CN110738581A - Campus comprehensive building power consumption rating method based on multiple linear regression - Google Patents

Campus comprehensive building power consumption rating method based on multiple linear regression Download PDF

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CN110738581A
CN110738581A CN201910675849.1A CN201910675849A CN110738581A CN 110738581 A CN110738581 A CN 110738581A CN 201910675849 A CN201910675849 A CN 201910675849A CN 110738581 A CN110738581 A CN 110738581A
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power consumption
annual
unit building
building area
building
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丁研
李沛霖
张震勤
朱能
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Tianjin University
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Abstract

The invention discloses an campus comprehensive building power consumption rating method based on multiple linear regression, which is applied to scientific research and engineering application in the relevant fields of buildings.

Description

Campus comprehensive building power consumption rating method based on multiple linear regression
Technical Field
The invention relates to public building power consumption rating methods, in particular to campus comprehensive building power consumption rating methods based on multiple linear regression.
Background
The campus is an important social component and also an energy-consuming household, the campus building energy consumption has the characteristics of multiple types, large total amount, large energy-saving potential and large influence, the campus building energy saving is an important ring in campus energy-saving work, the high education industry in China develops rapidly at present, the energy use is increased rapidly, however, the campus facility operation level is lower, the education buildings account for about 34 percent of the total amount of public buildings, the education building energy consumption accounts for 39.33 percent of the total amount of the public buildings, the university campus building amount accounts for more than 85 percent of the total amount of the education buildings, the colleges and universities consume about 3000 million tons of standard coal all the year round, and the index is reduced, the per-capita energy consumption of the Chinese campus building is four times that of the residential building, so the management of the university campus energy consumption is very important.
The comprehensive building is multipurpose building types, the main functions of the university comprehensive building comprise scientific research, office space provision and the like, the university comprehensive building is characterized by excessive schedule arrangement, diversified power consumption types and higher unit building area power consumption compared with other office buildings, and therefore, scheme is necessary for reducing the power consumption of the campus comprehensive building.
The existing invention has common characteristics that the standard of the total energy consumption or the power consumption of the building is established without analyzing the secondary power consumption in detail, the branch power consumption generally comprises the power consumption of lighting and indoor equipment, the power consumption of a heating ventilation air conditioning system and the power consumption of other power systems, the total power consumption reflects the whole power consumption condition of the building, and the branch power consumption reflects the power consumption, the power consumption details and the power demand of the building.
Therefore, the key problem to be solved urgently is to grade the building power consumption by adopting a reasonable building power consumption analysis mode and improve the current energy-saving mode.
Disclosure of Invention
In view of the above, the present invention provides methods for rating power consumption of campus buildings, so as to solve the above technical problems.
Based on the prior invention, the invention is improved as follows: selecting the influence factors of the electricity consumption; the multiple linear regression method is used for establishing the power consumption index of the unit building area; standardizing the power consumption index of a unit building area; establishing a power consumption standard of the unit building area based on the standardized power consumption index of the unit building area; correcting the reference; then, the construction electricity amount is rated.
The invention provides an campus comprehensive building power consumption rating method based on multiple linear regression, which comprises the following steps:
the method comprises the steps of collecting data including basic building information and real-time building power consumption, wherein the basic building information is divided into two types, namely information directly influencing the building power consumption, such as the running time and power of power equipment, and information related to air conditioner load, and information indirectly influencing the building power consumption, such as building type, position, building material and direction.
Factors affecting electricity usage, including age, building area, building height, floor number, building structure, exterior wall material, exterior window type, air conditioning system form, office area, are determined.
, based on multiple linear regression modeling, making the annual total power consumption and branch power consumption index of unit building area
Y＝a+b1X1+b2X2+bnXn+ε
Wherein: xi independent continuous variables representing the influence of unit building area annual power consumption index;
y represents an output (here, a power consumption index);
a represents the intercept;
b represents a regression parameter;
ε represents the residual error.
, determining the effect of the classification variables on the annual power consumption index standardization of the unit building area, and standardizing the annual power consumption index of the unit building area by adopting a method
Figure BDA0002143243840000021
Figure BDA0002143243840000022
Wherein:
Figure BDA0002143243840000023
representing a normalized independent variable;
Xi independent continuous variables representing the influence of unit building area annual power consumption index;
Figure BDA0002143243840000024
representing the mean of the sampled data;
Sirepresenting the standard deviation of the sampled data;
EUInorm(kWh/(m) represents a standardized annual power consumption index per unit building area2·a))；
EUI represents the annual power consumption index of a unit building area (kWh/(m)2·a))。
, determining the unit building area annual power consumption reference based on the standardized unit building area annual power consumption index.
, correcting the standard based on the multi-factor analysis of variance, because the building structure, the outer wall material, the type of the outer window and the form of the air conditioning system are classification factors, the standardization can not be carried out by using multiple regression analysis, therefore, the multi-factor analysis of variance is adopted to research the influence of classification variables on the standardized annual power consumption index of the unit building area, and determine the correction coefficient.
The total electricity consumption benchmark of unit building area and year and the branch electricity consumption benchmark of unit building area and year are respectively regarded as -level and second-level indexes, -level indexes are helpful for better understanding the building electricity consumption level of the same type of buildings, the second-level indexes can help to evaluate the electricity consumption condition in detail and carry out targeted energy-saving reconstruction and management, -level indexes are established by a linear grading model, and secondary indexes are established by a three-dimensional grading model.
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FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram of a reference measurement of annual total power consumption of a building area of a building unit;
FIG. 3 is a diagram of annual lighting and equipment power consumption reference measurement of the building area of a building complex;
FIG. 4 is a diagram of a reference measurement of electricity consumption of an annual air conditioning system in the unit building area of a complex building;
FIG. 5 is a diagram of a reference measurement of power consumption of an annual power system in the unit building area of a complex building;
FIG. 6 is an EUI of different air conditioning systemsAC，normA distribution diagram;
FIG. 7 is a reference diagram for correcting electricity consumption of a complex building;
FIG. 8 is a schematic view of a linear hierarchical model;
fig. 9 is a schematic view of a stereoscopic hierarchical model.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to specific embodiments and the accompanying drawings.
As shown in fig. 1, the present embodiment provides methods for ranking campus comprehensive building power consumption based on multiple linear regression, which includes the following steps:
step 1: and collecting data including basic building information and real-time building power consumption. The basic building information can be obtained by investigation. The building electricity consumption data can be directly downloaded from the monitoring system by means of the real-time monitoring system;
the building selected for the case study was a complex building located at 13 universities of Anhui province in China.
The total electricity consumption of a building can be subdivided into various sub-categories of electricity consumption, namely lighting and indoor equipment, air conditioning systems, electrical systems and special equipment. The power consumption of the special equipment refers to the power consumed by special professional equipment (such as large scientific research equipment) and high-power-consumption equipment (such as a data center server).
Step 2: determining factors influencing electricity consumption;
the preliminary determination of the annual power usage indicator level for a unit building area of the target building is based on building information from the survey and a record of power consumption obtained using the monitoring system.
The power consumption influencing factors of the case building include service life, building area, building height, floor number, building structure, exterior wall material, exterior window type, air conditioning system form, area of research office area and area of laboratory area. Factors affecting the sampled building electricity usage may be divided into a continuous factor describing a particular value and a categorical factor describing a qualitative description. The continuous factors include service life, total building area, building height, floor number, area of scientific research office area and area of laboratory area. The remaining factors, such as building construction and exterior wall materials, are classification factors.
And step 3: the method comprises the following steps of (1) formulating an annual total power consumption and a subentry power consumption index of a unit building area based on multivariate linear regression modeling, wherein the adopted method is shown as a formula (1);
Y＝a+b1X1+b2X2+bnXn+ε (1)
wherein: xi independent continuous variables representing the influence of unit building area annual power consumption index;
y represents an output (here, a power consumption index);
a represents the intercept;
b represents a regression parameter;
ε represents the residual error.
In this case, the annual total power consumption and the subentry power consumption of the unit building area of the integrated building are defined as dependent variables, and six continuous factors (including the building area, the service life, the floor number, the building height, the area of the scientific research office area and the area of the laboratory area) are defined as independent variables, which are respectively represented by X1、X2、…、X6And (4) showing.
Where the linear relationship between the power usage indicator and the independent variables including laboratory area, building area and floor number are significant, the factors represented by these variables are retained in the model. The laboratory area is mainly reflected in the total power consumption index, the lighting and indoor equipment power consumption index, the building area is reflected in the lighting and indoor equipment power consumption index and the air conditioning system power consumption index, and the floor number is reflected in the power system power consumption index. On the basis, the final regression equation of each index is as follows:
EUIT＝36.464+0.017×LA (2)
EUILR＝7.940+0.046×LA＝0.011×GFA (3)
EUIAC＝14.202+0.001×GFA (4)
EUIPSequal to 0.262+0.137 multiplied by floor number (5)
In the formula: EUITThe total annual power consumption index of the unit building area is expressed, (kWh/(m)2·a))；
EUILRExpresses the annual illumination of unit building area and the electricity consumption index of indoor equipment, (kWh/(m)2·a))；
EUIACThe power consumption index of the air conditioning system in the unit building area year is expressed, (kWh/(m)2·a))；
EUIPSThe power consumption index of the annual power system of the unit building area is expressed, (kWh/(m)2·a))；
LA denotes the laboratory area, m2；
GFA denotes the building area, m2。
Step 4, carrying out standardization processing on the annual total power consumption and the subentry power consumption indexes of the unit building area;
and determining the influence of the classification variables on the standardization of the annual power consumption index of the unit building area, and standardizing the annual power consumption index of the unit building area by adopting a method shown as a formula (6) and a formula (7).
Figure BDA0002143243840000051
Figure BDA0002143243840000052
Wherein:
Figure BDA0002143243840000053
representing a normalized independent variable;
Xi independent continuous variables representing the influence of unit building area annual power consumption index;
representing the mean of the sampled data;
Sirepresenting the standard deviation of the sampled data;
EUInormm(kWh/(m) represents a standardized annual power consumption index per unit building area2·a))。
The building-integrated standardized model of the 13 sampled building continuity factors in the case is expressed according to the following equation:
Figure BDA0002143243840000055
Figure BDA0002143243840000056
Figure BDA0002143243840000057
Figure BDA0002143243840000058
in the formula, X1Represents the building area GFA;
X3indicating the number of floors;
X6representing the laboratory area.
Step 5, determining the annual power consumption standard of the unit building area based on the standardized annual power consumption index of the unit building area;
in this case, the expressions (8) to (11) represent standardized models of the complex building, and the criterion is calculated by using a calculation average method based on an annual power consumption index of a unit building area. The calculation results are shown in fig. 2 to 5.
As can be seen from FIGS. 2-5, the total electricity consumption of buildings No. 9-13 in 13 buildings exceeds the annual total electricity consumption standard (EUI) of unit building areaT，norm) Wherein the annual lighting and the electricity consumption of the indoor equipment of the unit building area also exceed the corresponding standards (EUI)LR，norm) The main reason for this is that the power consumption of lighting and indoor equipment accounts for about 71.8% of the total power consumption of these buildings. The electricity consumption of the air conditioning systems of the buildings 9 and 13 exceeds the electricity consumption standard (EUI) of the air conditioning system of the unit building area annual degree of the five buildingsAC，norm). For the electricity consumption of the power system, except the No. 13 building, the other four buildings exceed the corresponding standard (EUI)PS，norm). For other buildings, although the total electricity consumption of the buildings does not exceed the reference, the electricity consumption of the air conditioning system and the electricity consumption of the power system of some of the buildings exceed the corresponding reference.
Step 6, analyzing and correcting a standard based on the multi-factor variance;
since building structures, exterior wall materials, exterior window types and air conditioning system forms are the classification factors, it is not possible to standardize using multivariate regression analysis. Therefore, a multi-factor analysis of variance is employed to study the effect of the classification variables on the normalized annual power usage index for a unit building area and to determine the correction coefficients.
The analysis result showed that EUIT，norm、EUILR，normAnd EUIPs，normThere is no statistical significance to these categorical variables, only the air conditioning system type for the EUIAC，normHas statistical significance. Therefore, the reference should be corrected according to the type of the air conditioning system. In the 13 sampling buildings of this case, 7 buildings adopt split air conditioning systems, 4 buildings adopt variable frequency multi-split air conditioning systems, and two buildings adopt fan coil systems. EUI of different air conditioning system typesAC，normAs shown in fig. 6.
As shown in fig. 6, the annual air conditioning system electricity consumption indexes of the average unit building area of the split air conditioning unit, the variable frequency multi-split air conditioning system and the fan coil system are respectively 17.98, 25.94 and 9.42 kWh/(m)2A), and furthermore the annual air-conditioning system power consumption index for the average unit building area of all buildings is 18.63 kWh/(m)2·a)。
According to EUIAC，normThe average value of (1) and the reference of the electricity consumption of the air conditioning system are corrected, and the calculation method of the correction coefficient is shown as the following formula:
Figure BDA0002143243840000061
the calculation results are as follows: CC (challenge collapsar)SAC＝0.965、CCVRV＝1.392、CCFC＝0.506
Wherein SAC represents split air-conditioning system, VRV represents variable frequency multi-split system, FC represents fan coil system.
As shown in FIG. 7, the corrected reference is 21.19 kWh/(m) compared with the reference in FIG. 42A) increased by 2.56 kWh/(m)2·a)。
Step 7, the established standard is used for grading the building electricity consumption;
the total electricity consumption benchmark of the unit building area year and the branch electricity consumption benchmark of the unit building area year are respectively regarded as grade and second grade indexes, the grade index is helpful for better understanding the building electricity utilization level of the similar buildings, and the second grade index can help to evaluate the electricity utilization condition in detail and carry out targeted energy-saving reconstruction and management.
In this case, for some buildings, the total power usage is below EUIT，normAnd the fractional power consumption exceeds the corresponding benchmark. Therefore, it is important to comprehensively estimate the power consumption of the building. The evaluation system is defined according to the actual power consumption data of the research integrated building according to the new power consumption standard.
The -grade index of the research building is divided into five grades from A to E, and the standard is 90.76 (kWh/(m)2A)) as the center, the maximum annual total electricity consumption of the unit building area is 180 (kWh/(m)/2The results of the grading are shown in FIG. 8. in the index of grade , "A" and "B" indicate that the total power consumption is relatively low, and the symbols "C" to "E" indicate that the total power consumption is relatively high.for the second-order index, grading is performed centering around the annual subentry power consumption per unit building area, below the reference "+" indicating that the subentry power consumption is qualified, above the reference "" indicating that the subentry power consumption is not qualified.
Table 1 lists the results of the ratings for this case of 13 buildings, as shown in Table 1, the total energy consumption of buildings 1, 3 and 8 is low and the individual power consumption is acceptable, for buildings 2, 4 and 5-7, the total energy consumption is low, but individual power consumptions require energy-saving modification, which can further reduce the total energy consumption by .
TABLE 1 rating of this case building
Figure BDA0002143243840000071
While the present invention has been described with reference to the accompanying drawings, the present invention is not limited to the above-described embodiments, which are illustrative only and not restrictive, and many modifications may be made by those skilled in the art without departing from the spirit of the present invention, within the scope of the appended claims.

Claims (7)

1. The campus comprehensive building power consumption rating method based on the multiple linear regression is characterized by comprising the following steps of:
(1) obtaining basic building information, including information directly influencing the building electricity consumption and information indirectly influencing the building electricity consumption;
(2) analyzing the basic building information obtained in the step (1) to obtain power consumption influence factors;
(3) determining the annual power consumption index of the unit building area based on multiple linear regression modeling according to the factors influencing the power consumption obtained in the step (2);
(4) standardizing the annual power consumption index of the unit building area obtained in the step (3);
(5) determining the annual power consumption standard of the unit building area on the basis of the step (4);
(6) correcting the annual electricity consumption benchmark of the unit building area obtained in the step (5);
(7) and (4) establishing a building power consumption rating system based on the benchmark obtained in the step (6).
2. The multiple linear regression-based campus comprehensive building power consumption rating method of claim 1, wherein the building power consumption rating system is established based on an annual power consumption benchmark for a unit building area.
3. The multiple linear regression-based campus composite building power consumption rating method of claim 2, wherein the annual power consumption benchmark for unit building area is based on the standardization of the power consumption index for unit building area.
4. The method of claim 3, wherein the electricity consumption index per unit building area comprises an annual lighting and indoor equipment power consumption index per unit building area, an annual air conditioning system terminal electricity consumption index per unit building area, and an annual power system electricity consumption index per unit building area.
5. The campus comprehensive building power rating method based on multiple linear regression of claim 1, wherein in the step (3), the multiple linear regression process is performed on all data for checking the latent factors of importance, and the multiple regression model with multiple classification variables is established by:
Y＝a+b1X1+b2X2+bnXn+ε
wherein: xi independent continuous variables representing the influence of unit building area annual power consumption index;
y represents an output (here, a power consumption index);
a represents the intercept;
b represents a regression parameter;
ε represents the residual error.
6. The method for grading power consumption of a campus comprehensive building based on multiple linear regression as claimed in claim 1, wherein in the step (4), annual power consumption index of unit building area is standardized by
Figure FDA0002143243830000011
Figure FDA0002143243830000021
Wherein:
Figure FDA0002143243830000022
representing a normalized independent variable;
Xi independent continuous variables representing the influence of unit building area annual power consumption index;
Figure FDA0002143243830000023
representing the mean of the sampled data;
Sirepresenting the standard deviation of the sampled data;
EUInorman annual power usage indicator representing a standardized unit building area;
EUI represents an annual power usage indicator for a unit building area.
7. The campus comprehensive building power consumption rating method based on multiple linear regression as claimed in claim 1, wherein the building power consumption rating system is established based on the basis of the reference in step (7), the unit building area annual total power consumption reference and the unit building area annual itemized power consumption reference are regarded as th-level and second-level indexes respectively, the method for establishing -level indexes is a linear grading model, namely, the unit building area annual total power consumption reference is taken as the center, the maximum value and the minimum value of the unit building area annual total power consumption are taken as the upper limit and the lower limit for grading, the method for establishing the second-level indexes is a three-dimensional grading model, and the unit building area annual itemized power consumption reference is taken as the center of a grading coordinate axis for grading.
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