Method and device for optimizing energy carbon and comfort, equipment and readable storage medium

Abstract

A method, apparatus, device, and computer-readable storage medium for synergistic optimization of energy, carbon emissions, and comfort are disclosed. The method involves acquiring indoor environmental data, energy consumption data, and occupant density over a preset time period. A comfort score is obtained based on the indoor environmental data and a first calculation formula. Total carbon emissions are calculated based on the energy consumption data and a second calculation formula. A target optimization scheme is obtained by combining the occupant density, comfort score, and total carbon emissions with a non-dominated genetic algorithm. This achieves synergistic optimization of building energy and carbon control and indoor comfort, effectively reducing building carbon emissions while ensuring a positive user experience, and avoiding the imbalance in experience or energy waste caused by optimizing a single indicator.

Description

Technical Field

[0001] This application relates to the field of building construction technology, specifically to a method, apparatus, equipment, and computer-readable storage medium for the synergistic optimization of energy, carbon emissions, and comfort. Background Technology

[0002] Currently, building energy conservation and indoor environmental comfort have become core requirements for the development of green buildings. In office, commercial and other building scenarios, it is urgent to achieve coordinated optimization of carbon emission control of energy consumption and indoor comfort assurance, so as to take into account both the requirements of low-carbon development and the user experience.

[0003] Most related technologies focus solely on controlling building carbon emissions, aiming to achieve carbon reduction targets by optimizing energy consumption structures. This approach, which focuses solely on carbon reduction, leads to the sacrifice of indoor comfort in order to reduce carbon emissions, and fails to achieve a balanced optimization of building carbon control and comfort assurance. Summary of the Invention

[0004] This application provides a method, apparatus, device, and computer-readable storage medium for the synergistic optimization of energy, carbon, and comfort, which can solve the technical problem in the prior art that it is impossible to achieve a balanced optimization of building energy and carbon control and comfort assurance.

[0005] In a first aspect, embodiments of this application provide a method for synergistic optimization of energy, carbon emissions, and comfort, the method comprising: Acquire indoor environmental data, energy consumption data, and personnel density within a preset time period, and obtain a comfort score based on the indoor environmental data and the first calculation formula; Based on the energy consumption data and the second calculation formula, the total carbon emissions are obtained; Based on the personnel density, comfort score, and total carbon emissions, a target optimization scheme is obtained using a non-dominated genetic algorithm.

[0006] In conjunction with the first aspect, in one embodiment, the indoor environmental data includes carbon dioxide concentration, light intensity, and indoor temperature, and the step of obtaining a comfort score based on the indoor environmental data and the first calculation formula includes: The indoor temperature is input into the thermal comfort calculation model to obtain the heat dissipation index; The first standard value is obtained by calculating the ratio of the carbon dioxide concentration to the preset carbon dioxide concentration; The ratio of the light intensity to the preset light intensity is calculated to obtain the second standard value; Substituting the heat dissipation index, the first standard value, and the second standard value into the first calculation formula, a comfort score is obtained, wherein the first calculation formula is:

[0007] in, , , For preset weighting coefficients, The heat dissipation index is... The first standard value, This is the second standard value.

[0008] In conjunction with the first aspect, in one embodiment, the energy consumption data includes electricity consumption, gas consumption, water consumption, cooling capacity, and photovoltaic self-generated and self-consumed electricity. The step of obtaining the total carbon emissions based on the energy consumption data and the second calculation formula includes: The carbon emissions from electricity generation are determined based on the aforementioned electricity consumption and the self-generated and self-consumed electricity from photovoltaic power. The carbon emissions from the gas are determined based on the gas consumption. The carbon emissions from water treatment are determined based on the water consumption. The carbon emissions from the cooling supply are determined based on the cooling capacity. Substituting the carbon emissions from electricity generation, gas combustion, water treatment, and cooling into the second calculation formula yields the total carbon emissions. The second calculation formula is as follows:

[0009] in, Total carbon emissions For carbon emissions from electricity, Carbon emissions from natural gas Carbon emissions from water treatment Carbon emissions from cooling.

[0010] In conjunction with the first aspect, in one implementation, the step of obtaining the target optimization scheme based on the personnel density, comfort score, and total carbon emissions, combined with a non-dominated genetic algorithm, includes: Based on the personnel density, comfort score, and total carbon emissions, a first set of optimal solutions is obtained by performing N rounds of solving using a non-dominated genetic algorithm. The first set of optimization schemes is sorted by non-dominated order and crowding is calculated to obtain the second set of optimization schemes; Determine whether there is a third optimization scheme in the second set of optimization schemes that satisfies the preset threshold for comfort score; If the third optimization scheme exists, then the third optimization scheme shall be taken as the target optimization scheme.

[0011] In conjunction with the first aspect, in one implementation, after obtaining the target optimization scheme based on the personnel density, comfort score, and total carbon emissions using a non-dominated genetic algorithm, the method further includes: Control commands are generated based on the target optimization scheme and sent to the corresponding energy-consuming devices for the devices to execute the control commands.

[0012] Secondly, embodiments of this application provide an energy-carbon and comfort synergistic optimization device, characterized in that the energy-carbon and comfort synergistic optimization device comprises: The first calculation module is used to acquire indoor environmental data, energy consumption data and personnel density within a preset time period, and to obtain a comfort score based on the indoor environmental data and the first calculation formula. The second calculation module is used to obtain the total carbon emissions based on the energy consumption data and the second calculation formula. The module is used to obtain the target optimization scheme based on the personnel density, comfort score, and total carbon emissions, combined with a non-dominated genetic algorithm.

[0013] In conjunction with the second aspect, in one implementation, the first computing module is specifically used for: The indoor temperature is input into the thermal comfort calculation model to obtain the heat dissipation index; The first standard value is obtained by calculating the ratio of the carbon dioxide concentration to the preset carbon dioxide concentration; The ratio of the light intensity to the preset light intensity is calculated to obtain the second standard value; Substituting the heat dissipation index, the first standard value, and the second standard value into the first calculation formula, a comfort score is obtained, wherein the first calculation formula is:

[0014] in, , , For preset weighting coefficients, The heat dissipation index is... The first standard value, This is the second standard value.

[0015] In conjunction with the second aspect, in one implementation, the obtaining module is specifically used for: Based on the personnel density, comfort score, and total carbon emissions, a first set of optimal solutions is obtained by performing N rounds of solving using a non-dominated genetic algorithm. The first set of optimization schemes is sorted by non-dominated order and crowding is calculated to obtain the second set of optimization schemes; Determine whether there is a third optimization scheme in the second set of optimization schemes that satisfies the preset threshold for comfort score; If the third optimization scheme exists, then the third optimization scheme shall be taken as the target optimization scheme.

[0016] Thirdly, embodiments of this application provide an energy-carbon and comfort co-optimization device, the energy-carbon and comfort co-optimization device including a processor, a memory, and an energy-carbon and comfort co-optimization program stored in the memory and executable by the processor, wherein when the energy-carbon and comfort co-optimization program is executed by the processor, it implements the steps of the energy-carbon and comfort co-optimization method as described in the first aspect.

[0017] Fourthly, embodiments of this application provide a computer-readable storage medium storing an energy-carbon and comfort co-optimization program, wherein when the energy-carbon and comfort co-optimization program is executed by a processor, it implements the steps of the energy-carbon and comfort co-optimization method described in the first aspect.

[0018] The beneficial effects of the technical solutions provided in this application include: By acquiring indoor environmental data, energy consumption data, and personnel density within a preset time period, and obtaining a comfort score based on the indoor environmental data and a first calculation formula; obtaining the total carbon emissions based on the energy consumption data and a second calculation formula; and obtaining a target optimization scheme based on the personnel density, comfort score, and total carbon emissions, combined with a non-dominated genetic algorithm, to achieve synergistic optimization of building energy and carbon control and indoor comfort. This effectively reduces building carbon emissions while ensuring the user experience, avoiding the problems of experience imbalance or energy waste caused by optimizing a single indicator. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating an embodiment of the energy, carbon, and comfort synergistic optimization method of this application. Figure 2 For this application Figure 1 A detailed flowchart of step S10; Figure 3 For this application Figure 1 A detailed flowchart of step S30; Figure 4 This is a schematic diagram of the functional modules of an embodiment of the energy-carbon and comfort synergistic optimization device of this application; Figure 5 This is a schematic diagram of the hardware structure of the energy, carbon, and comfort synergistic optimization device involved in the embodiments of this application. Detailed Implementation

[0020] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0022] In a first aspect, embodiments of this application provide a method for synergistic optimization of energy, carbon emissions, and comfort.

[0023] In one embodiment, reference is made to Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the energy, carbon, and comfort synergistic optimization method of this application. Figure 1 As shown, the methods for synergistic optimization of energy, carbon emissions, and comfort include: Step S10: Obtain indoor environmental data, energy consumption data, and personnel density within a preset time period, and obtain a comfort score based on the indoor environmental data and the first calculation formula. In one embodiment, various sensing and metering devices deployed within the building continuously collect data at preset time intervals. The preset time interval can be flexibly set according to the building's usage scenarios and control requirements. After the data collection is completed, the indoor environmental data is substituted into the first calculation formula, and the comfort score of the building as a whole or each functional area is directly obtained through formula calculation, providing data support for subsequent collaborative optimization.

[0024] Further, in one embodiment, the indoor environmental data includes carbon dioxide concentration, light intensity, and indoor temperature, and the step of obtaining a comfort score based on the indoor environmental data and the first calculation formula includes: The indoor temperature is input into the thermal comfort calculation model to obtain the heat dissipation index; The first standard value is obtained by calculating the ratio of the carbon dioxide concentration to the preset carbon dioxide concentration; The ratio of the light intensity to the preset light intensity is calculated to obtain the second standard value; Substituting the heat dissipation index, the first standard value, and the second standard value into the first calculation formula, a comfort score is obtained, wherein the first calculation formula is:

[0025] in, , , For preset weighting coefficients, The heat dissipation index is... The first standard value, This is the second standard value.

[0026] In one embodiment, such as Figure 2 As shown, Figure 2 For this application Figure 1 A detailed flowchart of step S10 shows that the indoor temperature is input into a preset thermal comfort calculation model. This model is built based on the principle of human thermal balance and obtains a heat dissipation index that characterizes the degree of human thermal comfort by comprehensively calculating environmental parameters such as indoor temperature, air velocity, and relative humidity. Assuming an indoor temperature of 25℃, the corresponding heat dissipation index is... The value is set to 0.4. The ratio of the real-time collected carbon dioxide concentration to the preset carbon dioxide concentration is calculated to obtain the first standard value. The preset carbon dioxide concentration is generally the standard value of 1000 ppm. Assuming the real-time carbon dioxide concentration is 800 ppm, then... =800 / 1000=0.8. Similarly, assuming the real-time illuminance is 400 lux and the preset illuminance is 500 lux, we obtain the second standard value. The value is 400 / 500 = 0.8; Assumption It is 0.6. It is 0.3. The value is 0.1. Substituting the obtained heat dissipation index, the first standard value, and the second standard value into the first calculation formula, the comfort score is obtained, i.e.: .

[0027] Step S20: Based on the energy consumption data and the second calculation formula, obtain the total carbon emissions; In one embodiment, the collected and verified energy consumption data are classified and statistically analyzed. The specific values ​​of each type of consumption data are sorted out according to the energy type. Then, the classified energy consumption data is substituted into a pre-set second calculation formula. Through the formula calculation, the total carbon emissions of the building within a preset time period are directly obtained, thereby realizing the quantitative calculation of building carbon emissions.

[0028] Further, in one embodiment, the energy consumption data includes electricity consumption, gas consumption, water consumption, cooling capacity, and photovoltaic self-generated and self-consumed electricity. The step of obtaining the total carbon emissions based on the energy consumption data and the second calculation formula includes: The carbon emissions from electricity generation are determined based on the aforementioned electricity consumption and the self-generated and self-consumed electricity from photovoltaic power. The carbon emissions from the gas are determined based on the gas consumption. The carbon emissions from water treatment are determined based on the water consumption. The carbon emissions from the cooling supply are determined based on the cooling capacity. Substituting the carbon emissions from electricity generation, gas combustion, water treatment, and cooling into the second calculation formula yields the total carbon emissions. The second calculation formula is as follows:

[0029] in, Total carbon emissions For carbon emissions from electricity, Carbon emissions from natural gas Carbon emissions from water treatment Carbon emissions from cooling.

[0030] In one embodiment, the carbon emissions of electricity are first determined based on the net electricity consumption after deducting the self-generated and self-consumed electricity from the electricity consumption, combined with the carbon emission accounting rules for electricity. Then, the carbon emissions of gas, water treatment, and cooling are determined sequentially based on the gas consumption, water consumption, and cooling capacity, combined with the carbon emission accounting rules corresponding to each type of energy source. Finally, the carbon emissions of electricity, gas, water treatment, and cooling are substituted into the second calculation formula one by one, and the total carbon emissions are obtained through summation. The second calculation formula is as follows: Total carbon emissions For carbon emissions from electricity, Carbon emissions from natural gas Carbon emissions from water treatment Carbon emissions from cooling.

[0031] Step S30: Based on the personnel density, comfort score, and total carbon emissions, and combined with a non-dominated genetic algorithm, obtain the target optimization scheme.

[0032] In one embodiment, the calculated personnel density, comfort score, and total carbon emissions are used as core input parameters and imported into the computational model of a non-dominated genetic algorithm. With the synergistic optimization of building energy, carbon, and comfort as the core guide, the algorithm solves the multi-objective optimization problem and directly outputs the target optimization scheme that meets the building operation needs, thereby realizing the intelligent generation of building control schemes under the fusion of multiple indicators.

[0033] Furthermore, in one embodiment, the step of obtaining the target optimization scheme based on the personnel density, comfort score, and total carbon emissions, combined with a non-dominated genetic algorithm, includes: Based on the personnel density, comfort score, and total carbon emissions, a first set of optimal solutions is obtained by performing N rounds of solving using a non-dominated genetic algorithm. The first set of optimization schemes is sorted by non-dominated order and crowding is calculated to obtain the second set of optimization schemes; Determine whether there is a third optimization scheme in the second set of optimization schemes that satisfies the preset threshold for comfort score; If the third optimization scheme exists, then the third optimization scheme shall be taken as the target optimization scheme.

[0034] In one embodiment, such as Figure 3 As shown, Figure 3 For this application Figure 1 The detailed flowchart of step S30 shows that: First, personnel density, comfort score, and total carbon emissions are input into a non-dominated genetic algorithm. With the optimization objectives of minimizing carbon emissions and maximizing comfort, the multi-objective optimization model is iterated N times to obtain a first set of optimization schemes containing multiple control strategies. Then, the first set of optimization schemes is hierarchically sorted according to the non-dominated sorting rules, and the crowding value of each scheme is calculated. Based on the sorting results and crowding values, a Pareto optimal second set of optimization schemes is obtained. Subsequently, a preset comfort threshold is retrieved, and the comfort score of each scheme in the second set of optimization schemes is checked one by one to see if it meets the threshold requirement. It is then determined whether there is a third optimization scheme that meets the requirements. If the third optimization scheme exists, the optimal third optimization scheme is selected as the final target optimization scheme.

[0035] Furthermore, in one embodiment, after obtaining the target optimization scheme based on the personnel density, comfort score, and total carbon emissions using a non-dominated genetic algorithm, the method further includes: Control commands are generated based on the target optimization scheme and sent to the corresponding energy-consuming devices for the devices to execute the control commands.

[0036] In one embodiment, the target optimization scheme is first analyzed, and the control parameters of each device are broken down according to the control area and device type of the building's energy-consuming equipment. Standardized control instructions are generated according to the device communication protocol. Then, through the building intelligent control network, the control instructions are accurately sent to the corresponding energy-consuming equipment such as air conditioning, lighting, ventilation, and cooling, so that the energy-consuming equipment can adjust its operating status according to the control instructions, realizing the implementation of the optimization scheme. After the energy-consuming equipment executes the control instructions, the operating status data of the equipment, as well as the indoor environmental data and energy consumption data in the building, are collected in real time. The data calculation, algorithm solution and scheme control are performed cyclically according to the process of steps S10 to S30. The target optimization scheme and equipment control instructions are dynamically updated according to the real-time data to realize the dynamic collaborative optimization of building energy carbon and comfort, and ensure the continuity and accuracy of the optimization effect.

[0037] In this embodiment, indoor environmental data, energy consumption data, and personnel density are acquired within a preset time period. A comfort score is obtained based on the indoor environmental data and a first calculation formula. The total carbon emissions are obtained based on the energy consumption data and a second calculation formula. Based on the personnel density, comfort score, and total carbon emissions, a target optimization scheme is obtained using a non-dominated genetic algorithm. This achieves synergistic optimization of building energy and carbon control and indoor comfort, effectively reducing building carbon emissions while ensuring the user experience, and avoiding the problems of experience imbalance or energy waste caused by optimizing a single indicator.

[0038] Secondly, embodiments of this application also provide a device for synergistic optimization of energy, carbon emissions, and comfort.

[0039] In one embodiment, reference is made to Figure 4 , Figure 4 This is a functional module diagram of an embodiment of the energy-carbon and comfort synergistic optimization device of this application. Figure 4 As shown, the energy, carbon, and comfort synergistic optimization device includes: The first calculation module 10 is used to acquire indoor environmental data, energy consumption data and personnel density within a preset time period, and to obtain a comfort score based on the indoor environmental data and the first calculation formula. The second calculation module 20 is used to obtain the total carbon emissions based on the energy consumption data and the second calculation formula; Module 30 is used to obtain the target optimization scheme based on the personnel density, comfort score, and total carbon emissions, combined with a non-dominated genetic algorithm.

[0040] Furthermore, in one embodiment, the indoor environmental data includes carbon dioxide concentration, light intensity, and indoor temperature, and the first calculation module 10 is used for: The indoor temperature is input into the thermal comfort calculation model to obtain the heat dissipation index; The first standard value is obtained by calculating the ratio of the carbon dioxide concentration to the preset carbon dioxide concentration; The ratio of the light intensity to the preset light intensity is calculated to obtain the second standard value; Substituting the heat dissipation index, the first standard value, and the second standard value into the first calculation formula, a comfort score is obtained, wherein the first calculation formula is:

[0041] in, , , For preset weighting coefficients, The heat dissipation index is... The first standard value, This is the second standard value.

[0042] Furthermore, in one embodiment, the energy consumption data includes electricity consumption, gas consumption, water consumption, cooling capacity, and photovoltaic self-generated and self-consumed electricity. The second calculation module 20 is used for: The carbon emissions from electricity generation are determined based on the aforementioned electricity consumption and the self-generated and self-consumed electricity from photovoltaic power. The carbon emissions from the gas are determined based on the gas consumption. The carbon emissions from water treatment are determined based on the water consumption. The carbon emissions from the cooling supply are determined based on the cooling capacity. Substituting the carbon emissions from electricity generation, gas combustion, water treatment, and cooling into the second calculation formula yields the total carbon emissions. The second calculation formula is as follows:

[0043] in, Total carbon emissions For carbon emissions from electricity, Carbon emissions from natural gas Carbon emissions from water treatment Carbon emissions from cooling.

[0044] Furthermore, in one embodiment, the obtaining module 30 is used for: Based on the personnel density, comfort score, and total carbon emissions, a first set of optimal solutions is obtained by performing N rounds of solving using a non-dominated genetic algorithm. The first set of optimization schemes is sorted by non-dominated order and crowding is calculated to obtain the second set of optimization schemes; Determine whether there is a third optimization scheme in the second set of optimization schemes that satisfies the preset threshold for comfort score; If the third optimization scheme exists, then the third optimization scheme shall be taken as the target optimization scheme.

[0045] Furthermore, in one embodiment, the energy, carbon, and comfort synergistic optimization device further includes a generation module for: Control commands are generated based on the target optimization scheme and sent to the corresponding energy-consuming devices for the devices to execute the control commands.

[0046] The functions of each module in the above-mentioned energy, carbon and comfort synergistic optimization device correspond to the steps in the above-mentioned energy, carbon and comfort synergistic optimization method embodiment, and their functions and implementation processes will not be described in detail here.

[0047] Thirdly, embodiments of this application provide an energy-carbon and comfort co-optimization device, which may be an adaptive MPC controller or similar device.

[0048] Reference Figure 5 , Figure 5 This is a schematic diagram of the hardware structure of the energy, carbon dioxide, and comfort co-optimization device involved in the embodiments of this application. In the embodiments of this application, the energy, carbon dioxide, and comfort co-optimization device may include a processor, a memory, a communication interface, and a communication bus.

[0049] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0050] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting devices within the energy, carbon, and comfort co-optimization device, as well as interfaces used for interconnecting the energy, carbon, and comfort co-optimization device with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0051] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0052] The processor can be a general-purpose processor, which can call the energy, carbon, and comfort co-optimization program stored in memory and execute the energy, carbon, and comfort co-optimization method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the energy, carbon, and comfort co-optimization program is called can be referred to the various embodiments of the energy, carbon, and comfort co-optimization method of this application, and will not be repeated here.

[0053] Those skilled in the art will understand that Figure 5 The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0054] Fourthly, embodiments of this application also provide a computer-readable storage medium.

[0055] The present application stores an energy, carbon and comfort co-optimization program on a computer-readable storage medium, wherein when the energy, carbon and comfort co-optimization program is executed by a processor, it implements the steps of the energy, carbon and comfort co-optimization method as described above.

[0056] The method implemented when the energy, carbon and comfort co-optimization procedure is executed can be referred to in various embodiments of the energy, carbon and comfort co-optimization method of this application, and will not be repeated here.

[0057] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0058] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0059] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0060] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0061] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0062] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0063] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method of carbon and comfort synergy optimization, characterized in that, The method for synergistic optimization of energy, carbon emissions, and comfort includes: Acquire indoor environmental data, energy consumption data, and personnel density within a preset time period, and obtain a comfort score based on the indoor environmental data and the first calculation formula; Based on the energy consumption data and the second calculation formula, the total carbon emissions are obtained; Based on the personnel density, comfort score, and total carbon emissions, a target optimization scheme is obtained using a non-dominated genetic algorithm.

2. The method of carbon and comfort synergy optimization of claim 1, wherein, The indoor environmental data includes carbon dioxide concentration, light intensity, and indoor temperature. The comfort score obtained based on the indoor environmental data and the first calculation formula includes: The indoor temperature is input into the thermal comfort calculation model to obtain the heat dissipation index; The first standard value is obtained by calculating the ratio of the carbon dioxide concentration to the preset carbon dioxide concentration; The ratio of the light intensity to the preset light intensity is calculated to obtain the second standard value; Substituting the heat dissipation index, the first standard value, and the second standard value into the first calculation formula, a comfort score is obtained, wherein the first calculation formula is: wherein, , , is a preset weight coefficient, is the heat dissipation index, is the first standard value, is the second standard value.

3. The method of carbon and comfort synergy optimization of claim 1, wherein, The energy consumption data includes electricity consumption, gas consumption, water consumption, cooling demand, and photovoltaic self-generated and self-consumed electricity. The total carbon emissions obtained based on the energy consumption data and the second calculation formula include: The carbon emissions from electricity generation are determined based on the aforementioned electricity consumption and the self-generated and self-consumed electricity from photovoltaic power. The carbon emissions from the gas are determined based on the gas consumption. The carbon emissions from water treatment are determined based on the water consumption. The carbon emissions from the cooling supply are determined based on the cooling capacity. Substituting the carbon emissions from electricity generation, gas combustion, water treatment, and cooling into the second calculation formula yields the total carbon emissions. The second calculation formula is as follows: wherein, is the total amount of carbon emissions, is the amount of carbon emissions from electricity, is the amount of carbon emissions from gas, is the amount of carbon emissions from water treatment, is the amount of carbon emissions from cooling.

4. The method of carbon and comfort synergy optimization of claim 1, wherein, The optimization scheme obtained by combining the personnel density, comfort score, and total carbon emissions with a non-dominated genetic algorithm includes: Based on the personnel density, comfort score, and total carbon emissions, a first set of optimal solutions is obtained by performing N rounds of solving using a non-dominated genetic algorithm. The first set of optimization schemes is sorted by non-dominated order and crowding is calculated to obtain the second set of optimization schemes; Determine whether there is a third optimization scheme in the second set of optimization schemes that satisfies the preset threshold for comfort score; If the third optimization scheme exists, then the third optimization scheme shall be taken as the target optimization scheme.

5. The method for synergistic optimization of energy, carbon emissions, and comfort as described in claim 1, characterized in that, After obtaining the target optimization scheme based on the personnel density, comfort score, and total carbon emissions using a non-dominated genetic algorithm, the process further includes: Control commands are generated based on the target optimization scheme and sent to the corresponding energy-consuming devices for the devices to execute the control commands.

6. A device for synergistic optimization of energy, carbon emissions, and comfort, characterized in that, The energy, carbon, and comfort synergistic optimization device includes: The first calculation module is used to acquire indoor environmental data, energy consumption data and personnel density within a preset time period, and to obtain a comfort score based on the indoor environmental data and the first calculation formula. The second calculation module is used to obtain the total carbon emissions based on the energy consumption data and the second calculation formula. The module is used to obtain the target optimization scheme based on the personnel density, comfort score, and total carbon emissions, combined with a non-dominated genetic algorithm.

7. The energy, carbon emissions, and comfort synergistic optimization device as described in claim 6, characterized in that, The first calculation module is specifically used for: The indoor temperature is input into the thermal comfort calculation model to obtain the heat dissipation index; The first standard value is obtained by calculating the ratio of the carbon dioxide concentration to the preset carbon dioxide concentration; The ratio of the light intensity to the preset light intensity is calculated to obtain the second standard value; Substituting the heat dissipation index, the first standard value, and the second standard value into the first calculation formula, a comfort score is obtained, wherein the first calculation formula is: in, , , For preset weighting coefficients, The heat dissipation index is... The first standard value, This is the second standard value.

8. The energy, carbon emissions, and comfort synergistic optimization device as described in claim 6, characterized in that, The obtained module is specifically used for: Based on the personnel density, comfort score, and total carbon emissions, a first set of optimal solutions is obtained by performing N rounds of solving using a non-dominated genetic algorithm. The first set of optimization schemes is sorted by non-dominated order and crowding is calculated to obtain the second set of optimization schemes; Determine whether there is a third optimization scheme in the second set of optimization schemes that satisfies the preset threshold for comfort score; If the third optimization scheme exists, then the third optimization scheme shall be taken as the target optimization scheme.

9. A device for synergistic optimization of energy, carbon emissions, and comfort, characterized in that, The energy, carbon and comfort co-optimization device includes a processor, a memory, and an energy, carbon and comfort co-optimization program stored in the memory and executable by the processor, wherein when the energy, carbon and comfort co-optimization program is executed by the processor, it implements the steps of the energy, carbon and comfort co-optimization method as described in any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores an energy, carbon, and comfort co-optimization program, wherein when the energy, carbon, and comfort co-optimization program is executed by a processor, it implements the steps of the energy, carbon, and comfort co-optimization method as described in any one of claims 1 to 5.

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