CN111539055B - Multi-perception intelligent photovoltaic roof, design method and design system thereof - Google Patents
Multi-perception intelligent photovoltaic roof, design method and design system thereof Download PDFInfo
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Abstract
The invention belongs to the technical field of photovoltaic power generation, and discloses a multi-perception intelligent photovoltaic roof, a design method and a design system thereof. According to the invention, solar power generation and skylight lighting are integrated, firstly, optimal solar cell inclination angle and optimal skylight arrangement information are obtained according to house information, and then, the design layout scheme of the roof is obtained by combining the optimal solar cell inclination angle and the optimal skylight arrangement information. The invention solves the problems of poor universality and low energy utilization rate of the photovoltaic roof in the prior art, has higher energy conversion efficiency and more reasonable lighting design, realizes the combination of energy conservation and universality, and can reduce the construction cost and the maintenance cost.
Description
Technical Field
The invention relates to the technical field of photovoltaic power generation, in particular to a multi-perception intelligent photovoltaic roof, a design method and a design system thereof.
Background
The energy conversion and energy conservation, environmental management and protection problems are particularly important under the current trend that the resource consumption and world population are increasingly increased along with the development of science and technology and society. Various energy-saving building components are born and developed, and are continuously optimized with the aim of improving the operation efficiency, the conversion efficiency and the total energy saving amount.
Photovoltaic roofs, which implement the process of converting solar radiation energy into electrical energy by integrating a common roof with solar cells, are widely used. However, large-area paving of photovoltaic roofs is only suitable for areas with sufficient and stable illumination. Because the conversion efficiency of the solar cell is lower, the highest working efficiency of the most advanced solar cell is also limited to about 20%, so that the energy-saving effect of the photovoltaic roof is poor in the areas with weaker illumination all the year round, frequent obvious change of illumination intensity and shorter illumination time all the year round. Photovoltaic roofs generally operate in a situation that differs greatly from ideal conditions. At present, most solar cells are laid in a concentrated mode, and the cells are driven by solar energy only, and no other parts are used for heat dissipation and can only be relieved through a physical structure. The temperature rise causes the battery to deviate from the optimum operating environment, so that the overall power generation efficiency is further lowered on the basis of the above point. In addition, the existing photovoltaic roof building has the problems of lack of scientific guidance and control, lack of intelligence and the like in design, so that the problems of low energy utilization rate and higher later manual maintenance cost are caused when photovoltaic equipment is designed and installed.
Most of the existing photovoltaic roofs are only suitable for specific seasons or environments, and energy conservation is greatly improved compared with a common roof, but the total energy conservation amount is still limited; the method cannot be widely used for all weather in the aspect of universality, and energy-saving resources are not fully utilized.
Disclosure of Invention
The embodiment of the application solves the problems of poor universality and low energy utilization rate of the photovoltaic roof in the prior art by providing the multi-perception intelligent photovoltaic roof, the design method and the design system thereof.
The embodiment of the application provides a design method of a multi-perception intelligent photovoltaic roof, which comprises the following steps:
obtaining an optimal solar cell inclination angle according to the geographical position information of the house;
obtaining optimal skylight arrangement information according to the parameter information of the house;
and obtaining a design layout scheme of the roof according to the optimal solar cell inclination angle and the optimal skylight arrangement information.
Preferably, the obtaining the optimal solar cell inclination angle according to the geographical position information of the house includes the following substeps:
according to the geographical position information of the house, combining a meteorological database to obtain radiation quantity information corresponding to each month;
obtaining an optimal dip angle corresponding to each month according to the radiation quantity information corresponding to each month by utilizing a Hay model;
and obtaining an annual optimal inclination angle according to the optimal inclination angle corresponding to each month by adopting an algorithm combining a Topsis method, an entropy weight method and a weighted average method, and taking the annual optimal inclination angle as the optimal inclination angle of the solar cell.
Preferably, the radiation quantity information comprises direct radiation quantity and scattered radiation quantity;
The annual optimal inclination angle calculating method comprises the following steps:
calculating the sum of the direct radiation quantity and the scattered radiation quantity of each month, and sorting months according to the size of the sum of the radiation quantities to obtain a first month vector M and a second month vector N; the first month vector M corresponds to six months with less sunlight, and the second month vector N corresponds to six months with more sunlight;
respectively obtaining a first matrix A and a second matrix B according to the first month vector M and the second month vector N; the first matrix A is composed of a direct radiation amount column vector corresponding to a first month vector M and a scattered radiation amount column vector corresponding to the first month vector M, and the second matrix B is composed of a direct radiation amount column vector corresponding to a second month vector N and a scattered radiation amount column vector corresponding to the second month vector N;
calculating to obtain a first index weight corresponding to the direct radiation quantity and a second index weight corresponding to the scattered radiation quantity through an entropy weight method;
calculating to obtain initial monthly weight by Topsis method;
correcting the initial monthly weight through the first index weight and the second index weight to obtain final monthly weight; the final monthly weight comprises a first weight vector C corresponding to the first month vector M and a second weight vector D corresponding to the second month vector N;
The first average optimal inclination Angle1 corresponding to the first month vector M is obtained by multiplying the optimal inclination Angle row vector of the month corresponding to the first month vector M by the first weight column vector C; the second average optimal inclination Angle2 corresponding to the second month vector N is obtained by multiplying the optimal inclination Angle row vector of the month corresponding to the second month vector N by the second weight column vector D;
setting a first sunlight weight W1 and a second sunlight weight W2, wherein W1 is smaller than W2;
the annual best tilt Angle is expressed as w1×angl1+w2×angl2.
Preferably, the obtaining the optimal skylight arrangement information includes: obtaining the optimal skylight area and the optimal skylight installation position.
Preferably, the obtaining the optimal sunroof area comprises the sub-steps of:
obtaining the total transmittance of the window according to the type and material of the selected window and the specification and type of glass;
obtaining a utilization coefficient according to the wall reflectivity, ceiling reflectivity and length, width and height of the house;
obtaining a window area ratio according to the selected light climate zone, lighting level and window type;
obtaining a lighting coefficient according to the total transmittance, the utilization coefficient and the window-to-ground area ratio;
and obtaining the optimal skylight area according to the total transmittance, the utilization coefficient, the window-to-ground area ratio and the lighting coefficient.
Preferably, the obtaining the optimal skylight installation position includes the following sub-steps:
dividing the roof area and the reference surface area into a first number of sections and a second number of sections respectively;
determining the coordinates of the center points of all sections according to the length, width and height of the house;
the skylight lighting is the integration of the linear light source to the vertical direction, and the illumination intensity of the linear light source to each point of the reference surface is combined to obtain the relation between the skylight lighting and the illumination intensity of each point of the reference surface, and meanwhile, the illumination intensity of each point of the reference surface forms a reference surface illumination matrix;
taking the skylight area matrix as an independent variable, taking the variance of the reference surface illumination matrix as an objective function, establishing a nonlinear equation set, obtaining a skylight area matrix when the variance of the reference surface matrix is minimum, and recording the skylight area matrix as a first skylight area matrix;
and obtaining the optimal skylight installation position according to the first skylight area matrix and the optimal skylight area.
The embodiment of the application provides a design system of multi-perception intelligent photovoltaic roof, including:
the user input module is used for obtaining the selection information of the house;
the input processing module is used for combining a weather database according to the selection information of the house to obtain house intermediate parameter information;
The optimal inclination angle calculation module is used for obtaining an optimal solar battery inclination angle according to the selection information of the house and the house intermediate parameter information;
the skylight arrangement calculation module is used for obtaining optimal skylight arrangement information according to the selection information of the house and the house intermediate parameter information;
the weather database is used for storing weather data;
the design system of the multi-perception intelligent photovoltaic roof is used for realizing the steps in the design method of the multi-perception intelligent photovoltaic roof.
Preferably, the selection information of the house comprises longitude and latitude of the geographical position of the house, type and material of a window, specification and type of glass, wall surface reflectivity, ceiling reflectivity, length and width of the house, light waiting areas and lighting grades;
the input processing module obtains direct radiation quantity, scattered radiation quantity, declination, lighting material transmittance, light blocking reduction coefficient of a window structure, utilization coefficient, window area ratio and lighting coefficient according to the longitude and latitude and by combining a meteorological database;
the optimal inclination angle calculation module obtains an optimal inclination angle corresponding to each month and an annual optimal inclination angle according to the declination, the direct radiation quantity and the scattered radiation quantity, and the annual optimal inclination angle is used as an optimal solar cell inclination angle;
The skylight arrangement calculation module comprises a skylight area calculation sub-module and a skylight position calculation sub-module;
the skylight area calculation sub-module obtains the optimal skylight area according to the transmittance of the lighting material, the light blocking reduction coefficient of the window structure, the lighting coefficient, the length, width and height of the house and the specification and type of the glass;
and the skylight position calculation sub-module is used for combining the optimal skylight area according to the length, width and height of the house to obtain the optimal skylight installation position.
The embodiment of the application provides a multi-perception intelligent photovoltaic roof, wherein a first area and a second area are arranged on the roof, the first area is used for installing a solar panel, and the second area is used for installing a skylight;
the solar panel is installed according to the optimal solar cell inclination angle obtained by the design method of the multi-perception intelligent photovoltaic roof;
and the skylight is installed according to the optimal skylight arrangement information obtained by the design method of the multi-perception intelligent photovoltaic roof.
Preferably, the skylight adopts an intelligent skylight, and the intelligent skylight consists of a skylight body structure, a baffle, a motor and an embedded main control;
the embedded main control is used for acquiring the time angle and the sun altitude angle of the roof in real time, and obtaining baffle angle information which enables the shadow area of the baffle to be minimum by adopting a minimized shadow algorithm;
And the motor controls the rotation angle of the baffle according to the baffle angle information.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
in the embodiment of the application, the design method of the multi-perception intelligent photovoltaic roof comprises the steps of obtaining an optimal solar cell inclination angle according to geographical position information of a house, obtaining optimal skylight arrangement information according to parameter information of the house, and obtaining a design layout scheme of the roof according to the optimal solar cell inclination angle and the optimal skylight arrangement information. The solar energy generation and skylight lighting design is integrated, and the optimal solar cell inclination angle and the optimal skylight arrangement are obtained, so that the solar energy generation and skylight lighting roof has higher energy conversion efficiency and more reasonable lighting design compared with the existing energy-saving roof, realizes the combination of energy conservation and universality, and can reduce the construction cost and the maintenance cost.
Drawings
In order to more clearly illustrate the technical solutions of the present embodiment, the drawings required for the description of the embodiment will be briefly described below, and it is obvious that the drawings in the following description are one embodiment of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of a multi-perception intelligent photovoltaic roof design system provided by an embodiment of the present invention;
FIG. 2 is a schematic diagram of an algorithm output result of an optimal skylight installation position obtained by using the multi-perception intelligent photovoltaic roof design method provided by the embodiment of the invention;
fig. 3 is a model diagram of a multi-perception intelligent photovoltaic roof according to an embodiment of the present invention.
Detailed Description
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments.
Example 1:
embodiment 1 provides a design method of a multi-perception intelligent photovoltaic roof, comprising:
(1) And obtaining the optimal solar cell inclination angle according to the geographical position information of the house.
One implementation is as follows: according to the geographical position information of the house, combining a meteorological database to obtain radiation quantity information corresponding to each month; obtaining an optimal dip angle corresponding to each month according to the radiation quantity information corresponding to each month by utilizing a Hay model; and obtaining an annual optimal inclination angle according to the optimal inclination angle corresponding to each month by adopting an algorithm combining a Topsis method, an entropy weight method and a weighted average method, and taking the annual optimal inclination angle as the optimal inclination angle of the solar cell.
Specifically, the radiation quantity information includes a direct radiation quantity and a scattered radiation quantity.
The annual optimal inclination angle calculating method comprises the following steps: calculating the sum of the direct radiation quantity and the scattered radiation quantity of each month, and sorting months according to the size of the sum of the radiation quantities to obtain a first month vector M and a second month vector N; the first month vector M corresponds to six months with less sunlight, and the second month vector N corresponds to six months with more sunlight; respectively obtaining a first matrix A and a second matrix B according to the first month vector M and the second month vector N; the first matrix A is composed of a direct radiation amount column vector corresponding to a first month vector M and a scattered radiation amount column vector corresponding to the first month vector M, and the second matrix B is composed of a direct radiation amount column vector corresponding to a second month vector N and a scattered radiation amount column vector corresponding to the second month vector N; calculating to obtain a first index weight corresponding to the direct radiation quantity and a second index weight corresponding to the scattered radiation quantity through an entropy weight method; calculating to obtain initial monthly weight by Topsis method; correcting the initial monthly weight through the first index weight and the second index weight to obtain final monthly weight; the final monthly weight comprises a first weight vector C corresponding to the first month vector M and a second weight vector D corresponding to the second month vector N; the first average optimal inclination Angle1 corresponding to the first month vector M is obtained by multiplying the optimal inclination Angle row vector of the month corresponding to the first month vector M by the first weight column vector C; the second average optimal inclination Angle2 corresponding to the second month vector N is obtained by multiplying the optimal inclination Angle row vector of the month corresponding to the second month vector N by the second weight column vector D; setting a first sunlight weight W1 and a second sunlight weight W2, wherein W1 is smaller than W2; the annual optimal inclination Angle is w1×angle1+w2×angle2.
(2) And obtaining optimal skylight arrangement information according to the parameter information of the house.
The obtaining of the optimal skylight arrangement information comprises the following steps: obtaining the optimal skylight area and the optimal skylight installation position.
Wherein said obtaining an optimal skylight area comprises the sub-steps of: obtaining the total transmittance of the window according to the type and material of the selected window and the specification and type of glass; obtaining a utilization coefficient according to the wall reflectivity, ceiling reflectivity and length, width and height of the house; obtaining a window area ratio according to the selected light climate zone, lighting level and window type; obtaining a lighting coefficient according to the total transmittance, the utilization coefficient and the window-to-ground area ratio; and obtaining the optimal skylight area according to the total transmittance, the utilization coefficient, the window-to-ground area ratio and the lighting coefficient.
Wherein, the obtaining the optimal skylight installation position comprises the following substeps: dividing the roof area and the reference surface area into a first number of sections and a second number of sections respectively; determining the coordinates of the center points of all sections according to the length, width and height of the house; the skylight lighting is the integration of the linear light source to the vertical direction, and the illumination intensity of the linear light source to each point of the reference surface is combined to obtain the relation between the skylight lighting and the illumination intensity of each point of the reference surface, and meanwhile, the illumination intensity of each point of the reference surface forms a reference surface illumination matrix; taking the skylight area matrix as an independent variable, taking the variance of the reference surface illumination matrix as an objective function, establishing a nonlinear equation set, obtaining a skylight area matrix when the variance of the reference surface matrix is minimum, and recording the skylight area matrix as a first skylight area matrix; and obtaining the optimal skylight installation position according to the first skylight area matrix and the optimal skylight area.
The first number and the second number may be the same or different, that is, the number of units divided by the roof and the number of units divided by the reference plane may be the same or different. For example, the roof is divided into 8 x 8 cells and the reference plane is divided into 10 x 10 cells. Can be selected according to the actual application requirement.
(3) And obtaining a design layout scheme of the roof according to the optimal solar cell inclination angle and the optimal skylight arrangement information.
It should be noted that, in addition to the above processing sequence, the obtaining of the optimal solar cell inclination angle and the obtaining of the optimal skylight arrangement information may be processed in parallel, as shown with reference to fig. 1. In addition, the optimal sun-roof arrangement information can be calculated first, and then the optimal solar cell inclination angle can be calculated.
Example 2:
embodiment 2 provides a design system of a multi-perception intelligent photovoltaic roof, which is used for realizing the steps in the design method of the multi-perception intelligent photovoltaic roof provided in embodiment 1.
A design system for a multi-perception intelligent photovoltaic roof, see fig. 1, comprising:
(1) And the user input module is used for obtaining the selection information of the house.
Specifically, the selection information of the house comprises longitude and latitude of the geographical position of the house, type and material of a window, specification and type of glass, wall surface reflectivity, ceiling reflectivity, length and width of the house, light waiting areas and lighting grades.
(2) And the input processing module is used for combining the weather database according to the selection information of the house to obtain the house intermediate parameter information.
Specifically, the input processing module obtains the direct radiation quantity, the scattered radiation quantity, the declination, the lighting material transmittance, the light blocking reduction coefficient, the utilization coefficient, the window area ratio and the lighting coefficient of the window structure according to the longitude and latitude and by combining a meteorological database.
(3) And the optimal inclination angle calculation module is used for obtaining the optimal solar battery inclination angle according to the selection information of the house and the house intermediate parameter information.
Specifically, the optimal inclination angle calculation module obtains an optimal inclination angle corresponding to each month and an annual optimal inclination angle according to the declination, the direct radiation amount and the scattered radiation amount, and the annual optimal inclination angle is used as an optimal solar cell inclination angle.
(4) And the skylight arrangement calculation module is used for obtaining optimal skylight arrangement information according to the house selection information and the house intermediate parameter information.
Specifically, the skylight arrangement calculation module comprises a skylight area calculation sub-module and a skylight position calculation sub-module. And the skylight area calculation sub-module obtains the optimal skylight area according to the transmittance of the lighting material, the light blocking reduction coefficient of the window structure, the lighting coefficient, the length, width and height of the house and the specification and type of the glass. And the skylight position calculation sub-module is used for combining the optimal skylight area according to the length, width and height of the house to obtain the optimal skylight installation position.
(5) And the weather database is used for storing weather data.
Example 3:
embodiment 3 provides a multi-perception intelligent photovoltaic roof, is provided with first district, second district on the roof, first district is used for installing solar cell panel, the second district is used for installing the skylight. The solar panel is installed according to the optimal solar cell inclination angle obtained by the design method of the multi-perception intelligent photovoltaic roof provided in the embodiment 1; the sunroof is installed according to the optimal sunroof arrangement information obtained by the design method of the multi-perception intelligent photovoltaic roof provided in the embodiment 1.
In the preferred scheme, the skylight adopts intelligent skylight, intelligent skylight comprises skylight body structure, baffle, motor, embedded master control. The embedded main control is used for acquiring the time angle and the sun altitude angle of the roof in real time, and obtaining baffle angle information which enables the shadow area of the baffle to be minimum by adopting a minimized shadow algorithm; and the motor controls the rotation angle of the baffle according to the baffle angle information.
The present invention is further described below.
The invention can provide the following design parameters for the user: 1. optimal solar cell tilt angle for a particular geographic location; 2. and the optimal skylight arrangement information (comprising optimal skylight area and optimal skylight installation position) required by the building corresponding to the specific parameters. The intelligent roof can be designed according to the calculation result, and intelligent operation of the skylight is realized.
The respective portions are described below.
(1) And obtaining the optimal inclination angle of the solar cell.
1.1, inquiring a weather database according to geographical position information (longitude and latitude) of a house input by a user to obtain corresponding information such as direct radiation quantity, scattered radiation quantity and the like.
Since the minimum resolution of data in a weather database (e.g., national weather service, NASA) is X degrees (e.g., x=0.5), discretization of latitude and longitude is performed, i.e., processing the location parameters entered by the user as the closest data points thereto.
And 1.2, calculating the optimal inclination angle corresponding to each month through an optimal inclination angle algorithm.
Specifically, the algorithm adopted is as follows: on the premise of the Hay model, the inclination angle when the radiation quantity is maximum is obtained by deriving the relation between the solar radiation quantity and the inclination angle on the inclined surface.
And 1.3, calculating an annual optimal inclination angle through an algorithm combining a Topsis method, an entropy weight method and a weighted average method, and taking the annual optimal inclination angle as an optimal solar cell inclination angle of the geographic position.
The algorithm of the annual optimal inclination angle is as follows: the sum of the direct radiation amount and the scattered radiation amount corresponding to the geographic position is sorted according to the size (meanwhile, the index is kept unchanged, the index can be month or month minus one), and the index is divided into two groups according to the total number of the indexes, namely M, N and M, N respectively corresponding to less-sunlight months and more-sunlight months. Grouping the direct radiation quantity and the scattered radiation quantity according to the index to obtain two matrixes which are respectively marked as A, B; and then Topsis analysis is carried out on the A and the B respectively, which is different from the common Topsis algorithm, the matrix is firstly subjected to entropy weight analysis before the minimum distance is calculated to obtain weights occupied by various variables (direct radiation quantity and scattered radiation quantity), then the weights are introduced into the calculation of the distances at two ends, and finally weight column vectors C, D occupied by all months are obtained, namely the matrix A, B is respectively analyzed by the entropy weight method, the matrix A corresponds to the weight column vector C, and the matrix B corresponds to the weight column vector D. Multiplying the optimal inclination Angle of each month corresponding to M by C and summing to obtain an average optimal inclination Angle of months with less sunlight, and marking as Angle1; and multiplying the optimal inclination Angle of each month corresponding to N by D and summing to obtain the average optimal inclination Angle of each month with more sunlight, and recording as Angle2. Since the inclination angle of the solar cell is determined during design and cannot be easily changed after installation, the annual optimal inclination angle is calculated as the final solar cell installation reference angle. In addition, since the solar cell plays an obvious role when the sunlight is more than large, two weights of 0.6, 0.4 and 0.4 x angle1+0.6 x angle2 can be taken as the optimal inclination angle throughout the year.
Compared with the traditional mode, the solar energy power generation system can utilize solar energy power generation to a greater extent. The inclination angle of the solar cell has a larger influence on the radiation quantity received by the panel, and in most cases, the inclination angle of the solar cell is fixedly at or slightly smaller than the latitude, so that the precise calculation aiming at specific environments is lacking, the efficiency of collecting solar energy of the solar cell is influenced to a certain extent, the invention combines seasonal characteristics, adopts entropy weight analysis and weighted average, optimizes according to regions by utilizing an optimized optimal inclination angle algorithm, has stronger applicability, ensures that the conversion efficiency of the solar cell is maintained at the highest level, realizes the maximum utilization of solar energy, improves the generated energy and improves the energy saving effect
(2) An optimal sunroof area is obtained.
2.1 determining the total transmittance of the window according to the type and material of the window, the type and material of the glass selected by the user.
Total transmittance t=t0×tc×tw, where t0 is the light-collecting material transmittance; tc is the light blocking reduction coefficient of the window structure; tw is the contamination reduction coefficient of the window pane. In the present invention, tw is 0.45; tc is determined by the tc value matrix, the row index is window type, and the column index is window material; t0 is determined by a list of t0 values, the list index being of the glass type and the element index being of the glass material.
And 2.2, determining the utilization coefficient according to the length, width and height of the house, and the wall reflectivity and the ceiling reflectivity selected by the user.
The coefficient CU is determined by a CU value array, and the array one-dimensional index is the wall reflectivity; the two-dimensional index is the ceiling reflectivity; the three-dimensional index is the room-to-space ratio RCR, which is calculated and rounded from the house length, width, height.
And 2.3, determining the window area ratio according to the light climate zone, the lighting level and the window type selected by the user.
The window-to-ground area ratio is determined by a window-to-ground ratio array, and the array one-dimensional index is the window type; the two-dimensional index is the lighting grade; three-dimensionally indexing the light climate zone.
And 2.4, determining the lighting coefficient according to the total transmittance, the utilization coefficient and the window area ratio.
And 2.5, determining the area of the window opening according to the total transmittance, the utilization coefficient, the window area ratio and the lighting coefficient, namely the optimal skylight area required by the building.
(3) An optimal sunroof mounting position is obtained.
And 3.1, dividing the roof area and the reference surface area into a certain number of sections respectively.
The number of divided rows and columns is equal and even. The ground is usually taken as a plane for receiving indoor light at 0.85 m and is used as a reference plane.
And 3.2, determining the coordinates of the center point of each section according to the length, width and height of the house input by the user.
And 3.3, determining the relation between the skylight lighting and the illumination intensity of each point of the reference surface through an algorithm.
The skylight lighting is the integration of a linear light source to the vertical direction, and the illumination intensity of each point is calculated by utilizing the position relation between the light source and the reference point.
And 3.4, determining a skylight area matrix when the variance of the reference plane matrix is minimized through an algorithm.
The calculated skylight area matrix is a normalized area, i.e., the numerical value therein represents the relative size of the skylight in each divided cell and is not the actual skylight size.
And 3.5, combining the optimal skylight area and the calculated skylight area matrix to obtain the optimal skylight installation position.
For example, a sunroof size having a value smaller than a preset value (e.g., the preset value is 0.1 times the maximum value in the matrix) is regarded as 0, i.e., no sunroof is placed in the cell. And (3) normalizing the rest values, and multiplying the values by the calculated optimal skylight area in the step (2) respectively to obtain the skylight area in each cell. If the calculated skylight area is larger than the cell area, the skylight area is changed to the cell area.
The skylight distribution constructed by the steps is the optimal skylight position distribution.
Compared with the traditional model, the invention can utilize the skylight to collect light to a greater extent. Lighting with skylights is another means of reducing the power consumption of indoor lighting. According to the invention, the optimal arrangement of the sunroof is realized through the arrangement algorithm of the position and the size of the sunroof, so that the illumination intensity on the indoor reference surface is most uniform when the sunroof is fully utilized for lighting, the optimization is performed from the practical use condition, the illumination power consumption consumed for maintaining the uniformity of the indoor illumination intensity is reduced, and the problem that the ideal condition and the practical performance of the existing energy-saving roof have a larger gap is solved. The solar cell is replaced by a skylight for lighting, and the whole power generation efficiency is improved.
(4) And designing an intelligent roof.
A first area for mounting solar panels and a second area for mounting skylights are provided on the roof.
The solar panel is installed according to the optimal solar cell inclination angle obtained by the design method of the multi-perception intelligent photovoltaic roof; the skylight is installed according to the optimal skylight arrangement information (namely the optimal skylight area and the optimal skylight installation position) obtained by the design method of the multi-perception intelligent photovoltaic roof.
According to the invention, the solar power generation and the skylight lighting design are fused, and the optimal solar cell inclination angle and the optimal skylight arrangement are obtained, so that the energy-saving roof has higher energy conversion efficiency and more reasonable lighting design compared with the existing energy-saving roof, and the combination of energy saving and universality is realized.
Compared with the existing energy-saving roof, the invention reduces the construction cost and the maintenance cost. The alternate arrangement of the solar cells and the skylights reduces the waste of resources, and reduces the installation cost of the solar cells from the installation quantity of the solar cells; the intelligent control system provides a solution for hidden dangers of problems caused by climate change or other reasons in the later period, and reduces the maintenance cost of the roof in terms of flexibility.
Compared with the existing energy-saving roof, the invention optimizes the structural defect without increasing the structural complexity. The reasonable arrangement of the solar cell and the skylight effectively reduces the heat dissipation problem of the solar cell, so that the conversion efficiency of the solar cell is maintained at a high level; the reasonable design of skylight position has effectively improved indoor illumination's homogeneity degree, has avoided the illumination distribution inequality scheduling problem that current energy-conserving roof appears.
Compared with the existing energy-saving roof, the invention has simpler design and control flow. The intelligent design system of the roof directly provides the results of proper solar battery inclination angles, skylight distribution conditions and the like for users according to parameters such as longitude and latitude, materials, house size, types and the like, and effectively reduces the consumption of time and energy of designers.
(5) And controlling intelligent operation of the skylight.
The skylight adopts intelligent skylight, intelligent skylight comprises skylight body structure, baffle, motor, embedded master control. The embedded main control is used for acquiring the time angle and the sun altitude angle of the roof in real time, and obtaining baffle angle information which enables the shadow area of the baffle to be minimum by adopting a minimized shadow algorithm; and the motor controls the rotation angle of the baffle according to the baffle angle information.
The shadow area is minimized under the current condition by adopting a minimized shadow algorithm and dynamically transmitting meteorological data in real time and taking the angle of the baffle as a variable.
According to the intelligent control system and the intelligent control method, the opening and closing state of the skylight can be intelligently controlled according to real-time weather, so that indoor lighting in sunny days is uniform in brightness, and the skylight baffle is closed in haze and windy weather to keep the skylight glass clean, so that indoor lighting electricity consumption and later cleaning cost are saved. In addition, it can be controlled manually. The method is simple and reduces the cost of post maintenance.
The following describes a multi-perception intelligent photovoltaic roof design system.
The design system comprises a user input module, an input processing module, an optimal inclination angle calculation module, a skylight arrangement calculation module and a skylight weather database, and is shown in figure 1.
The user input module is used for selecting and inputting an interface for a user, and inputting longitude, latitude, solar cell azimuth angle and length, width and height of a house; specification and type of glass, type and material of window, ceiling reflectivity, wall reflectivity, light weather area, lighting grade.
The input processing module obtains direct radiation quantity, scattered radiation quantity, declination, lighting material transmittance, light blocking reduction coefficient of a window structure, utilization coefficient, window-to-ground area ratio and lighting coefficient by utilizing longitude and latitude output by the user input module and inquiring the weather database or adopting a related algorithm.
The specific algorithm of the input processing module comprises the following steps:
(1) And processing the longitude and latitude output by the user input module, namely processing the last 2 bits of the decimal point into 25 or 75 so as to enable the decimal point to meet the input form of inquiring the weather database.
(2) Querying the weather database for direct and scattered radiation and screening, i.e., deleting unobserved data (generally represented by-999).
(3) And calculating declination.
Date n is [17,47,75,105,135,162,198,228,258,288,318,344].
(4) And obtaining the transmittance of the lighting material.
The transmittance t0 is obtained by indexing the glass specification and type output by the user input module.
(5) And obtaining the light blocking reduction coefficient of the window structure.
The reduction coefficient tc is obtained through the index of the type and the material of the window output by the user input module.
(6) The utilization coefficient is obtained.
The two-dimensional index RCR of the coefficient array is calculated by the length, width and height of the house.
(7) The window area ratio is obtained.
The array indexes are respectively the light waiting area, the lighting grade and the window type output by the user input module.
(8) And (3) calculating an average lighting coefficient Cav by using the returned results of the steps (4), (5), (6) and (7).
The optimal inclination angle calculation module obtains an optimal inclination angle and an annual optimal inclination angle corresponding to each month by using the declination, the direct radiation quantity and the scattered radiation quantity output by the input processing module, the latitude of the user input module and the azimuth angle of the solar cell, and the annual optimal inclination angle is used as the optimal solar cell inclination angle.
A specific implementation of the optimal tilt calculation module is provided below.
The calculation method comprises the following steps:
(1) The sum of the monthly direct radiation amount and the scattered radiation amount is calculated from the data of the direct radiation amount Hb and the scattered radiation amount Hd.
For example, the data of the direct radiation Hb and the scattered radiation Hd corresponding to 12 months in a certain area are shown in the following table:
| Month of month | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
| Hb | 3.23 | 3.34 | 3.57 | 4.54 | 4.37 | 3.97 | 6.37 | 6.64 | 5.88 | 4.29 | 3.84 | 3.58 |
| Hd | 1.14 | 1.44 | 1.82 | 2.13 | 2.4 | 2.53 | 2.21 | 1.93 | 1.71 | 1.5 | 1.19 | 1.04 |
(2) And (3) sequencing the array (12 elements) obtained in the step (1).
The results of Hb+Hd are sorted according to the size to obtain:
| month of month | 1 | 12 | 2 | 11 | 3 | 10 | 6 | 4 | 5 | 9 | 8 | 7 |
| Hb | 3.23 | 3.58 | 3.34 | 3.84 | 3.57 | 4.29 | 3.97 | 4.54 | 4.37 | 5.88 | 6.64 | 6.37 |
| Hd | 1.14 | 1.04 | 1.44 | 1.19 | 1.82 | 1.5 | 2.53 | 2.13 | 2.4 | 1.71 | 1.93 | 2.21 |
(3) Dividing the result obtained in the step (2) into two groups to obtain two groups of matrixes formed by combining Hb column vectors and Hd column vectors.
For example: month [1 12 2 11 3 10] is divided into a group, and is marked as M; month [6 4 5 9 8 7] was divided into another group, denoted N. The two groups of matrices obtained are respectively:
first matrixSecond matrix->
Namely A and B are derived from M and N, A (i,1) ＝Hb Mi ，A (i,2) ＝Hd Mi ，B (i,1) ＝Hb Ni ，B (i,2) ＝Hd Ni 。
(4) And calculating the weight of each index of the matrix by using an entropy weight method.
There are two indices in the present optimum tilt algorithm, hb and Hd. The result returned by the entropy weight method is the weight occupied by two indexes, the calculated result is Hb of 0.195 and Hd of 0.805.
(5) And calculating the weight corresponding to each month by using Topsis, namely a good and bad solution distance method.
The essence of the Topsis algorithm is to calculate the scores of the elements in the index by using the distances between the elements in the index and the maximum and minimum values of the elements in the index.
In the calculation of the maximum distance (Dp), dp is calculated as the first behavior of the first matrix A 2 ＝(3.23-Hb_max) 2 +(1.14-Hd_max) 2 Where Hb_max is the maximum value of Hb, i.e., 4.29, and Hd_max is the maximum value of Hd, i.e., 1.82.The calculation of the maximum distance of other rows is the same.
In the calculation of the minimum distance (Dn), hb_max and hd_max in the previous stage are replaced by hb_min and hd_min.
Score (i.e., weight) is related to maximum and minimum distances by score=dn/(dp+dn).
The calculated weight of each row is the weight of the optimal inclination angle of the month corresponding to the row. Month corresponds to the result after the grouping ordering in (2).
In order to make the result accurate, an entropy weight correction is adopted. I.e. the distance of each column should be multiplied by the corresponding weight when calculating the distance, for example, the actual maximum distance of the first row of the first matrix a when the calculation result in (4) is adopted is:
the maximum and minimum distances for each row are similarly available. Further, the weight of each row is calculated.
To sum up, a weight vector [0.052 0.032 0.205 0.096 0.359 0.256] of month [1 12 2 11 3 10] is finally obtained, and is denoted as C, and a weight vector [0.119 0.103 0.123 0.173 0.229 0.253] of month [6 4 59 8 7] is obtained, and is denoted as D.
(6) Multiplying the weight vector obtained in the step (5) by the optimal inclination angles of the months respectively and summing the multiplied weight vector to obtain the average optimal inclination Angle1 of the month [1 12 2 11 3 10] and the average optimal inclination Angle2 of the month [6 4 59 8 7 ].
Wherein, the optimal inclination angle vector takes a Hay model as a premise, and the solar radiation quantity H on the inclined plane is calculated T The relation between the inclination angle beta and the inclination angle beta is derived to obtain the inclination angle corresponding to the maximum solar radiation quantity, and the obtained result is the optimal inclination angle per month. The specific calculation formula adopted is as follows:
wherein h=h b +H d Indicating the total solar radiation quantity on the horizontal plane, H b Indicating the amount of direct radiation in the horizontal plane, H o Representing the solar radiation quantity on the outer level of the atmosphere, R b Represents the direct radiation ratio of the inclined plane to the horizontal plane, beta represents the inclined angle,indicating the surface reflectivity of the ground object. R is R b The method is calculated by parameters such as local latitude, solar cell azimuth angle and the like, and specifically adopts a prior art formula.
For example, the monthly optimum tilt calculation is [57.656 47.375 31.943 14.431-0.715-6.922-4.867 7.563 26.470 43.581 55.455 60.367].
Since the weight vector of month [1 12 2 11 3 10] is calculated as [0.052 0.032 0.205 0.096 0.359 0.256], the average optimum tilt angle corresponding to the calculated weight vector is:
0.052*57.656+0.032*60.367+0.205*47.375+0.096*55.455+0.359*31.943+0.256*43.581＝52.592。
similarly, month [6 45 9 8 7] had an average optimum tilt of 5.665.
(7) The month grouping has the meaning of distinguishing the more sun months from the less sun months, and in order to achieve optimal conversion of energy, the photovoltaic panel should be made more suitable for the more sun months. Therefore, the weight W1 is given the average optimal inclination of month [1 12 2 11 3 10], the weight W2 is given the average optimal inclination of month [6 45 9 8 7], and W1 is smaller than W2.
The annual optimum tilt Angle is denoted w1×angle1+w2×angle2. For example, when w1=0.4, w2=0.6, the annual optimum tilt Angle w1×angl1+w2×angl2= 20.436 is obtained.
And the skylight area calculating submodule obtains the optimal skylight area by utilizing the lighting material transmittance output by the input processing module, the light blocking reduction coefficient of the window structure, the lighting coefficient, the house length, width and height output by the user input module and the specification and type of glass.
The specific algorithm of the skylight area calculation sub-module comprises the following steps:
(1) The area of the house is calculated.
And calculating the house area by using the house length and the house width output by the user input module.
(2) The area of the sunroof under typical conditions was calculated.
Fitting a relation diagram of the house area and the typical skylight area to obtain typical skylight area calculation formulas under different house heights, judging the range of the house heights output by the user input module, adopting formulas corresponding to the corresponding ranges, and substituting the return value in the step (1) into calculation to obtain the skylight area under typical conditions.
(3) And calculating the skylight area.
And (3) calculating the skylight area by using the return value in the step (2), the average lighting coefficient under typical conditions (for example, the average lighting coefficient under typical conditions is 1%), the lighting coefficient output by the input processing module and the lighting material transmittance.
And the skylight position calculating submodule obtains the optimal installation position of the skylight by combining the optimal skylight area by utilizing the house length, width and height output by the user input module.
The specific algorithm of the skylight position calculation sub-module comprises the following steps:
(1) The roof and house reference surfaces are segmented.
(2) And calculating the optimal skylight area of each division surface.
And calculating the optimal solution of the skylight area of each dividing surface on the condition that the skylight lighting illumination variance is minimum, wherein the independent variable is the width of the skylight. As can be seen from symmetry, the illumination intensity distribution of the reference surface of the house can be obtained by only calculating the illumination intensity distribution of 1/8 area of the roof, so that only 1/8 area of the roof is calculated during calculation, and the improvement leads to remarkable improvement of the code operation efficiency.
The problem of solving the optimal solution in the step (2) is a nonlinear programming problem, so an optimal solution algorithm aiming at nonlinear programming is adopted. And carrying out central symmetry and the like on the obtained result to obtain a window area distribution matrix, and analyzing the matrix to obtain the optimal skylight installation position and size proportion.
That is, on the basis of the obtained optimal skylight area, the invention provides an optimal skylight arrangement algorithm for calculating skylight positions, which divides a house model, equally divides 10 x 10 of a receiving plane (the international standard is 0.85m on the ground), and provides a receiving matrix R= [ R 1 ,R 2 ,...R 100 ]Storing the light intensity received by the receiving plane; dividing the top 8 x 8 equally, we propose a coefficient matrix l= [ L1, L2 … L64]And w= [ W1, W2 … W64]Representing the length and width of the skylight of each block after the top block; in a daylighting model of the simulated skylight after double integration of the point light source, the j-th monitor on the ground corresponding to the L and W of the block is integrated to obtain the light intensity R of the j-th monitor on the ground, which receives the whole top j . The ratio quality factor Q of the variance and arithmetic mean of the receiving matrix is introduced as a measure, the smaller the quality factor Q, the closer the light intensity distribution on the receiving plane is to be flat. The invention solves the optimal matrix L and W by means of a nonlinear programming tool, which can guide the placement of the top skylight.
Taking a certain area as an example, a user inputs 30.8 degrees of latitude, 139.3 degrees of longitude and 0 degree of solar cell azimuth angle (usually defaulting to 0 degree), the glass selects single-layer common white glass, the window frame selects single-layer wood window, the length, width and height of a house are respectively input 5 meters, 5 meters and 2.8 meters, the reflectivity of a ceiling is selected to be 0.8, the reflectivity of a wall surface is selected to be 0.5, the light climate area is selected to be a III area, and the lighting level is selected to be a III level.
Obtaining roof design parameters: the optimum sunroof area was 3.41 square meters, the optimum solar cell tilt angle was 20.44 degrees, and the optimum sunroof mounting position as shown in fig. 2. The corresponding roof model is shown in figure 3.
The embodiment of the invention provides a multi-perception intelligent photovoltaic roof, a design method and a design system thereof, which at least comprise the following technical effects:
1. according to the invention, skylight lighting and photovoltaic power generation are combined, so that indoor lighting power consumption is reduced to the greatest extent, and energy-saving efficiency is improved; meanwhile, the electricity consumption per se is far smaller than that of an openable roof, and the energy consumption is reduced.
2. The invention optimizes the algorithm of the optimal inclination angle of the solar cell, can calculate the optimal inclination angle of the annual solar cell in the middle-low latitude area more clearly and reasonably, furthest utilizes solar radiation to generate electricity and reduces energy consumption.
3. The invention can quantitatively provide the optimal skylight area for the building designer, and saves the time and effort from qualitative analysis to repeated debugging.
4. The invention can provide the best skylight installation position, so that the indoor illumination stability is best, a comfortable environment is provided for users, the power consumption of lamps for compensating unstable illumination is reduced, and the energy consumption is reduced.
5. According to the invention, the radiation receiving amount of the solar cell is improved through the optimal solar cell inclination angle algorithm, and the use quantity of the solar cell is reduced through reasonably paving the skylights, so that the manufacturing cost is effectively reduced.
Finally, it should be noted that the above-mentioned embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same, and although the present invention has been described in detail with reference to examples, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention, and all such modifications and equivalents are intended to be encompassed in the scope of the claims of the present invention.
Claims (6)
1. The design method of the multi-perception intelligent photovoltaic roof is characterized by comprising the following steps of:
obtaining an optimal solar cell inclination angle according to the geographical position information of the house;
obtaining optimal skylight arrangement information according to the parameter information of the house;
obtaining a design layout scheme of the roof according to the optimal solar cell inclination angle and the optimal skylight arrangement information;
wherein, the obtaining the optimal skylight arrangement information includes: obtaining an optimal skylight area and an optimal skylight installation position;
the obtaining of the optimal skylight area comprises the following substeps: obtaining the total transmittance of the window according to the type and material of the selected window and the specification and type of glass; obtaining a utilization coefficient according to the wall reflectivity, ceiling reflectivity and length, width and height of the house; obtaining a window area ratio according to the selected light climate zone, lighting level and window type; obtaining a lighting coefficient according to the total transmittance, the utilization coefficient and the window-to-ground area ratio; obtaining the optimal skylight area according to the total transmittance, the utilization coefficient, the window-to-ground area ratio and the lighting coefficient;
The obtaining of the optimal skylight installation position comprises the following substeps: dividing the roof area and the reference surface area into a first number of sections and a second number of sections respectively; determining the coordinates of the center points of all sections according to the length, width and height of the house; the skylight lighting is the integration of the linear light source to the vertical direction, and the illumination intensity of the linear light source to each point of the reference surface is combined to obtain the relation between the skylight lighting and the illumination intensity of each point of the reference surface, and meanwhile, the illumination intensity of each point of the reference surface forms a reference surface illumination matrix; taking the skylight area matrix as an independent variable, taking the variance of the reference surface illumination matrix as an objective function, establishing a nonlinear equation set, obtaining a skylight area matrix when the variance of the reference surface matrix is minimum, and recording the skylight area matrix as a first skylight area matrix; and obtaining the optimal skylight installation position according to the first skylight area matrix and the optimal skylight area.
2. The method for designing a multi-perception intelligent photovoltaic roof according to claim 1, wherein the obtaining the optimal solar cell tilt angle according to the geographical location information of the house comprises the following sub-steps:
according to the geographical position information of the house, combining a meteorological database to obtain radiation quantity information corresponding to each month;
Obtaining an optimal dip angle corresponding to each month according to the radiation quantity information corresponding to each month by utilizing a Hay model;
and obtaining an annual optimal inclination angle according to the optimal inclination angle corresponding to each month by adopting an algorithm combining a Topsis method, an entropy weight method and a weighted average method, and taking the annual optimal inclination angle as the optimal inclination angle of the solar cell.
3. The method for designing a multi-perception intelligent photovoltaic roof according to claim 2, wherein the radiation amount information includes direct radiation amount and scattered radiation amount;
the annual optimal inclination angle calculating method comprises the following steps:
calculating the sum of the direct radiation quantity and the scattered radiation quantity of each month, and sorting months according to the size of the sum of the radiation quantities to obtain a first month vector M and a second month vector N; the first month vector M corresponds to six months with less sunlight, and the second month vector N corresponds to six months with more sunlight;
respectively obtaining a first matrix A and a second matrix B according to the first month vector M and the second month vector N; the first matrix A is composed of a direct radiation amount column vector corresponding to a first month vector M and a scattered radiation amount column vector corresponding to the first month vector M, and the second matrix B is composed of a direct radiation amount column vector corresponding to a second month vector N and a scattered radiation amount column vector corresponding to the second month vector N;
Calculating to obtain a first index weight corresponding to the direct radiation quantity and a second index weight corresponding to the scattered radiation quantity through an entropy weight method;
calculating to obtain initial monthly weight by Topsis method;
correcting the initial monthly weight through the first index weight and the second index weight to obtain final monthly weight; the final monthly weight comprises a first weight vector C corresponding to the first month vector M and a second weight vector D corresponding to the second month vector N;
the first average optimal inclination Angle1 corresponding to the first month vector M is obtained by multiplying the optimal inclination Angle row vector of the month corresponding to the first month vector M by the first weight column vector C; the second average optimal inclination Angle2 corresponding to the second month vector N is obtained by multiplying the optimal inclination Angle row vector of the month corresponding to the second month vector N by the second weight column vector D;
setting a first sunlight weight W1 and a second sunlight weight W2, wherein W1 is smaller than W2;
the annual best tilt Angle is expressed as w1×angl1+w2×angl2.
4. A design system for a multi-perception intelligent photovoltaic roof, comprising:
the user input module is used for obtaining the selection information of the house;
The input processing module is used for combining a weather database according to the selection information of the house to obtain house intermediate parameter information;
the optimal inclination angle calculation module is used for obtaining an optimal solar battery inclination angle according to the selection information of the house and the house intermediate parameter information;
the skylight arrangement calculation module is used for obtaining optimal skylight arrangement information according to the selection information of the house and the house intermediate parameter information;
the weather database is used for storing weather data;
the house selection information comprises longitude and latitude of the geographical position of the house, type and material of a window, specification and type of glass, wall surface reflectivity, ceiling reflectivity, length, width and height of the house, a light waiting area and lighting level;
the input processing module obtains direct radiation quantity, scattered radiation quantity, declination, lighting material transmittance, light blocking reduction coefficient of a window structure, utilization coefficient, window area ratio and lighting coefficient according to the longitude and latitude and by combining a meteorological database;
the optimal inclination angle calculation module obtains an optimal inclination angle corresponding to each month and an annual optimal inclination angle according to the declination, the direct radiation quantity and the scattered radiation quantity, and the annual optimal inclination angle is used as an optimal solar cell inclination angle;
The skylight arrangement calculation module comprises a skylight area calculation sub-module and a skylight position calculation sub-module;
the skylight area calculation sub-module obtains the optimal skylight area according to the transmittance of the lighting material, the light blocking reduction coefficient of the window structure, the lighting coefficient, the length, width and height of the house and the specification and type of the glass;
the skylight position calculation sub-module combines the optimal skylight area according to the length, width and height of the house to obtain an optimal skylight installation position;
the design system of the multi-perception intelligent photovoltaic roof is used for realizing the steps in the design method of the multi-perception intelligent photovoltaic roof as claimed in any one of claims 1 to 3.
5. The multi-perception intelligent photovoltaic roof is characterized in that a first area and a second area are arranged on the roof, the first area is used for installing a solar panel, and the second area is used for installing a skylight;
the solar panel is installed according to an optimal solar cell inclination angle obtained by the design method of the multi-perception intelligent photovoltaic roof according to any one of claims 1-3;
the skylight is installed according to optimal skylight arrangement information obtained by the design method of the multi-perception intelligent photovoltaic roof according to any one of claims 1-3.
6. The multi-perception intelligent photovoltaic roof according to claim 5, wherein the skylight is an intelligent skylight, and the intelligent skylight is composed of a skylight body structure, a baffle, a motor and an embedded main control;
the embedded main control is used for acquiring the time angle and the sun altitude angle of the roof in real time, and obtaining baffle angle information which enables the shadow area of the baffle to be minimum by adopting a minimized shadow algorithm;
and the motor controls the rotation angle of the baffle according to the baffle angle information.
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| CN108959690A (en) * | 2018-04-26 | 2018-12-07 | 西安建筑科技大学 | Buildings model window wall area based on BIM compares automatic optimization method |
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