Mongolian land building group intelligent site selection and generation method based on wind-solar-thermal comprehensive model

Abstract

The invention discloses an intelligent site selection and generation method for a building group in a covered land by using a wind, light and heat comprehensive model. The method includes the steps of Mongolian land energy data acquisition, Mongolian land building group layout pattern recognition, wind-solar-thermal comprehensive model construction, energy benefit maximized Mongolian land building intelligent site selection, energy equipment and Mongolian land building integrated scheme generation and multi-scheme scoring and output. According to the method, firstly, a scheme of integrating energy equipment and the masked building is generated by acquiring masked energy data, identifying a layout mode of the masked building group, calculating total energy supply by using a wind-solar-thermal comprehensive model, calculating intelligent site selection of the masked building with maximized energy benefit by using an algorithm, and performing multi-scheme evaluation output. According to the system, regional wind, light and heat clean energy can be comprehensively utilized, site selection and arrangement of the covered-ground buildings are carried out according to energy benefits, a covered-ground building and energy equipment integration scheme is generated, continuous and stable energy supply is provided for the covered-ground buildings, the life happiness index of covered-ground residents is increased, and carbon emission is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of urban planning, and in particular to an intelligent site selection and generation method for Montenegro building complexes based on a comprehensive wind, solar and thermal model. Background Art

[0002] The Inner Mongolia Autonomous Region is vast and sparsely populated, making it difficult for traditional power grid systems to cover it. At the same time, the Inner Mongolia Autonomous Region is one of China's wind energy resource concentration areas, with about 250 million kilowatts of wind energy resources that can be developed and utilized; it has abundant solar energy resources, with annual sunshine hours between 2,600 and 3,400 hours, and solar radiation energy of about 140 to 190 kWh / square meter; geothermal energy resources have great potential and broad application prospects. The rich renewable energy resources in the Inner Mongolia Autonomous Region provide a strong guarantee for the sustainable development of the region. The distributed energy supply system with coordinated multiple energy sources can be used as a reliable energy supply method to provide local herdsmen with a stable and continuous energy supply, meet their daily electricity and heating needs, and improve their quality of life.

[0003] At present, the site selection and generation methods of building complexes mainly consider the overall volume of the building, the building shape and combination of building forms, building sunshine and orientation, etc. at the level of individual buildings, and mainly consider socioeconomic factors such as population distribution, economic development level, infrastructure, and evaluate the traffic conditions around the building complex and the functional requirements of the building complex at the group level. For example, CN114519537A "A site selection method for a photovoltaic charging and storage sharing building system" uses regional population density, housing price cost, and traffic accessibility as the evaluation system for building site selection. This invention is only applicable to urban areas and does not consider the distribution of renewable resources; CN116702261 "A photovoltaic building complex optimization layout method and system" uses artificial intelligence methods to pre-layout the building complex, and selects the optimal solution based on the key factor of surface irradiance. The invention focuses on the optimization design calculation of community building complexes from the perspective of photovoltaic utilization. Existing methods have not yet proposed a layout method that can retain the layout characteristics of the Mongolian building complex and take into account the comprehensive distribution and availability of renewable energy in the Inner Mongolia Autonomous Region.

[0004] In terms of building complex generation, existing technologies focus on energy-saving transformation of Monte buildings, shifting building energy demand to clean energy such as solar energy and wind energy. For example, CN109995082B "Building Phase Change Energy Storage Electricity and Heat Joint Scheduling Method Taking into Account Wind and Solar Fluctuations" establishes a thermodynamic model and an electric-heat joint scheduling model for a building phase change energy storage system. The invention focuses on establishing a model to determine the scheduling plan for the purchase and sale of electricity for a building; CN116111946 "A Wind-Solar-Heat Storage Direct Flexible Energy Production Building System" uses photovoltaics, air thermal energy combined with new energy-producing building materials and breeze power generation technology to provide electricity and heat. The invention focuses on using new energy-producing materials to achieve energy-producing buildings. However, there is currently no method for integrating energy equipment and buildings for Monte buildings. The existing technology can neither preserve the distinctive features of Monte buildings nor solve the problems of low energy density, intermittency and randomness in the use of a single renewable energy source in Monte buildings. Summary of the invention

[0005] The present invention aims to provide a method for intelligent site selection and generation of Montenegrin building complexes based on a comprehensive wind, solar and thermal model to solve the problems raised in the above-mentioned background technology.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A method for intelligent site selection and generation of Monte building complexes based on a comprehensive wind, solar and thermal model, the method comprising the following steps:

[0008] S1: Monte Energy Data Acquisition

[0009] The solar energy data of Mongolia was obtained through the global meteorological inversion database of the weather forecast research institute, the wind energy data under typical meteorological conditions were obtained from the benchmark ground meteorological observation station, and the geothermal energy data of Mongolia was obtained from the energy authorities. The energy data was summarized based on the 103 county-level administrative divisions of the Inner Mongolia Autonomous Region as statistical units, with the names of the county-level administrative regions and prefecture-level administrative regions marked and saved in plain text format;

[0010] S2: Layout pattern recognition of Montenegro buildings

[0011] Construct a case collection of Mongolian buildings including traditional Mongolian yurts and improved Mongolian yurts. The case collection includes the location information of typical building complexes and high-precision oblique photography models. The morphological indicators of each building are extracted and calculated. The layout pattern of Mongolian building complexes is obtained by clustering using machine learning algorithms, and the top 10% of them are used as the typical layout pattern of Mongolian building complexes.

[0012] S3: Construction of wind, solar and thermal integrated model

[0013] A wind-solar-thermal integrated model is constructed by integrating wind energy, solar energy and ground-source thermal energy. The model includes breeze power generation, photovoltaic power generation, ground-source heat pump heating and wind-solar-thermal integrated regulation system. A shallow ground temperature monitor is installed on the ground within a radius of 100m of the Mengdi building complex to monitor the soil depth and corresponding temperature. Intelligent sensors are deployed at the connection between Tao Nao and Uni to monitor wind speed, air temperature and light changes. The energy supply mode of the wind-solar-thermal integrated model is adjusted in real time through the monitoring results and the intelligent central control switch, and the total energy supply is calculated using the wind-solar-thermal integrated model energy supply formula E, which includes wind power generation, photovoltaic power generation and ground-source heat pump heating;

[0014] S4: Intelligent site selection for Monte Carlo buildings to maximize energy efficiency

[0015] Obtain the geographic information data, terrain data and climate data of the target area, where the geographic information data includes the administrative division information and longitude and latitude information, and call S1 to obtain the energy data of the target area. Input the above data into the geographic information system for spatial matching, divide the target area into grids of 100m×100m, generate a center point for each grid and label it 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 for each grid A, standardize and add them to obtain the energy potential value AE and assign it to the center point of the grid. Use the Getis-Ord GI* algorithm to find high-value energy efficiency areas, record the grid numbers of the high-value areas and delineate the site selection boundaries for maximizing energy efficiency;

[0016] S5: Generation of energy equipment and Monte building integration solution

[0017] The Monte building complex layout pattern of S2 is placed in the boundary of the energy efficiency maximization site selection to form a benchmark plan. The number of photovoltaic power generation equipment, wind power generation equipment and ground source heat pump equipment is set, and the S3 wind, solar and thermal comprehensive model is called to calculate the energy supply of the plan to determine whether the energy supply meets the minimum energy consumption requirement of the benchmark plan. For the schemes that meet the threshold, energy equipment and Monte building are integrated and generated. The monocrystalline silicon 250W photovoltaic panels are supported by wooden strips on the Monte building Uni, and a support tube is inserted into the skylight to form an umbrella-like structure. The inclination of the photovoltaic panels is adjusted according to the latitude. The 8-turbine small vertical axis wind turbine is combined with the Hana and installed on the side of the Monte building. The ground source heat pump system composed of a 10Kw heat pump is installed underground in the Monte building, and the pipeline is connected to the domestic water and heating pipelines. The solar wind energy storage equipment composed of lithium electronic batteries is installed inside the Monte building and connected to the photovoltaic power generation equipment and wind power generation equipment. The intelligent central control switch is fixed on the Hana of the Monte building; among them, 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 set of parameters NPV, NWG and NGH come from the full set of positive integers generated by random functions, NPV≤30, NWG≤30, NGH≤1;

[0018] S6: Multiple scheme scoring and output

[0019] The generated multiple schemes are scored according to the energy efficiency dimension, economic dimension and safety dimension and a radar chart is generated. The intelligent generation scheme of the Monte building complex is printed, including the Monte building complex plane layout, energy system plane layout, energy equipment distribution map and scheme scoring radar chart;

[0020] Preferably, the step S1 collects 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 location and ground temperature.

[0021] Preferably, the step S2 extracts and calculates the morphological indicators of each building, and uses a machine learning algorithm to cluster to obtain the layout pattern of the Monte building complex, wherein the building morphological indicators include the number of yurts in the Monte building complex, the coordinates of the center of the bottom circle of the yurt and the direction of the connecting line, the difference between the length of the bottom connecting center line and the sum of the radius, the outer contour of each yurt plane, the bottom diameter, the height of the Khana wall, and the total height of the yurt. The morphological indicators are input into a hierarchical clustering algorithm, and the Euclidean distance is used as the distance measurement to automatically determine the number of clusters, and the clustering interval is set to 50-100.

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

[0023]

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

[0025]

[0026] Among them, 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 turbines, ρ is the air density coefficient, A is the swept area of ​​a single wind turbine, V is the average wind speed, C p is the wind energy conversion rate, t WG Working hours for wind power equipment, 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.

[0027] Preferably, the energy potential value AE is obtained by adding the normalized values ​​in step S4, that is, Where 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 center point of the grid to the geothermal well.

[0028] Preferably, the Getis-Ord GI* algorithm described in S1 is used to find the high energy efficiency area, that is, using the formula Calculate, where x j is the energy potential value of element j, w i,j is the spatial weight between features i and j, n is the total number of features, and The higher the z value obtained, the higher the wind and solar thermal potential energy value of the region it represents.

[0029] Preferably, the step S5 inserts the Monte building complex layout pattern of S2, that is, aligns the center of gravity of the typical Monte building complex layout pattern obtained by S2 with the center of gravity of the energy efficiency maximization site selection boundary in the geographic information platform. If the building complex layout exceeds the energy efficiency maximization site selection boundary, this layout pattern is deleted.

[0030] Preferably, the minimum energy consumption requirement in step S5 is calculated based on the limit values ​​of indoor calculated temperature, ventilation frequency, heating system operation time, lighting power density, equipment power, and thermal efficiency specified in the "Design Standards for Residential Buildings in Severe Cold and Cold Regions". The minimum energy consumption requirement value is

[0031]

[0032] Where K is the heat transfer coefficient of the exterior wall (W / m 3 ·K), U is the volume of the exterior 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 rate (m 3 / h), ρ is air density (kg / m 3 ), Cp is the specific heat capacity (J / (kg·℃)), 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, and the energy efficiency score = 100 × (N PV η PV +N WG η WG +N GH η GH ), Where N PV is the number of photovoltaic devices, N WG Set 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 the energy equipment.

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

[0035] 1. The present invention uses the Getis-Ord GI* algorithm to find high-value energy efficiency areas. The higher the z value obtained, the higher the regional wind, solar and thermal potential value. Based on this, the site selection boundary for maximizing energy efficiency is delineated, achieving efficient energy utilization from the level of Monte building site selection, and at the same time increasing the scientificity and speed of Monte building site selection;

[0036] 2. The present invention constructs a case collection of Mongolian buildings including traditional Mongolian yurts and improved Mongolian yurts, which includes location information of typical building complexes and high-precision oblique photography models, extracts and calculates the morphological indicators of each building, and uses machine learning algorithms to cluster to obtain the layout pattern of the Mongolian building complex. The top 10% of the layout patterns are used as the typical layout pattern of the Mongolian building complex, which increases the aesthetics of the layout of the Mongolian buildings and avoids the destruction of the texture of the Mongolian buildings caused by the chaotic layout;

[0037] 3. The present invention constructs a wind-solar-heat integrated model by integrating wind energy, solar energy and ground-source thermal energy. The model includes breeze power generation, photovoltaic power generation, ground-source heat pump heating and wind-solar-heat integrated regulation system. A shallow ground temperature monitor is installed on the ground within a radius of 100m of the Monte building complex to monitor the soil depth and corresponding temperature. An intelligent sensor is deployed at the connection between Tao Nao and Uni to monitor wind speed, air temperature and light changes. The wind-solar-heat integrated model energy supply mode is adjusted in real time through the monitoring results and the intelligent central control switch, and the wind-solar-heat integrated model energy supply formula E is used to calculate the total energy supply, providing a stable and continuous energy supply for Monte buildings, eliminating the problem of insufficient energy supply caused by insufficient single energy, and improving the living comfort of Monte buildings;

[0038] 4. The present invention uses a comprehensive wind, solar and thermal model to calculate the energy supply of the solution, determine whether the energy supply meets the minimum energy consumption requirement of the benchmark solution, and integrates energy equipment and Monte buildings for solutions that meet the threshold. This design solution of wind, solar and thermal energy complementation and integration with Monte buildings can make the most of wind, solar and geothermal energy, while meeting the needs of residents' lives, reducing carbon emissions, making extensive use of clean energy, reducing the use of traditional energy such as coal, and being more green and environmentally friendly. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a flowchart of a method for intelligent site selection and generation of Monte building complex based on wind, solar and thermal integrated model;

[0040] Figure 2 A case diagram of Montenegro building complex site selection in the target area of ​​a Montenegro building complex intelligent site selection and generation method based on a wind-solar-thermal integrated model;

[0041] Figure 3 A typical Montenegrin building complex layout pattern case diagram of the target area of ​​a Montenegrin building complex intelligent site selection and generation method based on a wind-solar-thermal integrated model;

[0042] Figure 4 This is a case diagram of the integrated generation plan of energy equipment and buildings in the target area of ​​a Monte building complex intelligent site selection and generation method based on a comprehensive wind, solar and thermal model. DETAILED DESCRIPTION

[0043] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments:

[0044] according to Figure 1-4 The specific implementation process is as follows:

[0045] The intelligent site selection and generation method of Monte building complex based on wind, solar and thermal comprehensive model specifically includes the following steps:

[0046] S1: Monte Energy Data Acquisition

[0047] The solar energy data of Mongolia was obtained through the global meteorological inversion database of the weather forecast research institute, the wind energy data under typical meteorological conditions was obtained from the benchmark ground meteorological observation station, and the geothermal energy data of Mongolia was obtained from the energy department. The energy data was summarized based on the 103 county-level administrative divisions of Inner Mongolia Autonomous Region as statistical units, the names of the county-level administrative regions and prefecture-level administrative regions were marked and saved in plain text format.

[0048] S2: layout pattern recognition of Montenegrin buildings;

[0049] Construct a case collection of Mongolian buildings including traditional Mongolian yurts and improved Mongolian yurts. The case collection includes the location information of typical building complexes and high-precision oblique photography models. The morphological indicators of each building are extracted and calculated. The layout pattern of Mongolian building complexes is obtained by clustering using machine learning algorithms, and the top 10% of them are used as the typical layout pattern of Mongolian building complexes.

[0050] S3: Construction of wind, solar and thermal integrated model

[0051] A wind-solar-thermal integrated model is constructed by integrating wind energy, solar energy and ground-source thermal energy. The model includes breeze power generation, photovoltaic power generation, ground-source heat pump heating and wind-solar-thermal integrated regulation system. A shallow ground temperature monitor is installed on the ground within a radius of 100m of the Mengdi building complex to monitor the soil depth and corresponding temperature. Intelligent sensors are deployed at the connection between Tao Nao and Uni to monitor wind speed, air temperature and light changes. The energy supply mode of the wind-solar-thermal integrated model is adjusted in real time through the monitoring results and the intelligent central control switch, and the total energy supply is calculated using the wind-solar-thermal integrated model energy supply formula E, which includes wind power generation, photovoltaic power generation and ground-source heat pump heating;

[0052] S4: Intelligent site selection for Monte Carlo buildings to maximize energy efficiency

[0053] Obtain geographic information data, terrain data and climate data of the target area, wherein the geographic information data includes administrative division information and longitude and latitude information, and call S1 to obtain energy data of the target area. 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 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 them after standardization to obtain the energy potential value AE and assign it to the center point of the grid. Use the Getis-Ord GI* algorithm to find high-value energy efficiency areas, record the grid numbers of the high-value areas and delineate the boundaries of the site selection that maximizes energy efficiency. This step is the core innovation of the present invention, which comprehensively considers the distribution and availability of solar energy, wind energy and geothermal energy resources, and then uses an intelligent algorithm to determine the site selection range of the Monte building complex;

[0054] S5: Generation of energy equipment and Monte building integration solution

[0055] The layout pattern of the Monte building complex in S2 is placed in the boundary of the site selection for maximizing energy efficiency to form a benchmark plan. The number of photovoltaic power generation equipment, wind power generation equipment and ground source heat pump equipment is set, and the S3 wind, light and heat comprehensive model is called to calculate the energy supply of the plan to determine whether the energy supply meets the minimum energy consumption requirements of the benchmark plan. For the plan that meets the threshold, the energy equipment and the Monte building are integrated. The 250W monocrystalline silicon photovoltaic power generation panel is supported and covered on the Monte building Uni with a tuft, and a support pipe is inserted into the skylight to form an umbrella-like structure. The inclination of the photovoltaic panel is adjusted according to the latitude. The 8-turbine small vertical axis wind turbine is combined with the Hana and installed on the side of the Monte building. The ground source heat pump system composed of a 10Kw heat pump is installed underground in the Monte building, and the pipeline is connected to the domestic water and heating pipelines. The solar wind energy storage equipment composed of lithium electronic batteries is installed inside the Monte building and connected to the photovoltaic power generation equipment and wind power generation equipment. The intelligent central control switch is fixed on the Hana of the Monte building. Among them, the number of monocrystalline silicon 250W photovoltaic panels is set as NPV, the number of 8-turbine small vertical axis wind turbines is set as NWG, and the number of heat pumps is set as NGH. A set of parameters NPV, NWG and NGH are from the full set of positive integers generated by random functions, NPV≤30, NWG≤30, NGH≤1. This step is the core innovation of the present invention. It organically combines the wind, solar and heat integrated energy supply system including photovoltaic power generation, breeze power generation, heat pump and intelligent central control switch with Monte buildings, which can solve the problems of low energy density, intermittency and randomness of renewable energy in the prior art, and better retain the style and characteristics of Monte buildings;

[0056] S6: Multiple scheme scoring and output

[0057] The generated multiple schemes are scored according to the energy efficiency dimension, economic dimension and safety dimension and a radar chart is generated. The intelligent generation scheme of the Monte building complex is printed, including the Monte building complex plan layout, energy system plan layout, energy equipment distribution map and scheme scoring radar chart.

[0058] The Monte energy data described in step S1 specifically includes:

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

[0060] Step S2 extracts and calculates the morphological index of each building, and uses machine learning algorithm to cluster to obtain the layout pattern of the Monte building complex. The implementation process is as follows:

[0061] The data extracted include the number of yurts in the Mongolian building complex, the coordinates of the center of the bottom circle of the yurt and the direction of the connecting line, the difference between the length of the bottom connecting center line and the sum of the radius, the outer contour of each yurt, the bottom diameter, the height of the Khana wall, and the total height of the yurt. The above morphological indicators are input into the hierarchical clustering algorithm, and the Euclidean distance is used as the distance measurement to automatically determine the number of clusters, and the clustering interval is set to 50-100.

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

[0063]

[0064]

[0065] The energy supply formula E of the wind-solar-thermal comprehensive model described in step S3 is implemented as follows:

[0066] The energy supply of the wind, solar and thermal comprehensive model is calculated by formula E.

[0067]

[0068] Among them, 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 turbines, ρ is the air density coefficient, A is the swept area of ​​a single wind turbine, V is the average wind speed, C p is the wind energy conversion rate, t WG Working hours for wind power equipment, AGH 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.

[0069] The energy potential value AE is obtained by adding the normalized values ​​in step S4, and the implementation process is as follows:

[0070] Normalize the real-time solar radiation intensity value to Normalize the real-time wind speed to The straight-line distance from the center of the grid to the geothermal well is standardized as The energy potential value AE is obtained by adding the above standardized results.

[0071] The Getis-Ord GI* algorithm described in step S4 searches for high energy efficiency areas, and the implementation process is as follows:

[0072] Using the formula Calculate, where x j is the energy potential value of element j, w i,j is the spatial weight between features i and j, n is the total number of features, and The higher the z value obtained, the higher the wind and solar thermal potential energy value of the region it represents.

[0073] Step S5 inserts the layout pattern of the Monte building complex in S2, and the implementation process is as follows:

[0074] In the geographic information platform, the center of gravity of the typical Monte building complex layout pattern obtained by S2 is aligned with the center of gravity of the energy efficiency maximization site selection boundary. If the building complex layout exceeds the energy efficiency maximization site selection boundary, this layout pattern will be deleted.

[0075] The minimum energy consumption requirement in step S5 is as follows:

[0076] The minimum energy consumption requirement is calculated based on the limit values ​​of indoor calculated temperature, ventilation times, heating system operation time, lighting power density, equipment power, and thermal efficiency specified in the "Design Standards for Residential Buildings in Severe Cold and Cold Regions". The calculation formula is:

[0077]

[0078] Where K is the heat transfer coefficient of the exterior wall (W / m3·K), U is the volume of the exterior wall, and 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 rate (m3 / h), ρ is the air density (kg / m3), Cp is the specific heat capacity (J / (kg·℃)), 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.

[0079] Step S6 is to score according to the energy efficiency dimension, the economic dimension and the safety dimension, and the specific implementation method is as follows:

[0080] The step S6 is to score according to the energy efficiency dimension, the economic dimension and the safety dimension, and the energy efficiency score = 100 × (N PV η PV +N WG η WG +N GH η GH ), Where N PV is the number of photovoltaic devices, N WG Set 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 the energy equipment.

[0081] The above is only an embodiment of the present invention, and the common knowledge such as the known specific technical solutions and / or characteristics in the solution is not described in detail here. It should be pointed out that for those skilled in the art, without departing from the technical solution of the present invention, several modifications and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. The intelligent site selection and generation method of Monte building complex based on wind, solar and thermal comprehensive model is characterized by: The method comprises the following steps: S1: Monte Energy Data Acquisition The solar energy data of Mongolia was obtained through the global meteorological inversion database of the weather forecast research institute, the wind energy data under typical meteorological conditions were obtained from the benchmark ground meteorological observation station, and the geothermal energy data of Mongolia was obtained from the energy authorities. The energy data was summarized based on the 103 county-level administrative divisions of the Inner Mongolia Autonomous Region as statistical units, with the names of the county-level administrative regions and prefecture-level administrative regions marked and saved in plain text format; S2: Layout pattern recognition of Montenegro buildings Construct a case collection of Mongolian buildings including traditional Mongolian yurts and improved Mongolian yurts. The case collection includes the location information of typical building complexes and high-precision oblique photography models. The morphological indicators of each building are extracted and calculated. The layout pattern of Mongolian building complexes is obtained by clustering using machine learning algorithms, and the top 10% of them are used as the typical layout pattern of Mongolian building complexes. S3: Construction of wind, solar and thermal integrated model A wind-solar-thermal integrated model is constructed by integrating wind energy, solar energy and ground-source thermal energy. The model includes breeze power generation, photovoltaic power generation, ground-source heat pump heating and wind-solar-thermal integrated regulation system. A shallow ground temperature monitor is installed on the ground within a radius of 100m of the Mengdi building complex to monitor the soil depth and corresponding temperature. Intelligent sensors are deployed at the connection between Tao Nao and Uni to monitor wind speed, air temperature and light changes. The energy supply mode of the wind-solar-thermal integrated model is adjusted in real time through the monitoring results and the intelligent central control switch, and the total energy supply is calculated using the wind-solar-thermal integrated model energy supply formula E, which includes wind power generation, photovoltaic power generation and ground-source heat pump heating; S4: Intelligent site selection for Monte Carlo buildings to maximize energy efficiency Obtain the geographic information data, terrain data and climate data of the target area, where the geographic information data includes the administrative division information and longitude and latitude information, and call S1 to obtain the energy data of the target area. Input the above data into the geographic information system for spatial matching, divide the target area into grids of 100m×100m, generate a center point for each grid and label it 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 for each grid A, standardize and add them to obtain the energy potential value AE and assign it to the center point of the grid. Use the Getis-Ord GI* algorithm to find high-value energy efficiency areas, record the grid numbers of the high-value areas and delineate the site selection boundaries for maximizing energy efficiency; S5: Generation of energy equipment and Monte building integration solution The Monte building complex layout pattern of S2 is placed in the boundary of the energy efficiency maximization site selection to form a benchmark plan. The number of photovoltaic power generation equipment, wind power generation equipment and ground source heat pump equipment is set, and the S3 wind, solar and thermal comprehensive model is called to calculate the energy supply of the plan to determine whether the energy supply meets the minimum energy consumption requirement of the benchmark plan. For the schemes that meet the threshold, energy equipment and Monte building are integrated and generated. The monocrystalline silicon 250W photovoltaic panels are supported by wooden strips on the Monte building Uni, and a support tube is inserted into the skylight to form an umbrella-like structure. The inclination of the photovoltaic panels is adjusted according to the latitude. The 8-turbine small vertical axis wind turbine is combined with the Hana and installed on the side of the Monte building. The ground source heat pump system composed of a 10Kw heat pump is installed underground in the Monte building, and the pipeline is connected to the domestic water and heating pipelines. The solar wind energy storage equipment composed of lithium electronic batteries is installed inside the Monte building and connected to the photovoltaic power generation equipment and wind power generation equipment. The intelligent central control switch is fixed on the Hana of the Monte building; among them, 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 set of parameters NPV, NWG and NGH come from the full set of positive integers generated by random functions, NPV≤30, NWG≤30, NGH≤1; S6: Multiple scheme scoring and output The generated multiple schemes are scored according to the energy efficiency dimension, economic dimension and safety dimension and a radar chart is generated. The intelligent generation scheme of the Monte building complex is printed, including the Monte building complex plan layout, energy system plan layout, energy equipment distribution map and scheme scoring radar chart.

2. The method for intelligent site selection and layout of Monte building complex based on wind, solar and thermal comprehensive model according to claim 1 is characterized by: The step S1 obtains the energy data, wherein the solar energy data includes the solar radiation intensity and the number of sunshine hours; the wind energy data includes the average wind speed and wind direction; and the geothermal energy data includes the geothermal well location and ground temperature.

3. The method for intelligent site selection and layout of Monte building complex based on wind, solar and thermal comprehensive model according to claim 1 is characterized by: The S2 extracts and calculates the morphological indicators of each building, and uses a machine learning algorithm to cluster to obtain the layout pattern of the Montenegro building complex, wherein the building morphological indicators include the number of yurts in the Montenegro building complex, the coordinates of the center of the bottom circle of the yurt and the direction of the connecting line, the difference between the length of the bottom connecting center line and the sum of the radius, the outer contour of each yurt plane, the bottom diameter, the height of the Khana wall, and the total height of the yurt. The morphological indicators are input into a hierarchical clustering algorithm, and the Euclidean distance is used as the distance measurement to automatically determine the number of clusters, and the clustering interval is set to 50-100.

4. The method for intelligent site selection and layout of Monte building complex based on wind, solar and thermal comprehensive model according to claim 1 is characterized by: The step S3 adjusts the energy supply mode of the wind-solar-heat comprehensive model in real time through the monitoring results and the intelligent central control switch, including two sub-modules of power generation and heating. The power generation module includes photovoltaic and wind power coordinated power generation, and the heating module includes geothermal energy supply and electric-heat conversion. All monitored environmental conditions and corresponding energy supply modes are shown in the following table:

5. The method for intelligent site selection and layout of Monte building complex based on wind, solar and thermal comprehensive model according to claim 1 is characterized by: The energy supply formula E of the wind, solar and thermal comprehensive model in step S3 is: Among them, 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 turbines, ρ is the air density coefficient, A is the swept area of ​​a single wind turbine, V is the average wind speed, C p is the wind energy conversion rate, t WG Working hours for wind power equipment, 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.

6. The method for intelligent site selection and layout of Monte building complex based on wind, solar and thermal comprehensive model according to claim 1 is characterized by: The energy potential value AE is obtained by adding the standardized S4, that is, Where 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 center point of the grid to the geothermal well.

7. The method for intelligent site selection and layout of Monte building complex based on wind, solar and thermal comprehensive model according to claim 1 is characterized in that: The Getis-Ord GI* algorithm in S1 finds high energy efficiency areas, that is, using the formula Calculate, where x j is the energy potential value of element j, w i,j is the spatial weight between features i and j, n is the total number of features, and The higher the z value obtained, the higher the wind and solar thermal potential energy value of the region it represents.

8. The method for intelligent site selection and layout of Monte building complex based on wind, solar and thermal comprehensive model according to claim 1 is characterized in that: The S5 inserts the Monte building complex layout pattern of S2, that is, aligns the center of gravity of the typical Monte building complex layout pattern obtained by S2 with the center of gravity of the energy efficiency maximization site selection boundary in the geographic information platform. If the building complex layout exceeds the energy efficiency maximization site selection boundary, this layout pattern is deleted.

9. The method for intelligent site selection and layout of Monte building complex based on wind, solar and thermal comprehensive model according to claim 1 is characterized by: The minimum energy consumption requirement in step S5 is calculated based on the limit values ​​of indoor calculated temperature, ventilation frequency, heating system operation time, lighting power density, equipment power, and thermal efficiency specified in the "Design Standards for Residential Buildings in Severe Cold and Cold Regions". The minimum energy consumption requirement value Where K is the heat transfer coefficient of the exterior wall (W / m 3 ·K), U is the volume of the exterior 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 rate (m 3 / h), ρ is air density (kg / m 3 ), Cp is the specific heat capacity (J / (kg·℃)), 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.

10. The method for intelligent site selection and layout of Monte building complex based on wind, solar and thermal comprehensive model according to claim 1 is characterized in that: The S6 is scored according to the energy efficiency, economic and safety dimensions, and the energy efficiency score = 100 × (N PV η PV +N WG η WG +N GH η GH ), Where N PV is the number of photovoltaic devices, N WG Set 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 the energy equipment.

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