Method, device and system for site selection of thermal power plant based on carbon footprint

Abstract

The present disclosure is about a method, device and system for selecting a thermal power plant site based on carbon footprint. The method includes: obtaining historical climate data of each candidate address; for each candidate address, using the historical climate data of the candidate address to predict climate forecast data within N years; obtaining target building parameters of the thermal power plant; the target building parameters are attribute parameters associated with buildings in the thermal power plant and carbon emissions; for each candidate address, inputting climate forecast data and target building parameters into a carbon footprint coupling model to obtain carbon emissions calculated by the carbon footprint coupling model based on climate forecast data and target building parameters; selecting the candidate address with the smallest carbon emissions to obtain the target plant address of the thermal power plant. This solution reduces the carbon emissions of thermal power plants.

Description

Technical Field

[0001] The present disclosure relates to the field of carbon emission technology, and in particular to a method, device and system for selecting a site for a thermal power plant based on carbon footprint. Background Art

[0002] The traditional site selection process for thermal power plants takes into account factors such as power development planning, transportation, water resources, and economic rationality. However, since thermal power plants will produce carbon emissions during their construction and operation, and the carbon emissions of thermal power plants are difficult to estimate, carbon emissions cannot be considered as a factor in site selection. Summary of the invention

[0003] In order to overcome the problems existing in the related art, the present disclosure provides a method, device and system for site selection of a thermal power plant based on carbon footprint.

[0004] According to a first aspect of an embodiment of the present disclosure, a method for selecting a site for a thermal power plant based on carbon footprint is provided, comprising:

[0005] Obtain historical climate data for each candidate address; wherein the candidate address is a candidate construction site for a thermal power plant;

[0006] For each candidate address, using the historical climate data of the candidate address, predict the climate forecast data within N years; the climate forecast data is the climate data associated with the carbon emissions of the thermal power plant; N is the expected life of the thermal power plant; N is an integer greater than 1;

[0007] Acquire target building parameters of the thermal power plant; the target building parameters are attribute parameters associated with buildings in the thermal power plant and carbon emissions;

[0008] For each candidate address, input the climate prediction data and the target building parameters into a carbon footprint coupling model to obtain carbon emissions calculated by the carbon footprint coupling model based on the climate prediction data and the target building parameters;

[0009] Select the candidate address with the smallest carbon emissions to obtain the target site for the thermal power plant.

[0010] In some embodiments of the present disclosure, the historical climate data includes any one or more of outdoor wind speed, outdoor temperature, and solar radiation data; the climate prediction data includes any one or more of outdoor wind speed, outdoor temperature, and solar radiation data; and the method of using the historical climate data of the candidate address to predict the climate prediction data within N years includes:

[0011] For each type of historical climate data, K time series are generated using the historical climate data; the historical climate data in each time series is the climate data of the same year; the historical climate data is the climate data within K years; K is an integer greater than 1;

[0012] For each of the K time series, the time series is divided into a plurality of subsequences according to a preset M period; M is an integer greater than 1;

[0013] Selecting a subsequence corresponding to the same preset period in each time series to obtain K subsequences; the preset period is any one of the M preset periods;

[0014] Predicting the climate prediction sub-data of the same preset period within N years based on the K sub-sequences;

[0015] The climate prediction sub-data of different preset periods corresponding to each year within the N years are spliced ​​in chronological order to obtain the climate prediction data within the N years.

[0016] In some embodiments of the present disclosure, the preset period includes multiple time nodes, each time node corresponds to a historical climate data; the predicting of the climate forecast sub-data of the same preset period within N years based on the K sub-sequences includes:

[0017] Selecting K historical climate data corresponding to the same time node in the K subsequences;

[0018] Generate a first trend chart according to the corresponding years using the K historical climate data;

[0019] Using a curve analysis algorithm, based on the first trend graph, predict the climate forecast sub-data at the same time node within the N years;

[0020] The climate forecast sub-data belonging to the same year are spliced ​​together to obtain the climate forecast sub-data of the same preset period within the N years.

[0021] In some embodiments of the present disclosure, the target building parameters may include any one or more of the following:

[0022] Convective heat transfer coefficient, radiation heat transfer coefficient between indoor surfaces, outer surface temperature of the envelope structure, indoor and outdoor air temperature, water vapor migration on the inner surface of the envelope structure, latent heat of evaporation, indoor fresh air volume, indoor air infiltration, time change rate of air infiltration, and heat generated by people and equipment in the room.

[0023] In some embodiments of the present disclosure, the step of inputting the climate prediction data and the target building parameters into a carbon footprint coupling model to obtain carbon emissions calculated by the carbon footprint coupling model based on the climate prediction data and the target building parameters includes:

[0024] For each year within the N years, determine the building energy consumption according to the climate prediction data and the target building parameters, calculate the initial carbon emissions using the building energy consumption and the carbon emission factor that matches the building energy consumption, and subtract the initial carbon emissions from the carbon reduction amount corresponding to the target building green space of the thermal power plant to obtain a first difference;

[0025] The first difference values ​​corresponding to the N years are added together to obtain the carbon emission amount.

[0026] According to a second aspect of an embodiment of the present disclosure, a device for selecting a site for a thermal power plant based on carbon footprint is provided, comprising:

[0027] An acquisition module is used to acquire historical climate data of each candidate address; wherein the candidate address is a candidate construction address of a thermal power plant;

[0028] A prediction module, for each candidate address, using the historical climate data of the candidate address to predict climate prediction data within N years; the climate prediction data is climate data associated with the carbon emissions of the thermal power plant; N is the expected life of the thermal power plant; N is an integer greater than 1;

[0029] The acquisition module is further used to acquire target building parameters of the thermal power plant; the target building parameters are attribute parameters associated with carbon emissions of buildings in the thermal power plant;

[0030] A calculation module, for inputting the climate prediction data and the target building parameters into a carbon footprint coupling model for each candidate address, to obtain carbon emissions calculated by the carbon footprint coupling model based on the climate prediction data and the target building parameters;

[0031] The selection module is used to select the candidate address with the smallest carbon emission and obtain the target site for building the thermal power plant.

[0032] In some embodiments of the present disclosure, the selection is specifically used for:

[0033] For each year within the N years, determine the building energy consumption according to the climate prediction data and the target building parameters, calculate the initial carbon emissions using the building energy consumption and the carbon emission factor that matches the building energy consumption, and subtract the initial carbon emissions from the carbon reduction amount corresponding to the target building green space of the thermal power plant to obtain a first difference;

[0034] The first difference values ​​corresponding to the N years are added together to obtain the carbon emission amount.

[0035] According to a third aspect of an embodiment of the present disclosure, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described in any one of the first aspects is implemented.

[0036] According to a fourth aspect of an embodiment of the present disclosure, there is provided a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the method according to any one of the first aspects is implemented.

[0037] According to a fifth aspect of an embodiment of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program implements the method as described in any one of the first aspects when executed by a processor.

[0038] The technical solution provided by the embodiments of the present disclosure may include the following beneficial effects: by obtaining the historical climate data of each candidate address; for each candidate address, using the historical climate data of the candidate address to predict the climate prediction data within N years; obtaining the target building parameters of the thermal power plant; the target building parameters are the attribute parameters associated with the building in the thermal power plant and carbon emissions; for each candidate address, inputting the climate prediction data and the target building parameters into the carbon footprint coupling model to obtain the carbon emissions calculated by the carbon footprint coupling model based on the climate prediction data and the target building parameters; selecting the candidate address with the smallest carbon emissions to obtain the target plant address of the thermal power plant. By performing climate prediction on the candidate address, the carbon emissions during the expected life of the thermal power plant are determined using the climate prediction data obtained by the climate prediction and the target building parameters, thereby selecting the candidate address with the lowest carbon emissions as the target plant address, effectively reducing the carbon emissions of the thermal power plant.

[0039] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0041] Figure 1 The present invention is a flow chart showing a method for selecting a thermal power plant site based on carbon footprint according to an exemplary embodiment.

[0042] Figure 2 The invention is a block diagram of a device for selecting a thermal power plant site based on carbon footprint according to an exemplary embodiment.

[0043] Figure 3It is a block diagram of an apparatus for a method for selecting a thermal power plant site based on carbon footprint according to an exemplary embodiment. DETAILED DESCRIPTION

[0044] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0045] The terms used in the disclosed embodiments are only for the purpose of describing specific embodiments, and are not intended to limit the disclosed embodiments. The singular forms "a", "an" and "the" used in the disclosed embodiments and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0046] It should be understood that although the terms first, second, third, etc. may be used to describe various information in the disclosed embodiments, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the disclosed embodiments, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at" or "when" or "in response to determination".

[0047] In addition, various forms of processes shown in the embodiments of the present disclosure may be used to reorder, add or delete steps. For example, the steps described in the present application may be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present disclosure can be achieved, and this document does not limit this.

[0048] In related technologies, as the global climate change problem becomes increasingly serious, carbon footprint, as an important indicator for measuring greenhouse gas emissions, has received extensive attention. However, the traditional site selection process for thermal power plants takes into account factors such as power development planning, transportation, water sources, and economic rationality. However, since thermal power plants will generate carbon emissions during construction and operation, and the carbon emissions of thermal power plants are difficult to estimate, carbon emissions cannot be considered as a factor in site selection.

[0049] In order to solve the above problems, the present disclosure provides a method, device and system for selecting a thermal power plant site based on carbon footprint, by obtaining the historical climate data of each candidate address; wherein the candidate address is the candidate site for the thermal power plant; for each candidate address, using the historical climate data of the candidate address, predicting the climate prediction data within N years; obtaining the target building parameters of the thermal power plant; the target building parameters are the attribute parameters associated with the building in the thermal power plant and carbon emissions; for each candidate address, inputting the climate prediction data and the target building parameters into the carbon footprint coupling model, obtaining the carbon emissions calculated by the carbon footprint coupling model based on the climate prediction data and the target building parameters; selecting the candidate address with the smallest carbon emissions, and obtaining the target site for the thermal power plant. By performing climate prediction on the candidate address, the carbon emissions during the expected life of the thermal power plant are determined using the climate prediction data and the target building parameters obtained by the climate prediction, thereby selecting the candidate address with the lowest carbon emissions as the target site for the thermal power plant, and effectively reducing the carbon emissions of the thermal power plant.

[0050] Figure 1 is a flow chart showing a method for selecting a thermal power plant site based on carbon footprint according to an exemplary embodiment. Figure 1 As shown, it should be noted that the method for selecting a thermal power plant site based on carbon footprint according to the embodiment of the present disclosure is applied to a thermal power plant site selection device based on carbon footprint. Figure 1 As shown, the method may include the following steps:

[0051] Step 101, obtaining historical climate data for each candidate address.

[0052] Among them, the candidate address is the candidate construction site of the thermal power plant.

[0053] In the disclosed embodiment, the historical climate data may be the climate data of the candidate address K years before. In order to better perform climate prediction, historical climate data of as many years as possible may be obtained.

[0054] It should be noted that the above historical climate data are climate data related to carbon emissions, such as historical outdoor wind speed, historical outdoor temperature, and historical solar radiation data.

[0055] Step 102: For each candidate address, use the historical climate data of the candidate address to predict the climate forecast data within N years.

[0056] In the embodiment of the present disclosure, the climate prediction data is climate data associated with carbon emissions from a thermal power plant; N is the expected life span of the thermal power plant; and N is an integer greater than 1.

[0057] It is understandable that the climate conditions at the same time each year vary, and there is a certain regularity in them. Therefore, the historical climate data of the candidate address can be used to predict the climate forecast data within N years.

[0058] In some embodiments of the present disclosure, the historical climate data includes any one or more of outdoor wind speed, outdoor temperature, and solar radiation data; the climate forecast data includes any one or more of outdoor wind speed, outdoor temperature, and solar radiation data. Step 102 may specifically include the following steps:

[0059] Step a1: for each type of historical climate data, generate K time series using the historical climate data.

[0060] Among them, the historical climate data in each time series are the climate data of the same year; the historical climate data are the climate data within K years; K is an integer greater than 1.

[0061] It is understandable that climate conditions change periodically according to the year. Therefore, the historical climate data can be divided according to the year to obtain the time series corresponding to each year in K years, that is, the historical climate data is stored in the time series according to the order of collection time.

[0062] Step a2: for each of the K time series, divide the time series into a plurality of subsequences according to the preset M periods.

[0063] Wherein, M is an integer greater than 1.

[0064] It is understandable that in the same year, different times (ie, different seasons) have different climate characteristics. Therefore, the time series can be divided according to the time periods corresponding to different climate characteristics to obtain multiple subsequences.

[0065] Step a3: select the subsequences corresponding to the same preset period in each time series to obtain K subsequences.

[0066] The preset period is any one of the M preset periods.

[0067] In one embodiment, subsequences corresponding to the same time period (ie, the same preset period) within K years are aggregated to obtain K subsequences.

[0068] Step a4: predicting the climate prediction sub-data of the same preset period within N years based on the K sub-sequences.

[0069] It can be understood that K subsequences corresponding to the same climate characteristics are used to determine the climate change trend in the corresponding period, so as to predict the climate forecast sub-data of the same preset period in the next N years based on the change trend, so as to accurately predict the climate and improve the accuracy of carbon emission calculation.

[0070] Step a5, splicing the climate prediction sub-data of different preset periods corresponding to each year within N years in chronological order to obtain the climate prediction data within N years.

[0071] In one embodiment, after obtaining the climate prediction sub-data of different preset periods corresponding to each year within N years, it is necessary to splice the climate prediction sub-data of multiple preset periods for each year within N years to obtain complete climate prediction data for each year.

[0072] In some embodiments of the present disclosure, the preset period includes multiple time nodes, each time node corresponds to a historical climate data, and step a4 may specifically include the following steps:

[0073] Step a41, selecting K historical climate data corresponding to the same time node in the K subsequences.

[0074] Step a42, using K historical climate data to generate a first trend chart according to the corresponding years.

[0075] Step a43, using a curve analysis algorithm to predict the climate forecast sub-data at the same time node within N years based on the first trend graph.

[0076] Step a44, concatenating the climate forecast sub-data belonging to the same year to obtain the climate forecast sub-data of the same preset period within N years.

[0077] In one embodiment, in order to further accurately determine the changing trend of climate data, the climate data of the same time node within N years can be used to generate a first trend graph including the mapping relationship between climate data and time. The climate forecast sub-data of the same time node within N years can be predicted based on the first trend graph using a curve analysis algorithm. The climate forecast sub-data of the same time node within N years are then spliced ​​together to obtain the climate forecast sub-data of the same preset period within N years.

[0078] Step 103, obtaining target building parameters of the thermal power plant.

[0079] Among them, the target building parameters are the attribute parameters associated with buildings in thermal power plants and carbon emissions.

[0080] In some embodiments of the present disclosure, the target building parameters may include any one or more of the following:

[0081] Convective heat transfer coefficient, radiation heat transfer coefficient between indoor surfaces, outer surface temperature of the envelope structure, indoor and outdoor air temperature, water vapor migration on the inner surface of the envelope structure, latent heat of evaporation, indoor fresh air volume, indoor air infiltration, time change rate of air infiltration, and heat generated by people and equipment in the room.

[0082] Step 104 , for each candidate address, input the climate prediction data and the target building parameters into the carbon footprint coupling model to obtain the carbon emissions calculated by the carbon footprint coupling model based on the climate prediction data and the target building parameters.

[0083] In one embodiment, the carbon footprint coupling model is a mathematical model that couples building energy consumption, climate factors and carbon footprint (ie, carbon emissions) to calculate the carbon emissions of the thermal power plant building.

[0084] In some embodiments of the present disclosure, step 104 may specifically include the following steps:

[0085] For each year within N years, determine the building energy consumption based on the climate forecast data and the target building parameters, calculate the initial carbon emissions using the carbon emission factor that matches the building energy consumption, and subtract the initial carbon emissions from the carbon reduction corresponding to the target building green space of the thermal power plant to obtain the first difference;

[0086] Add up the first differences corresponding to each of the N years to obtain the carbon emissions.

[0087] It should be noted that the carbon emission factor is used to estimate the amount of carbon dioxide emitted in a specific activity or process. This coefficient is usually expressed in terms of unit activity (such as per kilogram of fuel, per kilowatt-hour of electricity, or per ton of product produced, etc.).

[0088] In addition, thermal power plant buildings usually consist of the following parts: main plant area (main plant, dust removal device, induced draft machine site and chimney flue, etc.), high-voltage distribution device area, fuel storage and transportation area, water supply and drainage facilities area, chemical water treatment area, auxiliary production and ancillary facilities area, management and living area, environmental protection facilities area and building green space. Buildings off the ground can play a role in reducing carbon emissions.

[0089] Therefore, after obtaining the initial carbon emissions at the extreme, it is also necessary to subtract the carbon reduction corresponding to the target building green space of the thermal power plant (that is, the carbon reduction that can be achieved by the planned building green space) to improve the accuracy of the calculation results.

[0090] It can be understood that the above first difference is the predicted carbon emissions of the thermal power plant due to the building itself in the corresponding year.

[0091] In one embodiment, the building energy consumption can be calculated by the following formula:

[0092]

[0093] Among them, E m is the building energy consumption of thermal power plants (kWh); h m0 、h c0 is the convection heat transfer coefficient of the inner and outer surfaces of the building envelope of the thermal power plant (W / m2 ℃); h r,j is the radiation heat transfer coefficient between the indoor surfaces of the thermal power plant building (W / m 2 ℃); T x＝0 is the outer surface temperature of the enclosure structure (℃) and is related to the solar radiation ψ received by the building surface of the thermal power plant; T n , T0 is the indoor and outdoor air temperature of the thermal power plant building (℃); m v,x =0 is the amount of water vapor migration on the inner surface of the enclosure structure (kg); L(T) is the latent heat of evaporation (J / kg); W is the outdoor wind speed of the thermal power plant building (m / s); V is the indoor fresh air volume of the thermal power plant building (Vm 3 / h); I is the indoor air permeability of thermal power plant buildings (Vm 3 / h); F sch is the air penetration time change rate (%); q int Heat generated by people and equipment in the room (W)

[0094] Step 105, selecting a candidate address with the smallest carbon emission to obtain a target site for building a thermal power plant.

[0095] It is understandable that, when there are multiple optional candidate addresses, selecting the candidate address with the lowest carbon emissions as the factory construction address can reduce carbon emissions.

[0096] According to the method for selecting a thermal power plant site based on carbon footprint proposed in the embodiment of the present disclosure, the historical climate data of each candidate address is obtained; wherein the candidate address is the candidate construction site of the thermal power plant; for each candidate address, the historical climate data of the candidate address is used to predict the climate prediction data within N years; the target building parameters of the thermal power plant are obtained; the target building parameters are the attribute parameters associated with the building in the thermal power plant and carbon emissions; for each candidate address, the climate prediction data and the target building parameters are input into the carbon footprint coupling model to obtain the carbon emissions calculated by the carbon footprint coupling model based on the climate prediction data and the target building parameters; the candidate address with the smallest carbon emissions is selected to obtain the target construction site of the thermal power plant. By performing climate prediction on the candidate address, the carbon emissions during the expected life of the thermal power plant are determined using the climate prediction data obtained by the climate prediction and the target building parameters, thereby selecting the candidate address with the lowest carbon emissions as the target construction site, which effectively reduces the carbon emissions of the thermal power plant.

[0097] Figure 2 is a block diagram of a thermal power plant site selection device based on carbon footprint according to an exemplary embodiment. Figure 2 The device includes an acquisition module 201, a prediction module 202, an acquisition module 203, a calculation module 204 and a selection module 205.

[0098] The acquisition module 201 is used to acquire the historical climate data of each candidate address; wherein the candidate address is a candidate construction site of a thermal power plant;

[0099] The prediction module 202 is used to predict the climate prediction data within N years for each candidate address by using the historical climate data of the candidate address; the climate prediction data is the climate data associated with the carbon emissions of the thermal power plant; N is the expected life of the thermal power plant; N is an integer greater than 1;

[0100] The acquisition module 203 is also used to acquire target building parameters of the thermal power plant; the target building parameters are attribute parameters associated with carbon emissions in the thermal power plant;

[0101] A calculation module 204 is used to input the climate prediction data and the target building parameters into the carbon footprint coupling model for each candidate address, and obtain the carbon emissions calculated by the carbon footprint coupling model based on the climate prediction data and the target building parameters;

[0102] The selection module 205 is used to select a candidate address with the smallest carbon emission to obtain a target site for building a thermal power plant.

[0103] In some embodiments of the present disclosure, the historical climate data includes any one or more of outdoor wind speed, outdoor temperature, and solar radiation data; the climate prediction data includes any one or more of outdoor wind speed, outdoor temperature, and solar radiation data. The prediction module 202 can be specifically used for:

[0104] For each type of historical climate data, K time series are generated using the historical climate data; the historical climate data in each time series is the climate data of the same year; the historical climate data is the climate data within K years; K is an integer greater than 1;

[0105] For each of the K time series, the time series is divided into a plurality of subsequences according to a preset M period; M is an integer greater than 1;

[0106] Select a subsequence corresponding to the same preset period in each time series to obtain K subsequences; the preset period is any one of the M preset periods;

[0107] Predict the climate prediction sub-data of the same preset period within N years based on K sub-sequences;

[0108] The climate prediction sub-data of different preset periods corresponding to each year within N years are spliced ​​in chronological order to obtain the climate prediction data within N years.

[0109] In some embodiments of the present disclosure, the preset period includes multiple time nodes, each time node corresponds to a historical climate data, and the prediction module 202 can also be used to:

[0110] Select K historical climate data corresponding to the same time node in the K subsequences;

[0111] Generate a first trend chart according to the corresponding years using K historical climate data;

[0112] Using the curve analysis algorithm, the climate prediction sub-data at the same time node within N years are predicted based on the first trend chart;

[0113] The climate forecast sub-data belonging to the same year are spliced ​​together to obtain the climate forecast sub-data of the same preset period within N years.

[0114] In some embodiments of the present disclosure, the target building parameters may include any one or more of the following:

[0115] Convective heat transfer coefficient, radiation heat transfer coefficient between indoor surfaces, outer surface temperature of the envelope structure, indoor and outdoor air temperature, water vapor migration on the inner surface of the envelope structure, latent heat of evaporation, indoor fresh air volume, indoor air infiltration, time change rate of air infiltration, and heat generated by people and equipment in the room.

[0116] In some embodiments of the present disclosure, the calculation module 204 may be specifically used to:

[0117] For each year within N years, determine the building energy consumption based on the climate forecast data and the target building parameters, calculate the initial carbon emissions using the carbon emission factor that matches the building energy consumption, and subtract the initial carbon emissions from the carbon reduction corresponding to the target building green space of the thermal power plant to obtain the first difference;

[0118] Add up the first differences corresponding to each of the N years to obtain the carbon emissions.

[0119] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0120] According to the thermal power plant site selection device based on carbon footprint proposed in the embodiment of the present disclosure, the historical climate data of each candidate address is obtained; wherein the candidate address is the candidate construction site of the thermal power plant; for each candidate address, the historical climate data of the candidate address is used to predict the climate prediction data within N years; the target building parameters of the thermal power plant are obtained; the target building parameters are the attribute parameters associated with the building in the thermal power plant and carbon emissions; for each candidate address, the climate prediction data and the target building parameters are input into the carbon footprint coupling model to obtain the carbon emissions calculated by the carbon footprint coupling model based on the climate prediction data and the target building parameters; the candidate address with the smallest carbon emissions is selected to obtain the target construction site of the thermal power plant. By performing climate prediction on the candidate address, the carbon emissions during the expected life of the thermal power plant are determined using the climate prediction data obtained by the climate prediction and the target building parameters, so that the candidate address with the lowest carbon emissions is selected as the target construction site, which effectively reduces the carbon emissions of the thermal power plant.

[0121] Figure 3 3 is a block diagram of a device for selecting a thermal power plant site based on carbon footprint according to an exemplary embodiment. For example, the device 300 may be an electronic device, such as a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0122] Reference Figure 3 , the device 300 may include one or more of the following components: a processing component 302 , a memory 304 , a power component 306 , a multimedia component 308 , an audio component 310 , an input / output (I / O) interface 312 , a sensor component 314 , and a communication component 316 .

[0123] The processing component 302 generally controls the overall operation of the device 300, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 302 may include one or more processors 320 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 302 may include one or more modules to facilitate the interaction between the processing component 302 and other components. For example, the processing component 302 may include a multimedia module to facilitate the interaction between the multimedia component 308 and the processing component 302.

[0124] The memory 304 is configured to store various types of data to support operations on the device 300. Examples of such data include instructions for any application or method operating on the device 300, contact data, phone book data, messages, pictures, videos, etc. The memory 304 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0125] The power component 306 provides power to the various components of the device 300. The power component 306 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 300.

[0126] The multimedia component 308 includes a screen that provides an output interface between the device 300 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 308 includes a front camera and / or a rear camera. When the device 300 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0127] The audio component 310 is configured to output and / or input audio signals. For example, the audio component 310 includes a microphone (MIC), and when the device 300 is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 304 or sent via the communication component 316. In some embodiments, the audio component 310 also includes a speaker for outputting audio signals.

[0128] I / O interface 312 provides an interface between processing component 302 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0129] The sensor assembly 314 includes one or more sensors for providing various aspects of status assessment for the device 300. For example, the sensor assembly 314 can detect the open / closed state of the device 300, the relative positioning of components, such as the display and keypad of the device 300, the sensor assembly 314 can also detect the position change of the device 300 or a component of the device 300, the presence or absence of user contact with the device 300, the orientation or acceleration / deceleration of the device 300, and the temperature change of the device 300. The sensor assembly 314 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 314 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 314 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0130] The communication component 316 is configured to facilitate wired or wireless communication between the device 300 and other devices. The device 300 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 316 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 316 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0131] In an exemplary embodiment, the apparatus 300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0132] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 304 including instructions, and the instructions can be executed by the processor 320 of the device 300 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0133] In an exemplary embodiment, a computer program product is also provided, comprising a computer program, which implements the above method when executed by the processor 320 of the device 300 .

[0134] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered exemplary only, and the true scope and spirit of the present invention are indicated by the following claims.

[0135] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for selecting a thermal power plant site based on carbon footprint, characterized in that: include: Obtain historical climate data for each candidate address; wherein the candidate address is a candidate construction site for a thermal power plant; For each candidate address, using the historical climate data of the candidate address, predict the climate forecast data within N years; the climate forecast data is the climate data associated with the carbon emissions of the thermal power plant; N is the expected life of the thermal power plant; N is an integer greater than 1; Acquire target building parameters of the thermal power plant; the target building parameters are attribute parameters associated with buildings in the thermal power plant and carbon emissions; For each candidate address, input the climate prediction data and the target building parameters into a carbon footprint coupling model to obtain carbon emissions calculated by the carbon footprint coupling model based on the climate prediction data and the target building parameters; Select the candidate address with the smallest carbon emissions to obtain the target site for the thermal power plant.

2. The method for selecting a thermal power plant site based on carbon footprint according to claim 1, characterized in that: The historical climate data includes any one or more of outdoor wind speed, outdoor temperature, and solar radiation data; the climate forecast data includes any one or more of outdoor wind speed, outdoor temperature, and solar radiation data; The method of using the historical climate data of the candidate address to predict climate prediction data within N years includes: For each type of historical climate data, K time series are generated using the historical climate data; the historical climate data in each time series is the climate data of the same year; the historical climate data is the climate data within K years; K is an integer greater than 1; For each of the K time series, the time series is divided into a plurality of subsequences according to a preset M period; M is an integer greater than 1; Selecting a subsequence corresponding to the same preset period in each time series to obtain K subsequences; the preset period is any one of the M preset periods; Predicting the climate prediction sub-data of the same preset period within N years based on the K sub-sequences; The climate prediction sub-data of different preset periods corresponding to each year within the N years are spliced ​​in chronological order to obtain the climate prediction data within the N years.

3. The method for selecting a thermal power plant site based on carbon footprint according to claim 2, characterized in that: The preset period includes a plurality of time nodes, each of which corresponds to a historical climate data; the climate prediction sub-data of the same preset period within N years predicted based on the K sub-sequences includes: Selecting K historical climate data corresponding to the same time node in the K subsequences; Generate a first trend chart according to the corresponding years using the K historical climate data; Using a curve analysis algorithm, based on the first trend graph, predict the climate forecast sub-data at the same time node within the N years; The climate forecast sub-data belonging to the same year are spliced ​​together to obtain the climate forecast sub-data of the same preset period within the N years.

4. The method for selecting a thermal power plant site based on carbon footprint according to claim 1, characterized in that: The target building parameters may include any one or more of the following: Convective heat transfer coefficient, radiation heat transfer coefficient between indoor surfaces, outer surface temperature of the envelope structure, indoor and outdoor air temperature, water vapor migration on the inner surface of the envelope structure, latent heat of evaporation, indoor fresh air volume, indoor air infiltration, time change rate of air infiltration, and heat generated by people and equipment in the room.

5. The method for selecting a thermal power plant site based on carbon footprint according to claim 1, characterized in that: The step of inputting the climate prediction data and the target building parameters into a carbon footprint coupling model to obtain carbon emissions calculated by the carbon footprint coupling model based on the climate prediction data and the target building parameters includes: For each year within the N years, determine the building energy consumption according to the climate prediction data and the target building parameters, calculate the initial carbon emissions using the building energy consumption and the carbon emission factor that matches the building energy consumption, and subtract the initial carbon emissions from the carbon reduction amount corresponding to the target building green space of the thermal power plant to obtain a first difference; The first difference values ​​corresponding to the N years are added together to obtain the carbon emission amount.

6. A device for selecting a thermal power plant site based on carbon footprint, characterized in that: include: An acquisition module is used to acquire historical climate data of each candidate address; wherein the candidate address is a candidate construction address of a thermal power plant; A prediction module, for each candidate address, using the historical climate data of the candidate address to predict climate prediction data within N years; the climate prediction data is climate data associated with the carbon emissions of the thermal power plant; N is the expected life of the thermal power plant; N is an integer greater than 1; The acquisition module is further used to acquire target building parameters of the thermal power plant; the target building parameters are attribute parameters associated with carbon emissions of buildings in the thermal power plant; A calculation module, for inputting the climate prediction data and the target building parameters into a carbon footprint coupling model for each candidate address, to obtain carbon emissions calculated by the carbon footprint coupling model based on the climate prediction data and the target building parameters; The selection module is used to select the candidate address with the smallest carbon emission and obtain the target site for building the thermal power plant.

7. The device for selecting a thermal power plant site based on carbon footprint according to claim 6, characterized in that: The selection is specifically used for: For each year within the N years, determine the building energy consumption according to the climate prediction data and the target building parameters, calculate the initial carbon emissions using the building energy consumption and the carbon emission factor that matches the building energy consumption, and subtract the initial carbon emissions from the carbon reduction amount corresponding to the target building green space of the thermal power plant to obtain a first difference; The first difference values ​​corresponding to the N years are added together to obtain the carbon emission amount.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

10. A computer program product, comprising a computer program, characterized in that The computer program implements the method according to any one of claims 1 to 5 when executed by a processor.

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