CN110751402B - Green building energy consumption quota determining method based on control strategy - Google Patents
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Abstract
A green building energy consumption quota determining method based on a control strategy belongs to the field of green buildings and the field of optimization control. Establishing an EnergyPlus model of the building based on actual operation data of the green building; secondly, performing model checking on the model by adopting a checking simulation method; thirdly, determining a building operation control strategy and dividing the strategy into three grades of 'good', 'common' and 'poor'; and finally, simulating the building energy consumption by using Monte Carlo analysis, and fitting an energy consumption probability density curve to determine the building energy consumption quota.
Description
Technical Field
The invention relates to the field of green buildings and the field of optimization control, in particular to a method for determining an energy consumption reference line of a green building based on a control strategy.
Background
The rapid urbanization of China leads to the rapid increase of building energy consumption, and the building area of China towns in 2017 reaches 591 hundred million m2The total energy consumption of the building is 9.63 hundred million tons of standard coal, which accounts for about 21 percent of the total energy consumption of national energy. The green building has the characteristics of energy conservation, land conservation, water conservation, material conservation and environmental protection, and can effectively meet the aims of energy conservation, emission reduction and climate change of the building. By 2017, 10927 identification projects exist in green buildings in China, and the total building area exceeds 10 hundred million square meters.
While green buildings in China are vigorously developed, the green buildings face a key problem of 'green design operation is not green', namely the difference problem between the actual performance of the green buildings and an energy consumption baseline. According to related researches, the actual operation energy consumption of the green building is 30-50% higher than the design energy consumption, and even the deviation is as high as 200%. The key problem of energy consumption performance difference is obvious that the difference influences the actual operation regulation and control effect of the green building, reduces the energy efficiency level of the green building and influences the comfort of the indoor environment of the green building. Therefore, the development of green buildings in China urgently needs to solve the key problem of energy consumption performance difference of the green buildings. The problem of energy consumption performance difference of green buildings is effectively solved, and promotion and guidance effects on building energy efficiency level improvement, operation regulation and control optimization and accurate regulation and control policy making are achieved.
At present, the establishment of the 'energy consumption rating' generally adopts a target method, namely, the average performance of a target building is compared with that of other similar buildings, so as to judge the running performance of the target building. However, even if the buildings are similar, the use modes, the operation boundary conditions and other factors of the buildings are greatly different, so that the method often causes large errors. Therefore, researchers have proposed a self-comparison "energy consumption rating" evaluation method, i.e., comparing the actual performance of the target building with the "energy consumption rating" representing the building. At present, the commonly adopted method for generating the 'energy consumption quota' is as follows: the building energy consumption model is established according to design data, the meteorological data of a standard meteorological year is used, annual energy consumption simulation is carried out according to the assumed personnel density and other parameters during design, and the simulation result is used as an energy consumption quota. However, changes in the construction process of green buildings, actual personnel density after the buildings are put into use, use time, loads of indoor equipment (lighting, office equipment, and the like), system control strategies, outdoor weather conditions, and the like all have a significant influence on design expectations. The existing method does not consider the influence of the series of variables, so that the evaluation and analysis of the actual performance of the building are one-sided and sometimes even wrong. Therefore, the existing method for establishing the 'energy consumption quota' is not beneficial to the evaluation of the actual performance of the green building and restricts the efficient development and application of the green building.
Disclosure of Invention
The invention aims to provide a method for determining the energy consumption quota of a green building based on a control strategy, which can determine the energy consumption quota of the green building according to the personnel density, the service time, the load of indoor equipment (lighting, office equipment and the like), a system control strategy, outdoor meteorological conditions and the like of the green building, so that the actual performance of the green building can be truly reflected, and the further development of the green building is promoted.
The technical scheme adopted by the invention for solving the technical problems is as follows: a green building energy consumption quota determining method based on a control strategy comprises the steps of firstly, establishing an EnergyPlus model of a green building based on actual operation data of the building; secondly, performing model checking on the model by adopting a checking simulation method; thirdly, determining a building operation control strategy and dividing the strategy into three grades of 'good', 'common' and 'poor'; and finally, simulating the building energy consumption by using Monte Carlo analysis, fitting an energy consumption probability density curve, and determining the building energy consumption quota. The method comprises the following specific steps:
firstly, establishing a model of the building in energy consumption simulation software EnergyPlus according to building design parameters, and setting actual parameters such as model building envelope materials, room area and window-wall ratio, personnel density, service time, loads of indoor energy consumption equipment (lighting, office equipment and the like), system control strategies of the indoor energy consumption equipment (each energy consumption equipment can correspond to the system control strategies of a plurality of respective equipment), outdoor meteorological conditions and the like which are related to energy consumption;
secondly, performing model checking on the model by adopting a checking simulation method; comparing actual monthly total energy consumption data of a building with monthly simulated total energy consumption obtained by a model established in the first step, and determining a monthly total energy consumption reference model after adjusting and correcting model input parameters for multiple times (the input parameters are adjusted according to the proportion range of plus or minus 15 percent of the actual parameters) so that the error between the simulated result energy consumption and the actual energy consumption is within the range specified by relevant standards; the present invention uses ASHRAE guidelines 14-2014 as calibration standards, where: the standard mean deviation (NMBE) should be in the range of-5% to 5%, and the root mean square Coefficient of Variation (CVRMSE) should be in the range of 0 to 15%.
The i represents a natural number with a month value of 1-12, n is 12, and y isiThe actual energy consumption for the month of i,
for the simulated energy consumption of the month i,
the average value is obtained for annual average actual energy consumption, namely the accumulated and summed monthly energy consumption of the actual energy consumption.
And thirdly, selecting a plurality of system control strategies of each energy consumption device in the first step, and dividing the strategies into three grades, namely 'good', 'normal' and 'poor'. "good" represents the design expectation or optimal performance of the building, "general" represents the ability of the building to perform well, but only poorly managed, "poor" building operation control strategies are inefficient;
fourthly, researching the influence of each control strategy on the annual energy consumption of the building, namely the influence of the sum of the total energy consumption per month, in the third step by using a Monte Carlo simulation method; the method adopts a sensitivity analysis method, only one control strategy is changed in each simulation, and other control strategies are kept unchanged, so that the simulated variation of the building energy consumption can directly reflect the influence of the control strategy on the building; based on uncertainty analysis, estimating a probability density function of annual energy consumption distribution by adopting a random sampling statistical method, and fitting a simulation result into a normal distribution curve to obtain a building energy consumption probability density curve; the number of sampling samples for random sampling is preferably 1000;
and fifthly, defining the building energy consumption corresponding to the maximum probability in the energy consumption probability density curve as the energy consumption quota of the building.
The invention has the beneficial effects that: (1) the energy consumption quota of the green building can be accurately calculated; (2) the energy consumption quota of green buildings in different regions, different types and different utilization rates can be calculated; (3) the actual operation performance of the green building can be reflected, and the energy-saving potential of the green building is excavated according to the increased operation management mode; (4) the operation is simple, and the applicability is strong.
Drawings
FIG. 1 is an EnergyPlus simulation model based on actual parameters of an office building in Beijing;
FIG. 2 is a flow chart of the operation of example 1 jEPlus;
fig. 3 is a graph of the energy consumption probability of the building of example 1.
Detailed Description
The following description is only a preferred embodiment of the present invention, and does not limit the scope of the present invention.
The method for determining the energy consumption quota of the green building based on the control strategy provided by the invention realizes accurate and reasonable determination of the energy consumption quota of the green building. The following will describe the following embodiments of the present invention in further detail with reference to the accompanying drawings, but the present invention is not limited to the following embodiments.
Example 1
(1) FIG. 1 is an EnergyPlus simulation model established based on actual parameters of a commercial building in Beijing, wherein the height of each floor of a room is 2.98m, the total length is 12m, the total width is 3.3m, the length of each room is 3m, and the area of each room is 9.9m2There are 4 rooms per floor. The wall adopts a brick wall with the thickness of 240mm, the window adopts three layers of glass windows, the concrete structure is 6mm of EC glass, 12mm of air layer (90% of argon and 10% of air), and 6mm of common glass, and the concrete parameters of the EC glass are shown in Table 1. The height of the windowsill is 0.98m from the ground, and the window area is 1.5 multiplied by 1.5m2The window-wall ratio is 0.25;
indoor air conditioners, lighting, equipment, personnel and the like are set according to corresponding numerical values in actual measurement results, a practical heat supply and air conditioner design manual and a building lighting design standard (GB 50034-2013). The power of the illuminating lamps is set to be 80W (1), the heat dissipation capacity of each computer is 50W (1), the heat dissipation capacity of each person is 89W (2 persons), the moving time of an air conditioner, illumination, equipment, personnel and the like of each house is 8: 00-18: 00, the indoor design temperature in a heating season is 18-22 ℃, and the indoor design temperature in a cooling season is 24-28 ℃; the simulated meteorological parameters adopt meteorological files of Beijing area in international energy consumption calculation typical meteorological year. The simulation is carried out in 3 time intervals throughout the year according to different indoor thermal environment requirements, taking the Beijing area as an example, the heating season (11 months and 15 days to the next year and 3 months and 15 days), the cooling season (5 months and 15 days to 9 months and 15 days), the transition season (the rest time) and the like are respectively shown in Table 1. And recording the actual energy consumption per month.
(2) Performing model checking on the model by adopting a checking simulation method; comparing actual monthly total energy consumption data of a building with monthly simulation total energy consumption obtained by a model established in the first step, and determining a monthly total energy consumption reference model after adjusting and correcting model input parameters for multiple times (the input parameters are adjusted according to the proportion of up to 15 percent of the actual parameters) so that the error between simulation result energy consumption and actual energy consumption is within a range specified by a relevant standard; the present invention uses ASHRAE guidelines 14-2014 as calibration standards, where: the standard mean deviation (NMBE) should be in the range of-5% to 5%, and the root mean square Coefficient of Variation (CVRMSE) should be in the range of 0 to 15%.
(3) Table 1 shows the control operation strategies set in EnergyPlus. Based on the function of Macro of EnergyPlus, a system control strategy is set respectively, and the control strategy is defined to have "good" of 1, "general" of 2, "poor" of 3, and the probabilities are 1/3.
(4) Fig. 2 is a flow chart of the operation of jEPlus. In jEPlus, 13 operation control strategies are defined as optimization variables, and discrete variables 1, 2 and 3 are set for each control strategy, and represent three different levels under the same control strategy respectively. The 13 control variables are divided into 3 levels, so 1594323 (3) exists13) And (4) combination. Considering the number of combinations is too large, 1000 samples randomly generated by sampling the Latin hypercube are used, and a Latin hypercube sampling simulation method is selected in the setting page. The jEPlus calls the imf file set in EnergyPlus and starts the simulation.
(5) Fig. 3 is a building energy consumption probability curve. Reading all energy consumption simulation results of the jEPlus, and fitting the energy consumption simulation results into an energy consumption probability density curve in the shape of normal distribution, wherein the curve represents the energy consumption usage size and the energy consumption probability of the building. And selecting the building energy consumption corresponding to the maximum probability as the building energy consumption quota of the building.
The embodiment of the invention can effectively restrict the total energy consumption of the building and has simple operation.
TABLE 1 Green building operation optimization control strategy corresponding parameter settings
Claims (4)
1. A green building energy consumption quota determining method based on a control strategy is characterized by comprising the following specific steps:
firstly, establishing a model of the building in energy consumption simulation software EnergyPlus according to building design parameters, and setting according to actual parameters;
secondly, performing model checking on the model by adopting a checking simulation method; comparing actual monthly total energy consumption data of a building with monthly simulation total energy consumption obtained by a model established in the first step, adjusting input parameters of the model after adjusting and correcting the input parameters of the model for multiple times according to the proportion range of plus or minus 15 percent of the actual parameters, and determining a monthly total energy consumption reference model if the error between the energy consumption of a simulation result and the actual energy consumption is within the range specified by relevant standards; ASHRAE guidelines 14-2014 were used as calibration standards, where: the standard average deviation NMBE is within the range of-5%, and the root mean square coefficient of variation CVRMSE is within the range of 0-15%;
the i represents a natural number with a month value of 1-12, n is 12, and y isiThe actual energy consumption for the month of i,
for the simulated energy consumption of month i,
the average value is taken for annual average actual energy consumption, namely the accumulated summation of the monthly energy consumption of the actual energy consumption;
thirdly, selecting a plurality of system control strategies of each energy consumption device in the first step, and dividing the strategies into three grades, namely 'good', 'normal', 'poor'; "good" represents the design expectation or optimal performance of the building, "general" represents the ability of the building to perform well, but only poorly managed, "poor" building operation control strategies are inefficient;
fourthly, researching the influence of each control strategy on the annual energy consumption of the building, namely the influence of the sum of the total energy consumption per month, in the third step by using a Monte Carlo simulation method; the method adopts a sensitivity analysis method, only one control strategy is changed in each simulation, other control strategies are kept unchanged, and the simulated variation of the building energy consumption can directly reflect the influence of the control strategy on the building; estimating a probability density function of annual energy consumption distribution by adopting a random sampling statistical method, and fitting a simulation result into a normal distribution curve to obtain a building energy consumption probability density curve;
and fifthly, defining the building energy consumption corresponding to the maximum probability in the energy consumption probability density curve as the energy consumption quota of the building.
2. A control strategy based green building energy consumption quota determining method as claimed in claim 1, wherein the first step comprises one or more of model building envelope material, room area and window-to-wall ratio, personnel density, age, indoor energy consumption equipment load, indoor energy consumption equipment system control strategy, outdoor weather conditions, wherein each energy consumption equipment corresponds to the system control strategy of a plurality of respective equipments.
3. A control strategy based green building energy consumption quota determining method according to claim 1, characterized in that the first step comprises at least personnel density, usage time, indoor energy consumption equipment load, indoor energy consumption equipment system control strategy, wherein each energy consumption equipment corresponds to the system control strategy of a plurality of respective equipments.
4. A control strategy based green building energy consumption quota determining method as claimed in claim 1, wherein the number of the sampling samples randomly sampled in the fourth step is 1000.
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| CN108830932A (en) * | 2018-06-15 | 2018-11-16 | 郑州大学 | A kind of volumed space building energy consumption prediction technique coupled based on EnergyPlus with CFD |
| CN109636677A (en) * | 2019-01-17 | 2019-04-16 | 天津大学 | Building thermal technique performance estimating method based on model calibration |
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| CN108830932A (en) * | 2018-06-15 | 2018-11-16 | 郑州大学 | A kind of volumed space building energy consumption prediction technique coupled based on EnergyPlus with CFD |
| CN109636677A (en) * | 2019-01-17 | 2019-04-16 | 天津大学 | Building thermal technique performance estimating method based on model calibration |
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| 人工智能算法在建筑能耗预测中应用综述;沙国荣等;《科技创新与生产力》;20180910(第09期);全文 * |
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