CN108830932B - Large-space building energy consumption prediction method based on EnergyPlus and CFD coupling - Google Patents
Large-space building energy consumption prediction method based on EnergyPlus and CFD coupling Download PDFInfo
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Abstract
The invention discloses a large-space building energy consumption prediction method based on EnergyPlus and CFD coupling. The method can reduce the energy consumption simulation error caused by the thermal stratification phenomenon, thereby increasing the accuracy, effectiveness and reliability of the energy consumption simulation, and having very important significance for analyzing and calculating the energy consumption of the green building.
Description
Technical Field
The invention relates to the technical field of building energy consumption prediction, in particular to a large-space building energy consumption prediction method based on EnergyPlus and CFD coupling.
Background
With the increasing of energy crisis, the design of energy-saving buildings gets more and more attention. In order to reduce energy consumption, the energy consumption level of a building needs to be predicted so as to perform energy-saving design. At the present stage, the method for simulating the building energy consumption by using energy consumption simulation software is the most common method for predicting the building energy consumption level. When the energy consumption simulation software calculates, the default indoor air is completely mixed, however, in the actual situation, for a large-space building, due to the action of gravity, the internal air can form natural convection, the hot air with low density rises, the cold air with high density falls, and finally the thermal stratification phenomenon is caused. Due to the influence of thermal stratification, certain errors are inevitably generated when the energy consumption simulation software calculates the energy consumption of the large-space building, so that the simulation result is inconsistent with the actual condition and even has a large difference.
Disclosure of Invention
Aiming at the problems in the prior art, in order to reduce the energy consumption simulation error caused by the thermal stratification phenomenon and increase the accuracy of energy consumption simulation, the invention provides a large-space building energy consumption prediction method based on the coupling of EnergyPlus (building energy consumption simulation software) and CFD (computational fluid dynamics software), which can reduce the simulation error and improve the accuracy.
The technical scheme adopted by the invention is as follows: a large space building energy consumption prediction method based on EnergyPlus and CFD coupling comprises the following steps:
(1) collecting information about the structure under study, including: building plan, building envelope information, typical weather year data of the building location, building indoor environment design parameters, heating ventilation air conditioning system information, indoor personnel activity information, power consumption equipment information and lighting lamp information;
(2) establishing a CFD model and an EnergyPlus model according to a building plan of a researched building;
(3) importing the EnergyPlus model established in the step (2) into EnergyPlus, setting a heat transfer algorithm, and sequentially inputting the building enclosure structure information, the typical weather year data of the location of the building, the design parameters of the indoor environment of the building, the heating, ventilation and air conditioning system information, the indoor personnel activity information, the power consumption equipment information and the lighting lamp information in the step (1); outputting a set result, and performing energy consumption simulation for one time to obtain the inner surface temperature of the building wall;
(4) importing the CFD model established in the step (2) into ICEM (pre-processing software), carrying out grid division, and importing the divided grid into Fluent; inputting the inner surface temperature of the building wall obtained in the step (3) into Fluent as a part of boundary conditions of CFD simulation, and performing indoor thermal environment simulation by combining with the tuyere boundary conditions set in Fluent;
(5) outputting the convection heat transfer coefficient of the inner surface of the building wall and an indoor temperature field from the Fluent, and integrating the indoor temperature field into an indoor temperature distribution curve;
(6) inputting the convection heat transfer coefficient of the inner surface of the building wall obtained in the step (5) and an indoor temperature distribution curve into EnergyPlus, and performing secondary EnergyPlus energy consumption simulation;
(7) monitoring the on-site energy consumption of a researched building to obtain the actually measured energy consumption data of the building;
(8) and (4) comparing and analyzing the primary energy consumption data obtained in the step (3), the secondary energy consumption data obtained in the step (6) and the actually measured energy consumption data obtained in the step (7).
Further, in the step (2), the CFD model is a three-dimensional geometric model established by SketchUp (draft master software), and the EnergyPlus model is a three-dimensional geometric model established by Legacy OpenStudio plug-in of SketchUp.
The invention has the following beneficial effects: according to the invention, the inner surface temperature of the large-space building wall is calculated by energy consumption simulation software and is used as a temperature boundary condition simulated by CFD software, the inner surface convective heat transfer coefficient and the indoor temperature field of the wall are calculated by CFD, and the inner surface convective heat transfer coefficient and the indoor temperature field are used as initial conditions of the energy consumption calculation to obtain an energy consumption simulation result based on the energy consumption simulation result of the energy consumption simulation strategy of the energy consumption simulation software and the CFD, so that the energy consumption simulation error caused by the thermal stratification phenomenon can be reduced, the accuracy, effectiveness and reliability of the energy consumption simulation are increased, and the method has very important significance for analyzing and calculating the energy consumption of green buildings.
Drawings
FIG. 1 is a model diagram of a building under study according to the present invention;
FIG. 2 is a flow chart of a method for energy consumption prediction according to the present invention;
FIG. 3 is a graph of the change law of the average temperature of indoor air obtained by one EnergyPlus energy consumption simulation of the present invention;
FIG. 4 is a cloud graph of the indoor air temperature stratification obtained from a one-time EnergyPlus energy consumption simulation of the present invention;
FIG. 5 is a graph of the average indoor temperature distribution of a building under study according to the present invention;
FIG. 6 is a plot of the percentage of binomial energy consumption obtained from the quadratic energy plus energy consumption simulation of the present invention.
Detailed Description
The invention discloses a large-space building energy consumption prediction method based on the coupling of EnergyPlus (building energy consumption simulation software) and CFD (computational fluid dynamics software), which comprises the following steps:
(1) collecting information about the structure under study, including: building plan, building envelope information, typical weather year data of the building location, building indoor environment design parameters, heating ventilation air conditioning system information, indoor personnel activity information, power consumption equipment information and lighting lamp information;
(2) from the building plan of the building under study, a CFD model, which is a three-dimensional geometric model built by SketchUp (draft master software), and an EnergyPlus model, which is a three-dimensional geometric model built by plug-in Legacy OpenStudio of SketchUp, were built.
(3) Importing the EnergyPlus model established in the step (2) into EnergyPlus, setting a heat transfer algorithm, and sequentially inputting building envelope information, typical weather year data of a building location, building indoor environment design parameters, heating, ventilating and air conditioning system information, indoor personnel activity information, power consumption equipment information and lighting lamp information in the step (1); outputting a set result, and performing energy consumption simulation for one time to obtain the inner surface temperature of the building wall;
(4) importing the CFD model established in the step (2) into ICEM (pre-processing software), carrying out grid division, and importing the divided grid into Fluent; inputting the inner surface temperature of the building wall obtained in the step (3) into Fluent as a part of boundary conditions of CFD simulation, and performing indoor thermal environment simulation by combining with tuyere boundary conditions set in Fluent;
(5) outputting the convection heat transfer coefficient of the inner surface of the building wall and an indoor temperature field from the Fluent, and integrating the indoor temperature field into an indoor temperature distribution curve;
(6) inputting the convection heat transfer coefficient of the inner surface of the building wall obtained in the step (5) and an indoor temperature distribution curve into EnergyPlus, and performing secondary EnergyPlus energy consumption simulation;
(7) monitoring the on-site energy consumption of a researched building to obtain the actually measured energy consumption data of the building;
(8) and (4) comparing and analyzing the primary energy consumption data obtained in the step (3), the secondary energy consumption data obtained in the step (6) and the actually measured energy consumption data obtained in the step (7).
The effectiveness and reliability of the method of the invention is described below in connection with a specific embodiment.
The embodiment takes a large-space gymnasium as a research object, the large-space gymnasium has three layers, the whole large-space gymnasium is a cylindrical building with through top and bottom, and the area of a building base is 7558m2Total building area 12233m2The total height of the building is 25.1m, the height of one floor is 6m, the height of two floors is 10.8m, and the height of three floors is 8.3 m. The first wall and a part of the third wall are straight outer walls, the second wall and the other part of the third wall are inclined outer walls, and specific building enclosure information is shown in table 1. According to the requirements of GB 50189-2015 'public building energy-saving design Standard', the thermal performance limit values of the roofs, the outer walls and the windows of the class B public buildings in the areas with hot summer and cold winter are respectively 0.7W/(m)2·K)、1W/(m2·K)、3W/(m2K). Heat transfer coefficient of the main enclosure of the gymnasium, except for the roofAre all below the specified limit value, so the stadium enclosure has better heat insulation performance.
And building models are built by SketchUp according to the building plan of the gymnasium, and as shown in figure 1, the building models used by EnergyPlus and Fluent software are built by the method.
The gymnasium adopts an all-air constant-air-volume air conditioning system, the air supply mode is top-nozzle side air supply, three-layer external windows are provided with axial-flow fans for mechanical air exhaust, and an air return port and a fireproof air return window are arranged in a spectator area. The main equipment and model parameters of the heating, ventilating and air conditioning system are shown in table 2.
And simulating the energy consumption of the gymnasium on a certain match day according to the building information and the air conditioning information of the gymnasium, wherein the weather data is local typical weather year data. In the game day, 8 basketball games are scheduled, and the game time is 8: 00-20: 00. during the game, 1700 people are in the gym, and the air conditioning system and internal lights and equipment of the gym are all turned on.
As shown in fig. 2, the detailed flow of the method of the present invention is that, after initial conditions such as weather, building information, peoples, Lights, appliances, HVAC, etc. are input, energy consumption simulation (uncoupled simulation) is performed by using energy plus to obtain energy consumption data and the inner surface temperature of the building wall.
And then, taking the inner surface temperature as a boundary condition of CFD simulation, carrying out flow field simulation of the indoor thermal environment of the building, outputting the convection heat transfer coefficient and the temperature field of the inner surface of the building after CFD software iterative convergence, analyzing temperature distribution and fitting a temperature distribution curve, inputting the slope of the curve into a Room Air module of EnergyPlus, inputting the convection heat transfer coefficient into EnergyPlus, and carrying out secondary energy consumption simulation (coupling simulation) to obtain secondary energy consumption data.
And finally, comparing the energy consumption data of the primary energy consumption simulation (non-coupled simulation) and the secondary energy consumption simulation (coupled simulation) with the actual energy consumption data, and analyzing the effectiveness of the coupled simulation strategy.
The main initial conditions of one energy consumption simulation are shown in table 3.
TABLE 1 Stadium Enclosure information
TABLE 2 main equipment and model parameters of heating, ventilation and air conditioning system
TABLE 3 initial conditions for one EnergyPlus energy consumption simulation
Fig. 3 shows the time-to-time variation of the average temperature of the air in the building room studied during the day of the competition, as a result of one EnergyPlus energy consumption simulation. As can be seen from fig. 3, the average temperature of the indoor air ranges between 23 ℃ and 26 ℃, and the average temperature of the indoor air is slightly lower at night, close to the outdoor ambient temperature, from 7: the temperature starts to gradually rise from 00 ℃ and finally stabilizes at about 25 ℃. This is because 7: 00 is the time point when the air conditioner is started, and the air supply temperature of the air conditioner is 25 ℃ to 20: 00, when the match is finished, all air conditioners and power consumption equipment are closed, players and audiences leave the field, and the average indoor air temperature is gradually reduced. And according to the temperature of the inner surface of the wall body output by EnergyPlus, carrying out flow field simulation on the internal thermal environment of the gymnasium by combining the boundary conditions of the air inlet and the air outlet of the air conditioner. The boundary conditions for the CFD software simulation are shown in table 4.
TABLE 4 boundary conditions for CFD simulation
Fig. 4 shows a partial cloud of air temperature stratification inside a stadium, in units of degrees celsius. As can be seen from FIG. 4, there is a significant temperature stratification in the interior of the gym in the vertical direction, with the indoor air temperature stabilizing at 25.5 ℃ in the range of 0-18 m; in the range of 18-25m, the room air temperature started to rise and reached 29 ℃ near the top. On one hand, heat dissipated by indoor personnel, equipment, lamps and the like is not completely treated by the air conditioning system, and hot air gradually rises and gathers at the top; on the other hand, since the huge circular roof of the gymnasium absorbs a large amount of solar radiation heat, part of the heat enters the inside of the gymnasium via heat transfer and is concentrated at the top, thus forming a more significant thermal stratification phenomenon.
The specific method for integrating the indoor temperature field into the indoor temperature distribution curve is as follows: a plane is taken every 1m in the longitudinal height, and 25 planes are taken in total. Fig. 5 shows the average temperature values of the respective planes, and two temperature gradient curves are fitted by using the scatter points, and the 18m point is just the boundary point of the two curves. The slope of the curve from 0 m to 18m is 0.0154, which shows that the temperature gradient in this region is 0.0154 ℃/m, and the temperature rise is slow. The slope of the 18-25m curve is 0.4471, which shows that the temperature gradient in the region is 0.447 ℃/m, and the temperature increase speed is faster than that in the region of 0-18 m. The slopes of the Two curves are input into a Two Gradient option of a rom Air module in EnergyPlus software, the option is suitable for the condition that temperature stratification is distributed according to a certain slope along with the height, and then the convective heat transfer coefficient calculated by CFD is input into EnergyPlus to carry out secondary energy consumption simulation, namely coupled energy consumption simulation.
After the secondary energy consumption simulation, namely the coupling energy consumption simulation is finished, the comparison and analysis are carried out, and the energy consumption data of the secondary simulation is found to be increased to a certain extent compared with the energy consumption data of the primary simulation. As shown by the data in table 5, the coupled simulation produced 269.19kW · h more total Cooling energy than the data calculated for the uncoupled simulation, which is a 24.31% increase. In addition to this, there are various degrees of increase in energy consumption associated with HVAC (heating, ventilating, and air conditioning) systems, such as: the energy consumption of Pumps is increased by 56.06 kW.h, which is increased by 24.31%; the Heat Rejection energy consumption is increased by 3.32 kW.h, and is increased by 24.23%, and the light, Equipment and Fans energy consumption is not increased. The energy consumption of the coupled simulation is 328.59 kW.h more than that of the uncoupled simulation, and the total energy consumption is increased by 15.27%.
TABLE 5 comparison of energy consumption data
The comparison of the actual energy consumption data, the primary energy consumption data (uncoupled simulation), and the secondary energy consumption data (coupled simulation) is shown in table 6, and the actual energy consumption data was obtained by field investigation. Gym match day 0: 00-24: the power consumed by 00 is 2641.23 kW.h, the energy consumption value obtained by the primary energy consumption simulation, namely the non-coupling energy consumption simulation, is 2152.45 kW.h, and the error is 18.51%, and the energy consumption value obtained by the secondary energy consumption simulation, namely the coupling simulation, is 2481.04 kW.h, and the error is 6.06%, and is reduced by 12.45%. Therefore, the accuracy of building energy consumption simulation can be improved by adopting the coupling strategy.
TABLE 6 comparison of actual energy consumption data, primary energy consumption data, and secondary energy consumption data
According to the building energy consumption data obtained by the coupling simulation, the total power consumption of the gymnasium in the competition day is 2481.04 kW.h. The energy consumption of the gym is divided into six items, namely Cooling, Lighting, Equipment, Fans, Pumps, and Heat Rejection, and the fractional power consumption accounts for the example shown in fig. 6. Wherein the cooking, Fans, Pumps and Heat Rejection are the energy consumption consumed by HVAC (heating, ventilation and air conditioning) systems. As can be seen from FIG. 6, most of the electric energy in the gym is consumed by the HVAC system, and the total consumption reaches 71%, so that most of the electricity consumed by the whole building is used for adjusting the indoor air quality. Next, the power consumptions of Lighting and Equipment are 16% and 13%, respectively. Therefore, in the following energy-saving design, energy-saving modification of an air conditioning system should be considered heavily, such as air conditioning adopting a layering strategy according to actual thermal stratification, adjusting the position and the air supply quantity of an air supply outlet, optimizing a temperature field and a speed field, and the like.
According to the invention, the inner surface temperature of the large-space building wall is calculated by energy consumption simulation software and is used as a temperature boundary condition simulated by CFD software, the inner surface convective heat transfer coefficient and the indoor temperature field of the wall are calculated by CFD, and the inner surface convective heat transfer coefficient and the indoor temperature field are used as initial conditions of the energy consumption calculation to obtain an energy consumption simulation result based on the energy consumption simulation result of the energy consumption simulation strategy of the energy consumption simulation software and the CFD, so that the energy consumption simulation error caused by the thermal stratification phenomenon can be reduced, the accuracy, effectiveness and reliability of the energy consumption simulation are increased, and the method has very important significance for analyzing and calculating the energy consumption of green buildings.
The above-mentioned embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solutions of the present invention by those skilled in the art should fall within the protection scope defined by the claims of the present invention without departing from the spirit of the present invention.
Claims (2)
1. A large space building energy consumption prediction method based on EnergyPlus and CFD coupling is characterized by comprising the following steps:
(1) collecting information about the structure under study, including: building plan, building envelope information, typical weather year data of the building location, building indoor environment design parameters, heating ventilation air conditioning system information, indoor personnel activity information, power consumption equipment information and lighting lamp information;
(2) establishing a CFD model and an EnergyPlus model according to a building plan of a researched building;
(3) importing the EnergyPlus model established in the step (2) into EnergyPlus, setting a heat transfer algorithm, and sequentially inputting the building enclosure structure information, the typical weather year data of the location of the building, the design parameters of the indoor environment of the building, the heating, ventilation and air conditioning system information, the indoor personnel activity information, the power consumption equipment information and the lighting lamp information in the step (1); outputting a set result, and performing energy consumption simulation for one time to obtain the inner surface temperature of the building wall;
(4) importing the CFD model established in the step (2) into ICEM, carrying out mesh division, and importing the divided meshes into Fluent; inputting the inner surface temperature of the building wall obtained in the step (3) into Fluent as a part of boundary conditions of CFD simulation, and performing indoor thermal environment simulation by combining with the tuyere boundary conditions set in Fluent;
(5) outputting the convection heat transfer coefficient of the inner surface of the building wall and an indoor temperature field from the Fluent, and integrating the indoor temperature field into an indoor temperature distribution curve;
(6) inputting the convection heat transfer coefficient of the inner surface of the building wall obtained in the step (5) and an indoor temperature distribution curve into EnergyPlus, and performing secondary EnergyPlus energy consumption simulation;
(7) monitoring the on-site energy consumption of a researched building to obtain the actually measured energy consumption data of the building;
(8) and (4) comparing and analyzing the primary energy consumption data obtained in the step (3), the secondary energy consumption data obtained in the step (6) and the actually measured energy consumption data obtained in the step (7).
2. The large space building energy consumption prediction method of claim 1, characterized in that: in the step (2), the CFD model is a three-dimensional geometric model established by SketchUp, and the EnergyPlus model is a three-dimensional geometric model established by Legacy OpenStudio of SketchUp.
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| CN109992908B (en) * | 2019-04-08 | 2023-02-17 | 东南大学 | Urban building energy consumption simulation system |
| CN110412871B (en) * | 2019-07-10 | 2020-07-03 | 北京天泽智云科技有限公司 | Energy consumption prediction processing method and system for auxiliary equipment in building area |
| CN112668071A (en) * | 2019-10-15 | 2021-04-16 | 润弘精密工程事业股份有限公司 | Building energy consumption information processing method and system |
| CN110751402B (en) * | 2019-10-23 | 2022-05-24 | 北京工业大学 | Green building energy consumption quota determining method based on control strategy |
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| CN111666284A (en) * | 2020-05-18 | 2020-09-15 | 天津科技大学 | Building energy consumption uncertainty database establishing method and application method based on SQLite |
| CN112836396B (en) * | 2021-03-10 | 2022-12-27 | 同济大学 | Building real-time energy consumption abnormity diagnosis system |
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