Method for intelligent site selection and generation of building group in Inner Mongolia autonomous region based on wind-solar-thermal comprehensive model

Abstract

The application discloses a kind of based on wind light heat integrated model's Inner Mongolia autonomous region building group intelligent site selection and generation method, first by obtaining Inner Mongolia autonomous region energy data, identification Inner Mongolia autonomous region building group layout mode, then utilize wind light heat integrated model calculation total energy supply, using algorithm calculation energy benefit maximization Inner Mongolia autonomous region building intelligent site selection, generate energy equipment and Inner Mongolia autonomous region building integration scheme, and multi-scheme evaluation output.The present application can comprehensively utilize the region wind light heat clean energy, according to energy benefit, Inner Mongolia autonomous region building site selection arrangement is generated, and Inner Mongolia autonomous region building and energy equipment integration scheme is generated, which provides continuous and stable energy supply for Inner Mongolia autonomous region building, improves the happiness index of Inner Mongolia autonomous region residents, and reduces carbon emissions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban planning, and particularly relates to an intelligent site selection and generation method for building groups in Inner Mongolia Autonomous Region based on a wind-sun-heat comprehensive model. BACKGROUND

[0002] The Inner Mongolia region is sparsely populated, and the traditional power grid system is difficult to cover. At the same time, the Inner Mongolia region is one of the wind energy resource concentration areas in China, and the exploitable wind energy resources are about 250 million kilowatts; the solar energy resources are abundant, with annual sunshine hours of 2600-3400 hours and solar radiation energy of about 140-190 kilowatt hours / square meter; the geothermal energy resources have great potential and broad application prospects. The abundant renewable energy resources in the Inner Mongolia region provide a strong guarantee for the sustainable development of the region. The distributed energy supply system with multiple energy sources in cooperation can be used as a reliable energy supply method to provide stable and sustainable energy supply for local herdsmen, meet their daily life electricity and heating needs, and improve the quality of life.

[0003] At present, the building group site selection and generation method mainly considers the overall volume of the building, the building shape and the combination of the building shape, the building sunshine and orientation at the building monomer level, and mainly considers the population distribution, the economic development level, the social and economic factors of infrastructure, and the traffic conditions and functional requirements of the building group at the group level. For example, CN114519537A "A site selection method for light charging and storage sharing building system", which takes the regional population density, housing cost, and traffic accessibility as the evaluation system for building site selection, is only applicable to urban areas, and does not consider the distribution of renewable resources; CN116702261 "A photovoltaic building group optimization layout method and system" uses artificial intelligence method to pre-layout the building group, and selects the optimal scheme based on the surface irradiance key factor, and the invention focuses on the optimization design calculation of community building group from the perspective of photovoltaic utilization. The existing method has not yet proposed a layout method that can both retain the layout characteristics of the building group in Inner Mongolia Autonomous Region and consider the comprehensive distribution and availability of renewable energy in the Inner Mongolia region.

[0004] In the aspect of building group generation, the prior art focuses on the energy saving reconstruction of buildings in Inner Mongolia Autonomous Region, and converts the building energy demand to clean energy such as solar energy and wind energy. For example, CN109995082B “Building phase change energy storage electric heating combined dispatching method considering wind and light fluctuation” establishes a thermodynamic model and an electric heating combined dispatching model of a building phase change energy storage system, and the invention focuses on establishing a model to determine the dispatching plan of building electricity purchase and sale; CN116111946 “A wind-light-heat-storage-direct-flexible energy building system” uses photovoltaic and air heat energy to provide electric energy and heat energy by combining new energy building materials and micro-wind power generation technology, and the invention focuses on using new energy materials to realize energy building. However, there is no energy equipment and building integration generation method for buildings in Inner Mongolia Autonomous Region at present, and the prior art cannot save the characteristic style of buildings in Inner Mongolia Autonomous Region, nor can it solve the problems of low energy density, intermittency and randomness of single renewable energy utilization of buildings in Inner Mongolia Autonomous Region. SUMMARY

[0005] The present application aims to provide a wind-light-heat comprehensive model based intelligent site selection and generation method for building groups in Inner Mongolia Autonomous Region, to solve the problems raised in the above background art.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical scheme:

[0007] A wind-light-heat comprehensive model based intelligent site selection and generation method for building groups in Inner Mongolia Autonomous Region, the method comprising the following steps:

[0008] S1: Obtain energy data in Inner Mongolia Autonomous Region

[0009] Obtain solar energy data in Inner Mongolia Autonomous Region from a global meteorological inversion database of a weather forecast research institution, obtain wind energy data under typical meteorological year conditions from a benchmark ground meteorological observation station, and obtain geothermal energy data in Inner Mongolia Autonomous Region from an energy authority. The energy data is summarized in 103 county-level administrative units in Inner Mongolia Autonomous Region as a statistical unit, the county-level administrative area and prefectural-level administrative area names are marked, and saved in plain text format;

[0010] S2: Identify building group layout mode in Inner Mongolia Autonomous Region

[0011] Construct a building case set in Inner Mongolia Autonomous Region including traditional Mongolian yurt and improved Mongolian yurt, the case set includes location information and high-precision oblique photography model of typical building groups, extract and calculate the shape index of each building, and use machine learning algorithm to cluster to obtain the building group layout mode in Inner Mongolia Autonomous Region, and the top 10% are taken as typical building group layout mode in Inner Mongolia Autonomous Region;

[0012] S3: Construct a wind-light-heat comprehensive model

[0013] A wind-solar-geothermal integrated model is constructed, which includes micro-wind power generation, photovoltaic power generation, ground source heat pump heating and wind-solar-geothermal integrated regulation system. The shallow stratum temperature monitor is installed on the ground within the radius of 100m of the building group in Inner Mongolia Autonomous Region to monitor the soil depth and corresponding temperature. The intelligent sensor is arranged at the connection of Taonao and Wuni to monitor the wind speed, air temperature and light changes. The energy supply mode of the wind-solar-geothermal integrated model is adjusted in real time through the monitoring results and intelligent central control switch, and the total energy supply is calculated by using the wind-solar-geothermal integrated model energy supply formula E, which includes wind power generation, photovoltaic power generation and ground source heat pump heating capacity;

[0014] S4: Inner Mongolia Autonomous Region building intelligent site selection with maximum energy benefit

[0015] The geographical information data, topographic data and climate data of the target area are obtained, wherein the geographical information data includes administrative division information and latitude and longitude information, and the energy data of the target area is obtained by calling S1. The above data is input into the geographic information system for spatial matching, and the target area is divided into grids according to 100m×100m. The center point of each grid is generated and labeled as An. The terrain data and climate data of the target area are input into the simulation model to obtain the real-time solar radiation intensity value, real-time wind speed and straight-line distance from the ground source heat well of each grid A. After standardization, the energy potential value AE is added and assigned to the grid center point. The Getis-Ord GI* algorithm is used to find the high value area of energy benefit, and the high value area grid number is recorded and the energy benefit maximum site selection boundary is drawn;

[0016] S5: Energy equipment and Inner Mongolia Autonomous Region building integration scheme generation

[0017] The Inner Mongolia Autonomous Region building group layout mode formed in the energy consumption benefit maximization site boundary S2 is inserted into the benchmark scheme, the number of photovoltaic power generation equipment, wind power generation equipment and ground source heat pump equipment is set, the S3 wind light heat comprehensive model is called to calculate the scheme energy, and it is judged whether the energy supply meets the minimum energy consumption demand of the benchmark scheme. For the scheme meeting the threshold, the energy equipment and the Inner Mongolia Autonomous Region building integration are generated, the monocrystalline silicon 250W photovoltaic panel is supported on the Inner Mongolia Autonomous Region building with a wooden strip, and a support pipe is inserted into the Inner Mongolia Autonomous Region building to form a umbrella rib structure, the inclination angle of the photovoltaic panel is adjusted according to the latitude, the 8-turbine small vertical axis wind turbine is installed on the side of the Inner Mongolia Autonomous Region building in combination with Han, the 10Kw ground source heat pump system equipment is installed underground of the Inner Mongolia Autonomous Region building and connected with the domestic water and heating pipeline, the lithium ion battery composed of solar and wind energy storage equipment is installed in the Inner Mongolia Autonomous Region building and connected with the photovoltaic power generation equipment, wind power generation equipment, and the intelligent central control switch is fixed on the Han of the Inner Mongolia Autonomous Region building; wherein, the number of monocrystalline silicon 250W photovoltaic panels is set as NPV, the number of 8-turbine small vertical axis wind turbine equipment is set as NWG, and the number of heat pumps is set as NGH, a group of parameters NPV, NWG and NGH are positive integers from a random function generated full set, NPV≤30, NWG≤30, and NGH≤1;

[0018] S6: Multi-scheme scoring and output

[0019] The generated multi-scheme is scored according to the energy efficiency dimension, economic dimension and safety dimension, and a radar chart is generated, and the Inner Mongolia Autonomous Region building group intelligent generation scheme is printed, including the Inner Mongolia Autonomous Region building group plane layout, energy system plane layout, energy equipment point layout and scheme scoring radar chart;

[0020] Preferably, in the step S1, the Inner Mongolia Autonomous Region energy data, wherein the solar energy data includes solar radiation intensity and sunshine hours; the wind energy data includes average wind speed and wind direction; and the geothermal energy data includes geothermal well position and ground temperature.

[0021] Preferably, in the step S2, the shape index of each building is extracted and calculated, and a machine learning algorithm is used to cluster to obtain the Inner Mongolia Autonomous Region building group layout mode, wherein the building shape index includes the number of Mongolian yurts in the Inner Mongolia Autonomous Region building group, the coordinates of the bottom center of the Mongolian yurt and the direction of the connecting line, the difference between the length of the bottom connecting line and the radius, the plane outer contour of each Mongolian yurt, the bottom diameter, the Han wall height, and the total height of the Mongolian yurt. The shape index is input into a hierarchical clustering algorithm, the Euclidean distance is used as the distance measurement, the number of clusters is automatically determined, and the clustering interval is set to 50-100.

[0022] Preferably, the step S3 adjusts the wind-solar-thermal comprehensive model energy supply mode in real time by monitoring results and intelligent central control switch, including two sub-modules of power generation and heat supply. The power generation module includes photovoltaic and wind power coordinated power generation, and the heat supply module includes geothermal energy supply and electric heating conversion. All monitoring environmental conditions and corresponding energy supply modes are shown in the following table:

[0023]

[0024] Preferably, the S3 wind-solar-thermal comprehensive model energy supply formula E is:

[0025] E = 3600P PV t PV + 3600P WG t WG + Q GH = 3600N pv P m ηt PV + 3600N WG t WG + A GH ΔT

[0026] Wherein, N pv is the number of photovoltaic devices, P m is the rated capacity of a single component of the photovoltaic device, η is the conversion efficiency of the photovoltaic component, t PV is the actual sunshine hours, N WG is the number of wind power devices, ρ is the air density coefficient, A is the wind sweeping area of a single wind power device, V is the average wind speed, is the wind energy conversion rate, t WG is the working hours of the wind power device, A GH is the ground source heat pump heating area, and ΔT is the temperature difference between the inlet and outlet water temperatures of the ground source heat pump.

[0027] Preferably, the step S4 is standardized and added to obtain the energy potential value AE, i.e. AE= , wherein Al is the real-time solar radiation intensity value, Aw is the real-time wind speed, and Ad is the straight-line distance from the grid center point to the ground source heat well.

[0028] Preferably, the Getis-Ord GI* algorithm according to S1 is used to find the high value area of energy benefit, i.e. the formula = is used for calculation, wherein x j is the energy potential value of element j, w i,j is the spatial weight between elements i and j, n is the total number of elements, and , , The higher the z value is, the higher the wind-solar-thermal potential value of the represented area is.

[0029] Preferably, the step S5 places the Inner Mongolia autonomous region building group layout mode of S2, that is, the center of gravity of the typical Inner Mongolia autonomous region building group layout mode obtained by S2 and the center of gravity of the energy benefit maximization site boundary are aligned in the geographic information platform, if the building group layout exceeds the range of the energy benefit maximization site boundary, the layout mode is deleted.

[0030] Preferably, the step S5 is the minimum energy consumption demand, that is, the minimum energy consumption demand is calculated according to the limit value of the indoor calculation temperature, air change frequency, heating system operation time, lighting power density, equipment power and thermal efficiency specified in the Design Standard of Residential Buildings in Severe Cold and Cold Regions, and the minimum energy consumption demand value

[0031] ,

[0032] Wherein, K is the heat transfer coefficient of the outer wall (W / m 3 ·K), U is the volume of the outer wall, T 室内 is the indoor temperature of the building, T 室外 is the outdoor temperature of the building, V is the air flow (m 3 / h), ρ is the air density (kg / m 3 ), Cp is the specific heat capacity (J / (kg·°C)), N is the number of lamps, W is the lamp power, T is the lighting system usage time, t is the heating time, α is the heating load, and η is the thermal efficiency.

[0033] Preferably, the step S6 is scored according to the energy efficiency dimension, the economic dimension and the safety dimension, the energy efficiency score = 100×(N PV η PV + N WG η WG + N GH η GH ), the economic score = , and the safety score = + + Wherein, N PV is the number of photovoltaic equipment, N WG is the number of wind power equipment, N GH is the number of ground source heat pump equipment, η PV is the energy utilization rate of photovoltaic equipment, η WG is the energy utilization rate of wind power equipment, η GH is the utilization rate of 10kw ground source heat pump, and P is the sum of the initial investment cost and maintenance cost of energy equipment.

[0034] Compared with the prior art, the present application has the following beneficial effects:

[0035] 1. The higher the z value obtained by using the Getis-Ord GI* algorithm to find the high value area of energy efficiency, the higher the regional wind and light potential value, and the maximum energy efficiency site boundary is determined accordingly, realizing the efficient use of energy from the building site level of Inner Mongolia Autonomous Region, and at the same time increasing the scientificity and site selection speed of building site selection in Inner Mongolia Autonomous Region;

[0036] 2. The present application builds an Inner Mongolia Autonomous Region building case set containing traditional Mongolian yurt and improved Mongolian yurt, the case set includes location information and high-precision oblique photography model of typical building group, extracts and calculates the shape index of each building, and uses machine learning algorithm to cluster to obtain Inner Mongolia Autonomous Region building group layout mode, and the first 10% of which is taken as a typical Inner Mongolia Autonomous Region building group layout mode, which increases the beauty of Inner Mongolia Autonomous Region building layout and avoids the damage of Inner Mongolia Autonomous Region building texture caused by chaotic layout;

[0037] 3. The present application builds a wind-solar-thermal comprehensive model by comprehensively utilizing wind energy, solar energy and geothermal energy, which includes micro-wind power generation, photovoltaic power generation, ground source heat pump heating and wind-solar-thermal comprehensive regulation system. Within the radius of 100m of the Inner Mongolia Autonomous Region building group, shallow stratum temperature monitor is installed on the ground to monitor the soil depth and corresponding temperature, and intelligent sensor is arranged at the connection of Taonao and Wuni to monitor wind speed, air temperature and light changes. The wind-solar-thermal comprehensive model energy supply mode is adjusted in real time through the monitoring results and intelligent central control switch, and the total energy supply is calculated by using the wind-solar-thermal comprehensive model energy supply formula E, which provides stable and continuous energy supply for Inner Mongolia Autonomous Region buildings, eliminates the problem of insufficient energy supply caused by insufficient single energy, and improves the comfort of Inner Mongolia Autonomous Region buildings;

[0038] 4. The present application calculates the energy supply of the scheme by using the wind-solar-thermal comprehensive model, judges whether the energy supply meets the minimum energy consumption demand of the benchmark scheme, and generates the energy equipment and Inner Mongolia Autonomous Region building integration for the scheme meeting the threshold. This design scheme of wind-solar-thermal energy complementary integration with Inner Mongolia Autonomous Region building can make the most of wind energy, solar energy and geothermal energy, meet the needs of residents' life, reduce carbon emissions, make full use of clean energy, reduce the use of traditional energy such as coal, and be more green and environmentally friendly. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 It is a flow chart of an Inner Mongolia Autonomous Region building group intelligent site selection and generation method based on a wind-solar-thermal comprehensive model;

[0040] Figure 2 It is a site selection case diagram of Inner Mongolia Autonomous Region building group in a target area of an Inner Mongolia Autonomous Region building group intelligent site selection and generation method based on a wind-solar-thermal comprehensive model;

[0041] Figure 3 Fig. 1 is a typical Inner Mongolia autonomous region building group layout mode case diagram of a target region of an Inner Mongolia autonomous region building group intelligent site selection and generation method based on a wind-solar-thermal comprehensive model;

[0042] Figure 4 Fig. 2 is an energy equipment and building integration generation scheme case diagram of a target region of an Inner Mongolia autonomous region building group intelligent site selection and generation method based on a wind-solar-thermal comprehensive model. DETAILED DESCRIPTION

[0043] The application will be described in further detail below in conjunction with the accompanying drawings and embodiments:

[0044] According to Figures 1-4 the specific implementation process is as follows:

[0045] The Inner Mongolia autonomous region building group intelligent site selection and generation method based on a wind-solar-thermal comprehensive model specifically includes the following steps:

[0046] S1: Inner Mongolia autonomous region energy data acquisition

[0047] Solar energy data of the Inner Mongolia autonomous region is obtained from a global meteorological inversion database of a weather forecast research institution, wind energy data under typical meteorological year conditions is obtained from a benchmark ground meteorological observation station, and geothermal energy data of the Inner Mongolia autonomous region is obtained from an energy authority. The energy data is collected in 103 county-level administrative units of the Inner Mongolia autonomous region as a statistical unit, the county-level administrative area and prefecture-level administrative area names are marked, and saved in a plain text format.

[0048] S2: Inner Mongolia autonomous region building group layout mode identification

[0049] A building case set of the Inner Mongolia autonomous region containing traditional Mongolian yurts and improved Mongolian yurts is constructed, the case set includes location information and high-precision oblique photography models of typical building groups, shape indexes of each building are extracted and calculated, a machine learning algorithm is used for clustering to obtain an Inner Mongolia autonomous region building group layout mode, and the first 10% of the Inner Mongolia autonomous region building group layout mode is taken as a typical Inner Mongolia autonomous region building group layout mode.

[0050] S3: Wind-solar-thermal comprehensive model construction

[0051] A wind-solar-geothermal integrated model is constructed, which includes micro-wind power generation, photovoltaic power generation, ground source heat pump heating and wind-solar-geothermal integrated regulation system. A shallow stratum temperature monitor is installed on the ground within a radius of 100m of the building group in Inner Mongolia Autonomous Region to monitor the soil depth and corresponding temperature. An intelligent sensor is arranged at the junction of Taonao and Wuni to monitor wind speed, air temperature and light changes. The energy supply mode of the wind-solar-geothermal integrated model is adjusted in real time through the monitoring results and intelligent central control switch, and the total energy supply is calculated by using the wind-solar-geothermal integrated model energy supply formula E, which includes wind power generation, photovoltaic power generation and ground source heat pump heating;

[0052] S4: Energy benefit maximization of Inner Mongolia Autonomous Region building intelligent site selection

[0053] Geographical information data, topographic data and climate data of the target area are obtained, wherein the geographical information data includes administrative division information and latitude and longitude information, and the energy data of the target area is obtained by calling S1. The above data is input into a geographic information system for spatial matching, and the target area is divided into grids according to 100m x 100m, a center point is generated for each grid and labeled as An. The terrain data and climate data of the target area are input into a simulation model to obtain the real-time solar radiation intensity value, real-time wind speed and straight-line distance from the ground source heat well of each grid A. After standardization, the energy potential value AE is added and assigned to the grid center point. The Getis-Ord GI* algorithm is used to find the high value area of energy benefit, and the high value area grid number is recorded and the energy benefit maximization site selection boundary is drawn. This step is the core innovation point of the present application, which comprehensively considers the distribution and availability of solar energy, wind energy and geothermal energy resources, and then uses intelligent algorithm to determine the site selection range of the building group in Inner Mongolia Autonomous Region;

[0054] S5: Energy equipment and Inner Mongolia Autonomous Region building integration scheme generation

[0055] The Inner Mongolia autonomous region building group layout mode benchmark scheme is formed by inserting S2 in the energy consumption benefit maximization site boundary. The number of photovoltaic power generation equipment, wind power generation equipment and ground source heat pump equipment is set, the S3 wind-solar-thermal comprehensive model is called to calculate the scheme energy, and it is judged whether the energy supply meets the minimum energy consumption demand of the benchmark scheme. For the scheme meeting the threshold, the energy equipment and the Inner Mongolia autonomous region building integration are generated, the monocrystalline silicon 250W photovoltaic panel is covered on the Inner Mongolia autonomous region building Wuni, and a support pipe is inserted into the skylight to form a rib structure, the inclination of the photovoltaic panel is adjusted according to the latitude, the 8-turbine small vertical axis wind turbine is installed on the side of the Inner Mongolia autonomous region building combined with Han, the 10Kw ground source heat pump system equipment is installed underground of the Inner Mongolia autonomous region building, and the pipeline is connected with the domestic water and heating pipeline, the solar wind energy storage equipment composed of lithium ion battery is installed in the Inner Mongolia autonomous region building and connected with the photovoltaic power generation equipment and the wind power generation equipment, and the intelligent central control switch is fixed on the Han of the Inner Mongolia autonomous region building. Among them, the number of monocrystalline silicon 250W photovoltaic panels is NPV, the number of 8-turbine small vertical axis wind turbines is NWG, and the number of heat pumps is NGH, a group of parameters NPV, NWG and NGH are positive integers from a random function generated set, NPV≤30, NWG≤30, and NGH≤1. This step is the core innovation point of the present application, which combines the wind-solar-thermal comprehensive energy supply system including photovoltaic power generation, micro-wind power generation, heat pump and intelligent central control switch with the Inner Mongolia autonomous region building, and can solve the problems of low energy density, intermittency and randomness of renewable energy in the prior art, and the style characteristics of the Inner Mongolia autonomous region building are well preserved.

[0056] S6: Multi-scheme scoring and output

[0057] The generated multi-scheme is scored according to the energy efficiency dimension, economic dimension and safety dimension, and a radar chart is generated, and the Inner Mongolia autonomous region building group intelligent generation scheme is printed, including the Inner Mongolia autonomous region building group plane layout, energy system plane layout, energy equipment distribution map and scheme scoring radar chart.

[0058] The Inner Mongolia autonomous region energy data in step S1 specifically includes:

[0059] The solar energy data includes solar radiation intensity and sunshine hours; the wind energy data includes average wind speed and wind direction; the geothermal energy data includes geothermal well position and ground temperature.

[0060] In step S2, the shape index of each building is extracted and calculated, and the Inner Mongolia autonomous region building group layout mode is obtained by using machine learning algorithm clustering, and the implementation process is as follows:

[0061] The extraction includes the number of Mongolian yurts in Inner Mongolia Autonomous Region, the coordinates of the center of the bottom of the Mongolian yurt and the direction of the connecting line, the difference between the length of the connecting line and the radius of the bottom, the outer contour of each Mongolian yurt plane, the diameter of the bottom, the height of the Hanan wall, and the total height of the Mongolian yurt. The above shape indexes are input into the hierarchical clustering algorithm, the Euclidean distance is used as the distance measurement, the number of clusters is automatically determined, and the clustering interval is set to 50-100.

[0062] The process of real-time adjustment of the wind-solar-thermal comprehensive model energy supply mode by monitoring results and intelligent central control switch in step S3 is as follows: the wind-solar-thermal comprehensive model includes two sub-modules of power generation and heat supply. The power generation module includes photovoltaic and wind power coordinated power generation, and the heat supply module includes geothermal energy supply and electric heating conversion. All monitoring environmental conditions and corresponding energy supply modes are shown in the following table:

[0063]

[0064] The process of realizing the energy supply formula E of the wind-solar-thermal comprehensive model in step S3 is as follows:

[0065] The energy supply of the wind-solar-thermal comprehensive model is calculated by formula E,

[0066] E= 3600P PV t PV +3600P WG t WG +Q GH =3600N pv P m ηt PV +3600N WG t WG +A GH ΔT

[0067] Where N pv is the number of photovoltaic devices, P m is the rated capacity of a single component of the photovoltaic device, η is the conversion efficiency of the photovoltaic component, t PV is the actual sunshine hours, N WG is the number of wind power equipment, ρ is the air density coefficient, A is the swept area of a single wind power equipment, V is the average wind speed, is the wind energy conversion rate, t WG is the working hours of the wind power equipment, A GH is the heat supply area of the ground source heat pump, and ΔT is the temperature difference between the inlet and outlet water temperatures of the ground source heat pump.

[0068] The process of realizing the energy potential value AE obtained by adding the standardized values in step S4 is as follows:

[0069] The real-time solar radiation intensity value is standardized as Al= , and the real-time wind speed is standardized as Aw= , the linear distance between the center point of the grid and the geothermal well is normalized as , and the normalized results are added to obtain the energy potential value AE.

[0070] Step S4: The Getis-Ord GI* algorithm is used to find the high-value area of energy benefits, and the implementation process is as follows:

[0071] The formula = is used for calculation, where x j is the energy potential value of element j, w i,j is the spatial weight between elements i and j, n is the total number of elements, and , , The higher the z value, the higher the wind and light potential energy value of the area.

[0072] Step S5: The building group layout mode of Inner Mongolia Autonomous Region is placed in S2, and the implementation process is as follows:

[0073] In the geographic information platform, the center of gravity of the typical building group layout mode of Inner Mongolia Autonomous Region obtained in S2 is aligned with the center of gravity of the energy benefit maximization site boundary. If the building group layout exceeds the energy benefit maximization site boundary range, the layout mode is deleted.

[0074] Step S5: The minimum energy consumption requirement, the specific content is as follows:

[0075] According to the limit values of indoor calculation temperature, air change frequency, heating system operation time, lighting power density, equipment power, and thermal efficiency specified in the Design Standard for Residential Buildings in Severe Cold and Cold Regions, the minimum energy consumption requirement is calculated, and the calculation formula is:

[0076] ,

[0077] Where K is the heat transfer coefficient of the outer wall (W / m3·K), U is the volume of the outer wall, T 室内 is the indoor temperature of the building, T 室外 is the outdoor temperature of the building, V is the air flow (m3 / h), ρ is the air density (kg / m3), Cp is the specific heat capacity (J / (kg·°C)), N is the number of lamps, W is the lamp power, T is the lighting system usage time, t is the heating time, α is the heating load, and η is the thermal efficiency.

[0078] Step S6: Scoring according to the energy efficiency dimension, economic dimension, and safety dimension, and the specific implementation method is as follows:

[0079] Step S6: Scoring according to the energy efficiency dimension, economic dimension, and safety dimension, and the energy efficiency score = 100×(N PVη PV + N WG η WG + N GH η GH ), economic score = 0.2η , safety score = 0.2η + + , wherein N PV is the number of photovoltaic devices, N WG is the number of wind power devices, N GH is the number of ground source heat pump devices, η PV is the energy utilization rate of photovoltaic devices, η WG is the energy utilization rate of wind power devices, η GH is the utilization rate of 10kw ground source heat pump, and P is the sum of initial investment cost and maintenance cost of energy devices.

[0080] The above only describes the embodiments of the present application, and the specific technical solutions and / or common knowledge of characteristics in the scheme are not described in detail. It should be noted that for those skilled in the art, without departing from the technical solutions of the present application, a number of modifications and improvements can be made, which should also be considered as the protection scope of the present application, which will not affect the effect and practicality of the present application. The protection scope of the present application should be subject to the content of its claims, and the specific embodiments in the specification can be used to explain the content of the claims.

Claims

1. A method for intelligent site selection and generation of building groups in Inner Mongolia Autonomous Region based on a wind-solar-thermal integrated model, characterized in that, The method comprises the following steps: S1: Obtain energy data of Inner Mongolia Autonomous Region Obtain solar energy data of Inner Mongolia Autonomous Region through the global meteorological inversion database of weather forecast research institutions, obtain wind energy data under typical meteorological year conditions from the benchmark ground meteorological observation station, obtain geothermal energy data of Inner Mongolia Autonomous Region from the energy authority, and aggregate the energy data in 103 county-level administrative units of Inner Mongolia Autonomous Region as the statistical unit, mark the county-level administrative area and prefecture-level administrative area name and save it in plain text format; S2: Identify the layout mode of building groups in Inner Mongolia Autonomous Region Build a building case set of Inner Mongolia Autonomous Region including traditional Mongolian yurt and improved Mongolian yurt, which includes the location information of typical building groups and high-precision oblique photography model, extract and calculate the shape index of each building, and use machine learning algorithm to cluster to get the layout mode of building groups in Inner Mongolia Autonomous Region, and the top 10% are selected as the typical layout mode of building groups in Inner Mongolia Autonomous Region; S3: Construction of wind-solar-thermal comprehensive model Construct a wind-solar-thermal comprehensive model including micro-wind power generation, photovoltaic power generation, geothermal heat pump heating and wind-solar-thermal comprehensive regulation system, install shallow stratum temperature monitors within 100m radius of the building group in Inner Mongolia Autonomous Region to monitor the soil depth and corresponding temperature, and lay intelligent sensors at the junction of Tao Nao and Wuni to monitor wind speed, air temperature and light changes, adjust the energy supply mode of the wind-solar-thermal comprehensive model in real time through the monitoring results and intelligent central control switch, and calculate the total energy supply E using the wind-solar-thermal comprehensive model energy supply formula, which includes wind power generation, photovoltaic power generation and geothermal heat pump heating capacity; S4: Inner Mongolia Autonomous Region building intelligent site selection for maximum energy benefit Obtain geographic information data, terrain data and climate data of the target area, wherein the geographic information data includes administrative division information and latitude and longitude information, obtain the energy data of the target area by calling S1, input the above data into the geographic information system for spatial matching, divide the target area into grids according to 100m×100m, generate a center point for each grid and label it as An, input the terrain data and climate data of the target area into the simulation model to obtain the real-time solar radiation intensity value, real-time wind speed and straight-line distance from the geothermal well of each grid A, add the standardized values to obtain the energy potential value AE and assign it to the grid center point, use Getis-Ord GI* algorithm to find the high value area of energy benefit, record the grid number of the high value area and draw the boundary of the maximum energy benefit site selection; S5: Energy equipment and Inner Mongolia Autonomous Region building integration scheme generation In the energy consumption benefit maximization site boundary, the Inner Mongolia autonomous region building group layout mode formed by S2 is set as the benchmark scheme, the number of photovoltaic power generation equipment, wind power generation equipment and ground source heat pump equipment is set, S3 wind light heat comprehensive model is called to calculate the scheme energy, it is judged whether the energy supply meets the minimum energy consumption demand of the benchmark scheme, and the energy equipment and the Inner Mongolia autonomous region building integration are generated for the scheme meeting the threshold value. The monocrystalline silicon 250W photovoltaic power generation panel is supported on the Inner Mongolia autonomous region building with a wooden strip, and a support pipe is inserted into the skylight to form a umbrella rib structure. According to the latitude, the inclination angle of the photovoltaic panel is adjusted. The 8 turbine small vertical axis wind turbine is installed on the side of the Inner Mongolia autonomous region building combined with Han. The 10Kw ground source heat pump system equipment is installed in the underground of the Inner Mongolia autonomous region building, and the pipeline is connected with the domestic water and heating pipeline. The lithium ion battery composed of solar energy and wind energy storage equipment is installed in the interior of the Inner Mongolia autonomous region building and connected with the photovoltaic power generation equipment, wind power generation equipment. The intelligent central control switch is fixed on the Han of the Inner Mongolia autonomous region building; wherein, the number of monocrystalline silicon 250W photovoltaic power generation panel is NPV, the number of 8 turbine small vertical axis wind turbine equipment is NWG, and the number of heat pump is NGH. A group of parameters NPV, NWG and NGH are positive integers from a random function generated set, NPV≤30, NWG≤30, NGH≤1; S6: Multi-scheme scoring and output The generated multi-scheme is scored according to energy efficiency dimension, economic dimension and safety dimension and radar chart is generated, and the Inner Mongolia autonomous region building group intelligent generation scheme is printed, including the Inner Mongolia autonomous region building group plane layout, energy system plane layout, energy equipment distribution map and scheme scoring radar chart.

2. The intelligent site selection and generation method for building groups in Inner Mongolia Autonomous Region based on a wind-solar-heat integrated model according to claim 1, characterized in that: The step S1 Inner Mongolia autonomous region energy data, wherein the solar energy data includes solar radiation intensity, sunshine hours; the wind energy data includes average wind speed, wind direction; the geothermal energy data includes geothermal well position, ground temperature. 3.The intelligent site selection and generation method for building groups in Inner Mongolia Autonomous Region based on a wind-solar-heat integrated model according to claim 1, characterized in that: The S2 extracts and calculates the shape index of each building, and uses machine learning algorithm to cluster to obtain the Inner Mongolia autonomous region building group layout mode, wherein the building shape index includes the number of Mongolian yurt of the Inner Mongolia autonomous region building group, the center coordinates and connecting line direction of the bottom of the Mongolian yurt, the difference between the bottom connecting line length and the radius sum, the plane outer contour of each Mongolian yurt, the bottom diameter, the Han wall height, the total height of the Mongolian yurt. The shape index is input into the hierarchical clustering algorithm, the Euclidean distance is used as the distance measurement, the number of clusters is automatically determined, and the clustering interval is set to 50-100.

4. The intelligent site selection and generation method for building groups in Inner Mongolia Autonomous Region based on a wind-solar-heat integrated model according to claim 1, characterized in that: The step S3 wind light heat comprehensive model energy supply formula E: E = 3600P PV t PV + 3600P WG t WG + Q GH = 3600N pv P m ηt PV + 3600N WG t WG + A GH ΔT Where, N pv is the number of photovoltaic devices, P m is the rated capacity of a single component of the photovoltaic device, η is the conversion efficiency of the photovoltaic component, t PV is the actual number of sunshine hours, N WG is the number of wind power devices, ρ is the air density coefficient, A is the wind sweeping area of a single wind power device, V is the average wind speed, is the wind energy conversion rate, t WG is the working hours of the wind power device, A GH is the heating area of the ground source heat pump, and ΔT is the temperature difference between the inlet and outlet water temperatures of the ground source heat pump.

5. The intelligent site selection and layout method for building groups in Inner Mongolia Autonomous Region based on a wind-solar-heat integrated model according to claim 1, characterized in that: The S4 standardization addition obtains the energy potential value AE, that is, AE= , wherein Al is the real-time solar radiation intensity value, Aw is the real-time wind speed, and Ad is the straight-line distance from the grid center point to the ground source heat well. 6.The intelligent site selection and generation method for building groups in Inner Mongolia Autonomous Region based on a wind-solar-heat integrated model according to claim 1, characterized in that, The Getis-Ord GI* algorithm in S1 searches for high-energy-efficiency regions, i.e., it uses the formula... = Perform the calculation, where x j It is the energy potential value of element j, w i,j is the spatial weight between elements i and j, and n is the sum of the elements, and , , The higher the z-value, the higher the potential energy value of wind, solar and thermal energy in the region. 7.The intelligent site selection and generation method for building groups in Inner Mongolia Autonomous Region based on a wind-solar-heat integrated model according to claim 1, characterized in that, The S5 Inner Mongolia autonomous region building group layout mode of S2 is inserted, that is, the center of gravity of the typical Inner Mongolia autonomous region building group layout mode obtained by S2 and the center of gravity of the energy benefit maximization site boundary are aligned in the geographic information platform. If the building group layout exceeds the energy benefit maximization site boundary range, the layout mode is deleted. 8.The intelligent site selection and generation method for building groups in Inner Mongolia Autonomous Region based on a wind-solar-heat integrated model according to claim 1, characterized in that: The step S5 minimum energy consumption demand value is: ， Wherein, K is the heat transfer coefficient of the external wall, unit W / m 3 ·K, U is the volume of the external wall, T 室内 is the indoor temperature of the building, T 室外 is the outdoor temperature of the building, V is the air flow, unit m 3 / h, ρ is the air density, unit kg / m 3 , Cp is the specific heat capacity, unit J / (kg·°C), N is the number of lamps, W is the power of the lamps, T is the use time of the lighting system, t is the heating time, α is the heating load, and η is the thermal efficiency. 9.The intelligent site selection and generation method for building groups in Inner Mongolia Autonomous Region based on a wind-solar-heat integrated model according to claim 1, characterized in that, The S6 is scored according to energy efficiency dimension, economic dimension and safety dimension, energy efficiency score = 100 x (N PV η PV + N WG η WG + N GH η GH ), economic score = , safety score = + + , wherein N PV is the number of photovoltaic devices, N WG is the number of wind power devices, N GH is the number of ground source heat pump devices, η PV is the energy utilization rate of photovoltaic devices, η WG is the energy utilization rate of wind power devices, η GH is the utilization rate of 10kw ground source heat pump, and P is the sum of initial investment cost and maintenance cost of energy equipment.

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