WO2017095101A1

WO2017095101A1 - System for measuring and evaluating building energy performance and method for driving same

Info

Publication number: WO2017095101A1
Application number: PCT/KR2016/013835
Authority: WO
Inventors: 송수원; 박천건
Original assignee: 한국건설기술연구원
Priority date: 2015-11-30
Filing date: 2016-11-29
Publication date: 2017-06-08
Also published as: CA3005184C; KR101622523B1; CA3005184A1

Abstract

A system for measuring and evaluating building energy performance according to the present invention conducts measurement and evaluation of performance regarding building energy using building management servers A and B (110, 120), and comprises: a measurement variable setting module (210) for receiving measurement data, which includes a measurement history, from the building management servers A and B (110, 120) and setting building energy and an outer-air temperature as variables; a measurement data analysis module (220) for receiving the variables, related to times before and after energy retrofit for saving the building energy, from the measurement variable setting module (210) and analyzing the correlation between the building energy and first and second outer-air temperatures; a regression model generating module (230) for plotting the variables related to the time before energy retrofit, which have been output from the measurement data analysis module (220), generating a building energy regression module in view of the transition point of the first outer-air temperature, and applying the second outer-air temperature, related to the time after energy retrofit, to the building energy regression model, thereby generating a reference model; a statistical analysis module (240) for statistically analyzing an energy retrofit model with regard to the building energy regression model and the reference model, which have been output from the regression model generating module (230); and an energy saving analysis module (250) for comparing the difference between the predicted amount of energy consumption, which is one of pieces of data resulting from statistical analysis by the statistical analysis module (240), and the actual amount of energy consumption, which has been output from the building management servers A and B (110, 120), thereby data-analyzing the amount of energy saving.

Description

Building Energy Performance Evaluation System and Driving Method

The present invention relates to a building energy performance measurement evaluation system and a driving method, and more particularly, based on measurement data of a building management A server (building automatic control system) or building management B server (energy monitoring system). Building energy performance assessments take into account changes in outside temperature that have a major impact on building energy consumption before and after energy recovery.

In the prior art, Kissock et al. (2003) is a statistical method of searching for the transformation point (or change point) of the building energy regression model according to the ambient temperature. By dividing by (e.g., 10), the grid is evenly set, and the outside temperature at which the root mean squared error (RMSE) of the building energy regression model is minimized for each interval is set as the change point.

However, when the difference between the maximum and the minimum of the outside temperature of the population is large, the grid of the outside temperature for searching for the conversion point becomes large, and as a result, the accuracy of the searched conversion point and the reliability of the regression model are inferior.

In addition, even when the grid of the outside temperature is arbitrarily set in the related art, the reliability of the searched regression point and the regression model does not always increase according to the distribution of the population. Narrow setting has the disadvantage of slowing down the calculation.

The present invention has been made to solve such a problem, the building energy performance to perform the building energy performance evaluation in consideration of the change in the outside temperature, which has a major influence on the building energy consumption before and after the building energy recovery using the server for the building management It is an object to provide a measurement evaluation system and a driving method.

Another object of the present invention is to provide a building energy performance measurement evaluation system and a driving method for generating a regression model having a transformation point according to the measured data and performing statistical analysis and reliability analysis. do.

In addition, the present invention has been devised to solve this problem, in order to search for the transformation point of the regression model generated according to the measurement data, the virtual transformation point is sequentially selected at the outside temperature of the population, and then the least square method is performed around each virtual transformation point. It is another object of the present invention to provide a building energy performance measurement evaluation system and a driving method for establishing an intersection point of a sinus regression model and a right regression model.

In order to achieve this object, the building energy performance measurement evaluation system of the present invention is a building energy performance measurement evaluation system that performs performance measurement and evaluation on building energy using A and B servers 110 and 120 for building management. A measurement variable setting module 210 for receiving the measurement data including the measurement history from the management A and B servers 110 and 120 and setting the building energy and the outside temperature as variables; The measurement variable setting module 210 receives the variable before and after the energy number for the energy saving of the building, and indicates a trend and correlation between the building energy and the outside temperature before and after the energy number. A measurement data analysis module 220 for analyzing a correlation between the building energy and the first and second outdoor temperatures using an analysis graph; Plot the variable before the number of energy output from the measured data analysis module 220 to generate a building energy regression model in consideration of the conversion point of the first outside temperature, and generate a second outside temperature for the energy after the energy number. A regression model generation module 230 for generating a reference model by applying the regression model; A statistical analysis module 240 for statistically analyzing the building energy regression model output from the regression model generation module 230 and an energy number model for the reference model; And energy saving to extract an energy saving amount by comparing a difference between the predicted energy usage, which is one of the statistical data analyzed by the statistical analysis module 240, and the actual energy usage output from the building management A and B servers 110 and 120. Analysis module 250 is included.

The building energy performance measurement and evaluation system registers one or more control points corresponding to the facility generating the building energy before analyzing the correlation, and then either a work tree configuration method or an individual energy metering installation method for the facility. Collect the measurement data using either one.

The analysis graph according to the correlation uses a time series plot or an X-Y axis scatter plot.

The building energy regression model may include an average model, a simple regression model, a heating regression model, a cooling regression model, a cooling and / or heating regression model, or a general regression according to the number of conversion points found from the analysis graph and the shape of the analysis graph. At least one of the models.

Virtual transformation points are sequentially selected from the minimum value to the maximum value of the measured data, and the sum of the residuals (MSE: Mean Squared Error) of the left data and the right data is minimized by using the least-square method around each virtual conversion point. Search for arguments.

The number of data of the measurement data, the number of change points of the building energy regression model, the coordinate of the change point of the building energy regression model, the slope of the left or right model among the building energy regression models, or the type of the building energy regression model Any one of the operations is calculated and derived, and at least one of R2, AdjR2, RMSE, or CV_RMSE is analyzed.

The energy saving amount equally adjusts the conditions of use of the first and second outside air temperatures before and after the implementation of the energy number, which includes the act of saving the building energy, including the replacement of building insulation, improvement of the installation or automatic control operation method. Determine the amount of energy savings compared to the actual usage of the building energy.

In addition, the method of driving the building energy performance measurement and evaluation system to achieve this purpose is a method of driving the building energy performance measurement and evaluation system for performing the performance measurement and evaluation of the building energy using the A, B server (110, 120) for building management. In response, the measurement variable setting module 210 receives the measurement data including the measurement history from the building management A and B servers 110 and 120 and sets the building energy and the outside temperature as variables; The measurement data analysis module 220 receives the variables before and after the energy number for the energy saving of the building from the measurement variable setting module 210 and receives the building energy and the outside air before and after the energy number. Analyzing a correlation between the building energy and the first and second outdoor air temperatures using an analysis graph showing a trend and a correlation of temperature; The regression model generation module 230 plots the variable before the number of energies output from the measurement data analysis module 220 to generate a building energy regression model in consideration of the conversion point of the first outside temperature, Generating a reference model by applying a second outside temperature to the building energy regression model; A statistical analysis module 240 performing statistical analysis on the building energy regression model output from the regression model generation module 230 and an energy number model for the reference model; And comparing the difference between the predicted energy usage, which is one of the result data statistically analyzed by the energy analysis module 250 by the statistical analysis module 240, and the actual energy usage output from the building management A and B servers 110 and 120. Extracting the amount of energy savings.

The building energy performance measurement and evaluation system registers one or more control points corresponding to the facility generating the building energy before analyzing the correlation, and then either a work tree configuration method or an individual energy metering installation method for the facility. The method may further include collecting the measurement data using any one.

The correlation graph according to the correlation includes using a time series plot or an X-Y axis scatter plot.

The building energy regression model includes at least one of an average model, a simple regression model, a heating regression model, a cooling regression model, a cooling and heating regression model, or a general regression model according to the number of conversion points found from the analysis graph and the shape of the analysis graph. It includes a step set to either.

The conversion point search of the analysis graph sequentially selects virtual conversion points from the minimum value to the maximum value of the measured data, and residuals of the left data and the right data (MSE: Mean Squared) using the least square method around each virtual conversion point. Search for the argument whose sum is Error).

The statistical analysis may include data of the measurement data, the number of change points of the building energy regression model, the coordinates of the change points of the building energy regression model, the slope of the left or right model of the building energy regression model, or the building. Computing and deriving any one of a type of energy regression model; And performing a reliability analysis on at least one of R2, AdjR2, RMSE, or CV_RMSE.

The energy saving amount equally adjusts the conditions of use of the first and second outside air temperatures before and after the implementation of the energy number, which includes the act of saving the building energy, including the replacement of building insulation, improvement of the installation or automatic control operation method. To further determine the amount of energy savings compared to the actual usage of the building energy.

The building energy performance measurement evaluation system and the driving method of the present invention can use a good energy-saving device or material to verify the energy saving effect and improve performance through efficient energy management.

In addition, the building energy performance measurement evaluation system and the driving method of the present invention, based on the measurement data of the server (A and / or B) for the building management, the change in the outside temperature which has a major influence on the building energy consumption before and after the energy number of the building. Considering the building energy performance evaluation method in consideration, the reliability of the energy saving effect through the number of energy of the existing building is secured can be guaranteed performance.

In addition, the building energy performance measurement evaluation system and the driving method of the present invention, if applied to the building management server (A and / or B) of the existing building, the installation cost of the separate monitoring sensor for the collection of the measurement data before and after the energy recovery It can be minimized, and the energy saving effect through continuous commissioning after energy recovery can be quickly and objectively analyzed and provided to the user to prevent indiscriminate replacement of equipment and efficient building energy management.

1 is a block diagram of a building energy performance measurement evaluation system according to an embodiment of the present invention;

FIG. 2 is a flow chart illustrating a performance evaluation driving method of the building energy performance measurement evaluation system according to FIG. 1;

3 is a detailed working flowchart of the measurement data analysis step S200 according to FIG. 2;

FIG. 4 is a flowchart illustrating a step (S300) of generating a reference model and a step (S400) of statistical analysis according to FIG. 2; FIG.

FIG. 5 is a detailed flowchart illustrating a method for searching for a transform point in the step 300 of generating a reference model according to FIG. 2;

6 is an exemplary view illustrating a heating energy conversion point model graph of a building energy performance measurement evaluation system and a driving method according to an embodiment of the present invention;

Hereinafter, an embodiment of the present invention will be described.

1 is a block diagram of a building energy performance measurement evaluation system according to an embodiment of the present invention, Figure 2 is a flow chart illustrating a performance evaluation driving method of the building energy performance measurement evaluation system according to Figure 1, Figure 3 2 is a detailed flowchart illustrating a measurement data analysis step S200 according to FIG. 2, and FIG. 4 is a flowchart illustrating a step S300 of generating a reference model and a step S400 of statistical analysis according to FIG. 2. 2 is a detailed flowchart of a method for searching for a conversion point in the step 300 of generating a reference model according to FIG. 2, and FIG. 6 is a graph of a heating energy conversion point model of a building energy performance measurement evaluation system and a driving method according to an embodiment of the present invention. The illustrated example is also shown.

Referring to Figure 1, the building management server according to an embodiment of the present invention is composed of a server A (110: building automatic system or BAS) and / or B server (120: building energy management system or BEMS) and building energy It is installed separately from the performance measurement and evaluation system 100.

The building energy performance measurement and evaluation system 100 includes a building energy performance evaluation unit 200 and an input / output unit 130, and the building energy performance evaluation unit 200 includes a measurement variable setting module 210 and measurement. It is configured to include a data analysis module 220, regression model generation module 230, statistical analysis module 240 and energy saving analysis module 250.

The input / output unit 140 may receive data output from the building management servers 110 and 120 and output or transmit data output from the energy saving effect analysis module 250 to the outside.

Each

module

210, 220, 230, 240, 250 may include a memory to perform a corresponding function, and a processor (not shown) provided in the building energy performance evaluation unit 200 may be configured outside each module, or the memory and the processor may be It may be integrated and provided inside each module.

First of all, a building's retrofit is a series of energy savings for a building that includes replacing existing low-performance windows, wall insulation and facility systems (chillers, boilers, pumps, etc.), or improving automatic control operations. It can be defined as any action of.

In this regard, the operation of building server A (110) for building management of existing buildings has been concentrated on simple operation and monitoring, and in the case of building server B (120: building energy management system), Energy saving management is limited, so even if a good energy saving device or material is used, it is not enough to verify the energy saving effect and to improve performance through efficient energy management.

In order to objectively measure and verify the energy savings through building energy counts, it is important to perform building energy performance assessments, taking into account changes in outside temperature that have a major impact on the building's cooling or heating energy consumption before and after the energy count. Do.

Building energy can be divided into cooling energy, which is largely influenced by outside temperature, or ground energy (apparatus and lighting energy) and other energy, which are hardly influenced by heating temperature.

This building energy (dependent variable) can be represented by a simple regression model that is described by the ambient temperature (independent variable). If the relationship between energy consumption and outside temperature is described by only one model, there is no change in the outside temperature. In this case, there is one change point (expressed in two models based on a specific outside air temperature).

Therefore, the conversion point setting is an important variable in determining the accuracy and reliability of the building energy regression model.

The building energy performance measurement and evaluation system 100 of the present invention is a building energy performance measurement and evaluation system for performing performance measurement and evaluation on building energy using A and B servers 110 and 120 for building management. A measurement variable setting module 210 for receiving the measurement data including the measurement history from the A and B servers 110 and 120 from the past to the present and setting the building energy and the outside temperature as variables; Measurement data analysis module for inputting variables before and after the number of energy for building energy saving from the measurement variable setting module 210 and analyzing the correlation between the building energy and the first and second outdoor air temperatures. 220; The building energy regression model is generated by considering the conversion point of the first outside air temperature using the variable before the number of energy output from the measurement data analysis module 220 (floating), and the second outside air temperature for the energy number after the building energy regression is calculated. A regression model generation module 230 for applying the model to generate a reference model; A statistical analysis module 240 for statistically analyzing the energy count model for the building energy regression model and the reference model output from the regression model generation module 230; And energy saving analysis for analyzing energy savings by comparing the difference between the predicted energy usage, which is one of the statistical data analyzed by the statistical analysis module 240, and the actual energy usage output from the A and B servers 110 and 120 for building management. Module 250.

Here, the first outside air temperature and the second outside air temperature generally have different data profiles, but there may be a case where they have the same data profile.

The building energy performance evaluation unit 200 registers one or more control points corresponding to the facility generating the building energy and configures the work tree type before analyzing the correlation, or installs individual energy meters in the facility. Collect measurement data using any one or more of the following.

Based on the range and parameters of the performance assessment by the number of energy, the building management server 110 and / or 120 and / or a separate energy monitoring system are used to collect measurement data on the building energy and outside temperature before and after the energy recovery. Analyze

The collection of measurement data is performed by calling the point of the existing automatic control system or registering the additionally installed monitoring system to the performance evaluation point to construct a work tree, and then based on the period of energy registration work separately registered. Analyzes using graphs (e.g. Time series plot, XY scatter plot) showing trends and correlations between building energy (dependent variable) and ambient temperature (independent variable) before and after repair.

Here, the energy metering separately installed performs energy repair work registration including information recorded before and after work on which energy repair was performed and work duration.

Analysis of correlations uses time series plots or X-Y-axis scatter plots.

The building energy regression model is at least one of an average model, a simple regression model, a heating regression model, a cooling regression model, or a cooling and heating regression model according to the number of conversion points and the graph shape according to the graph analysis of the measured data.

The transformation point search method sequentially selects virtual transformation points from the minimum value to the maximum value of the population, and uses the least-squares method around each virtual transformation point to determine the factor of which the sum of the residuals (MSE) of the left data and the right data is minimum. Search.

The left regression model and the right regression model of the searched parameters are analyzed to establish the final point of change (the intersection of the two regression models).

Statistical analysis computes any one or more of the number of data in the population, the number of change points in the regression model, the coordinates of the change point in the regression model, the slope of the left or right regression model, or the type of the regression model, and the reliability analysis includes R2, AdjR2, Analyze one or more of RMSE or CV_RMSE.

The amount of energy savings is determined by the same adjustment of the conditions of use of the outside temperature before and after the implementation of the number of energy consisting of energy saving activities of the building, including improvements in insulation, equipment, or automatic control operation. .

Referring to FIG. 2, the driving method of the building energy performance measurement evaluation system 100 according to an embodiment of the present invention uses the building energy performance measurement evaluation system 100 of FIG. 1 to measure and drive the building energy performance of the present application. It can be seen that the functions and steps of performing the method are the same as those of the system of FIG. 1.

Building energy performance measurement and evaluation method of the building energy performance measurement evaluation system 100 using the building management server (110, 120) is the energy number performance evaluation range and variable setting step (S100), before and after the energy count measurement data collection and analysis step (S200), building energy regression model and reference model generation step considering the conversion point (S300), building energy regression model and reference model statistical analysis and reliability analysis step (S400) and building energy saving effect analysis step before and after the energy count (S500) It is configured to include).

Each step may be performed by the building energy performance evaluation unit 200 or each module.

In the setting of the energy count performance evaluation range and the variable (S100), the energy management is measured by receiving measurement data including the measurement history from the past to the present from the building management servers 110 and 120.

The performance evaluation range according to the number of energy is basically set in consideration of the collection method and range of measurement data according to the type and range of energy saving factors applied to the target building.

In addition, the dependent variables of the building energy regression model for evaluating the energy performance before and after the energy recovery are classified by energy source (electric or gas energy) or by energy use of the building (cooling, heating, lighting, hot water supply, equipment, etc.). , Set the outside temperature as an independent variable.

Measurement data collection and analysis step (S200) sets the variable and receives the measurement data of the variable before and after the number of energy for building energy saving and analyzes the correlation between the building energy and the outside temperature.

Referring to FIG. 3, measurement data on building energy and outside temperature before and after energy recovery are collected and analyzed using the BAS 110 and / or a separate energy monitoring system based on the performance evaluation range and parameters according to the number of energy. do.

The collection of measurement data is performed by calling a point of an existing automatic control system (S110) or registering an additionally installed monitoring system (S140) to a performance evaluation point (S120) and constructing a work tree (S130). Using graphs (eg, time series plot, XY scatter plot) that show trends and correlations between building energy (dependent variables) and ambient temperature (independent variables) before and after energy recovery, based on the duration of energy reclamation Analyze (S200).

Here, separately installed energy metering (S140) performs the energy repair operation registration (S160) at step S200 that includes information recorded before and after the energy repair (S150) the job name and the duration of the operation.

For example, if you want to replace two older turbocoolers with a high-efficiency turbocooler to save the electrical energy of the target building during summer cooling, and want to evaluate the cooling energy (electricity) savings, first of all Check the operating conditions (such as uptime) and the power consumption of the freezers for two existing freezers (or energy management systems) that are being measured and register them as points for performance evaluation.

However, if the power consumption of one freezer is not measured, install a separate energy meter (watt-hour meter) and register it in the performance evaluation point.

In addition, the points for each registered machine generate virtual points through some computational operations (e.g., the sum of the power consumption points of the refrigerator 1 and the refrigerator 2 is reconfigured to one point as 'freezer power consumption'). Performance Assessment Construct a 'work tree'.

In addition, based on the registered work (operation name and duration) on the number of energy replacements, the data measured before and after the energy recovery are correlated with the dependent variable (cooler power consumption) and the independent variable (outside air temperature). Graph the relationship.

Generating a reference model (S300) analyzes the correlation by using the measured data of the variable before the number of energy, creates a building energy regression model in consideration of the conversion point of the outside temperature, and applies the outside temperature after the number of energy to the regression model To create a reference model.

That is, a building energy regression model is created based on the measurement data before the energy repair and the baseline is generated by applying the outside temperature after the energy repair to the regression model before the repair.

In the present invention, the building energy regression model is a function (expressed as a graph) of air-conditioning energy (electricity or gas consumption) with respect to the outside air temperature before the number of energy, while the baseline model is a regression model before the number of the outside air after the number of energy. It can be defined as a regression model for predicting heating and cooling energy (electricity or gas consumption) with respect to outside temperature after energy recovery.

In other words, if the baseline energy model is prepared based on the baseyear energy model before the repair and compared with the energy consumption after the repair, the amount of energy saving after the repair can be calculated (evaluated).

Building energy regression model (or reference model) preparation method considering the conversion point of the present invention is characterized in that it further comprises setting the first building energy regression model type and the number of conversion points, and second the conversion point of the building energy regression model.

Referring to FIG. 4, in the population, air temperature is set as an independent variable and cooling or heating energy is set as a dependent variable (S200).

When the building energy regression model is selected according to the graph of the population (S393a), it is classified into an average model, a simple regression model, a heating regression model, a cooling regression model, a cooling / heating regression model, and a general regression model (S393b).

Set the number of change points (or change points) of the outside temperature of the regression model (S393c), determine whether the change point number is 0 (S393d), determine whether it is 1 if not O (S393e), and if it is 1, search for one change point. (S393f) Building energy regression model statistical analysis (S400) and reliability analysis (S400a) are performed.

Here, the average model and the simple regression model of step S393b proceed to step S400, and if the number of change points is 0 in step S393d, the step proceeds to step S400.

In step S393e, if the number of change points is not 1, it is determined whether the number of change points is 2 (S393i), if not 2, the process returns to step S393c, and if 2, two change points are searched (S393j) and step S400 Proceed to

Referring to FIG. 5, the procedure of searching for a transform point of a building energy regression model or a reference model is classified according to the number of transform points of the set regression model.

The average model or the simple regression model having the first 0 transformation points performs statistical analysis of the regression model (or average model) directly without going through a separate transformation point search process.

In the case of the regression model, virtual transformation points are sequentially selected from the minimum outside temperature (2nd data) to the maximum outside temperature (n-2nd data) of the population in the regression model. The sum of the residuals (MSE) for the left data and the right data is searched for the minimum-value factor (outside temperature) and the left and right regression models of the searched factors are analyzed. Intersection point).

In the third regression model having two conversion points, the method further includes setting an imaginary equilibrium point (the outside temperature) that separates the two conversion points, and the subsequent conversion point search method is the same as the one conversion point search process.

Referring to FIG. 5, in the conversion point search method, first, the number of conversion points is set (S340), and if there is one, one conversion point is searched (S370), and i (data) = 2 starts (S371).

It is determined whether i <n (S372) and if it is small, the left [x (1), ---, x (i)] [y (1), ---, y (i)]

Set data to the right [x (i + 1), ---, x (n)] [y (i + 1), ---, y (n)] (S373).

Calculate the sum of the left and right data MSE using the least squares method in the regression model. MSE (i-1) = MSE (L) + MSE (R) (S374), i = i + 1 (S375) MES ( i) searches for the smallest argument i K = arg min MSE (i) (S376) and sets the data left [x (1), ---, x (k)] [y (1), ---, y (k)] right [x (k + 1), ---, x (n)] [y (k + 1), ---, y (n)] (S377) The intercept and slope calculation of the right regression model (S378), the left and right regression model analysis are performed to calculate the change point (S379), and the regression model statistical analysis is performed (S379a).

If the conversion point of step S340 is 0, the process proceeds to step S379a. If the conversion point is 2, two conversion points are searched (S390), and an imaginary equilibrium point (ambient temperature) distinguishing the two conversion points (change point). Set (S390a), left change point [x (1), ---, x (m-1)] [y (1), ---, y (m-1)] right change point [x (m +1), ---, x (n)] [y (m + 1), ---, y (n)] Set the data (S390b) and return to ①.

If i <n in step S372, the flow advances to step S376.

In step S400, the statistical analysis and the reliability may be analyzed for the building energy regression model and the reference model.

Statistical analysis of the building energy regression model (or reference model) including the searched transformation points, the number of data in the population, the number of changes in the regression model, the coordinates of the points of change in the regression model, the slope of the left or right regression model, and the regression model. We derive the type of, and analyze R2, AdjR2, RMSE, CV_RMSE, etc. to verify the reliability of the regression model.

6 and Table 1 show heating energy regression models and statistical analysis examples of the present invention.

Example of statistical results of heating energy conversion point model

좌측 모델Left model	통계 분석 결과값Statistical analysis result	우측 모델Right model	통계 분석 결과값Statistical analysis result
NN	49.000049.0000	NN	5151
y-절편y-intercept	20.124920.1249	y-절편y-intercept	8.58628.5862
기울기inclination	-2.0195-2.0195	기울기 inclination	00
RMSERMSE	0.93260.9326	RMSERMSE	0.95890.9589
R2R2	0.99050.9905	R2 R2	00
AdjR2AdjR2	0.99030.9903	AdjR2 AdjR2	00
CVRMSECVRMSE	4.12464.1246	CVRMSECVRMSE	11.167511.1675
변환점(CP)Convert Point (CP)	5.71375.7137

Analyzing the energy saving amount (S250) performs statistical analysis and reliability analysis and quantitatively analyzes the energy saving effect before and after the number of energy by comparing the difference between the predicted energy consumption and the actual energy consumption of the reference model.

Energy saving effect (energy saving rate and energy saving rate) by creating a building energy reference model by applying the outside temperature after energy recovery to the building energy regression model before energy generation and calculating the difference with the actual measured energy consumption after energy recovery. Analyze

Claims

Building energy performance measurement and evaluation system that performs performance measurement and evaluation on building energy using A and B servers 110 and 120 for building management.

A measurement variable setting module 210 which receives measurement data including measurement history from the building management A and B servers 110 and 120 and sets building energy and outdoor temperature as variables;

The measurement variable setting module 210 receives the variable before and after the energy number for the energy saving of the building, and indicates a trend and correlation between the building energy and the outside temperature before and after the energy number. A measurement data analysis module 220 for analyzing a correlation between the building energy and the first and second outdoor temperatures using an analysis graph;

Plot the variable before the number of energy output from the measured data analysis module 220 to generate a building energy regression model in consideration of the conversion point of the first outside temperature, and generate a second outside temperature for the energy after the energy number. A regression model generation module 230 for generating a reference model by applying the regression model;

A statistical analysis module 240 for statistically analyzing the building energy regression model output from the regression model generation module 230 and an energy number model for the reference model; And

Energy saving analysis for extracting energy savings by comparing the difference between the predicted energy usage, which is one of the statistical data analyzed by the statistical analysis module 240, and the actual energy usage output from the building management A and B servers 110 and 120. Building energy performance measurement evaluation system comprising a module (250).
According to claim 1, The building energy performance measurement evaluation system,

Prior to analyzing the correlations, one or more control points corresponding to the facility generating the building energy are registered, and then the measured data is collected using either a work tree configuration method or an individual energy metering installation method for the facility. Building energy performance measurement evaluation system characterized in that the collection.
The method of claim 1, wherein the analysis graph according to the correlation,

Building energy performance measurement and evaluation system using time series plot or X-Y axis scatter plot.
The method of claim 1, wherein the building energy regression model,

Building according to at least one of an average model, a simple regression model, a heating regression model, a cooling regression model, a cooling and heating regression model, or a general regression model according to the number of transformation points found from the analysis graph and the shape of the analysis graph. Energy performance measurement and evaluation system.
The method of claim 4, wherein the search for the conversion point of the analysis graph,

Virtual transformation points are sequentially selected from the minimum value to the maximum value of the measured data, and the sum of the residuals (MSE: Mean Squared Error) of the left data and the right data is minimized by using the least-square method around each virtual conversion point. A building energy performance measurement evaluation system characterized by searching for factors.
The method of claim 1, wherein the statistical analysis,

The number of data of the measurement data, the number of change points of the building energy regression model, the coordinate of the change point of the building energy regression model, the slope of the left or right model among the building energy regression models, or the type of the building energy regression model And calculating and deriving any one of them, and analyzing at least one of R2, AdjR2, RMSE, and CV_RMSE.
According to claim 1, wherein the energy saving amount,

The use of the building energy by adjusting the conditions of use of the first and second outside air temperatures before and after the implementation of the energy number, which includes the act of reducing the building energy, including the replacement of building insulation and the improvement of the installation or automatic control operation method. Building energy performance measurement and evaluation system characterized by identifying the amount of energy savings compared to actual usage.
As a driving method of a building energy performance measurement and evaluation system for performing performance measurement and evaluation on building energy using A and B servers 110 and 120 for building management,

A measurement variable setting module 210 receiving measurement data including measurement history from the building management A and B servers 110 and 120 and setting building energy and outside temperature as variables;

The measurement data analysis module 220 receives the variables before and after the energy number for the energy saving of the building from the measurement variable setting module 210 and receives the building energy and the outside air before and after the energy number. Analyzing a correlation between the building energy and the first and second outdoor air temperatures using an analysis graph showing a trend and a correlation of temperature;

The regression model generation module 230 plots the variable before the number of energies output from the measurement data analysis module 220 to generate a building energy regression model in consideration of the conversion point of the first outside temperature, Generating a reference model by applying a second outside temperature to the building energy regression model;

A statistical analysis module 240 performing statistical analysis on the building energy regression model output from the regression model generation module 230 and an energy number model for the reference model; And

The energy saving analysis module 250 compares the difference between the predicted energy usage, which is one of the statistical data analyzed by the statistical analysis module 240, and the actual energy usage output from the building management A and B servers 110 and 120. A method of driving a building energy performance measurement evaluation system comprising the step of extracting energy savings.
According to claim 8, The building energy performance measurement evaluation system,

Prior to analyzing the correlations, one or more control points corresponding to the facility generating the building energy are registered, and then the measured data is collected using either a work tree configuration method or an individual energy metering installation method for the facility. And collecting the building energy performance measurement evaluation system.
The method of claim 8, wherein the analysis graph according to the correlation,

Using a time series plot or an X-Y axis scatter plot.
The method of claim 8, wherein the building energy regression model,

Setting at least one of an average model, a simple regression model, a heating regression model, a cooling regression model, a cooling and heating regression model, or a general regression model according to the number of transformation points found from the analysis graph and the shape of the analysis graph. A drive method for a building energy performance measurement evaluation system, characterized in that.
The method of claim 11, wherein the search for the conversion point of the analysis graph,

Virtual transformation points are sequentially selected from the minimum value to the maximum value of the measured data, and the sum of the residuals (MSE: Mean Squared Error) of the left data and the right data is minimized by using the least-square method around each virtual conversion point. A method of driving a building energy performance measurement and evaluation system characterized by searching for a factor.
The method of claim 8, wherein the statistical analysis,

The number of data of the measurement data, the number of change points of the building energy regression model, the coordinate of the change point of the building energy regression model, the slope of the left or right model among the building energy regression models, or the type of the building energy regression model Computing and deriving any one of them; And

And conducting a reliability analysis on at least one of R2, AdjR2, RMSE, or CV_RMSE.
The method of claim 8, wherein the energy saving amount,

The use of the building energy by adjusting the conditions of use of the first and second outside air temperatures before and after the implementation of the energy number, which includes the act of reducing the building energy, including the replacement of building insulation and the improvement of the installation or automatic control operation method. The method of driving a building energy performance measurement and evaluation system, characterized in that it further comprises the step of identifying the amount of energy savings compared to the actual usage.