildings requiring central air conditioning systems, including commercial buildings, residential buildings, schools, and hospitals, each have different heating and cooling load requirements. Traditional load calculation methods require detailed calculations for each project, resulting in a large computational workload and limited accuracy.
[0004] In related technologies, predicting air conditioning load data for buildings is a labor-intensive process with limited accuracy, which poses difficulties for the planning of central air conditioning systems. Summary of the Invention
[0005] To overcome, to at least part of the problems in related technologies, the large workload and limited accuracy of predicting air conditioning load data for buildings, this application provides a method and apparatus for predicting air conditioning load data.
[0006] According to a first aspect of the embodiments of this application, a method for predicting air conditioning load data is provided, comprising the following steps:
[0007] The initial load data of the air conditioning system is determined based on the building's basic parameters; these basic parameters include: the building type and / or shape coefficient.
[0008] The design non-guaranteed hours and design load index of the building are obtained; both the design non-guaranteed hours and the design load index are preset values.
[0009] The scaling factor is determined based on the initial load data, design load parameters, and design non-guaranteed hours.
[0010] The initial load data is scaled using a scaling factor to obtain the load forecast data.
[0011] Furthermore, determining the initial load data of the air conditioning system based on the building's basic parameters includes the following steps:
[0012] The system performs a matching query in the standard database based on the basic parameters to retrieve the matching initial load data. The standard database is a pre-established database that stores the initial load data corresponding to various types and shape coefficients of buildings.
[0013] Furthermore, the scaling factor is determined based on the initial load data, design load indicators, and design non-guaranteed hours, including the following steps:
[0014] Determine the initial value of the upper limit scaling factor;
[0015] The upper limit scaling factor is iteratively updated based on the initial load data, design load indicators, and design non-guaranteed hours.
[0016] Determine the initial value of the lower bound scaling factor;
[0017] The lower and upper scaling factors are iteratively updated based on the initial load data, design load indicators, and design non-guaranteed hours.
[0018] The final scaling factor is determined based on the iterative lower and upper scaling factors.
[0019] Furthermore, the upper limit scaling factor is iteratively updated based on the initial load data, design load indicators, and design non-guaranteed hours, including the following steps:
[0020] Determine the current load data based on the initial load data and the upper limit scaling factor;
[0021] The number of hours not guaranteed at present is determined based on current load data and design load indicators;
[0022] Compare the design non-guaranteed hours with the current non-guaranteed hours;
[0023] When the design-guaranteed hours are less than the current unguaranteed hours, the upper limit scaling factor and the current load data are iteratively updated until the design-guaranteed hours are greater than the current unguaranteed hours.
[0024] Further, the current load data is determined based on the initial load data and the upper limit scaling factor, including the following steps:
[0025] Q i =a max ×Q i，0 ;
[0026] Among them, Q i For current load data, a maxQ is the upper bound scaling factor. i，0 This is the initial load data.
[0027] Furthermore, the upper limit scaling factor and the current load data are iteratively updated, including the following steps:
[0028] The upper limit scaling factor a max The value of a is increased by a preset increment, based on the increased value of a. max The value determines the new current load data.
[0029] Furthermore, the lower and upper scaling factors are iteratively updated based on the initial load data, design load indicators, and design non-guaranteed hours, including the following steps:
[0030] The current scaling factor is determined based on the lower and upper scaling factors.
[0031] Determine the current load data based on the initial load data and the current scaling factor;
[0032] The number of hours not guaranteed at present is determined based on current load data and design load indicators;
[0033] Compare the design non-guaranteed hours with the current non-guaranteed hours;
[0034] When the design does not guarantee the number of hours less than the current number of hours not guaranteed, update the lower bound scaling factor; when the design does not guarantee the number of hours greater than the current number of hours not guaranteed, update the upper bound scaling factor.
[0035] Determine whether the difference between the updated lower scaling factor and the upper scaling factor is less than a preset threshold;
[0036] If the values are less than the specified values, stop the iteration and output the current lower and upper scaling factors; otherwise, continue the iteration.
[0037] Further, determining the current scaling factor based on the lower and upper scaling factors includes the following steps:
[0038] a=(a max +a min ) / 2;
[0039] Where a is the current scaling factor, a max a is the upper limit scaling factor; min This is the lower bound scaling factor.
[0040] Furthermore, the step to update the lower bound scaling factor is: let a min =a; The steps to update the upper bound scaling factor are: Let a max =a.
[0041] Furthermore, based on current load data and design load indicators, the number of hours currently not guaranteed is determined, including the following steps:
[0042] Input the current load data Q i and design load index Q d ;
[0043] Let the number of unguaranteed hours h = 0;
[0044] Iterate through i, when any Q i >Q d At that time, let h = h + 1;
[0045] Output the final, unguaranteed number of hours, h.
[0046] According to a second aspect of the embodiments of this application, an apparatus for predicting air conditioning load data is provided, comprising:
[0047] An initial data module is used to determine the initial load data of the air conditioning system based on the building's basic parameters; the basic parameters include: the building type and / or shape coefficient;
[0048] The acquisition module is used to acquire the design non-guarantee hours and design load index of the building; the design non-guarantee hours and the design load index are both preset values;
[0049] The determination module is used to determine the scaling factor based on the initial load data, design load indicators, and design non-guaranteed hours.
[0050] The scaling module is used to scale the initial load data using a scaling factor to obtain load forecast data.
[0051] According to a third aspect of the embodiments of this application, a computer device is provided, comprising: a memory for storing a computer program; and a processor for executing the computer program in the memory to implement the operation steps of the method as described in any of the above embodiments.
[0052] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided, on which a computer program is stored, wherein when the computer program is executed by a processor, it implements the operation steps of the method as described in any of the above embodiments.
[0053] The technical solutions provided by the embodiments of this application have the following beneficial effects:
[0054] The proposed solution is based on pre-collected and organized initial load data. When predicting the air conditioning load of a building, it finds the matching initial load data through basic parameters, and then scales the initial load data to obtain the predicted air conditioning load data of the building. This solution does not require complex modeling, has a small computational load, and provides accurate and reliable prediction results for the building load.
[0055] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description
[0056] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0057] Figure 1 This is a flowchart illustrating a method for predicting air conditioning load data according to an embodiment of the present invention.
[0058] Figure 2 This is a logic diagram of a building's hourly load algorithm module throughout the year, as shown in an embodiment of the present invention.
[0059] Figure 3 This is a logic diagram of a method for calculating the hourly load of a building throughout the year, as shown in an embodiment of the present invention.
[0060] Figure 4 This is a logic diagram of an algorithm module that does not guarantee the number of hours, as shown in an embodiment of the present invention.
[0061] Figure 5 This is a block diagram of an air conditioning load data prediction device shown in an embodiment of the present invention. Detailed Implementation
[0062] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of methods and apparatus consistent with some aspects of this application as detailed in the appended claims.
[0063] Figure 1 This is a flowchart illustrating a method for predicting air conditioning load data according to an exemplary embodiment. The method may include the following steps:
[0064] Step S1: Determine the initial load data of the air conditioning system based on the building's basic parameters; the basic parameters include: building type and / or shape coefficient;
[0065] Step S2: Obtain the design non-guarantee hours and design load index of the building; the design non-guarantee hours and the design load index are both preset values;
[0066] Step S3: Determine the scaling factor based on the initial load data, design load indicators, and design non-guaranteed hours;
[0067] Step S4: Scale the initial load data using a scaling factor to obtain load forecast data.
[0068] The proposed solution is based on pre-collected and organized initial load data. When predicting the air conditioning load of a building, it finds the matching initial load data through basic parameters, and then scales the initial load data to obtain the predicted air conditioning load data of the building. This solution does not require complex modeling, has a small computational load, and provides accurate and reliable prediction results for the building load.
[0069] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.
[0070] The hourly load forecasting scheme for buildings proposed in this application is based on a pre-established standard database of building loads. It is applicable to a variety of building projects, eliminates the need for manual calculation of hourly building loads, saves time and effort, and provides accurate and reliable forecasting results for building loads.
[0071] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.
[0072] 1. Reference Figure 2 This patent proposes a method for calculating the hourly load of a building throughout the year. Based on existing engineering data, a standard database of hourly loads of buildings is established. After selecting information such as building type and building scale, the corresponding hourly load data of the building throughout the year is retrieved from the database. At the same time, the initial load data size is adjusted according to the design load and the design value of the number of hours not guaranteed. Then, the hourly load data of each functional area is calculated according to the area ratio of the functional areas.
[0073] Step S1, which determines the initial load data of the air conditioning system based on the building's basic parameters, includes the following steps: performing a matching query in a standard database based on the basic parameters to retrieve matching initial load data; wherein, the standard database is a pre-established database that stores initial load data corresponding to various types and shape coefficients of buildings.
[0074] 2. The establishment of a building load standard database includes the following steps:
[0075] (2.1) Collect typical historical central air conditioning system engineering project data, including reliable data exported from building load simulation software and central air conditioning system simulation platform; data types include building hourly load throughout the year, meteorological parameters such as outdoor dry and wet bulb temperatures, shape coefficient and other data related to building energy consumption.
[0076] (2.2) The collected engineering data were verified, classified, and standardized according to different geographical locations, building uses, and shape coefficients. A building load standard database was established, and the above engineering data were stored in the database. The data establishment process was mainly carried out manually. The shape coefficient is an architectural design term that refers to the ratio of the external surface area of a building in contact with the outdoor atmosphere to the volume it encloses. The external surface area does not include the area of the ground, the interior walls of unheated stairwells, and the entrance doors.
[0077] Regarding the shape coefficient, since it affects building energy consumption indicators, several shape coefficient ranges are set for various types of buildings to improve prediction accuracy. Each shape coefficient range corresponds to a set of initial building load data, denoted as Q. i，0 .
[0078] For example, in practical applications, building types can include: factories, office buildings, hospitals, shopping malls, etc. For example, for multi-story buildings such as office buildings, a set of hourly load data is given with a shape factor of 0.2 to 0.25, and a set of hourly load data is given with a shape factor of 0.25 to 0.3; for single-story buildings such as factories, the shape factor does not need to be considered.
[0079] (2.3) When it is necessary to predict the building load for a new project, relevant data can be retrieved from the building load standard database based on the building information of the new project for subsequent load calculation.
[0080] 3. The method for predicting the hourly load of buildings throughout the year includes the following steps:
[0081] (3.1) Based on the actual engineering parameters, input the building location, building type, building area and building shape coefficient data.
[0082] (3.2) Based on the building location, building type, and building shape coefficient, retrieve the corresponding hourly data for the entire year from the building load standard database as the initial load data Q. i，0 Perform subsequent calculations.
[0083] (3.3) Call the building functional zoning template based on the project information.
[0084]
[0085]
[0086] The building design cooling index is calculated based on the cooling load index of each functional zone as follows:
[0087] Q d =S·∑α j ·q j
[0088] Where S represents the building area, in m². 2 ;α j q represents the area percentage of the j-th functional zone; j This represents the cooling load index for the j-th functional zone, in W / m². 2 .
[0089] (3.4) Adjust the building's hourly initial load data Q throughout the year according to the standard of design-unguaranteed hours h0. i，0 The initial data is scaled synchronously according to the scale, and the scaling factor is determined by the scaling factor 'a'. Here, h0 can be customized according to project requirements.
[0090] As attached Figure 3 As shown, an algorithm is proposed to calculate the hourly load of a building throughout the year using a scaling factor 'a'. After achieving a certain level of accuracy, the calculated value Q of the hourly load throughout the year is output. i .
[0091] In some embodiments, step S3 is based on the initial load data and the design load index Q. d The design does not guarantee the number of hours h0. Determining the scaling factor includes the following steps:
[0092] Step S301: Determine the upper limit scaling factor a max The initial value; based on the initial load data Q i，0 Design load index Q d The design does not guarantee the number of hours h0 relative to the upper limit scaling factor a. max Perform iterative updates;
[0093] Step S302: Determine the lower limit scaling factor a min The initial value; based on the initial load data Qi，0 Design load index Q d The design does not guarantee the number of hours h0 relative to the lower limit scaling factor a. min and the upper limit scaling factor a max Perform iterative updates;
[0094] Step S303: Based on the iterative lower bound scaling factor a min and the upper limit scaling factor a max Determine the final scaling factor.
[0095] In practical applications, step S301 specifically includes the following steps: based on the initial load data Q i，0 and the upper limit scaling factor a max Determine the current load data Q i Based on current load data Q i and design load index Q d Determine the current unguaranteed hours h; compare the design unguaranteed hours h0 with the current unguaranteed hours h; when the design unguaranteed hours h0 is less than the current unguaranteed hours h, adjust the upper limit scaling factor a. max and current load data Q i Perform iterative updates until the design does not guarantee the number of hours h0, which is greater than the current number of hours h.
[0096] Reference Figure 3 Based on the initial load data Q i，0 and the upper limit scaling factor a max Determine the current load data Q i The specific calculation method is as follows: Q i =a max ×Q i，0 .exist Figure 3 In the embodiment shown, a max The initial value is 1. In other embodiments, a max The initial value can also be set to other values.
[0097] Scaling factor a of the upper limit max Iterative updates are performed on the current load data, specifically including the following steps: The upper limit scaling factor a... max The value of a is increased by a preset increment, based on the increased value of a. max The value determines the new current load data. Figure 3 In the embodiment shown, the preset increment is 0.5, that is, each iteration a max The value increases by 0.5. In other embodiments, the preset increment can also be set to other values.
[0098] In practical applications, step S302 includes the following steps: based on the lower limit scaling factor a minand the upper limit scaling factor a max Determine the current scaling factor 'a'; based on the initial load data Q. i，0 The current load data Q is determined by the current scaling factor 'a'. i Based on current load data Q i and design load index Q d Determine the current unguaranteed hours h; compare the design unguaranteed hours h0 with the current unguaranteed hours h; when the design unguaranteed hours h0 is less than the current unguaranteed hours h, update the lower limit scaling factor a. min When the design does not guarantee the number of hours h0, which is greater than the current number of hours not guaranteed h, update the upper limit scaling factor a. max Determine the updated lower bound scaling factor 'a'. min and the upper limit scaling factor a max Check if the difference between the values is less than a preset threshold; if it is, stop the iteration and output the current lower bound scaling factor 'a'. min and the upper limit scaling factor a max Otherwise, continue iterating.
[0099] Reference Figure 3 In step S302, the lower limit scaling factor a is used. min and the upper limit scaling factor a max The current scaling factor 'a' is determined by the following calculation method: a = (a max +a min ) / 2.
[0100] Reference Figure 3 The steps to update the lower bound scaling factor are: Let a min =a; The steps to update the upper bound scaling factor are: Let a max =a. In Figure 3 In the embodiment shown, a min The initial value is 0, and the preset threshold is set to 0.001. In other embodiments, a min The initial value can also be set to other values, and the preset threshold can also be set to other values.
[0101] Reference Figure 3 Step S303 determines the final scaling factor by letting a = (a max +a min ) / 2; use a as the final scaling factor.
[0102] The algorithm for the module that does not guarantee the number of hours is attached. Figure 4 As shown, the input is the hourly load and design load for the whole year, and the output is the number of hours not guaranteed (h).
[0103] Reference Figure 4In steps S301 and S302, the number of hours not guaranteed at present is determined based on the current load data and design load indicators, including the following steps: Input the current load data Q i and design load index Q d Let the current unguaranteed number of hours h = 0; iterate through i, when any Q i >Q d Then, let h = h + 1; output the final number of unguaranteed hours h. Unguaranteed hours are the design load data Q. d The current hourly load data Q is not met. i Q i >Q d The number of times this situation occurs.
[0104] (3.5) Obtain the calculated value Q of the building's hourly load throughout the year. i Then, by calling the building functional zoning template, the building load is allocated to each functional zone, and the hourly load for each functional zone throughout the year can be obtained as follows:
[0105] Q i，j =α j ·Q i
[0106] Q i α represents the calculated hourly load of the building throughout the year, in W. j Let be the area percentage of the j-th functional zone.
[0107] This application proposes a method for calculating the hourly load of a building throughout the year. This method requires the prior collection of existing typical engineering data, the establishment of a standard database of building loads based on a large number of typical engineering building load-related data, and the classification and storage of various data in the database according to engineering type and building scale.
[0108] When predicting building air conditioning load data, the building type and scale are selected based on the input building basic parameters. The corresponding annual hourly initial load data is retrieved from the standard database. Simultaneously, the initial load data ratio is adjusted according to the design load and the design value for non-guaranteed hours, and the initial load data is scaled to obtain the annual hourly load calculation result. Then, based on the functional zoning area ratio, the annual hourly load data for each zone is calculated.
[0109] Figure 5 This is a block diagram illustrating an air conditioning load data prediction device according to an exemplary embodiment. (Refer to...) Figure 5 The device includes: an initial data module 501, an acquisition module 502, a determination module 503, and a scaling module 504.
[0110] The initial data module 501 is used to determine the initial load data of the air conditioning system based on the basic parameters of the building; the basic parameters include: the type of building and / or the shape coefficient.
[0111] The acquisition module 502 is used to acquire the design non-guaranteed hours and design load index of the building; the design non-guaranteed hours and the design load index are both preset values.
[0112] The determination module 503 is used to determine the scaling factor based on the initial load data, design load index, and design non-guaranteed hours.
[0113] The scaling module 504 is used to scale the initial load data using a scaling factor to obtain load forecast data.
[0114] Regarding the apparatus in the above embodiments, the specific steps for each module to perform operations have been described in detail in the embodiments related to the method, and will not be elaborated further here. Each module in the above-described air conditioning load data prediction apparatus can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.
[0115] In one embodiment, a computer device is provided, comprising a processor, memory, and a database connected via a system bus. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device can be used to store a building load standard database; the database in the above embodiment can also be implemented using a database device independent of the computer device. When the computer program is executed by the processor, it implements a method for predicting air conditioning load data.
[0116] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements a method for predicting air conditioning load data.
[0117] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.
[0118] It should be noted that in the description of this application, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this application, unless otherwise stated, "a plurality of" means at least two.
[0119] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the function involved, as will be understood by those skilled in the art to which embodiments of this application pertain.
[0120] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.
[0121] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.
[0122] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.
[0123] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.
[0124] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.
[0125] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.
Claims
1. A method for predicting air conditioning load data, characterized in that, Includes the following steps: Determining the initial load data of the air conditioning system based on the basic parameters of the building includes the following steps: performing a matching query in a standard database according to the basic parameters to retrieve the matching initial load data; wherein, the standard database is a pre-established database that stores the initial load data corresponding to various types of buildings with different shape coefficients; the basic parameters include: the type of building and / or shape coefficient; The design non-guaranteed hours and design load index of the building are obtained; both the design non-guaranteed hours and the design load index are preset values. The scaling factor is determined based on initial load data, design load parameters, and design non-guaranteed hours, including the following steps: determining the initial value of the upper limit scaling factor; iteratively updating the upper limit scaling factor based on the initial load data, design load parameters, and design non-guaranteed hours; determining the initial value of the lower limit scaling factor; iteratively updating both the lower and upper limit scaling factors based on the initial load data, design load parameters, and design non-guaranteed hours; and determining the final scaling factor based on the iteratively updated lower and upper limit scaling factors. The process of iteratively updating the upper limit scaling factor based on initial load data, design load indicators, and design non-guaranteed hours includes the following steps: determining the current load data based on the initial load data and the upper limit scaling factor; determining the current non-guaranteed hours based on the current load data and design load indicators; comparing the design non-guaranteed hours with the current non-guaranteed hours; and iteratively updating the upper limit scaling factor and the current load data when the design non-guaranteed hours are less than the current non-guaranteed hours, until the design non-guaranteed hours are greater than the current non-guaranteed hours. The initial load data is scaled using a scaling factor to obtain the load forecast data.
2. The method according to claim 1, characterized in that, Determining the current load data based on the initial load data and the upper limit scaling factor includes the following steps: in, This is the current load data. This is the upper limit scaling factor. This is the initial load data.
3. The method according to claim 1, characterized in that, The upper limit scaling factor and current load data are iteratively updated, including the following steps: upper limit scaling factor The value is increased by a preset increment, based on the increased value. The value determines the new current load data.
4. The method according to claim 1, characterized in that, The lower and upper scaling factors are iteratively updated based on the initial load data, design load indicators, and design non-guaranteed hours, including the following steps: The current scaling factor is determined based on the lower and upper scaling factors. Determine the current load data based on the initial load data and the current scaling factor; The number of hours not guaranteed at present is determined based on current load data and design load indicators; Compare the design non-guaranteed hours with the current non-guaranteed hours; When the design does not guarantee the number of hours less than the current number of hours not guaranteed, update the lower bound scaling factor; when the design does not guarantee the number of hours greater than the current number of hours not guaranteed, update the upper bound scaling factor. Determine whether the difference between the updated lower scaling factor and the upper scaling factor is less than a preset threshold; If the values are less than the specified values, stop the iteration and output the current lower and upper scaling factors; otherwise, continue the iteration.
5. The method according to claim 4, characterized in that, Determining the current scaling factor based on the lower and upper scaling factors includes the following steps: in, This is the current scaling factor. This is the upper limit scaling factor; This is the lower bound scaling factor.
6. The method according to claim 4, characterized in that, The steps to update the lower bound scaling factor are: Let ; The steps to update the upper limit scaling factor are: Let .
7. The method according to any one of claims 1-6, characterized in that, Determining the number of hours not guaranteed based on current load data and design load specifications includes the following steps: Enter current load data and design load index ; The number of hours is not guaranteed at present. ; Traversal i When any season ; Output the final current unguaranteed hours. .
8. A device for predicting air conditioning load data, characterized in that, include: The initial data module is used to determine the initial load data of the air conditioning system based on the building's basic parameters. The basic parameters include: building type and / or shape coefficient; specifically used to perform matching queries in a standard database based on the basic parameters to retrieve matching initial load data; wherein, the standard database is a pre-established database that stores initial load data corresponding to various types and shape coefficients of buildings; The acquisition module is used to acquire the design non-guarantee hours and design load index of the building; the design non-guarantee hours and the design load index are both preset values; The determination module is used to determine the scaling factor based on initial load data, design load indicators, and design non-guaranteed hours. Specifically, it is used to determine the initial value of the upper limit scaling factor; iteratively update the upper limit scaling factor based on the initial load data, design load indicators, and design non-guaranteed hours; determine the initial value of the lower limit scaling factor; determine the current load data based on the initial load data and the upper limit scaling factor; determine the current non-guaranteed hours based on the current load data and design load indicators; compare the design non-guaranteed hours with the current non-guaranteed hours; when the design non-guaranteed hours are less than the current non-guaranteed hours, iteratively update the upper limit scaling factor and the current load data until the design non-guaranteed hours are greater than the current non-guaranteed hours; and determine the final scaling factor based on the iterated lower limit scaling factor and upper limit scaling factor. The scaling module is used to scale the initial load data using a scaling factor to obtain load forecast data.
9. A computer device, characterized in that, include: Memory, used to store computer programs; A processor for executing a computer program in the memory to implement the operational steps of the method according to any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the operational steps of the method according to any one of claims 1 to 7.