CN115241933B - Artificial intelligence-based method for determining the installation capacity of zero-carbon building energy control systems - Google Patents
Artificial intelligence-based method for determining the installation capacity of zero-carbon building energy control systemsInfo
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- CN115241933B CN115241933B CN202210797346.3A CN202210797346A CN115241933B CN 115241933 B CN115241933 B CN 115241933B CN 202210797346 A CN202210797346 A CN 202210797346A CN 115241933 B CN115241933 B CN 115241933B
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for AC mains or AC distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/48—Controlling the sharing of the in-phase component
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F17/10—Complex mathematical operations
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for AC mains or AC distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for AC mains or AC distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for AC mains or AC distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
- H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
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Abstract
本发明提供的一种基于人工智能的零碳建筑能源控制系统安装容量确定方法所述系统包括发电模块、化学储能模块及物理储能模块；所述安装容量确定方法的步骤包括步骤S10：获取建筑物自身有功计算数据data1；步骤S20：获取建筑所在地气象数据data2；步骤S30：计算所述发电模块的安装容量Pg；步骤S40：计算所述化学储能模块的安装容量Pb；步骤S50：计算所述物理储能模块安装容量Ps。是以，步骤S50通过采用水蓄能设备储能而具有更高效的能源转换效率；步骤S30通过设置电动汽车及充电桩使得发电模块和化学储能模块的配置更加准确并提高系统的柔韧性，且本发明采用人工智能算法反复训练，具有高可靠性及自学习能力，能适应于不同项目应用。
The present invention provides an artificial intelligence-based method for determining the installed capacity of a zero-carbon building energy control system. The system includes a power generation module, a chemical energy storage module, and a physical energy storage module. The method comprises steps S10: obtaining the building's own active power calculation data (data1); step S20: obtaining meteorological data (data2) for the building's location; step S30: calculating the installed capacity Pg of the power generation module; step S40: calculating the installed capacity Pb of the chemical energy storage module; and step S50: calculating the installed capacity Ps of the physical energy storage module. Therefore, step S50 utilizes water storage equipment for energy storage, resulting in higher energy conversion efficiency. Step S30 utilizes electric vehicles and charging stations to ensure more accurate configuration of the power generation module and chemical energy storage module, improving system flexibility. Furthermore, the present invention utilizes artificial intelligence algorithms for repeated training, resulting in high reliability and self-learning capabilities, making it adaptable to diverse project applications.
Description
Technical Field
The invention relates to the technical field of zero-carbon construction, in particular to an artificial intelligence-based zero-carbon construction energy control system installation capacity determining method.
Background
The zero-carbon building is a building which can be operated independently of a power grid and can be operated by means of renewable energy sources such as solar energy or wind energy, wherein annual energy production is greater than or equal to annual energy utilization of the building, and primary energy is not used at all.
Notably, much of the current academic discussion is still low carbon construction, with less subject research being conducted on zero carbon construction. At present, no good scheme exists for how to configure an energy system for a zero-carbon building, for example, how much scale of photovoltaic is needed to be matched for the zero-carbon building, how much large-capacity storage battery is needed to be configured, whether other energy sources are needed to be matched for meeting the zero-carbon requirement, and the like.
In addition, in the existing zero-carbon building energy system research, the defects still need to be solved:
First, the energy consumption of the air conditioner cannot be effectively allocated. The energy consumption of the air conditioner is the main energy consumption of the current building, and the energy consumption accounts for about 70 percent. At present, the accumulator is used for storing energy and then used for an air conditioning system, so that the accumulator is quite huge in arrangement. If the energy consumption of the air conditioner cannot be effectively regulated, the efficiency of the zero-carbon building energy system is very low, so that the system is high in manufacturing cost and difficult to popularize.
Second, electric vehicles are not considered for system deployment. The number of electric vehicles will be very large in the future, and the electric quantity of the electric vehicles will not be small. According to the existing research, the electric automobile can be charged in the future and can also discharge to the power grid, and the discharge does not affect the service life of the battery. Therefore, the electric automobile is ensured to be charged at the power generation peak value, and is properly discharged to the power grid at the power utilization peak value, so that the capacity configuration of an energy system can be reduced, and the system economy is improved.
Third, the zero-carbon building energy system subsystems are not matched sufficiently. The zero-carbon building energy system consists of a plurality of subsystems, such as a power generation module, a chemical energy storage module, a physical energy storage module, an electric automobile module and the like, wherein the subsystems have close matching relations. If the configuration is made according to the highest capacity without considering the cooperation between each other, energy saving potential is wasted, resulting in high system cost. If the logic relation among the modules is considered in detail, the capacity data are reasonably allocated, so that the economy of the system can be improved, and the purpose of comprehensively popularizing the zero-carbon building to society is achieved.
Disclosure of Invention
In view of the above, the present invention aims to provide an artificial intelligence-based method for determining the installation capacity of a zero-carbon building energy control system, so as to improve the above technical problems.
In order to achieve the above purpose, the invention adopts the technical scheme that the invention provides an artificial intelligence-based zero-carbon building energy control system installation capacity determining method, wherein the system comprises a power generation module, a chemical energy storage module and a physical energy storage module, and the method comprises the following steps:
step S10, acquiring active calculation data1 of a building;
step S20, obtaining the meteorological data2 of the ground where the building is located;
Step S30, calculating the installation capacity P g of the power generation module;
Step S40, calculating the installation capacity P b of the chemical energy storage module;
And S50, calculating the installation capacity P s of the physical energy storage module.
The invention further improves the method for determining the installation capacity of the zero-carbon building energy control system, wherein the step S10 further comprises the following steps:
Step S11, extracting active calculation power P 0 of the transformer according to a transformer load factor calculation table;
Step S12, acquiring the active calculation power of the lighting, air conditioning, electric power and charging pile, and distributing transformer active calculation power P 0 according to a load factor calculation table, wherein P 0＝P1+P2+P3+P4 is shown in the following formula (1), P 0 is transformer active calculation power, P 1 is lighting active calculation power, P 2 is air conditioning active calculation power, P 3 is electric active calculation power, and P 4 is charging pile active calculation power.
The invention discloses a method for determining the installation capacity of a zero-carbon building energy control system, which is further improved in that a photovoltaic power supply unit and a wind power supply unit are arranged in a power generation module, wherein the step S20 further comprises the following steps:
S21, calculating a graph of total daily energy consumption P e0 of the whole year by adopting an artificial intelligence algorithm according to building properties, meteorological data and national holiday information;
Step S22, calculating and obtaining a graph of illumination energy consumption, air conditioner energy consumption, electric power energy consumption and charging pile energy consumption by adopting an artificial intelligence algorithm according to a graph of total daily energy consumption P e0, wherein the total daily energy consumption P e0 is represented by the following formula (2), P e0＝Pe1+Pe2+Pe3+Pe4, P e0 is total daily energy consumption, P e1 is illumination energy consumption, P e2 is air conditioner energy consumption, P e3 is electric power energy consumption and P e4 is charging pile energy consumption;
Step S23, calculating a graph of photovoltaic annual energy generation capacity P V0 of a unit photovoltaic power supply unit according to meteorological data2 of the place where the building is located;
And S24, calculating a graph of annual energy generation P W0 of wind power of the unit wind power supply unit according to the meteorological data2 of the place where the building is located.
The invention relates to a method for determining the installation capacity of a zero-carbon building energy control system, which is further improved in that the calculation step of the total daily energy consumption P e0 comprises the following steps:
providing annual energy consumption data of each type of building;
dividing all data into spring and autumn, summer and winter according to weather;
classifying the energy consumption data of each season by combining with corresponding meteorological data, and respectively providing a combination of working days/rest days, a combination of sunny days/cloudy days/rainy days;
And training corresponding data through an artificial intelligence algorithm to realize data fitting, and respectively extracting different combinations of each quarter to obtain a graph of the typical 24-hour single-day total energy consumption P e0.
The invention further improves the method for determining the installation capacity of the zero-carbon building energy control system, wherein the step S30 further comprises the following steps:
S31, calculating the installation capacity P g of the power generation module according to the following formula (3), wherein alpha is the installation unit number of the photovoltaic power supply units, P V is the power generation amount of the unit photovoltaic power supply units, beta is the installation unit number of the wind power supply units, and P W is the power generation amount of the unit wind power supply units;
And S32, calculating annual total energy consumption data W 0, lighting annual total energy consumption W 1, air conditioner annual energy consumption W 2, electric power annual energy consumption W 3 and charging pile annual energy consumption W 4 according to the following formulas (4) to (8), wherein:
formula (4) total annual energy consumption W 0,
Formula (5) total annual energy consumption W 1 of illumination,
The energy consumption W 2 of the air conditioner,
The formula (7) is that the power consumption W 3,
The energy consumption W 4 of the charging pile is shown in the formula (8),
And S33, calculating annual energy generation W G of the power generation module, annual energy generation W V of the photovoltaic power supply unit and annual energy generation W W of the wind power supply unit according to the following formulas (9) to (11), wherein:
Formula (9) annual energy production W G,WG＝WV+WW of the power generation module,
Formula (10) annual energy production W V of the photovoltaic power supply unit,
Formula (11) annual energy production W W of the wind power supply unit,
Step S34, ensuring that the generated energy of the system is more than or equal to the electricity consumption, and considering the k e times of the installed allowance, and calculating according to the following formula (12) W V+WW≥keW0, wherein W V+WW is the generated energy of the system, and k eW0 is the electricity consumption of the system;
and S35, constraint conditions of the photovoltaic power supply unit and the wind power supply unit are used for ensuring the best economic benefit.
The invention relates to a method for determining the installation capacity of a zero-carbon building energy control system, which is further improved in the step S35, wherein the minimum value of the cost in the service life of a power generation module is calculated according to the following formula (13), wherein lambda is the cost in the service life of a unit photovoltaic power supply unit, mu is the cost in the service life of a unit wind power supply unit, alpha is the installation unit number of the photovoltaic power supply unit, and beta is the installation unit number of the wind power supply unit.
The invention relates to a method for determining the installation capacity of a zero-carbon building energy control system, which is further improved in that the system further comprises an electric automobile module, the electric automobile module comprises an electric automobile and a charging pile, a storage battery is arranged in the chemical energy storage module, and the step S40 further comprises:
Step S41, on the basis that the installation capacity P b of the chemical energy storage module meets the requirement of electric equipment except an air conditioner and the use of the charging pile is not counted at peak time, the installation capacity P b of the chemical energy storage module is calculated according to the following formula (14) by P b＝Pe1+Pe3, wherein P e1 is illumination energy consumption and P e3 is electric energy consumption;
Step S42, considering the electric automobile discharge at a peak stage, and supplementing the electric power use of electric equipment except an air conditioner, wherein the duty ratio coefficient of the capacity of the discharge equipment in the installation capacity of the charging pile is k 4;
Step S43, except for the use of electric equipment, the installation capacity P b of the chemical energy storage module deducts the real-time electric quantity distribution part of the power generation module, the distribution coefficient k g1 is considered to be k g1, the distribution coefficient k g1 is calculated according to the following formula (15) k g1＝(W1+W3)/W0, wherein W 1 is total energy consumption of illumination in the whole year, and W 3 is electric energy consumption;
step S44, the constraint condition of the storage battery is the optimal use interval;
And step S45, considering the steps S41-S44, enabling the installation capacity P b of the chemical energy storage module to meet the following formula (16) namely P b＝1.25(Pe1+Pe3-kg1Pg-k4P4), wherein P e1 is illumination energy consumption, P e3 is electric power energy consumption, k g1Pg is a first distribution coefficient k g1 multiplied by the installation capacity P g of the power generation module, the product is the direct use power of the illumination load of the power generation module, k 4P4 is a duty ratio coefficient k 4 multiplied by the active calculation power P 4 of the charging pile, and the product is the discharge power of the electric automobile at the peak time.
The method for determining the installation capacity of the zero-carbon building energy control system is further improved in the step S44, wherein the optimal use interval of the storage battery is 60% -80% of the installation capacity.
A still further improvement of the method for determining the installation capacity of the zero-carbon building energy control system according to the present invention is that the step S50 further includes:
Step S51, enabling the installation capacity P s of the physical energy storage module to meet the air conditioner energy consumption P e2;
Step S52, removing the use of air-conditioning electric equipment, wherein the installation capacity P s of the physical energy storage module deducts the real-time electric quantity distribution part of the power generation module, and the distribution coefficient is considered to be k g2, and the distribution coefficient k g1 is calculated according to the following formula (17) k g2＝W2/W0, wherein W 2 is air-conditioning energy consumption, and W 0 is total annual energy consumption;
Step S53, setting the use coefficient of the installation capacity P s of the physical energy storage module as k s;
and S54, considering the steps S51-S53, enabling the installation capacity P s of the physical energy storage module to meet the following formula (18) P s＝(Pe2-kg2Pg)/ks, wherein P e2 is used for intelligently calculating air conditioner energy consumption by manpower, k g2Pg is a second distribution coefficient k g2 multiplied by the installation capacity P g of the power generation module, and the obtained product is the air conditioner load direct use power of the power generation module, and k s is the use coefficient of the water energy storage capacity P s.
The invention provides a method for determining the installation capacity of a zero-carbon building energy control system, which is further improved in that the system comprises a system controller, the system controller is used for electrically controlling a power generation module, a chemical energy storage module, a physical energy storage module and an electric automobile module through a data bus, wherein the power generation module comprises the power generation module controller which is connected with the data bus, the power generation module controller is further connected with a photovoltaic power supply unit and a wind power supply unit, the chemical energy storage module comprises the chemical energy storage module controller which is connected with the data bus, the chemical energy storage module controller is further connected with a storage battery, the physical energy storage module comprises the physical energy storage module controller which is connected with the data bus, the physical energy storage module controller is further connected with a water energy storage device, the electric automobile module comprises the electric automobile module controller which is connected with the data bus, and the electric automobile module controller is further connected with an electric automobile and a charging pile.
The invention adopts the technical proposal, which has the following beneficial effects:
(1) The invention adopts the water energy storage equipment, only 1 conversion, namely electric energy-heat energy is needed, so the energy conversion efficiency of the water energy storage equipment is higher.
(2) According to the zero-carbon building energy control system, the electric automobile module is arranged so that the energy characteristics of the electric automobile are different from those of other energy-consuming equipment, and the photovoltaic power supply unit, the wind power supply unit, the storage battery of the chemical energy storage module and other equipment of the power generation module are more accurately configured and the flexibility of the system is improved on the basis of considering the characteristics of the electric automobile alone.
(3) The method for determining the installation capacity of the zero-carbon building energy control system adopts an artificial intelligent algorithm, annual energy consumption data obtained through repeated training has high reliability, and the method has a self-learning function through application in different projects, so that the application accuracy in the zero-carbon building energy system is higher and higher.
These and other objects, features and advantages of the present invention will become more fully apparent from the following detailed description and appended claims, and may be learned by the practice of the invention as set forth hereinafter.
Drawings
FIG. 1 is a schematic diagram of a configuration architecture for zero-carbon building energy control according to the present invention.
FIG. 2 is a schematic diagram of the calculation flow of the zero-carbon building energy system of the present invention.
FIG. 3 is a schematic diagram of the process of acquiring active computing data of a building according to the present invention.
FIG. 4 is a schematic diagram of a building meteorological data acquisition process according to the present invention.
Fig. 5 is a schematic diagram of a power generation module installation capacity acquisition flow of the present invention.
Fig. 6 is a schematic diagram of a battery mounting capacity acquisition flow of the present invention.
FIG. 7 is a schematic diagram of a water storage installation capacity acquisition process of the present invention.
Fig. 8 is a schematic diagram of the architecture of the zero-carbon building energy system of the present invention.
Fig. 9 is a schematic diagram of the system controller workflow of the zero-carbon building of the present invention.
The correspondence of the reference numerals with the components is as follows:
The system controller 10, the data bus 11, the power generation module 20, the power generation module controller 21, the photovoltaic power supply unit 22, the wind power supply unit 23, the chemical energy storage module 30, the chemical energy storage module controller 31, the storage battery 32, the physical energy storage module 40, the physical energy storage module controller 41, the water energy storage device 42, the electric automobile module 50, the electric automobile module controller 51, the electric automobile 52, the charging pile 53, the building energy consumption management module 60, the energy consumption management module receiver 61 data design input stage A, the energy configuration output stage B, the building self-power calculation data1 and the building site meteorological data2.
Detailed Description
Detailed embodiments of the present invention will be disclosed herein. It is to be understood, however, that the disclosed embodiments are merely exemplary of the invention, which may be embodied in various and alternative forms. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present invention.
In order to facilitate the understanding of the present invention, the following description will be given with reference to fig. 1 to 9 and the embodiments.
As shown in fig. 1, the installation capacity determining method of the zero-carbon building energy control system of the invention is to further produce and output corresponding or required data in an energy configuration output stage B according to the data input in the data design input stage a. The data design input stage A comprises the step of inputting building self active calculation data1 and building site meteorological data2. After the two parts of data are acquired, the energy configuration output stage B can be entered to develop the system energy configuration calculation.
As shown in fig. 1, the system configuration of the present invention mainly includes a power generation module 20, a chemical energy storage module 30, and a physical energy storage module 40. The power generation module 20 includes the photovoltaic power supply unit 22 and the wind power supply unit 23, and the configuration ratio of the two is described in detail in the installation capacity determination method described later. The chemical energy storage module 30 uses the storage battery 32 in the embodiment of the present invention, but is not limited thereto, and a lithium battery or other electric storage device meeting the foregoing requirements can be used. The physical energy storage module 40 adopts the water energy storage device 42 in the embodiment of the present invention, but is not limited thereto, and other energy storage devices such as ice storage devices, etc. that can perform the same function are adopted in the situation that the economic technology is more advantageous for the place where the climate condition is suitable.
As shown in fig. 2 to 7, a flowchart showing steps of the method for determining installation capacity of the artificial intelligence-based zero-carbon construction energy control system according to the present invention is shown.
Referring to fig. 2, the method for determining the installation capacity of the zero-carbon building energy control system based on artificial intelligence according to the present invention includes steps S10 to S50, wherein steps S10 and S20 correspond to the data design input stage a in fig. 1, and steps S30, S40 and S50 correspond to the energy configuration output stage B in fig. 1.
Specifically, as shown in fig. 2, the steps S10 to S50 include:
step S10, acquiring active calculation data1 of a building;
step S20, obtaining the meteorological data2 of the ground where the building is located;
Step S30, the installation capacity P g of the power generation module 20 is calculated. In the embodiment of the present invention, the power generation module 20 specifically adopts a photovoltaic power supply unit 22 and a wind power supply unit 23 to realize zero-carbon power generation/supply.
Step S40 calculates the installation capacity P b of the chemical energy storage module 30 (i.e., the battery 32). In the embodiment of the present invention, the chemical energy storage module 30 specifically uses the storage battery 32 to realize chemical energy storage.
Step S50, calculating the installation capacity P s of the physical energy storage module 40 (i.e., the water storage device 42). In the embodiment of the present invention, the physical energy storage module 40 specifically adopts the water energy storage device 42 to realize physical energy storage.
In the embodiment of the invention, the electricity consumption of the zero-carbon building is divided into illumination electricity consumption (illumination for short), air-conditioning electricity consumption (air-conditioning for short), other electricity consumption (electricity for short) and electricity consumption of the charging pile 53 (charging pile for short) for charging the electric automobile 52.
Specifically, as shown in fig. 3, the step S10 further includes sub-steps S11 to S12. The substeps include:
And S11, extracting active calculation power P 0 of the transformer according to a transformer load factor calculation table.
And S12, acquiring the active calculation power of the lighting, air conditioning, electric power and charging pile, and distributing the active calculation power P 0 of the transformer according to the load factor calculation table, wherein the active calculation power P 0＝P1+P2+P3+P4 is shown in the following formula (1). Wherein, P 0 is the transformer active calculation power, P 1 is the lighting active calculation power, P 2 is the air conditioner active calculation power, P 3 is the electric active calculation power, and P 4 is the charging pile active calculation power.
The transformer load factor calculation table in step S11 is a design file related to the building, which is generated immediately when the design of the building is completed according to the "depth of construction design documentation (2016 edition)" rule, 3.6.5 th rule. The calculated power of the lighting, air conditioning, electric power, and charging pile for the building in step S12 may be directly obtained from the transformer load factor calculation table corresponding to the building.
Specifically, as shown in fig. 4, the step S20 further includes sub-steps S21 to S24. The substeps include:
And S21, calculating a graph of the total energy consumption P e0 in a single day by adopting an artificial intelligence algorithm according to the building properties, meteorological data and national holiday information.
Further, the step of calculating the total daily energy consumption P e0 by the artificial intelligence algorithm includes, first, the need to provide annual energy consumption data of each type of building. Second, all data are divided into 3 quarters according to climate, namely spring and autumn, summer and winter. Thirdly, the energy consumption data of each season are classified by combining the corresponding meteorological data, and a combination of working days/rest days, a combination of sunny days/cloudy days/rainy days is provided respectively. Fourth, training corresponding data through an artificial intelligence algorithm to realize data fitting, and respectively extracting different combinations of each quarter, so that a graph of the total daily energy consumption P e0 of a typical 24h period is obtained.
And S22, calculating and obtaining graphs of illumination energy consumption, air conditioner energy consumption, electric power energy consumption and charging pile energy consumption by adopting an artificial intelligence algorithm according to the graph of the total daily energy consumption P e0. The sum of the total energy consumption per day P e0 is represented by the following formula (2) P e0＝Pe1+Pe2+Pe3+Pe4. Wherein, P e0 is the total energy consumption of a single day, P e1 is the illumination energy consumption, P e2 is the air conditioner energy consumption, P e3 is the electric power energy consumption, and P e4 is the charging pile energy consumption.
Step S23, calculating a graph of photovoltaic annual energy production P V0 of the unit photovoltaic power supply unit 22 according to the meteorological data2 of the place where the building is located.
Step S24, calculating a graph of annual energy production P W0 of wind power of the unit wind power supply unit 23 according to the meteorological data2 of the place where the building is located.
In the embodiment of the invention, step S22 and step S21 train the existing data through an artificial intelligence algorithm to obtain graphs of illumination energy consumption, air conditioner energy consumption, electric power energy consumption and charging pile energy consumption. In addition, the step S21 and the step S22 are based on the mastered large data of various areas and various buildings, new configuration data is obtained through the existing large data, and the operation data of a new project is used as one of the basic data of the next project, so that the accurate zero-carbon building energy system configuration data is further obtained through a self-learning program.
Specifically, as shown in fig. 5, the step S30 further includes sub-steps S31 to S34. The substeps include:
In the step S31, the installation capacity P g of the power generation module 20 is calculated according to the following formula (3), wherein alpha is the installation unit number of the photovoltaic power supply units 22, P V is the power generation amount of the unit photovoltaic power supply units 22, beta is the installation unit number of the wind power supply units 23, and P W is the power generation amount of the unit wind power supply units 23.
And S32, calculating annual total energy consumption data W 0, lighting annual total energy consumption W 1, air conditioner annual energy consumption W 2, electric power annual energy consumption W 3 and charging pile annual energy consumption W 4 according to the following formulas (4) to (8).
Wherein:
formula (4) total annual energy consumption W 0,
Formula (5) total annual energy consumption W 1 of illumination,
The energy consumption W 2 of the air conditioner,
The formula (7) is that the power consumption W 3,
The energy consumption W 4 of the charging pile is shown in the formula (8),
And step S33, annual energy production W G of the power generation module 20, annual energy production W V of the photovoltaic power supply unit 22 and annual energy production W W of the wind power supply unit 23 are calculated according to the following formulas (9) to (11).
Wherein:
annual energy production W of the power generation module 20 G,WG＝WV+WW
Formula (10) annual energy production W V of the photovoltaic power unit 22,
Formula (11) annual energy production W W of the wind power supply unit 23,
And step S34, ensuring that the generated energy of the system is more than or equal to the electricity consumption, and considering the installed margin k e times, and calculating according to the following formula (12) W V+WW≥keW0. Wherein W V+WW is the generated energy of the system, and k eW0 is the used energy of the system.
The installed margin k e is a certain margin which is set on the basis of the construction electric design principle and is in addition to the installed capacity which ensures the electric capacity.
In step S35, the constraint conditions of the photovoltaic power supply unit 22 and the wind power supply unit 23 are the best to ensure the economic benefit. Specifically, assuming that the cost per unit photovoltaic power unit 22 is λ and the cost per unit wind power unit 23 is μ in the service life, the minimum value of the cost per unit wind power unit 23 in the service life is calculated as αλ+βμ in the following formula (13). Where α is the number of installation units of the photovoltaic power supply unit 22, and β is the number of installation units of the wind power supply unit 23.
Specifically, as shown in fig. 6, the step S40 further includes sub-steps S41 to S45. The substeps include:
In the step S41, on the basis that the installation capacity P b of the chemical energy storage module 30 meets the requirement of electric equipment except an air conditioner and the charging pile 53 is not counted into the use at the peak time, the installation capacity P b of the chemical energy storage module 30 is calculated according to the following formula (14) by P b＝Pe1+Pe3, wherein P e1 is illumination energy consumption and P e3 is electric energy consumption.
And S42, considering the electric automobile 52 to discharge at a peak stage, and supplementing the electric power use of electric equipment except an air conditioner, wherein the duty ratio coefficient (discharge coefficient) of the capacity of the discharge equipment in the installation capacity of the charging pile is k 4.
In step S43, except for the use of the electric device, the installation capacity P b of the chemical energy storage module 30 deducts the real-time power distribution portion of the power generation module 20, and the distribution coefficient k g1 is considered. The partition coefficient k g1 is calculated as k g1＝(W1+W3)/W0, where W 1 is the total annual energy consumption of the illumination and W 3 is the electrical energy consumption.
And S44, the constraint condition of the storage battery 32 is the optimal use interval, namely 60% -80% of the installation capacity.
In the step S45, considering the steps S41-S44, the installation capacity P b of the chemical energy storage module 30 is made to meet the following formula (16) P b＝1.25(Pe1+Pe3-kg1Pg-k4P4), wherein P e1 is illumination energy consumption, P e3 is electric power energy consumption, k g1Pg is a first distribution coefficient k g1 multiplied by the installation capacity P g of the power generation module 20, the product is obtained by directly using power for illumination loads of the power generation module 21, k 4P4 is a duty ratio coefficient (k 4) multiplied by the active calculation power P 4 of the charging pile, and the product is obtained by obtaining the discharge power of the electric automobile 52 at the peak moment.
In the formula (16), if the battery constraint condition is the optimal use interval, that is, 60% -80% of the capacity, the calculated capacity should be 80% of the installation capacity, that is, the installation capacity is 1.25 times of the calculated capacity. The calculation of P b is referred to in steps S41 to S44.
Specifically, as shown in fig. 7, the step S50 further includes sub-steps S51 to S54. The substeps include:
Step S51, the installation capacity P s of the physical energy storage module 40 is enabled to meet the air conditioner energy consumption P e2.
Step S52, excluding the use of air-conditioning electric equipment, where the installation capacity P s of the physical energy storage module 40 deducts the real-time power distribution portion of the power generation module 20, and considers that the distribution coefficient is k g2. The distribution coefficient k g1 is calculated by the following formula (17) k g2＝W2/W0, wherein W 2 is air conditioning energy consumption and W 0 is total annual energy consumption.
Step S53, setting the use coefficient of the installation capacity P s of the physical energy storage module 40 as k s.
Wherein, as the use efficiency exists for all devices, the energy loss comprises heat conduction, auxiliary device energy consumption and the like. The usage coefficient of the device is the coefficient obtained by subtracting the loss on the basis of the installation power.
And step S54, considering the steps S51-S53, enabling the installation capacity P s of the physical energy storage module 40 to meet the following formula (18) P s＝(Pe2-kg2Pg)/ks, wherein P e2 is used for intelligently calculating air conditioner energy consumption by manpower, k g2Pg is a second distribution coefficient k g2 multiplied by the installation capacity P g of the power generation module 20, the product is obtained, the air conditioner load direct use power of the power generation module is obtained, and k s is a use coefficient of the water energy storage capacity P s.
The method for determining the installation capacity of the zero-carbon building energy control system based on artificial intelligence is described above, and the control system architecture for calculating the method is described below.
As shown in fig. 8, the zero-carbon construction energy control system of the present invention includes a system controller 10, and the system controller 10 electrically controls the power generation module 20, the chemical energy storage module 30, the physical energy storage module 40, the electric vehicle module 50, and the construction energy consumption management module 60 through a data bus 11. Wherein:
The power generation module 20 comprises a power generation module controller 21 connected with the data bus 11, and the power generation module controller 21 is further connected with a photovoltaic power supply unit 22 and a wind power supply unit 23.
The chemical energy storage module 30 includes a chemical energy storage module controller 31 connected to the data bus 11, and the chemical energy storage module controller 31 is further connected to a storage battery 32.
The physical energy storage module 40 includes a physical energy storage module controller 41 connected to the data bus 11, where the physical energy storage module controller 41 is further connected to a water energy storage device 42.
The electric automobile module 50 includes an electric automobile module controller 51 connected to the data bus 11, and the electric automobile module controller 51 is further connected to an electric automobile 52 and a charging pile 53.
The building energy consumption management module 60 comprises an energy consumption management module receiver 61 connected with the data bus 11, and the energy consumption management module receiver 61 obtains the energy consumption data of the zero-carbon building energy system through the data bus 11.
Referring to fig. 9, a system controller 10 workflow of the zero-carbon building energy control system of the present invention is shown. The method comprises the steps of starting parameter setting, sequentially inputting basic parameters, acquiring historical data, acquiring prediction data, randomly generating initial PSO particle data of a storage battery, judging whether particles meet storage battery constraint conditions after the initial PSO particle data of the storage battery are generated, starting iteration when the constraint conditions are met, and adjusting initial values (parameter setting and/or basic parameter inputting) to meet the constraint conditions when the constraint conditions are not met, and starting iteration after adjustment is completed.
And then sequentially calculating the adaptive value of the storage battery and randomly generating water energy storage initial PSO particle data, judging whether the particles meet the water energy storage constraint condition after the water energy storage initial PSO particle data are generated, starting iteration when the constraint condition is met, and adjusting initial values (parameter setting and/or basic parameter input) to meet the constraint condition when the constraint condition is not met, and starting iteration after the adjustment is completed.
And then, sequentially calculating a water energy storage adaptive value, randomly generating initial PSO particle data of the electric automobile, judging whether particles meet the constraint condition of the electric automobile after the initial PSO particle data of the electric automobile are generated, starting iteration when the constraint condition is met, and adjusting initial values (parameter setting and/or basic parameter input) to meet the constraint condition when the constraint condition is not met, and starting iteration after the adjustment is completed.
Then, the steps of calculating the adaptation value of the electric vehicle, starting the next iteration, judging whether the iteration number is reached, returning to the step of randomly generating the initial PSO particle data of the storage battery when the iteration number is not reached, and ending the operation of the system controller 10 when the iteration number is reached are sequentially performed.
Wherein PSO refers to a particle swarm Optimization algorithm (PARTICLE SWARM Optimization), and is a random Optimization technology based on population. The particle swarm optimization algorithm mimics the swarm behavior of insects, herds, shoals, fish, etc., which search for food in a collaborative manner, each member of the swarm constantly changing its search pattern by learning its own experience and the experience of other members.
In the embodiment of the present invention, the water storage device 42 of the physical energy storage module 40 is used as an energy storage device of an air conditioning system, and the specific usage method is to store the cold energy of the air conditioning system in the electricity consumption low-peak period (the period with lower electricity charge) and release the stored energy in the electricity consumption peak period (the period with higher electricity charge) by using the water storage device 42, and then use the energy for air conditioning. Therefore, the problems of huge storage battery arrangement and poor economy caused by the method that the energy consumption of an air conditioning system is high and the storage battery is used for storing energy and converting the stored energy into cold and hot energy required by the air conditioner are solved, meanwhile, the problems of excessive storage battery arrangement, improvement of storage battery equipment faults and fire safety are avoided. In addition, the storage battery is used for storing energy to provide energy consumption for the air conditioning system, 3 times of energy conversion are needed, namely electric energy, chemical energy, electric energy and heat energy, and the water energy storage device 42 is used as the energy storage device of the air conditioning system, only 1 time of conversion is needed, namely electric energy and heat energy, so that the energy conversion efficiency of the water energy storage device is higher.
In the embodiment of the present invention, the electric automobile module 50 is used to increase the accuracy and flexibility of the artificial intelligence-based zero-carbon building energy control system of the present invention. The electric quantity of the electric vehicles is not small in size in the future, and according to the existing research, the electric vehicles can be charged and discharged to a power grid in the future, and the service life of the battery is not influenced by the discharging. Therefore, the zero-carbon building energy control system of the invention utilizes the energy consumption characteristics of the electric automobile 52 to be different from other energy consumption equipment by arranging the electric automobile module 50, so that the equipment configuration of the photovoltaic power supply unit 22, the wind power supply unit 23, the storage battery 32 of the chemical energy storage module 30 and the like of the power generation module 20 is more accurate on the basis of considering the characteristics of the electric automobile independently. In addition, when the building energy consumption is higher, the electric automobile module controller 51 can also control the electric automobile 52 and/or the charging pile 53 to discharge, so that the electric energy is supplemented into the zero-carbon building energy control system, and the flexibility of the system is improved.
The zero-carbon building energy system is composed of a plurality of modules, including a power generation module, a chemical energy storage module, a physical energy storage module, an electric automobile module and the like, wherein the operation logic of each module is completely different, and a mature and practically applicable system design cannot be formed on the basis that the mathematical model and the constraint condition of each module and the constraint relation among the modules are not found. Therefore, in the embodiment of the invention, the zero-carbon building energy control system is based on the system architecture, and a mathematical model for allocating energy data is provided according to the logical relation among the modules, and the mathematical model has high accuracy and high reliability, and is a feasible method for determining the installation capacity of the zero-carbon building energy control system, which can be applied to specific projects.
Because different buildings have different annual energy consumption curves due to the building properties and the local meteorological data, and the national holidays change, the annual energy consumption data also change. In the embodiment of the invention, the annual energy consumption data (annual energy consumption graph) obtained by repeated training by adopting an artificial intelligence algorithm is provided, and the method has high reliability. The algorithm has a self-learning function through application in different projects, so that the application accuracy in the zero-carbon building energy system is higher and higher. Specifically, the artificial intelligence algorithm principle adopted by the invention is that under the guidance of a certain algorithm, the system searches the optimal result by itself, and the purpose of the algorithm is to guide the system to search the optimal result suitable for energy consumption data retrieval by itself.
The present invention has been described in detail with reference to the drawings and embodiments, and one skilled in the art can make various modifications to the invention based on the above description. Accordingly, certain details of the illustrated embodiments are not to be taken as limiting the invention, which is defined by the appended claims.
Claims (8)
1. The method for determining the installation capacity of the zero-carbon building energy control system based on artificial intelligence is characterized by comprising a power generation module, a chemical energy storage module and a physical energy storage module, wherein the method comprises the following steps:
step S10, acquiring active calculation data1 of a building;
step S20, obtaining the meteorological data2 of the ground where the building is located;
Step S30, calculating the installation capacity P g of the power generation module;
Step S40, calculating the installation capacity P b of the chemical energy storage module;
S50, calculating the installation capacity P s of the physical energy storage module;
the system further comprises an electric automobile module, wherein the electric automobile module comprises an electric automobile and a charging pile, a storage battery is arranged in the chemical energy storage module, and the S40 comprises:
Step S41, on the basis that the installation capacity P b of the chemical energy storage module meets the requirement of electric equipment except an air conditioner and the use of the charging pile is not counted at peak time, the installation capacity P b of the chemical energy storage module is calculated according to the following formula (14) by P b＝Pe1+Pe3, wherein P e1 is illumination energy consumption and P e3 is electric energy consumption;
Step S42, considering the electric automobile discharge at a peak stage, and supplementing the electric power use of electric equipment except an air conditioner, wherein the duty ratio coefficient of the capacity of the discharge equipment in the installation capacity of the charging pile is k 4;
Step S43, except for the use of air conditioner, the installation capacity P b of the chemical energy storage module deducts the real-time electric quantity distribution part of the power generation module, and the first distribution coefficient k g1 is considered as k g1, wherein the first distribution coefficient k g1 is calculated according to the following formula (15) k g1＝(W1+W3)/W0, W 1 is total annual illumination energy consumption, W 3 is total annual electric energy consumption, and W 0 is total annual energy consumption;
step S44, the constraint condition of the storage battery is the optimal use interval;
Step S45, considering the steps S41-S44, enabling the installation capacity P b of the chemical energy storage module to meet the following formula (16), wherein P b＝1.25(Pe1+Pe3-kg1Pg-k4P4 is P e1 which is illumination energy consumption, P e3 which is electric power energy consumption, k g1Pg which is a first distribution coefficient k g1 multiplied by the installation capacity P g of the power generation module, obtaining direct use power of the illumination load of the power generation module, and k 4P4 which is a duty ratio coefficient k 4 multiplied by the active calculation power P 4 of the charging pile, and obtaining electric vehicle discharge power when the product is peak time;
s50 includes:
Step S51, enabling the installation capacity P s of the physical energy storage module to meet the air conditioner energy consumption P e2;
Step S52, removing the use of air-conditioning electric equipment, wherein the installation capacity P s of the physical energy storage module deducts the real-time electric quantity distribution part of the power generation module, and the second distribution coefficient k g2 is considered to be k g2, and is calculated according to the following formula (17) k g2＝W2/W0, wherein W 2 is the annual energy consumption of the air conditioner, and W 0 is the annual total energy consumption;
Step S53, setting the use coefficient of the installation capacity P s of the physical energy storage module as k s;
and S54, considering the steps S51-S53, enabling the installation capacity P s of the physical energy storage module to meet the following formula (18) P s＝(Pe2-kg2Pg)/ks, wherein P e2 is used for intelligently calculating air conditioner energy consumption by manpower, k g2Pg is a second distribution coefficient k g2 multiplied by the installation capacity P g of the power generation module, and the obtained product is the air conditioner load direct use power of the power generation module, and k s is the use coefficient of the water energy storage capacity P s.
2. The method for determining the installation capacity of an artificial intelligence-based zero-carbon construction energy control system according to claim 1, wherein the step S10 further comprises:
Step S11, extracting active calculation power P 0 of the transformer according to a transformer load factor calculation table;
Step S12, acquiring the active calculation power of the lighting, air conditioning, electric power and charging pile, and distributing transformer active calculation power P 0 according to a load factor calculation table, wherein P 0＝P1+P2+P3+P4 is shown in the following formula (1), P 0 is transformer active calculation power, P 1 is lighting active calculation power, P 2 is air conditioning active calculation power, P 3 is electric active calculation power, and P 4 is charging pile active calculation power.
3. The method for determining the installation capacity of the artificial intelligence-based zero-carbon building energy control system according to claim 1 or 2, wherein a photovoltaic power supply unit and a wind power supply unit are arranged in the power generation module, and the step S20 further comprises:
S21, calculating a graph of total annual energy consumption P e0 by adopting an artificial intelligence algorithm according to building properties, meteorological data and national holiday information;
Step S22, calculating and obtaining a graph of illumination energy consumption, air conditioner energy consumption, electric power energy consumption and charging pile energy consumption by adopting an artificial intelligence algorithm according to a graph of total daily energy consumption P e0, wherein the total daily energy consumption P e0 is represented by the following formula (2), P e0＝Pe1+Pe2+Pe3+Pe4, P e0 is total daily energy consumption, P e1 is illumination energy consumption, P e2 is air conditioner energy consumption, P e3 is electric power energy consumption and P e4 is charging pile energy consumption;
Step S23, calculating a graph of annual photovoltaic power generation capacity P V0 of a unit photovoltaic power supply unit according to the meteorological data2 of the place where the building is located;
and S24, calculating a graph of annual wind power generation capacity P W0 of the unit wind power supply unit according to the meteorological data2 of the place where the building is located.
4. The artificial intelligence based zero-carbon construction energy control system installation capacity determination method according to claim 3, wherein the calculating step of the single day total energy consumption P e0 comprises:
providing annual energy consumption data of each type of building;
dividing all data into spring and autumn, summer and winter according to weather;
classifying the energy consumption data of each season by combining with corresponding meteorological data, and respectively providing a combination of working days/rest days, sunny days/cloudy days/rainy days;
And training corresponding data through an artificial intelligence algorithm to realize data fitting, and respectively extracting different combinations of each quarter to obtain a graph of the typical 24-hour single-day total energy consumption P e0.
5. The method for determining the installation capacity of an artificial intelligence-based zero-carbon construction energy control system according to claim 3, wherein the step S30 further comprises:
S31, calculating the installation capacity P g of the power generation module according to the following formula (3), wherein alpha is the installation unit number of the photovoltaic power supply units, P V is the power generation amount of the unit photovoltaic power supply units, beta is the installation unit number of the wind power supply units, and P W is the power generation amount of the unit wind power supply units;
And S32, calculating annual total energy consumption W 0, illumination annual total energy consumption W 1, air conditioner annual energy consumption W 2, electric power annual energy consumption W 3 and charging pile annual energy consumption W 4 according to the following formulas (4) to (8), wherein:
formula (4) total annual energy consumption W 0,
Formula (5) total annual energy consumption W 1 of illumination,
The annual energy consumption W 2 of the air conditioner,
The annual energy consumption W 3 of the electric power is shown in the formula (7),
The annual energy consumption W 4 of the charging pile is shown in the formula (8),
And S33, calculating annual energy generation W G of the power generation module, annual energy generation W V of the photovoltaic power supply unit and annual energy generation W W of the wind power supply unit according to the following formulas (9) to (11), wherein:
Annual energy production W of the power generation module G,WG＝WV+WW
Formula (10) annual energy production W V of the photovoltaic power supply unit,
Formula (11) annual energy production W W of the wind power supply unit,
Step S34, ensuring that the generated energy of the system is more than or equal to the used energy, considering the installed allowance k e times, and calculating according to the following formula (12) W V+WW≥keW0, wherein W V+WW is annual generated energy of the power generation module, and W 0 is annual total energy consumption;
and S35, constraint conditions of the photovoltaic power supply unit and the wind power supply unit are used for ensuring the best economic benefit.
6. The artificial intelligence based zero-carbon building energy control system installation capacity determination method according to claim 5, wherein:
The minimum value of the cost in the service life of the power generation module is calculated according to the following formula (13), wherein lambda is the cost in the service life of the unit photovoltaic power supply unit, mu is the cost in the service life of the unit wind power supply unit, alpha is the installation unit number of the photovoltaic power supply unit, and beta is the installation unit number of the wind power supply unit.
7. The artificial intelligence based zero-carbon construction energy control system installation capacity determination method according to claim 3, wherein:
And in the step S44, the optimal use interval of the storage battery is 60% -80% of the installation capacity.
8. The artificial intelligence based zero-carbon building energy control system installation capacity determination method according to claim 1, wherein:
the system comprises a system controller, wherein the system controller electrically controls a power generation module, a chemical energy storage module, a physical energy storage module and an electric automobile module through a data bus,
The power generation module comprises a power generation module controller connected with the data bus, wherein the power generation module controller is additionally connected with a photovoltaic power supply unit and a wind power supply unit;
the chemical energy storage module comprises a chemical energy storage module controller connected with the data bus, wherein the chemical energy storage module controller is additionally connected with a storage battery;
the physical energy storage module comprises a physical energy storage module controller connected with the data bus, wherein the physical energy storage module controller is additionally connected with water energy storage equipment;
The electric automobile module comprises an electric automobile module controller connected with the data bus, and the electric automobile module controller is additionally connected with an electric automobile and a charging pile.
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