KR101915209B1 - Method for energy consumption simulation based big data considering characteristic factor - Google Patents

Abstract

The present invention relates to an energy consumption simulation method based on big data comprising the steps of: setting a data table consisting of characteristic factors affecting the energy consumption to predict the energy consumption for adjusting an internal temperature of the building to be inspected to a specific temperature under a certain outdoor temperature condition; and searching for data matching the characteristic factor of the building to be inspected from the data table. The energy consumption simulation method is able to more easily and simply calculate the amount of energy consumed by the building to be inspected and is estimated by a predetermined linear interpolation method when there exists a characteristic factor which does not coincide with the data table.

Description

METHOD FOR ENERGY CONSUMPTION SIMULATION BASED BIG DATA CONSIDERING CHARACTERISTIC FACTOR BACKGROUND OF THE INVENTION [0001]

The present invention includes a step of setting a data table composed of characteristic factors affecting energy consumption for predicting the energy consumption for adjusting the internal temperature of the building to be inspected to a specific temperature under a certain outdoor temperature condition, And searching for data that matches the characteristic factor of the property of the building, thereby making it possible to more easily and simply calculate the energy consumption of the building to be analyzed. If there is a characteristic factor inconsistent with the data table, It is a simulation method of energy consumption based on big data that is estimated.

In general, many techniques are used to predict energy consumption in buildings such as buildings. For example, FORWARD simulation and BACKWARD simulation are used.

In the case of the FORWARD simulation technique, energy consumption of each element constituting the building is calculated after assuming a predetermined building, and the energy consumption of the entire building is predicted. For example, after building a building having components such as a predetermined azimuth and an area, a characteristic value for each of the building components is calculated to predict energy consumption of the building.

In the case of the BACKWARD simulation technique, the energy consumption of the existing building is measured and data is constructed, and the energy consumption characteristic value for each component is calculated by the data to predict the energy saving rate.

However, in the case of such a conventional technology, as described above, two simulation techniques are required, so that it takes a lot of time and effort to predict the energy consumption, and it is difficult to calculate the energy consumption of the building because the components constituting the building are too complicated .

Meanwhile, the above-described energy simulation technique itself is widely known and is described in detail in the following prior art documents, and a description thereof will be omitted.

Korean Patent No. 10-1636203 Korean Patent No. 10-1652797 Korean Patent No. 10-1670471 Korean Patent No. 10-1717756 Korean Patent No. 10-1741015 Korean Patent Publication No. 10-2016-0133183

SUMMARY OF THE INVENTION The present invention has been made to solve the above-mentioned problems, and it is an object of the present invention to provide a data table having characteristic factors affecting energy consumption for predicting the energy consumption for adjusting the internal temperature of a building to be inspected to a specific temperature under a certain outside- And searching for data matching the characteristic factor of the building to be analyzed from the data table so as to more easily and simply calculate the energy consumption of the building to be analyzed, A method of simulating energy consumption based on a big data, which is estimated by a predetermined interpolation method, is provided.

It is to be understood, however, that the intention is not to limit the scope of the invention as defined in the appended claims, but that the invention may also be embodied in other specific forms without departing from the spirit or essential characteristics thereof. will be.

In order to achieve the above object, the present invention is to secure energy consumption data from a substantial number of actual buildings and to predict energy consumption by searching for data matching the building to be analyzed among the data, (P3) to the ratio of the external area of the building to the window area (P3), the exterior factor (PA) to the external temperature of the building, the bearing factor (P1) A first step (S100) of setting a data table including a shadow factor (P4) for a ratio of an exterior area of a building and a shadow area, and an area factor (P5) for a total area of the building; And a second step (S200) of finding the data corresponding to each characteristic factor of the building to be analyzed from the data table set by the step S100 The first step S100 arranges the analysis data of the actual building, arranges the energy consumption for adjusting the internal temperature of the building to a specific temperature under a certain outdoor temperature condition according to the number of cases of each characteristic factor, In a second step (S200), when some of the characteristic factors of the data table and the analysis target building do not match, the energy consumption data of the disagreement characteristic factor is secured and the data The present invention provides a big data-based energy consumption simulation method for estimating an energy consumption amount by calculating an inconsistent characteristic factor value using an interpolation method.

In order to arrange the plurality of characteristic factors in the first step S100, the energy consumption amount according to the respective orientation parameters P1 is first arranged, and the shape factor (P1) is calculated for each of the orientation factors P1 (P3) is arranged for each of the shape factors (P2) and the energy consumption amount according to the shadow factor (P4) is set for each of the opening / closing factors (P3) And arranges the energy consumption according to the area factor (P5) and the energy consumption according to the outside air factor (PA) for each of the shadow factors (P4), and the energy consumption according to the area factor (P5) And the energy consumption according to the outside air factor (PA) is expressed by the unit energy consumption of each indoor unit (P4) And the outside air temperature is divided into a specific range and indicated by a plurality of numbers.

In the above, the outside temperature of the outside air factor P1 is divided by an interval of 5 占 폚 in a period between -50 占 폚 and 40 占 폚, and the bearing parameter P1 divides the bearing of the building into eight parts.

If data corresponding to one characteristic factor of the orientation factor P1, the shape factor P2, the opening / closing factor P3 and the shadow factor P4 in the characteristic factor do not appear in the data table, And calculating energy consumption data for a characteristic factor that is inconsistent by the interpolation method using Equation (1).

[Equation 1]

Y: Energy consumption of the analyzed building to be calculated for the disagreeable characteristic factor

L11, L12: the ratio of the low value and the high value adjacent to the data of the mismatched characteristic factor,

E11, E12: the energy consumption of each of the low and high values adjacent to the data of the mismatching characteristic factor

If data corresponding to two characteristic factors of the orientation factor P1, the shape factor P2, the open / close factor P3 and the shadow factor P4 in the characteristic factor do not appear in the data table, Calculating a plurality of energy consumption data for characteristic factors that are inconsistent by the interpolation method using Equation 2; and calculating an average of the plurality of calculated energy consumption amounts to calculate an energy consumption amount of the building to be analyzed.

&Quot; (2) "

Y1, Y2: energy consumption of the analyzed building to be calculated for the disagree characteristic factor

L11, L12, L21, L22, L23, L24: the ratio of the low value and the high value adjacent to the data of the mismatched characteristic factor,

E11, E12, E21, E22: energy consumption amount corresponding to the low value and the high value adjacent to the data of the disagreement characteristic factor

If data corresponding to three characteristic factors of the orientation factor P1, the shape factor P2, the open / close factor P3 and the shadow factor P4 in the characteristic factor do not appear in the data table, Calculating a plurality of energy consumption data for a characteristic factor that is inconsistent by an interpolation method using Equation 3; and calculating an average of the plurality of calculated energy consumption amounts to calculate an energy consumption amount of the analysis target building.

&Quot; (3) "

Y1, Y2, Y3, Y4: The energy consumption of the analyzed building to be calculated for the mismatching characteristic factor

L33, L33, L34, L35, L36, L37, L38: a ratio of a ratio of a low value and a high value adjacent to data of an inconsistent characteristic factor,

E11, E12, E21, E22, E31, E32, E41, and E42: Each energy consumption amount corresponding to a low value and a high value adjacent to data of an inconsistent characteristic factor

The features and advantages of the present invention will become more apparent from the following detailed description based on the accompanying drawings.

Prior to that, terms and words used in the present specification and claims should not be construed in a conventional and dictionary sense, and the inventor may properly define the concept of the term in order to best explain its invention It is to be understood that the technical terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

Also, the technical terms used herein should be interpreted in a sense generally understood by a person skilled in the art to which the present invention belongs, unless otherwise defined in this specification, and it should be understood that an overly comprehensive It should not be construed as a meaning or an overly reduced meaning.

Furthermore, the singular forms " a " as used herein include plural referents unless the context clearly indicates otherwise. In the present application, the term "comprising" or "comprising" or the like should not be construed as necessarily including the various elements or steps described in the specification, Or may include additional components or steps, and should be construed as meaning and concept consistent with technical thought.

According to the present invention, the energy consumption of the building to be analyzed can be easily and easily predicted.

1 is a schematic diagram showing a data table based on a big data according to an embodiment of the present invention.
2 is a schematic diagram showing an actual part of a big data-based data table according to an embodiment of the present invention.
FIG. 3 is a data table in the case where there is one inconsistency characteristic factor of the Big Data based energy consumption simulation method according to an embodiment of the present invention.
FIG. 4 is a data table in the case where there are two discrete characteristic factors of a Big Data based energy consumption simulation method according to an embodiment of the present invention.
FIG. 5 is a data table in a case where there are three characteristic factors of inconsistency of energy consumption simulation method based on Big Data according to an embodiment of the present invention.

Hereinafter, preferred embodiments of the present invention will be described with reference to the accompanying drawings. In this process, the thicknesses of the lines and the sizes of the components shown in the drawings may be exaggerated for clarity and convenience of explanation.

In addition, the terms described below are defined in consideration of the functions of the present invention, which may vary depending on the intention or custom of the user, the operator. Therefore, definitions of these terms should be made based on the contents throughout this specification.

In addition, the following embodiments are not intended to limit the scope of the present invention, but merely as exemplifications of the constituent elements set forth in the claims of the present invention, and are included in technical ideas throughout the specification of the present invention, Embodiments that include components replaceable as equivalents in the elements may be included within the scope of the present invention.

The present invention secures energy consumption data from a substantial number of actual buildings, and finds data matching the building to be analyzed among the data to predict energy consumption. To this end, a first step (S100) of acquiring a substantial amount of data (big data) from an actual building and setting a data table for sorting in a predetermined format, a second step (S100) of finding a energy consumption amount of a building (Step S200).

In the first step S100, the data table is a characteristic factor affecting the energy consumption. The data table includes an outside factor (PA) for the outside temperature of the building, a bearing factor (P1) for the orientation of the building, (P3), the shadow factor (P4) on the ratio of the outside area of the building to the shadow area (P4), and the area factor (P5) on the total area of the building .

At this time, the data table arranges analysis data of an actual building, and arranges the energy consumption for adjusting the internal temperature of the building to a specific temperature under a certain outdoor temperature condition according to the number of cases of each characteristic factor.

Then, in step S200, data corresponding to each characteristic factor of the building to be analyzed is found from the data table to predict the energy consumption amount.

In the second step S200, if some of the characteristic factors of the analysis target building do not coincide with each other, the energy consumption data of the disagreement characteristic factor is secured and the data adjacent to the disagreement characteristic factor And calculates the disagreement characteristic factor value by using the linear interpolation method to predict the energy consumption amount, which will be described later.

As shown in FIG. 1, in order to arrange the plurality of characteristic factors in the first step S100, an energy consumption amount according to each orientation factor P1 is firstly arranged, and each of the orientation factors P1, (P3) is arranged for each of the shape factors (P2), and a shadow factor (P4) is calculated for each of the opening / closing factors (P3) And the energy consumption according to the area factor (P5) and the energy consumption according to the outdoor factor (PA) are arranged for each of the shadow factors (P4).

As shown in FIG. 1, energy consumption according to each orientation factor P1 is first arranged. P1_1 and P1_n1 shown in FIG. 1 are energy consumption amounts according to respective bearing parameters P1 such as east, west, south, north, and the like.

For each of the orientation parameters P1, the energy consumption according to the shape factor P2 is arranged. For example, the energy consumption for each shape factor P2 for the orientation parameter P1 is east, respectively. In addition, the energy consumption amount according to the open / close factor P3 is arranged for each of the shape factors P2. A shadow parameter (P4) is arranged in a similar manner.

Meanwhile, the energy consumption according to the area factor P5 and the energy consumption according to the outside air factor PA are arranged for each of the shadow factors P4. Here, the energy consumption according to the area factor (P5) is expressed as a value divided by a unit area. For example, if energy consumption for various areas is converted into energy consumption for 100 py, then data for other areas will be used. For example, when energy consumption for 200 py is sought, It is convenient to use.

As described above, the outside air factor PA is the energy required to maintain the internal temperature of the building at a predetermined temperature at a certain external temperature, and the outside air factor PA corresponds to the outside air temperature for each of the shadow factors P4, The amount of energy consumption is indicated, and the outside air temperature is divided for each outside air temperature. At this time, the outside air temperature can be expressed for each temperature, and it is also possible to divide the outside air temperature into a specific range and display a plurality of the outside air temperatures.

The temperature range PA_1 to PA_n1 of the outside air factor PA can be divided at intervals of 5 DEG C in the interval between -50 DEG C and 40 DEG C and the bearing parameter P1 is divided into 45 DEG clockwise It is divided into 8 orientations.

In addition, the shape factor (P2) for the shape of the building is expressed by the ratio of the roadside to the longitudinal side (the length of the longitudinal side / the percentage of the lateral side length) by setting the building as a square. At this time, the shape factor P2 appears as shape factors P2_1 to P2_n3 of respective ratios according to the respective bearing factors P1_1 to P1_n2.

The opening / closing factor (P3) is expressed by the ratio of the outside area of the building to the window area (percentage of the window area / percentage of the outside area of the building) and the opening / closing factors (P3_1 to P3_n4) of each ratio with respect to the shape factors (P2_1 to P2_n3) Respectively.

In addition, the shadow factor (P4) is expressed as the ratio of the area outside the building to the area illuminated by the shadow (percentage of area shaded / outside area of the building), and the shadow factor for each ratio (P3_1 ~ P3_n4) P4_1 to P4_n5), respectively.

In the data table completed in the first step (S100), an outside air factor (PA) for the outside temperature of the building, a bearing factor (P1) for the direction of the building and a shape (P3), the shadow factor (P4) and the area factor (P5) for the total area of the building, with respect to the factor (P2) and the ratio of the exterior area of the building to the window area (P3) ) Are stored in a single set, and a set of data corresponding to each factor of interest is stored for all buildings of interest.

Therefore, since the actual energy consumption amount corresponding to each characteristic factor is stored for a plurality of actual buildings in the table acquired in the first step S100, the characteristic factor of the building to be analyzed is determined, The energy consumption of the building to be analyzed can be predicted by checking the energy consumption of the actual building corresponding to the energy consumption of the building.

For example, as shown in FIG. 2, when the building to be analyzed has a bearing factor P1 of 45 degrees in the clockwise direction with respect to the north and a ratio of the shape factor P2 (lengthwise length / lengthwise length) is 20% And the ratio of the opening factor P3 (window area / outside area of the building) is 40% and the ratio of the shadow factor P4 (shadow area / outside area of the building) is 60% The consumption is 1000kWh, and when the outdoor temperature is 40 ℃, the energy consumption is 2000kWh. On the other hand, since the energy consumption per 100 square meters is 200 pyeong, the energy consumption of the analyzed building can be calculated as twice.

Incidentally, in the second step (S200), some of the characteristic factors of the data table and the analysis target building may not coincide with each other. In this case, the energy consumption amount data of the disagreement characteristic factor is obtained, and the inconsistent characteristic factor value is calculated by using the data adjacent to the inconsistency characteristic factor using the linear interpolation method to estimate the energy consumption amount.

In the first step S100, the outside temperature of the outside air factor P1 is divided by 5 占 폚 intervals in a range between -50 占 폚 and 40 占 폚. The energy consumption for setting the internal temperature of the building to a specific temperature according to the external temperature is acquired from a plurality of actual buildings for each of the remaining characteristic factors, and a data table is set.

However, there is a case where the orientation factor of the building subject to analysis in the set data table does not correspond to the eight orientations. In this case, the energy consumption data of the disagreement characteristic factor is obtained, and the disagreement characteristic factor value is calculated by interpolation using the data adjacent to the disagreement characteristic factor to predict the energy consumption amount.

That is, when data corresponding to one characteristic factor of the orientation factor P1, the shape factor P2, the open / close factor P3 and the shadow factor P4 in the characteristic factor does not appear in the data table, Using the equation (1), the energy consumption data for the disagreement characteristic factor is calculated by the interpolation method.

[Equation 1]

Here, Y is the energy consumption of the building to be analyzed, L11 and L12 are the ratios of the low and high values adjacent to the data of the mismatching characteristic factor, E11 and E12, respectively, The energy consumption of each of the low value and the high value.

As shown in FIG. 3, if the outside air temperature is 20 ° and the disagreement characteristic factor is 48 ° as the bearing parameter P1, it can not be found in the data table. In this case, the energy consumption for the case where the orientation factor is 48 degrees is calculated by Equation (1).

Here, L11 and L12 are ratios for the low value and the high value adjacent to the data of the disagreement characteristic factor, respectively, and therefore, in this embodiment, the low value is 45 deg. And the high value is 50 deg. At this time, since the mismatching orientation factor is 48 degrees, the ratio L11 is 3 and L12 is 2. The weighting value existing between the data of the disagreement characteristic factor and the data adjacent to each other can be reflected in the energy consumption to be calculated.

Further, E11 and E12 are energy consumption amounts corresponding to the low value and the high value adjacent to the data of the disagreement characteristic factor, respectively. At this time, there are a plurality of energy consumption data corresponding to the low value and high energy consumption data corresponding to the high value. In this case, the average value of the energy consumption data is taken. That is, the energy consumption corresponding to the low value (45 °) is 12,12,13,14,15,16,17,18. Taking an average of the above values, it becomes 14.625, and this value becomes E11. Also, the energy consumption against the high value (50 °) is 13,15,17,18,19,20,21,22 respectively. Taking the average of these values, it is 18.125, and this value becomes E12.

Therefore, Y to be obtained becomes 16.725 according to Equation (1), and if there is one characteristic factor that is inconsistent by this method, the energy consumption of the building to be analyzed can be calculated.

4, data corresponding to two characteristic factors of the orientation factor P1, the shape factor P2, the open / close factor P3, and the shadow factor P4 are not displayed in the data table A step of calculating a plurality of energy consumption data for a characteristic factor that is inconsistent by an interpolation method using Equation 2 below, and a step of calculating an energy consumption amount of the building to be analyzed by obtaining the average of the plurality of calculated energy consumption amounts .

&Quot; (2) "

L11, L12, L21, L22, L23, and L24 of the analysis target building to be calculated for the disagreement characteristic factors Y1 and Y2 are values of the low and high values adjacent to the data of the disagreement characteristic factor, Each of the ratios E11, E12, E21, and E22 is the energy consumption amount corresponding to the low value and the high value adjacent to the data of the disagreement characteristic factor.

On the other hand, when there are two disagreement characteristic factors, there is one characteristic factor, for example, a low value and a high value for the disagreement orientation factor, and another disagreement characteristic factor, for example, a shape factor exists for the low value Since there is also the shape factor for a high value, the energy consumption calculated is two (Y1, Y2), and the energy consumption of the building to be analyzed is calculated by averaging the energy consumption.

For example, when the orientation factor P1 is 48 degrees and the shape factor P2 is 30 as a disagreeable characteristic factor, L11 and L12 become 3 and 2, respectively. Since L21 and L22 and L23 and L24 are proportional to the adjacent low and high values of the shape factor P2, L21 and L22 are 1 and L23 and L24 are 8 and 13, respectively. The weighting value existing between the data of the disagreement characteristic factor and the data adjacent to each other can be reflected in the energy consumption to be calculated.

In addition, there are two energy consumption amounts (Y1, Y2) to be calculated, and the adjacent energy consumption amounts to two pairs. That is, since a pair of low values E11 of two pairs is a number, the average is 12.75, and the high value E12 is 16,75. Also, the lower value E21 of the other pair of energy consumption amounts to 15.75 and the higher value E22 becomes 20.5. The energy consumption of the building to be analyzed can be calculated by calculating the average of the values of Y1 and Y2 obtained by putting these values into the equation (2).

5, data corresponding to three characteristic factors of the orientation factor P1, the shape factor P2, the open / close factor P3 and the shadow factor P4 in the characteristic factor appear in the data table A step of calculating a plurality of pieces of energy consumption data for disagreement characteristic factors by an interpolation method using Equation 3 below and calculating an average of the plurality of calculated energy consumption amounts to calculate an energy consumption amount of the analysis target building .

&Quot; (3) "

L11, L12, L21, L22, L23, L24, L31, L32, L33, L34, L35, L36 are the energy consumption of the analyzed building to be calculated with respect to the mismatching characteristic factor. , L37 and L38 are the respective ratios for the low and high values adjacent to the data of the mismatching characteristic factor and E11, E12, E21, E22, E31, E32, E41 and E42 are adjacent to the data of the mismatching characteristic factor The energy consumption of each of the low value and the high value.

On the other hand, when there are three disagreement characteristic factors, the energy consumption calculated is four (Y1, Y2, Y3, Y4), and the energy consumption of the building to be analyzed is averaged to calculate the energy consumption of the building to be analyzed. Is omitted.

Of course, there may be four cases in which there are disagreement characteristic factors, which can be used to estimate the energy consumption of the building to be analyzed by analogy with the above-described method of the present invention.

While the present invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, You will understand.

It is therefore to be understood that the embodiments described above are to be considered in all respects only as illustrative and not restrictive, the scope of the invention being described in the foregoing specification is defined by the appended claims, Ranges and equivalents thereof are to be construed as being included within the scope of the present invention.

Claims (5)

Simulation of the energy consumption of a building using a simulation device that predicts energy consumption,
(PA) with respect to the outside air temperature of the actual building, a bearing factor (P1) with respect to the direction of the actual existing building, (P3) with respect to the ratio of the outside area of the actual existing building to the window area and the shadow factor (P3) with respect to the ratio of the outside area of the actual existing building to the shadow area P4), an area factor (P5) with respect to the total area of the actual existing buildings, and energy consumption, and a table is created by using data for a plurality of actual buildings
In order to analyze the energy consumption of the building to be analyzed by using the table, it is necessary to determine a bearing factor for the direction of the building to be analyzed, a shape factor for the shape of the building to be analyzed, A shadow factor for a ratio of an external area of the building to be analyzed and an area illuminated by the shadow and an area factor for a total area of the building to be analyzed, As a method for simulating the energy consumption of the building to be analyzed as compared with the characteristic factor of the table
By applying the simulation apparatus
(PA), a bearing factor (P1) with respect to the direction of the building, and a shape factor (P2) with respect to the shape of the building as characteristic factors affecting the energy consumption of the actual existing building (P3), the shadow factor (P4) on the ratio of the area outside the building to the shadow area, and the area factor (P5) on the total area of the building A first step S100 of forming a data table,
And a second step (S200) of searching for data matching each characteristic factor of the building to be analyzed from the data table formed by the first step (S100) and estimating the energy consumption amount,
The step of forming the data table in the first step (S100)
Arranging the energy consumption amount according to the orientation factor (P1), arranging the energy consumption amount according to the shape factor (P2) for each of the orientation factors (P1) The energy consumption amount according to the opening factor P3 is arranged for each of the shape factors P2 and the energy consumption amount according to the shadow factor P4 is arranged for each of the opening and closing factors P3, The energy consumption according to the area factor (P5) and the energy consumption according to the outside air factor (PA)
In the second step (S200), when some of the characteristic factors of the data table and the analysis target building do not coincide, the energy consumption data of the disagreement characteristic factor is secured
Estimating an energy consumption amount by calculating the disagreement characteristic factor value using linear interpolation by data adjacent to the disagreement characteristic factor,
If data corresponding to one characteristic factor of the orientation factor P1, the shape factor P2, the open / close factor P3 and the shadow factor P4 in the characteristic factor does not appear in the data table
And calculating energy consumption data for disagree characteristic factors by an interpolation method using Equation (1) below. ≪ EMI ID = 1.0 >
[Equation 1]

Y: Energy consumption of the analyzed building to be calculated for the disagreeable characteristic factor
L11, L12: the ratio of the low value and the high value adjacent to the data of the mismatched characteristic factor,
E11, E12: the energy consumption of each of the low and high values adjacent to the data of the mismatching characteristic factor delete The method according to claim 1,
The external temperature of the outside air factor (PA) is divided by 5 DEG C intervals in the interval between -50 DEG C and 40 DEG C,
Wherein the bearing factor (P1) is a Big Data-based energy consumption simulation method that takes into account the characteristic factor of dividing the bearing of the building into eight. delete The method according to claim 1,
If data corresponding to two characteristic factors of the orientation factor P1, the shape factor P2, the open / close factor P3 and the shadow factor P4 in the characteristic factor do not appear in the data table
Calculating a plurality of pieces of energy consumption data for disagreement characteristic factors by an interpolation method using Equation (2) below;
And calculating an average of the plurality of calculated energy consumption amounts and calculating an energy consumption amount of the analysis target building.
&Quot; (2) "

Y1, Y2: energy consumption of the analyzed building to be calculated for the disagree characteristic factor
L11, L12, L21, L22, L23, L24: the ratio of the low value and the high value adjacent to the data of the mismatched characteristic factor,
E11, E12, E21, E22: energy consumption amount corresponding to the low value and the high value adjacent to the data of the disagreement characteristic factor

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