Building load prediction method, training method and system considering random thermal disturbance

Abstract

The invention provides a building load prediction method, training method and system considering random thermal disturbance, which can be applied to the technical field of building energy supply system control. The method comprises the steps of obtaining historical operation information, attribute parameter information and historical environmental factor information of a building radiation energy supply system for a target building corresponding to a target time period in response to a received building load prediction demand for the target time period; constructing a first model according to the historical operation information, the attribute parameter information and the historical environment factor information; performing continuous time processing on the variable parameters of the first model to obtain target parameter information; the target parameter information is processed according to a target model, a thermal disturbance predicted value of the target building is obtained, and the target model is constructed by coupling a first model and a second model; and inputting the thermal disturbance prediction value of the target building into the first model to obtain a load prediction value of the target building.

Description

Technical Field

[0001] The present invention relates to the technical field of building energy supply system control, and in particular to a thermal disturbance prediction method, a training method and a system for a building. Background Art

[0002] Building radiant energy supply systems embed heat / cold fluid pipes inside the building envelope and use these pipes to store and transfer energy, thereby achieving control of the building's indoor environment. Building energy supply terminals based on internally embedded heat / cold fluid pipes have great energy storage potential, and the envelope structure can adjust the heat storage / release, which is conducive to the implementation of flexible energy use in buildings. However, thermal disturbances in building radiant energy supply systems usually affect the indoor environment and energy consumption.

[0003] In the process of implementing the concept of the present invention, the inventors found that the dynamic characteristics of thermal disturbances in the absence of measurement conditions are usually not taken into account in the relevant technologies, and the impact of different thermal disturbances on the building load is often simplified by integrating thermal disturbance terms, resulting in low prediction accuracy of the target building load, which in turn makes it difficult for the control strategy to adapt to rapidly changing environments or complex system requirements, making the overall performance of the building radiation energy supply system in actual operation lower than expected. Summary of the invention

[0004] In view of the above problems, the present invention provides a building load prediction method, training method and system considering random thermal disturbances.

[0005] According to a first aspect of the present invention, a method, a training method and a system for predicting building loads taking into account random thermal disturbances are provided, the method comprising: in response to receiving a building load prediction demand for a target time period, obtaining historical operation information, attribute parameter information and historical environmental factor information of a building radiation energy supply system for a target building corresponding to the target time period; constructing a first model according to the historical operation information, attribute parameter information and historical environmental factor information, wherein the first model comprises variable parameters; performing continuous time processing on the variable parameters of the first model to obtain target parameter information; processing the target parameter information according to the target model to obtain a thermal disturbance prediction value of the target building, wherein the target model is constructed by coupling the first model and the second model, the second model being obtained by training an initial model using sample historical operation information, sample historical environmental factor information and sample time parameter information with the thermal disturbance prediction value as a label; and inputting the thermal disturbance prediction value of the target building into the first model to obtain a load prediction value of the target building.

[0006] According to an embodiment of the present invention, target parameter information is processed according to a target model to obtain a thermal disturbance prediction value of a target building, including: coupling a first model with a second model to obtain a target model; in the target model, determining a predicted indoor temperature value for a target period of time according to an initial disturbance prediction value output by the second model; determining a loss function value according to the predicted indoor temperature value for the target period of time and an actual value of the indoor temperature for the target period of time; and obtaining a thermal disturbance prediction value for the target building when the loss function value is less than a preset loss function value.

[0007] According to an embodiment of the present invention, a loss function value is determined based on a predicted value of indoor temperature during a target period and an actual value of indoor temperature during the target period, including: determining a target loss function based on a predicted value of indoor temperature during a target period and an actual value of indoor temperature during the target period; determining a loss function value based on the target loss function.

[0008] According to an embodiment of the present invention, the first model also includes: constant parameters, wherein the first model is coupled with the second model to obtain a target model, including: determining target parameter information based on the constant parameters and variable parameters of the first model; and coupling the first model and the second model to obtain the target model based on the target parameter information and the initial disturbance prediction value.

[0009] According to an embodiment of the present invention, historical operation information includes temperature information of the enclosure structure, temperature information of the surface of the floor, temperature information of the lumped nodes of the embedded pipe plane, water supply temperature information, return water temperature information, and water flow and heating capacity; attribute parameter information includes thermal capacity of the enclosure structure, thermal capacity of indoor air, thermal capacity of the floor, thermal capacity of the embedded pipe, thermal resistance between outdoor air and the enclosure structure, thermal resistance between indoor air and the enclosure structure, thermal resistance of the external window, thermal resistance between the embedded pipe and the floor, thermal resistance between the embedded pipe and the soil, thermal capacity of water, conversion coefficient of solar radiation and area of ​​the external window; historical environmental factor information includes indoor temperature information and outdoor temperature information; wherein, based on the historical operation information, attribute parameter information and historical environmental factor information, a first model is constructed, including: based on the temperature information of the enclosure structure, indoor temperature information, thermal capacity of the enclosure structure, thermal resistance between outdoor air and the enclosure structure, thermal resistance between indoor air and the enclosure structure and the first process disturbance, a first equilibrium equation is constructed; based on the temperature information of the enclosure structure, indoor temperature information, outdoor temperature information, The second balance equation is constructed according to the temperature information on the upper surface of the floor, the heat capacity of the indoor air, the thermal resistance between the indoor air and the enclosure structure, the thermal resistance between the indoor air and the floor, the thermal resistance of the external window, the first thermal disturbance parameter and the second process disturbance; the third balance equation is constructed according to the indoor temperature information, the temperature information on the upper surface of the floor, the temperature information of the lumped nodes of the embedded pipe plane, the area of ​​the external window, the conversion coefficient of solar radiation, the heat capacity of the floor, the thermal resistance between the indoor air and the floor, the thermal resistance between the embedded pipe and the floor, the second thermal disturbance parameter and the third process disturbance; the fourth balance equation is constructed according to the temperature information of the soil, the temperature information of the lumped nodes of the embedded pipe plane, the temperature information on the upper surface of the floor, the heat capacity of the embedded pipe, the thermal resistance between the embedded pipe and the floor, the thermal resistance between the embedded pipe and the soil, the heating amount and the fourth process disturbance; the fifth balance equation is constructed according to the water supply temperature information, the return water temperature information, the heat capacity of water, the water flow rate and the heating amount; the first model is constructed according to the first balance equation, the second balance equation, the third balance equation, the fourth balance equation and the fifth balance equation.

[0010] According to an embodiment of the present invention, continuous time processing is performed on the variable parameters of the first model to obtain target parameter information, including: continuous time processing is performed on the variable parameters of the first model to obtain a continuous time first model; historical energy supply information, attribute parameter information and historical environmental factor information are input into the continuous time first model to obtain target parameter information.

[0011] The second aspect of the present invention provides a method for training a target model based on a building radiation energy supply system, comprising: obtaining training samples, the training samples including sample historical operation information, sample attribute parameter information, sample historical environmental factor information and sample disturbance parameter information; using the target model to process the sample historical operation information, sample attribute parameter information, sample historical environmental factor information and sample disturbance parameter information, and outputting target parameter information; when the target parameter information is determined, using a feedforward neural network to train the target model to obtain a trained target model.

[0012] A third aspect of the present invention provides a building load prediction system considering random thermal disturbances, comprising: a first acquisition module, a construction module, a first obtaining module, a second obtaining module and a third obtaining module.

[0013] The first acquisition module is used to obtain historical operation information, attribute parameter information and historical environmental factor information of the building radiation energy supply system for the target building corresponding to the target time period in response to receiving a thermal disturbance prediction demand for the target time period.

[0014] The construction module is used to construct a first model according to historical operation information, attribute parameter information and historical environmental factor information, wherein the first model includes variable parameters.

[0015] The first obtaining module is used to perform continuous time processing on the variable parameters of the first model to obtain target parameter information.

[0016] The second obtaining module is used to process the target parameter information according to the target model to obtain the thermal disturbance prediction value of the target building, wherein the target model is constructed by coupling the first model and the second model, and the second model is obtained by training the historical operation information, historical environmental factor information and time parameter information with the thermal disturbance prediction value as the label.

[0017] The third obtaining module is used to input the thermal disturbance prediction value of the target building into the first model to obtain the load prediction value of the target building.

[0018] A fourth aspect of the present invention provides a training device based on a target model of a building radiation energy supply system, comprising: a second acquisition module, an output module and a training module.

[0019] The second acquisition module is used to acquire training samples, where the training samples include sample historical operation information, sample attribute parameter information, sample historical environmental factor information and sample disturbance parameter information.

[0020] The output module is used to process the sample historical operation information, sample attribute parameter information, sample historical environmental factor information and sample disturbance parameter information using the target model, and output the target parameter information.

[0021] The training module is used to train the target model using a feedforward neural network when the target parameter information is determined to obtain a trained target model.

[0022] A fifth aspect of the present invention provides an electronic device, comprising: one or more processors; a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0023] The sixth aspect of the present invention further provides a computer-readable storage medium on which a computer program or instruction is stored, and the steps of the above method are implemented when the above computer program or instruction is executed by a processor.

[0024] The seventh aspect of the present invention also provides a computer program product, including a computer program or instructions, which implement the steps of the above method when executed by a processor.

[0025] According to the building load prediction method, training method and system considering random thermal disturbances provided by the present invention, by constructing a first model based on the acquired historical operation information, attribute parameter information and historical environmental factor information, and performing continuous time processing on the variable parameters of the first model, the target parameter information can be obtained. The target parameter information is processed using the target model to obtain the thermal disturbance prediction value of the target building. Since the target model is constructed by the fusion of the first model and the second model, it can accurately predict multivariate uncertain thermal disturbances under limited measurement conditions, thereby improving the accuracy of the disturbance prediction value of the target building, and further improving the accuracy of the load prediction value of the target building, so that the overall performance of the building radiation energy supply system during actual operation meets expectations. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above contents and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:

[0027] Figure 1 An application scenario diagram of a building load prediction method considering random thermal disturbances according to an embodiment of the present invention is shown.

[0028] Figure 2 A flow chart of a building load prediction method considering random thermal disturbances according to an embodiment of the present invention is shown.

[0029] Figure 3 A schematic diagram of a first model according to an embodiment of the present invention is shown.

[0030] Figure 4 A flow chart of a building load prediction method considering random thermal disturbances according to yet another embodiment of the present invention is shown.

[0031] Figure 5 A flow chart for constructing a target model according to an embodiment of the present invention is shown.

[0032] Fig. 6A A comparison diagram of thermal disturbance prediction results due to occupancy by personnel according to an embodiment of the present invention is shown.

[0033] Figure 6B A comparison diagram of solar radiation heat disturbance prediction results according to an embodiment of the present invention is shown.

[0034] Figure 7 A flow chart of a training method for a target model based on a building radiation energy supply system according to an embodiment of the present invention is shown.

[0035] Figure 8 A structural block diagram of a building load prediction system considering random thermal disturbances according to an embodiment of the present invention is shown.

[0036] Fig. 9 A structural block diagram of a training device based on a target model of a building radiation energy supply system according to an embodiment of the present invention is shown.

[0037] Fig.10 A block diagram of an electronic device suitable for implementing a building load prediction method considering random thermal disturbances according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0038] Below, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of embodiments of the present invention. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of concepts of the present invention.

[0039] The terms used herein are only for describing specific embodiments and are not intended to limit the present invention. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0040] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0041] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0042] In the technical solution of the present invention, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0043] In the process of implementing the present invention, it is found that the related technologies usually do not consider the dynamic characteristics of thermal disturbances in the absence of measurement conditions, and often simplify the impact of different thermal disturbances on building loads by integrating thermal disturbance terms, resulting in low prediction accuracy of the target building load, which in turn causes the control strategy to make the overall performance of the building radiation energy supply system in actual operation lower than expected. Alternatively, machine learning methods are used to predict building thermal disturbances, but their prediction accuracy has a strong dependence on historical data.

[0044] In view of this, an embodiment of the present invention provides a building load prediction method considering random thermal disturbances, including: in response to receiving a thermal disturbance prediction demand for a target time period, obtaining historical operation information, attribute parameter information and historical environmental factor information of a building radiation energy supply system for a target building corresponding to the target time period; constructing a first model according to the historical operation information, attribute parameter information and historical environmental factor information, wherein the first model includes variable parameters; performing continuous time processing on the variable parameters of the first model to obtain target parameter information; processing the target parameter information according to the target model to obtain a thermal disturbance prediction value of the target building, wherein the target model is constructed by coupling the first model and the second model, the second model is obtained by training an initial model with a thermal disturbance prediction value as a label using sample historical operation information, sample historical environmental factor information and sample time parameter information, and the thermal disturbance prediction value of the target building is input into the first model to obtain a load prediction value of the target building.

[0045] Figure 1 An application scenario diagram of a building load prediction method considering random thermal disturbances according to an embodiment of the present invention is shown.

[0046] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used to provide a medium for a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0047] The user can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only for example).

[0048] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0049] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0050] It should be noted that the building load prediction method considering random thermal disturbances provided in the embodiment of the present invention can generally be executed by the server 105. Accordingly, the building load prediction system considering random thermal disturbances provided in the embodiment of the present invention can generally be set in the server 105. The building load prediction method considering random thermal disturbances provided in the embodiment of the present invention can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the building load prediction system considering random thermal disturbances provided in the embodiment of the present invention can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0051] It should be understood that Figure 1The number of the first terminal device, the second terminal device, the third terminal device, the network and the server in the embodiment is only for illustration. According to the implementation requirements, there may be any number of the first terminal device, the second terminal device, the third terminal device, the network and the server.

[0052] Figure 2 A flow chart of a building load prediction method considering random thermal disturbances according to an embodiment of the present invention is shown.

[0053] like Figure 2 As shown, the building load prediction method 200 considering random thermal disturbances of this embodiment includes operations S210 to S250.

[0054] In operation S210, in response to receiving a building load forecast demand for a target period, historical operation information, attribute parameter information, and historical environmental factor information of a building radiant energy supply system for a target building corresponding to the target period are obtained.

[0055] In operation S220, a first model is constructed based on the historical operation information, the attribute parameter information, and the historical environmental factor information.

[0056] In operation S230, continuous time processing is performed on the variable parameters of the first model to obtain target parameter information.

[0057] In operation S240, the target parameter information is processed according to the target model to obtain a thermal disturbance prediction value of the target building.

[0058] In operation S250, the thermal disturbance prediction value of the target building is input into the first model to obtain a load prediction value of the target building.

[0059] According to an embodiment of the present invention, the building radiation energy supply system of the target building may represent a system in which water pipes with cold / hot water flowing therethrough are embedded inside the building envelope.

[0060] According to an embodiment of the present invention, the target period may represent a certain period of time in the future. For example, the target period may be the first month of the third quarter of the next year. For another example, the target period may be 24 hours in one day on next Friday. Thermal disturbances may represent temperature changes or disturbances in heat transfer caused by external or internal factors. Thermal disturbances may be instantaneous or continuous, periodic or non-periodic, and may cause changes in the state or parameters of the building's radiation energy supply system. Since thermal disturbances may be instantaneous, random, or non-periodic, it is difficult to accurately predict the predicted value of thermal disturbances. Thermal disturbances may be multivariate, for example, they may be thermal disturbances caused by occupancy of personnel. For another example, they may also be solar radiation thermal disturbances.

[0061] According to an embodiment of the present invention, the historical operation information may represent the operation information of the building radiation energy supply system during operation in the previous period of time. The historical operation information may be different for different target time periods. The attribute parameter information may represent the attribute information of each part of the building radiation energy supply system, and the attribute information may be a fixed value. The historical environmental factor information may represent the ambient temperature information in the previous period of time.

[0062] According to an embodiment of the present invention, a first model may be constructed based on historical operation information, attribute parameter information, and historical environmental factor information. The first model may also be referred to as a gray box model. The first model may be used to predict the load of a target building.

[0063] According to an embodiment of the present invention, the first model may include variable parameters, and the variable parameters may represent variable parameters in the first model.

[0064] According to an embodiment of the present invention, the variable parameters of the first model may be processed continuously in time, so as to obtain target parameter information, which may include multiple target parameter information. For example, the target parameter information may include three.

[0065] According to an embodiment of the present invention, the target model can be constructed by coupling the first model and the second model. The second model can be obtained by training the initial model using the thermal disturbance prediction value as a label and using the sample historical operation information, the sample historical environmental factor information and the sample time parameter information. The second model can include a neural network model.

[0066] According to an embodiment of the present invention, the disturbance prediction value of the target building can be obtained by processing the target parameter information using the target model. For example, the disturbance prediction value of the target building within one day in the future can be obtained by processing the target parameter information using the target model.

[0067] According to an embodiment of the present invention, the thermal disturbance prediction value of the target building may be input into the first model, and the thermal disturbance prediction value of the target building may be processed by the first model to obtain the load prediction value of the target building.

[0068] According to an embodiment of the present invention, by constructing a first model based on the acquired historical operation information, attribute parameter information and historical environmental factor information, and performing continuous time processing on the variable parameters of the first model, the target parameter information can be obtained. By processing the target parameter information using the target model, the thermal disturbance prediction value of the target building can be obtained. Since the target model is constructed by the fusion of the first model and the second model, it can accurately predict multivariate uncertain thermal disturbances under limited measurement conditions, thereby improving the accuracy of the disturbance prediction value of the target building, and further improving the accuracy of the load prediction value of the target building, so that the overall performance of the building radiation energy supply system during actual operation meets expectations.

[0069] Figure 3 A schematic diagram of a first model according to an embodiment of the present invention is shown.

[0070] like Figure 3 As shown in FIG. 1 , a first model based on thermal resistance and thermal capacity (RC) is constructed. The first model can be used to describe a simplified model of the thermal dynamics of the target building. The first model includes the indoor side of the target building, the envelope structure of the target building, and the outdoor side of the target building. The first model is divided into an above-ground part and an underground part through the ground, and the underground part belongs to an embedded pipe structure.

[0071] According to an embodiment of the present invention, historical operation information may include temperature information of the enclosure structure, temperature information of the upper surface of the floor, temperature information of the lumped node of the embedded pipe plane, return water temperature information, and water flow and heating supply. Attribute parameter information may include thermal capacity of the enclosure structure, thermal capacity of indoor air, thermal capacity of the floor, thermal capacity of the embedded pipe, thermal resistance between outdoor air and the enclosure structure, thermal resistance between indoor air and the enclosure structure, thermal resistance of the external window, thermal resistance between the embedded pipe and the floor, thermal resistance between the embedded pipe and the soil, thermal capacity of water, conversion coefficient of solar radiation, and area of ​​the external window. Historical environmental factor information may include indoor temperature information and outdoor temperature information.

[0072] According to an embodiment of the present invention, a first model is constructed based on historical operation information, attribute parameter information and historical environmental factor information, including: constructing a first equilibrium equation based on the temperature information of the enclosure structure, the indoor temperature information, the heat capacity of the enclosure structure, the thermal resistance between the outdoor air and the enclosure structure, the thermal resistance between the indoor air and the enclosure structure and the first process disturbance; constructing a second equilibrium equation based on the temperature information of the enclosure structure, the indoor temperature information, the outdoor temperature information, the temperature information on the upper surface of the floor, the heat capacity of the indoor air, the thermal resistance between the indoor air and the enclosure structure, the thermal resistance between the indoor air and the floor, the thermal resistance between the indoor air and the floor, the thermal resistance of the external window, the first thermal disturbance parameter and the second process disturbance; constructing a second equilibrium equation based on the indoor temperature information, the temperature information on the upper surface of the floor, the lumped value of the embedded pipe plane The third balance equation is constructed based on the temperature information of the nodes, the area of ​​the external windows, the conversion coefficient of solar radiation, the heat capacity of the floor, the thermal resistance between the indoor air and the floor, the thermal resistance between the embedded pipe and the floor, the second thermal disturbance parameter and the third process disturbance. The fourth balance equation is constructed based on the temperature information of the soil, the temperature information of the lumped nodes of the embedded pipe plane, the temperature information of the upper surface of the floor, the heat capacity of the embedded pipe, the thermal resistance between the embedded pipe and the floor, the thermal resistance between the embedded pipe and the soil, the heating amount and the fourth process disturbance. The fifth balance equation is constructed based on the water supply temperature information, the return water temperature information, the heat capacity of water, the water flow rate and the heating amount. The first model is constructed based on the first balance equation, the second balance equation, the third balance equation, the fourth balance equation and the fifth balance equation.

[0073] According to an embodiment of the present invention, a first equilibrium equation may be constructed based on the temperature information of the enclosure structure, the indoor temperature information, the heat capacity of the enclosure structure, the thermal resistance between the outdoor air and the enclosure structure, the thermal resistance between the indoor air and the enclosure structure, and the first process disturbance, as shown in the following formula (1).

[0074] (1);

[0075] in, Represents the temperature information of the envelope structure of the target building, Indicates outdoor temperature information; Indicates indoor temperature information. represents the heat capacity of the enclosure structure, represents the thermal resistance between the outdoor air and the building envelope, represents the thermal resistance between indoor air and the building envelope, represents the first process disturbance.

[0076] According to an embodiment of the present invention, a second equilibrium equation can be constructed based on the temperature information of the enclosure structure, the indoor temperature information, the outdoor temperature information, the temperature information of the floor surface, the heat capacity of the indoor air, the thermal resistance between the indoor air and the enclosure structure, the thermal resistance between the indoor air and the floor, the thermal resistance of the external window, the first thermal disturbance parameter and the second process disturbance, as shown in the following formula (2).

[0077] (2);

[0078] in, Indicates the temperature information on the floor surface. represents the first thermal disturbance parameter; is the heat capacity of the indoor air, represents the thermal resistance of the exterior window, represents the thermal resistance between indoor air and floor, represents the second process disturbance.

[0079] According to an embodiment of the present invention, a third equilibrium equation can be constructed based on indoor temperature information, temperature information on the upper surface of the floor, temperature information of the lumped nodes of the embedded pipe plane, the area of ​​the external window, the conversion coefficient of solar radiation, the heat capacity of the floor, the thermal resistance between the indoor air and the floor, the thermal resistance between the embedded pipe and the floor, the second thermal disturbance parameter and the third process disturbance, as shown in the following formula (3).

[0080] (3);

[0081] in, Represents the temperature information of the lumped nodes of the embedded pipe plane, represents the area of ​​the exterior window, represents the conversion coefficient of solar radiation, represents the second thermal disturbance parameter; is the heat capacity of the floor, represents the thermal resistance between the embedded pipe and the floor, represents the third process disturbance.

[0082] According to an embodiment of the present invention, the first thermal disturbance parameter may be a human occupancy thermal disturbance parameter. The second thermal disturbance parameter may be a solar radiation thermal disturbance parameter.

[0083] According to an embodiment of the present invention, a fourth equilibrium equation can be constructed according to the temperature information of the soil, the temperature information of the lumped nodes of the embedded pipe plane, the temperature information of the surface of the floor, the heat capacity of the embedded pipe, the thermal resistance between the embedded pipe and the floor, the thermal resistance between the embedded pipe and the soil, the heat supply and the fourth process disturbance, as shown in the following formula (4).

[0084] (4);

[0085] in, Indicates soil temperature information, Indicates the heat supply of the building's radiation energy supply system. represents the thermal resistance between the embedded pipe and the soil, represents the heat capacity of the embedded pipe, Represents the fourth process disturbance.

[0086] According to an embodiment of the present invention, a fifth balance equation may be constructed based on the water supply temperature information, the return water temperature information, the heat capacity of water, and the water flow rate and heat supply, as shown in the following formula (5).

[0087] (5);

[0088] in, Indicates water supply temperature information. represents the return water temperature information, c represents the heat capacity of water, and m represents the water flow rate.

[0089] According to an embodiment of the present invention, a heat pump may be used for water supply. The first process disturbance, the second process disturbance, the third process disturbance and the fourth process disturbance may be random process disturbances, and their influence on the building radiation energy supply system may be ignored.

[0090] According to an embodiment of the present invention, a first model may be further constructed based on the first balance equation, the second balance equation, the third balance equation, the fourth balance equation and the fifth balance equation constructed above.

[0091] According to an embodiment of the present invention, continuous time processing is performed on the variable parameters of the first model to obtain target parameter information, including: continuous time processing is performed on the variable parameters of the first model to obtain a continuous time first model; historical energy supply information, attribute parameter information and historical environmental factor information are input into the continuous time first model to obtain target parameter information.

[0092] According to an embodiment of the present invention, by performing continuous time processing on the variable parameters of the first model, a continuous time first model can be obtained, as shown in the following formula (6).

[0093] (6);

[0094] in, represents the state vector, , represents the control input vector, ; represents the perturbation vector, , denotes independent normally distributed random disturbances.

[0095] According to an embodiment of the present invention, the random disturbance of independent normal distribution can be ignored in the building radiation energy supply system. By inputting the historical energy supply information, the attribute parameter information and the historical environmental factor information into the continuous time first model for processing, the target parameter information can be obtained. The target parameter information can include the first target parameter information, the second target parameter information and the third target parameter information.

[0096] According to an embodiment of the present invention, the first target parameter information A may be expressed as the following formula (7).

[0097] (7);

[0098] According to an embodiment of the present invention, the second target parameter information B may be expressed as the following formula (8).

[0099] (8);

[0100] According to an embodiment of the present invention, the third target parameter information E can be expressed as the following formula (9).

[0101] (9);

[0102] According to an embodiment of the present invention, a first model can be constructed based on the first balance equation, the second balance equation, the third balance equation, the fourth balance equation and the fifth balance equation. By performing continuous-time processing on the variable parameters of the first model, a continuous-time first model can be obtained. By using the continuous-time first model to process historical energy supply information, attribute parameter information and historical environmental factor information, target parameter information can be obtained, thereby improving the accuracy of the target parameter information.

[0103] According to an embodiment of the present invention, target parameter information is processed according to a target model to obtain a thermal disturbance prediction value of a target building, including: coupling a first model with a second model to obtain a target model; in the target model, determining a predicted indoor temperature value for a target period of time according to an initial disturbance prediction value output by the second model; determining a loss function value according to the predicted indoor temperature value for the target period of time and an actual value of the indoor temperature for the target period of time; and obtaining a thermal disturbance prediction value for the target building when the loss function value is less than a preset loss function value.

[0104] According to an embodiment of the present invention, a target model may be obtained by coupling the first model and the second model.

[0105] According to an embodiment of the present invention, the above formula (7) can be discretized using the zero-order hold method, that is, the control signal is kept unchanged within the sampling period span and converted from a digital signal to an analog signal. For example, the sampling time can be 1 hour, that is, Ts=1h. Interval [ , ] can represent a time period. Keeping the control signal unchanged within the sampling time span can indicate that the heating amount remains unchanged within the sampling time span. The initial moment of the sampling period and control signals The following are formulas (8) and (9).

[0106] (8);

[0107] (9);

[0108] According to an embodiment of the present invention, k represents the number of the initial time in the sampling period, and the value range of k is greater than or equal to 0.

[0109] According to an embodiment of the present invention, the following formulas (10) to (15) can be obtained by discretizing the above formula (7).

[0110] (10);

[0111] (11);

[0112] (12);

[0113] (13);

[0114] (14);

[0115] (15);

[0116] in, represents the discretized state vector at the k+1th moment, The discretized state vector at the kth moment is, represents the control signal after discretization at the kth moment, represents the perturbation vector after discretization at the kth moment, represents the random disturbance after discretization at the kth moment, represents the observation matrix, Represents the first target parameter information after discretization, Represents the second target parameter information after discretization, Represents the third target parameter information after discretization, Indicates the indoor temperature information at the kth moment.

[0117] According to an embodiment of the present invention, when the target parameter information is determined, the target model can be constructed based on the first model and the second model. The state space equation is as shown in the above formulas (10) and (11), and the input , , and , you can get .

[0118] According to an embodiment of the present invention, the initial disturbance prediction value can be output by the second model. In the target model, the indoor temperature prediction value of the target period can be determined according to the initial disturbance prediction value, that is, the initial disturbance prediction value is input into the following formula (16), and the initial disturbance prediction value is used to calculate the indoor temperature prediction value of the target period. and Replace thermal disturbance prediction value and , so that the predicted value of indoor temperature in the target period can be determined as follows:

[0119] (16);

[0120] (17);

[0121] in, represents the first initial disturbance prediction value, , represents the second initial disturbance prediction value, , Represents the predicted value of indoor temperature at the kth moment.

[0122] According to an embodiment of the present invention, a loss function value is determined based on a predicted value of indoor temperature during a target period and an actual value of indoor temperature during the target period, including: determining a target loss function based on a predicted value of indoor temperature during a target period and an actual value of indoor temperature during the target period; determining a loss function value based on the target loss function.

[0123] According to an embodiment of the present invention, the target loss function L may be determined according to the predicted value of the indoor temperature during the target period and the actual value of the indoor temperature during the target period, as shown in the following formula (18).

[0124] (18);

[0125] According to an embodiment of the present invention, when the target loss function is determined, the loss function value can be determined.

[0126] According to an embodiment of the present invention, the preset loss function value can be set according to experience or according to demand, and the setting method of the preset loss function is not limited here. When the loss function value is less than the preset loss function value, the thermal disturbance prediction value of the target building can be obtained.

[0127] According to an embodiment of the present invention, a target model can be constructed by coupling the first model and the second model, and the indoor temperature prediction value can be determined based on the initial disturbance value output by the second model, so that the target loss function can be determined in combination with the actual value of the indoor temperature in the target period, and then the loss function value can be determined, and when the loss function value is less than the preset loss function value, the thermal disturbance prediction value of the target building can be obtained, thereby improving the accuracy of the thermal disturbance prediction value of the target building, and when the target model is constructed, the prediction process of the thermal disturbance does not rely on historical data.

[0128] Figure 4 A flow chart of a building load prediction method considering random thermal disturbances according to yet another embodiment of the present invention is shown.

[0129] like Figure 4 As shown, the building load prediction method 400 considering random thermal disturbances of this embodiment includes operations S410 to S490.

[0130] In operation S410, in response to receiving a building load forecast demand for a target period, historical operation information, attribute parameter information, and historical environmental factor information of a building radiant energy supply system for a target building corresponding to the target period are obtained.

[0131] In operation S420, a first model is constructed based on the historical operation information, the attribute parameter information, and the historical environmental factor information.

[0132] In operation S430, continuous time processing is performed on the variable parameters of the first model to obtain target parameter information.

[0133] In operation S440, the first model is coupled with the second model to obtain a target model.

[0134] In operation S450, in the target model, a predicted value of the indoor temperature in the target period is determined according to the initial disturbance predicted value output by the second model.

[0135] In operation S460, a target loss function is determined according to the predicted value of the indoor temperature during the target period and the actual value of the indoor temperature during the target period.

[0136] In operation S470, a loss function value is determined according to the target loss function.

[0137] In operation S480, when the loss function value is less than a preset loss function value, a thermal disturbance prediction value of the target building is obtained;

[0138] In operation S490, the thermal disturbance prediction value of the target building is input into the first model to obtain a load prediction value of the target building.

[0139] According to an embodiment of the present invention, the first model is coupled with the second model to obtain a target model, including: determining target parameter information based on the constant parameters of the first model; and coupling the first model and the second model to obtain the target model based on the target parameter information and the initial disturbance prediction value.

[0140] According to an embodiment of the present invention, the first model may further include a constant parameter, and the constant parameter of the first model may represent an invariant parameter in the first model, such as the area of ​​the outer ring.

[0141] According to an embodiment of the present invention, the target parameter information can be determined according to the constant parameters and variable parameters of the first model using the above equations (7) to (9).

[0142] According to an embodiment of the present invention, the first model and the second model can be coupled according to the target parameter information and the initial disturbance prediction value using the above formulas (16) and (17) to obtain the target model.

[0143] According to the embodiment of the present invention, a target model can be constructed by coupling the first model and the second model, and the accuracy of the thermal disturbance prediction value of the target building can be improved by using the target model.

[0144] According to an embodiment of the present invention, a building load prediction method considering random thermal disturbances can predict a thermal disturbance that is difficult to predict, and then predict the building load. For example, it can predict the thermal disturbance caused by human occupancy. For another example, it can predict the thermal disturbance caused by solar radiation.

[0145] Figure 5 A flow chart for constructing a target model according to an embodiment of the present invention is shown.

[0146] like Figure 5As shown, the system of the target model includes an embedded tubular building 510 and a thermal disturbance predictor 520. The thermal disturbance predictor 520 is a device applied to a building load prediction method considering random thermal disturbances. The embedded tubular building 510 is also the above-mentioned building radiation energy supply system. The historical operation information, attribute parameter information and historical environmental factor information of the embedded tubular building 510 can be used to construct a first model, and the variable parameters and constant parameters of the first model can be determined. The variable parameters of the first model are processed in a continuous time to obtain target parameter information. The target parameter information is input into the thermal disturbance predictor 520. The first model and the second model are coupled to obtain a target model. The target model processes the target parameter information to obtain a thermal disturbance prediction value.

[0147] Fig. 6A A comparison diagram of thermal disturbance prediction results due to occupancy by personnel according to an embodiment of the present invention is shown. Figure 6B A comparison diagram of solar radiation heat disturbance prediction results according to an embodiment of the present invention is shown.

[0148] like Fig. 6A and 6B As shown in the figure, the building load prediction method considering random thermal disturbance can achieve accurate prediction for both human occupancy thermal disturbance and solar radiation thermal disturbance.

[0149] Figure 7 A flow chart of a training method for a target model based on a building radiation energy supply system according to an embodiment of the present invention is shown.

[0150] like Figure 7 As shown, the training method 700 based on the target model of the building radiant energy supply system includes operations S710 to S730.

[0151] In operation S710, a training sample is acquired.

[0152] In operation S720, the sample historical operation information, the sample attribute parameter information, the sample historical environmental factor information, and the sample disturbance parameter information are processed using the target model to output target parameter information.

[0153] In operation S730, when the target parameter information is determined, a target model is trained using a feedforward neural network to obtain a trained target model.

[0154] According to an embodiment of the present invention, the training sample may include sample historical operation information, sample attribute parameter information, sample historical environmental factor information and sample disturbance parameter information.

[0155] According to an embodiment of the present invention, when the target parameter information is determined, a feedforward neural network can be used to train the target model, thereby obtaining a trained target model. The training of the target model includes forward propagation and backward propagation.

[0156] According to an embodiment of the present invention, the forward propagation process includes the following formulas (19) to (21).

[0157] (19);

[0158] (20);

[0159] (twenty one);

[0160] Where x represents the input state vector, n represents the number of state vectors, represents the set of state vectors, Represents the hidden layer The input of node j, Represents the hidden layer The output of node j, l represents the hidden layer index, represents the number of nodes in the l-1th layer, Indicates that from Layer Node To Layer Node The weight of represents the bias term, Indicates the selected activation function.

[0161] According to an embodiment of the present invention, the target loss function L can be determined according to the above formula (18), and the back propagation process includes the following formulas (22) to (24).

[0162] (twenty two);

[0163] (twenty three);

[0164] (twenty four);

[0165] in, represents the derivative of the activation function, represents the learning rate, Represents an index item.

[0166] According to an embodiment of the present invention, the input state vector may include outdoor temperature information, time, date type, indoor occupancy, solar radiation time series characteristics, etc., wherein the date type may be whether it is a weekend. Each weight and bias is updated as (22) to (24), traversing all samples in the sample.

[0167] Based on the above-mentioned building load prediction method considering random thermal disturbance, the present invention also provides a building load prediction system considering random thermal disturbance. Figure 8 The system is described in detail.

[0168] Figure 8 A structural block diagram of a building load prediction system considering random thermal disturbances according to an embodiment of the present invention is shown.

[0169] like Figure 8 As shown, the building load prediction system 800 considering random thermal disturbances of this embodiment includes a first acquisition module 810 , a construction module 820 , a first obtaining module 830 , a second obtaining module 840 and a third obtaining module 850 .

[0170] The first acquisition module 810 is used to, in response to receiving the building load forecast demand for the target period, acquire the historical operation information, attribute parameter information and historical environmental factor information of the building radiation energy supply system for the target building corresponding to the target period. In one embodiment, the first acquisition module 810 can be used to perform the operation S210 described above, which will not be repeated here.

[0171] The construction module 820 is used to construct a first model according to the historical operation information, the attribute parameter information and the historical environmental factor information, wherein the first model includes variable parameters. In one embodiment, the construction module 820 can be used to perform the operation S220 described above, which will not be described in detail here.

[0172] The first obtaining module 830 is used to perform continuous time processing on the variable parameters of the first model to obtain target parameter information. In one embodiment, the first obtaining module 830 can be used to perform the operation S230 described above, which will not be described in detail here.

[0173] The second obtaining module 840 is used to process the target parameter information according to the target model to obtain the thermal disturbance prediction value of the target building, wherein the target model is constructed by coupling the first model and the second model, and the second model is obtained by training the historical operation information, the historical environmental factor information and the time parameter information with the thermal disturbance prediction value as the label. In one embodiment, the second obtaining module 840 can be used to perform the operation S240 described above, which will not be repeated here.

[0174] The third obtaining module 850 is used to input the thermal disturbance prediction value of the target building into the first model to obtain the load prediction value of the target building. In one embodiment, the third obtaining module 850 can be used to perform the operation S250 described above, which will not be repeated here.

[0175] According to an embodiment of the present invention, the second obtaining module 840 includes: a first obtaining submodule, a second obtaining submodule, a third obtaining submodule and a fourth obtaining submodule.

[0176] The first obtaining submodule is used to couple the first model with the second model to obtain a target model.

[0177] The second obtaining submodule is used to determine the indoor temperature prediction value of the target time period in the target model according to the initial disturbance prediction value output by the second model.

[0178] The third submodule is used to determine the loss function value according to the predicted value of the indoor temperature in the target period and the actual value of the indoor temperature in the target period.

[0179] The fourth obtaining submodule is used to obtain the thermal disturbance prediction value of the target building when the loss function value is less than the preset loss function value.

[0180] According to an embodiment of the present invention, the third obtaining submodule includes: a first obtaining unit and a second obtaining unit.

[0181] A first obtaining unit is used to determine a target loss function according to a predicted value of indoor temperature in a target period and an actual value of indoor temperature in a target period;

[0182] The second obtaining unit is used to determine the loss function value according to the target loss function.

[0183] According to an embodiment of the present invention, the first model further includes: a constant parameter, and a first obtaining submodule includes: a third obtaining unit and a fourth obtaining unit.

[0184] A third obtaining unit is used to determine target parameter information according to the constant parameters of the first model;

[0185] The fourth obtaining unit is used to couple the first model and the second model according to the target parameter information and the initial disturbance prediction value to obtain the target model.

[0186] According to an embodiment of the present invention, historical operation information includes temperature information of the enclosure structure, temperature information of the upper and lower surfaces of the floor, temperature information of the lumped nodes of the embedded pipe plane, water supply temperature information, return water temperature information, and water flow and heating capacity; attribute parameter information includes thermal capacity of the enclosure structure, thermal capacity of indoor air, thermal capacity of the floor, thermal capacity of the embedded pipe, thermal resistance between outdoor air and the enclosure structure, thermal resistance between indoor air and the enclosure structure, thermal resistance of external windows, thermal resistance between embedded pipes and the floor, thermal resistance between embedded pipes and soil, thermal capacity of water, conversion coefficient of solar radiation and area of ​​external windows; historical environmental factor information includes indoor temperature information and outdoor temperature information; wherein, construction module 820 includes: a first construction submodule, a second construction submodule, a third construction submodule, a fourth construction submodule, a fifth construction submodule and a sixth construction submodule.

[0187] The first construction submodule is used to construct a first equilibrium equation according to the temperature information of the enclosure structure, the indoor temperature information, the heat capacity of the enclosure structure, the thermal resistance between the outdoor air and the enclosure structure, the thermal resistance between the indoor air and the enclosure structure, and the first process disturbance.

[0188] The second construction submodule is used to construct a second equilibrium equation based on the temperature information of the enclosing structure, the indoor temperature information, the outdoor temperature information, the temperature information on the floor surface, the heat capacity of the indoor air, the thermal resistance between the indoor air and the enclosing structure, the thermal resistance between the indoor air and the floor, the thermal resistance of the external window, the first thermal disturbance parameter and the second process disturbance.

[0189] The third construction submodule is used to construct a third equilibrium equation according to indoor temperature information, temperature information on the surface of the floor, temperature information of the lumped nodes of the embedded pipe plane, the area of ​​the external window, the conversion coefficient of solar radiation, the heat capacity of the floor, the thermal resistance between the indoor air and the floor, the thermal resistance between the embedded pipe and the floor, the second thermal disturbance parameter and the third process disturbance.

[0190] The fourth construction submodule is used to construct a fourth equilibrium equation according to the temperature information of the soil, the temperature information of the lumped nodes of the embedded pipe plane, the temperature information of the upper surface of the floor, the heat capacity of the embedded pipe, the thermal resistance between the embedded pipe and the floor, the thermal resistance between the embedded pipe and the soil, the heat supply and the fourth process disturbance.

[0191] The fifth construction submodule is used to construct a fifth balance equation according to the water supply temperature information, the return water temperature information, the heat capacity of water, the water flow rate and the heat supply.

[0192] The sixth construction submodule is used to construct the first model according to the first equilibrium equation, the second equilibrium equation, the third equilibrium equation, the fourth equilibrium equation and the fifth equilibrium equation.

[0193] According to an embodiment of the present invention, the first obtaining module 830 includes: a fifth obtaining submodule and a sixth obtaining submodule.

[0194] The fifth obtaining submodule is used to perform continuous-time processing on the variable parameters of the first model to obtain a continuous-time first model.

[0195] The sixth obtaining submodule is used to input historical energy supply information, attribute parameter information and historical environmental factor information into the continuous time first model to obtain target parameter information.

[0196] Based on the above-mentioned training method of the target model based on the building radiation energy supply system, the present invention also provides a training device based on the target model of the building radiation energy supply system. Fig. 9 The device is described in detail.

[0197] Fig. 9 A structural block diagram of a training device based on a target model of a building radiation energy supply system according to an embodiment of the present invention is shown.

[0198] like Fig. 9 As shown, the training device 900 based on the target model of the building radiation energy supply system of this embodiment includes a second acquisition module 910 , an output module 920 and a training module 930 .

[0199] The second acquisition module 910 is used to acquire training samples, which include sample historical operation information, sample attribute parameter information, sample historical environmental factor information, and sample disturbance parameter information. In one embodiment, the second acquisition module 910 can be used to perform the operation S710 described above, which will not be repeated here.

[0200] The output module 920 is used to process the sample historical operation information, the sample attribute parameter information, the sample historical environmental factor information and the sample disturbance parameter information using the target model, and output the target parameter information. In one embodiment, the output module 920 can be used to perform the operation S720 described above, which will not be repeated here.

[0201] The training module 930 is used to train the target model using a feedforward neural network to obtain a trained target model when the target parameter information is determined. In one embodiment, the training module 930 can be used to perform the operation S730 described above, which will not be described in detail here.

[0202] According to an embodiment of the present invention, any multiple modules among the modules, submodules, units, and subunits can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present invention, at least one of the modules, submodules, units, and subunits can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, at least one of the modules, submodules, units, and subunits can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be executed.

[0203] Fig.10 A block diagram of an electronic device suitable for implementing a building load prediction method considering random thermal disturbances according to an embodiment of the present invention is shown.

[0204] like Fig.10 As shown, the electronic device 1000 according to an embodiment of the present invention includes a processor 1001, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1002 or the program loaded from the storage part 1008 to the RAM (Random Access Memory). The processor 1001 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 1001 may also include an onboard memory for caching purposes. The processor 1001 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0205] In RAM 1003, various programs and data required for the operation of electronic device 1000 are stored. Processor 1001, ROM 1002 and RAM 1003 are connected to each other via bus 1004. Processor 1001 performs various operations of the method flow according to the embodiment of the present invention by executing the program in ROM 1002 and / or RAM 1003. It should be noted that the program can also be stored in one or more memories other than ROM 1002 and RAM 1003. Processor 1001 can also perform various operations of the method flow according to the embodiment of the present invention by executing the program stored in the one or more memories.

[0206] According to an embodiment of the present invention, the electronic device 1000 may further include an input / output (I / O) interface 1005, which is also connected to the bus 1004. The electronic device 1000 may further include one or more of the following components connected to the I / O interface 1005: an input portion 1006 including a keyboard, a mouse, etc.; an output portion 1007 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 1008 including a hard disk, etc.; and a communication portion 1009 including a network interface card such as a LAN card, a modem, etc. The communication portion 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the I / O interface 1005 as needed. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1010 as needed, so that a computer program read therefrom is installed into the storage portion 1008 as needed.

[0207] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiment; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the above method according to the embodiment of the present invention is implemented.

[0208] According to an embodiment of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: a portable computer disk, a hard disk, a RAM (Random Access Memory), a ROM (Read Only Memory), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present invention, the computer-readable storage medium may include the ROM 1002 and / or RAM 1003 described above and / or one or more memories other than ROM 1002 and RAM 1003.

[0209] The embodiment of the present invention also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the building load prediction method considering random thermal disturbances provided by the embodiment of the present invention.

[0210] The computer program executes the above functions defined in the system / device of the embodiment of the present invention when it is executed by the processor 1001. According to the embodiment of the present invention, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0211] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 1009, and / or installed from the removable medium 1011. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0212] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 1009, and / or installed from the removable medium 1011. When the computer program is executed by the processor 1001, the above functions defined in the system of the embodiment of the present invention are performed. According to the embodiment of the present invention, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0213] According to an embodiment of the present invention, the program code for executing the computer program provided by the embodiment of the present invention can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0214] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0215] It will be appreciated by those skilled in the art that the features described in the various embodiments of the present invention may be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in the present invention. In particular, without departing from the spirit and teachings of the present invention, the features described in the various embodiments of the present invention may be combined and / or combined in various ways. All of these combinations and / or combinations fall within the scope of the present invention.

[0216] The embodiments of the present invention are described above. However, these embodiments are only for the purpose of illustration, and are not intended to limit the scope of the present invention. Although each embodiment is described above, it does not mean that the measures in each embodiment cannot be used in combination advantageously. Without departing from the scope of the present invention, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

Claims

1. A building load prediction method considering random thermal disturbances, characterized in that: The method comprises: In response to receiving a building load forecast demand for a target period, acquiring historical operation information, attribute parameter information, and historical environmental factor information of a building radiation energy supply system for a target building corresponding to the target period; Constructing a first model according to the historical operation information, the attribute parameter information and the historical environmental factor information, wherein the first model includes variable parameters; Performing continuous time processing on the variable parameters of the first model to obtain target parameter information; Processing the target parameter information according to the target model to obtain the thermal disturbance prediction value of the target building, wherein the target model is constructed by coupling the first model and the second model, and the second model is obtained by training the initial model using the thermal disturbance prediction value as a label and using sample historical operation information, sample historical environmental factor information and sample time parameter information; The thermal disturbance prediction value of the target building is input into the first model to obtain the load prediction value of the target building.

2. The method according to claim 1, characterized in that The step of processing the target parameter information according to the target model to obtain the thermal disturbance prediction value of the target building includes: coupling the first model with the second model to obtain the target model; In the target model, determining the indoor temperature prediction value of the target period according to the initial disturbance prediction value output by the second model; Determining a loss function value according to the predicted value of the indoor temperature during the target period and the actual value of the indoor temperature during the target period; When the loss function value is less than a preset loss function value, a thermal disturbance prediction value of the target building is obtained.

3. The method according to claim 2, characterized in that The step of determining the loss function value according to the predicted value of the indoor temperature in the target time period and the actual value of the indoor temperature in the target time period comprises: Determining a target loss function according to the predicted value of the indoor temperature during the target period and the actual value of the indoor temperature during the target period; According to the target loss function, the loss function value is determined.

4. The method according to claim 2, characterized in that: The first model further includes: a constant parameter, wherein coupling the first model with the second model to obtain the target model includes: Determining the target parameter information according to the constant parameters of the first model and the variable parameters; According to the target parameter information and the initial disturbance prediction value, the first model and the second model are coupled to obtain a target model.

5. The method according to claim 1, characterized in that: The historical operation information includes the temperature information of the enclosure structure, the temperature information of the upper surface of the floor, the temperature information of the lumped nodes of the embedded pipe plane, the water supply temperature information, the return water temperature information, and the water flow and heating supply; the attribute parameter information includes the heat capacity of the enclosure structure, the heat capacity of the indoor air, the heat capacity of the floor, the heat capacity of the embedded pipe, the thermal resistance between the outdoor air and the enclosure structure, the thermal resistance between the indoor air and the enclosure structure, the thermal resistance of the external window, the thermal resistance between the embedded pipe and the floor, the thermal resistance between the embedded pipe and the soil, the heat capacity of water, the conversion coefficient of solar radiation and the area of ​​the external window; the historical environmental factor information includes the indoor temperature information and the outdoor temperature information; The step of constructing a first model according to the historical operation information, the attribute parameter information and the historical environmental factor information includes: Constructing a first equilibrium equation according to the temperature information of the enclosure structure, the indoor temperature information, the heat capacity of the enclosure structure, the thermal resistance between the outdoor air and the enclosure structure, the thermal resistance between the indoor air and the enclosure structure, and the first process disturbance; Constructing a second equilibrium equation according to the temperature information of the enclosure structure, the indoor temperature information, the outdoor temperature information, the temperature information of the floor surface, the heat capacity of the indoor air, the thermal resistance between the indoor air and the enclosure structure, the thermal resistance between the indoor air and the floor, the thermal resistance of the external window, the first thermal disturbance parameter and the second process disturbance; A third equilibrium equation is constructed according to the indoor temperature information, the temperature information of the upper surface of the floor, the temperature information of the lumped node of the embedded pipe plane, the area of ​​the external window, the conversion coefficient of the solar radiation, the heat capacity of the floor, the thermal resistance between the indoor air and the floor, the thermal resistance between the embedded pipe and the floor, the second thermal disturbance parameter and the third process disturbance; Constructing a fourth equilibrium equation according to the temperature information of the soil, the temperature information of the lumped nodes of the embedded pipe plane, the temperature information of the upper surface of the floor, the heat capacity of the embedded pipe, the thermal resistance between the embedded pipe and the floor, the thermal resistance between the embedded pipe and the soil, the heat supply and the fourth process disturbance; constructing a fifth balance equation according to the water supply temperature information, the return water temperature information, the heat capacity of the water, the water flow rate and the heat supply; The first model is constructed according to the first equilibrium equation, the second equilibrium equation, the third equilibrium equation, the fourth equilibrium equation and the fifth equilibrium equation.

6. The method according to claim 1, characterized in that The step of performing continuous time processing on the variable parameters of the first model to obtain target parameter information includes: Performing continuous-time processing on the variable parameters of the first model to obtain a continuous-time first model; The historical energy supply information, the attribute parameter information and the historical environmental factor information are input into the continuous time first model to obtain the target parameter information.

7. A training method for a target model based on a building radiation energy supply system, characterized in that: The method comprises: Acquire a training sample, wherein the training sample includes sample historical operation information, sample attribute parameter information, sample historical environmental factor information, and sample disturbance parameter information; Processing the sample historical operation information, the sample attribute parameter information, the sample historical environmental factor information and the sample disturbance parameter information using the target model, and outputting target parameter information; When the target parameter information is determined, the target model is trained using a feedforward neural network to obtain a trained target model.

8. A building load prediction system considering random thermal disturbances, characterized in that: The system comprises: A first acquisition module is used to, in response to receiving a building load forecast demand for a target period, acquire historical operation information, attribute parameter information and historical environmental factor information of a building radiation energy supply system for a target building corresponding to the target period; A construction module, configured to construct a first model according to the historical operation information, the attribute parameter information and the historical environmental factor information, wherein the first model includes variable parameters; A first obtaining module is used to perform continuous time processing on the variable parameters of the first model to obtain target parameter information; A second obtaining module is used to process the target parameter information according to a target model to obtain a thermal disturbance prediction value of the target building, wherein the target model is constructed by coupling the first model and the second model, and the second model is obtained by training the historical operation information, the historical environmental factor information and the time parameter information with the thermal disturbance prediction value as a label; The third obtaining module is used to input the thermal disturbance prediction value of the target building into the first model to obtain the load prediction value of the target building.

9. A training device based on a target model of a building radiation energy supply system, characterized in that: The device comprises: A second acquisition module is used to acquire training samples, wherein the training samples include sample historical operation information, sample attribute parameter information, sample historical environmental factor information and sample disturbance parameter information; An output module, used to process the sample historical operation information, the sample attribute parameter information, the sample historical environmental factor information and the sample disturbance parameter information using the target model, and output target parameter information; The training module is used to train the target model using a feedforward neural network to obtain a trained target model when the target parameter information is determined.

10. An electronic device, comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 6 or 7.

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