CA3005184A1  System for measuring and evaluating building energy performance and method for driving same  Google Patents
System for measuring and evaluating building energy performance and method for driving same Download PDFInfo
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 CA3005184A1 CA3005184A1 CA3005184A CA3005184A CA3005184A1 CA 3005184 A1 CA3005184 A1 CA 3005184A1 CA 3005184 A CA3005184 A CA 3005184A CA 3005184 A CA3005184 A CA 3005184A CA 3005184 A1 CA3005184 A1 CA 3005184A1
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 G—PHYSICS
 G06—COMPUTING; CALCULATING; COUNTING
 G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
 G06Q20/00—Payment architectures, schemes or protocols
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 G06Q20/14—Payment architectures specially adapted for billing systems

 G—PHYSICS
 G06—COMPUTING; CALCULATING; COUNTING
 G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
 G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
 G06Q50/06—Electricity, gas or water supply

 G—PHYSICS
 G06—COMPUTING; CALCULATING; COUNTING
 G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
 G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
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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 outerair 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 outerair 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 outerair temperature, and applying the second outerair 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 dataanalyzing the amount of energy saving.
Description
SYSTEM FOR MEASURING AND EVALUATING BUILDING ENERGY
PERFORMANCE AND METHOD FOR DRIVING SAME
TECHNICAL FIELD
The present invention relates to a system for measuring and evaluating building energy performance and method for driving same, more particularly, to perform building energy performance evaluation in view of a change of outerair temperature which has a major influence on building energy consumption before and after an energy retrofit of building, based on the measurement data of building management server A
(building automatic control system) or building management server B (energy monitoring system).
BACKGROUND ART
In the prior art (paper), Kissock et al. (2003) describes a statistical method to search change point (or turning point) of building energy regression model according to outerair temperature, in which interval (Grid) is set evenly by dividing difference between maximum and minimum values of outerair temperature of population by a predetermined number (e.g.
10) and outerair temperature at which a residual (root mean squared error, RMSE) of the building energy regression model for each interval is lowest is set as a change point.
However, when the difference between the maximum and minimum values of outerair temperature of population is large, the interval (Grid) of the outerair temperature for searching the change point becomes large. As a result, there are disadvantages in that an accuracy of a searched change point and a reliability of a regression model are inferior.
Further, in the prior art, there are disadvantages in that the accuracy of the searched change point and the reliability of the regression model according to a distribution of the population are not always increased even if the interval (Grid) of the outerair temperature is arbitrarily set to be narrow, and if the interval (Grid) of the outerair temperature for searching the change point is set too narrow, then calculation speed is slowed down.
DISCLOSURE
TECHNICAL PROBLEM
The present invention has been made to solve such a problem, and it is an object of the present invention to provide a system for measuring and evaluating building energy performance and method for driving same which conducts building energy performance evaluation considering a change in outerair temperature that has a major influence on building energy consumption before and after energy retrofit of building using building management server.
Further, the present invention has been made to solve such a problem, and it is other object of the present invention to provide a system for measuring and evaluating building energy performance and method for driving same which conducts a statistical analysis and a reliability analysis by generating a regression model having a change point according to measured data.
Further, the present invention has been made to solve such a problem, and it is an another object of the present invention to provide a system for measuring and evaluating building energy performance and method for driving same which select virtual change points sequentially for outerair temperature of population and set an intersection point of a left side regression model and a right side regression model using a least square method with
PERFORMANCE AND METHOD FOR DRIVING SAME
TECHNICAL FIELD
The present invention relates to a system for measuring and evaluating building energy performance and method for driving same, more particularly, to perform building energy performance evaluation in view of a change of outerair temperature which has a major influence on building energy consumption before and after an energy retrofit of building, based on the measurement data of building management server A
(building automatic control system) or building management server B (energy monitoring system).
BACKGROUND ART
In the prior art (paper), Kissock et al. (2003) describes a statistical method to search change point (or turning point) of building energy regression model according to outerair temperature, in which interval (Grid) is set evenly by dividing difference between maximum and minimum values of outerair temperature of population by a predetermined number (e.g.
10) and outerair temperature at which a residual (root mean squared error, RMSE) of the building energy regression model for each interval is lowest is set as a change point.
However, when the difference between the maximum and minimum values of outerair temperature of population is large, the interval (Grid) of the outerair temperature for searching the change point becomes large. As a result, there are disadvantages in that an accuracy of a searched change point and a reliability of a regression model are inferior.
Further, in the prior art, there are disadvantages in that the accuracy of the searched change point and the reliability of the regression model according to a distribution of the population are not always increased even if the interval (Grid) of the outerair temperature is arbitrarily set to be narrow, and if the interval (Grid) of the outerair temperature for searching the change point is set too narrow, then calculation speed is slowed down.
DISCLOSURE
TECHNICAL PROBLEM
The present invention has been made to solve such a problem, and it is an object of the present invention to provide a system for measuring and evaluating building energy performance and method for driving same which conducts building energy performance evaluation considering a change in outerair temperature that has a major influence on building energy consumption before and after energy retrofit of building using building management server.
Further, the present invention has been made to solve such a problem, and it is other object of the present invention to provide a system for measuring and evaluating building energy performance and method for driving same which conducts a statistical analysis and a reliability analysis by generating a regression model having a change point according to measured data.
Further, the present invention has been made to solve such a problem, and it is an another object of the present invention to provide a system for measuring and evaluating building energy performance and method for driving same which select virtual change points sequentially for outerair temperature of population and set an intersection point of a left side regression model and a right side regression model using a least square method with
2 each virtual change point as center in order to search for a change point of a regression model generated according to measured data.
TECHNICAL SOLUTION
In order to achieve the above objects, 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 outerair temperature as variables; a measurement data analysis module 220 for receiving the variables, related to occasions before and after energy retrofit for saving the building energy, from the measurement variable setting module 210 and analyzing a correlation between the building energy and first and second outerair temperatures using an analysis graph showing trend of and correlation between the building energy and outerair temperature before and after energy retrofit; a regression model generating module 230 for plotting the variables related to the occasion before energy retrofit, which have been output from the measurement data analysis module 220, generating a building energy regression model with a change point of the first outerair temperature, and applying the second outerair temperature, related to the occasion 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
TECHNICAL SOLUTION
In order to achieve the above objects, 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 outerair temperature as variables; a measurement data analysis module 220 for receiving the variables, related to occasions before and after energy retrofit for saving the building energy, from the measurement variable setting module 210 and analyzing a correlation between the building energy and first and second outerair temperatures using an analysis graph showing trend of and correlation between the building energy and outerair temperature before and after energy retrofit; a regression model generating module 230 for plotting the variables related to the occasion before energy retrofit, which have been output from the measurement data analysis module 220, generating a building energy regression model with a change point of the first outerair temperature, and applying the second outerair temperature, related to the occasion 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
3 saving analysis module 250 for comparing a difference between a predicted amount of energy consumption, which is one of data resulting from statistical analysis by the statistical analysis module 240, and an actual amount of energy consumption, which has been output from the building management servers A and B 110, 120, thereby extracting an amount of energy saving.
The system for measuring and evaluating building energy performance registers one or more monitoring and control points corresponding to facility generating the building energy before analyzing the correlation, and then collects the measurement data using any one of a work tree type configuration method or an individual energy metering installation method for the facility.
The analysis graph according to the correlation uses a time series plot or an XY
scatter plot.
The building energy regression model is at least any 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 shape of the analysis graph and a number of the change points searched from the analysis graph.
A virtual change point is sequentially selected from a minimum value to a maximum value of the measurement data and a factor in which sum of residual (Mean Squared Error, MSE) for left side data and right side data is minimum is searched using a least square method with each virtual change point as center.
Any one of a number of the measurement data, a number of the change points of the building energy regression model, coordinates of the change point of the building energy regression model, slope of the left side model or right side model of the building energy
The system for measuring and evaluating building energy performance registers one or more monitoring and control points corresponding to facility generating the building energy before analyzing the correlation, and then collects the measurement data using any one of a work tree type configuration method or an individual energy metering installation method for the facility.
The analysis graph according to the correlation uses a time series plot or an XY
scatter plot.
The building energy regression model is at least any 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 shape of the analysis graph and a number of the change points searched from the analysis graph.
A virtual change point is sequentially selected from a minimum value to a maximum value of the measurement data and a factor in which sum of residual (Mean Squared Error, MSE) for left side data and right side data is minimum is searched using a least square method with each virtual change point as center.
Any one of a number of the measurement data, a number of the change points of the building energy regression model, coordinates of the change point of the building energy regression model, slope of the left side model or right side model of the building energy
4 regression model, or type of the building energy regression model is calculated and derived, and reliability analysis is carried out on at least any one of R2, AdjR2, RMSE
or CV_RMSE.
With regard to the amount of energy saving, use conditions of the first and second outerair temperatures before and after execution of energy retrofit which is comprised of building energy saving actions including a replacement of building heat insulating material, an improvement in equipment or automatic control operation method, are adjusted to be same, thereby, the amount of energy saving is identified based on comparison with actual amount of the building energy consumption.
Also, in order to achieve the above objects, a method for driving 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 following steps:
receiving, by a measurement variable setting module 210, measurement data, which includes a measurement history, from the building management servers A and B 110, 120 and setting building energy and an outerair temperature as variables; receiving, by a measurement data analysis module 220, the variables, related to occasions before and after energy retrofit for saving the building energy, from the measurement variable setting module 210 and analyzing a correlation between the building energy and first and second outerair temperatures using an analysis graph showing trend of and correlation between the building energy and outerair temperature before and after energy retrofit; plotting, by a regression model generating module 230, the variables related to the occasion before energy retrofit, which have been output from the measurement data analysis module 220, generating a building energy regression model with a change point of the first outerair temperature, and applying the second outerair temperature, related to the occasion after energy retrofit, to the building energy regression model, thereby generating a reference model;
statistically analyzing, by a statistical analysis module 240, 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 comparing, by an energy saving analysis module 250, a difference between a predicted amount of energy consumption, which is one of data resulting from statistical analysis by the statistical analysis module 240, and an actual amount of energy consumption, which has been output from the building management servers A and B 110, 120, thereby extracting an amount of energy saving.
The method further comprises a step wherein the system for measuring and evaluating building energy performance registers one or more monitoring and control points corresponding to facility generating the building energy before analyzing the correlation, and then collects the measurement data using any one of a work tree type configuration method or an individual energy metering installation method for the facility.
The method further comprises a step wherein the analysis graph according to the correlation uses a time series plot or an XY scatter plot.
The method further comprises a step wherein the building energy regression model is set as at least any 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 shape of the analysis graph and a number of the change points searched from the analysis graph.
In the method, searching for change point of the analysis graph is to sequentially select a virtual change point from a minimum value to a maximum value of the = =
measurement data and to search a factor in which sum of residual (Mean Squared Error, MSE) for left side data and right side data is minimum using a least square method with each virtual change point as center.
The method further comprises steps wherein the statistical analysis comprises:
calculating and deriving any one of a number of the measurement data, a number of the change points of the building energy regression model, coordinates of the change point of the building energy regression model, slope of the left side model or right side model of the building energy regression model, or type of the building energy regression model; and conducting a reliability analysis on at least any one of R2, AdjR2, RMSE or CV_RMSE.
The method further comprises a step wherein with regard to the amount of energy saving, use conditions of the first and second outerair temperatures before and after execution of energy retrofit which is comprised of building energy saving actions including a replacement of building heat insulating material, an improvement in equipment or automatic control operation method, are adjusted to be same, thereby, the amount of energy saving is identified based on comparison with actual amount of the building energy consumption.
ADVANTAGEOUS EFFECTS
A system for measuring and evaluating building energy performance and method for driving same of the present invention can verify the energy saving effect when using good energy saving devices or materials and improve the performance through efficient energy management.
A system for measuring and evaluating building energy performance and method for = =
driving same of the present invention relates to a method for evaluating building energy performance considering a change in outerair temperature that has a major influence on building energy consumption before and after energy retrofit of building based on measurement data of building management server (A and/or B), and it can secure the reliability of the energy saving effect through the energy retrofit of the existing building, thereby can guarantee the performance.
In addition, when a system for measuring and evaluating building energy performance and method for driving same of the present invention is applied to an existing building management server (A and/or B) of building, it is possible to minimize an installation cost of a separate monitoring sensor for collecting measurement data of before/after energy retrofit. Even after the energy retrofit, it can quickly and objectively analyze the energy saving effect through continuous commissioning and provide it to users, thereby able to prevent imprudent equipment replacement and able to efficient building energy management.
BRIEF DESCRIPTION OF DRAWINGS
FIG. 1 is a block diagram of a system for measuring and evaluating building energy performance according to one embodiment of the present invention.
FIG. 2 is a flow chart showing a method for driving a performance evaluation of a system for measuring and evaluating building energy performance according to FIG. 1.
FIG. 3 is a specific operation flow chart of measurement data analysis step (S200) according to FIG. 2.
FIG. 4 is an operation flow chart illustrating a reference model generating step (S300) and statistical analysis step (S400) according to FIG. 2.
. .
FIG. 5 is a specific operation flow chart of a method of searching for a change point in a reference model generating step (S300) according to FIG. 2.
FIG. 6 is an exemplary drawing illustrating a graph of heating energy change point model of a system for measuring and evaluating building energy performance and method for driving same according to one embodiment of the present invention.
DETAILED DESCRIPTION OF THE EMBODIMENTS
Hereinafter, one embodiment of the present invention will be described.
FIG. 1 is a block diagram of a system for measuring and evaluating building energy performance according to one embodiment of the present invention, FIG. 2 is a flow chart showing a method for driving a performance evaluation of a system for measuring and evaluating building energy performance according to FIG. 1, FIG. 3 is a specific operation flow chart of measurement data analysis step (S200) according to FIG. 2, FIG.
4 is an operation flow chart illustrating a reference model generating step (S300) and statistical analysis step (S400) according to FIG. 2, FIG. 5 is a specific operation flow chart of a method of searching for a change point in a reference model generating step (S300) according to FIG. 2, and FIG. 6 is an exemplary drawing illustrating a graph of heating energy change point model of a system for measuring and evaluating building energy performance and method for driving same according to one embodiment of the present invention.
Referring to FIG. 1, a building management server according to an embodiment of the present invention includes a server A 110 (a building automatic system or BAS) and/or a server B 120 (a building energy management system or BEMS), and it is installed separately from a system for measuring and evaluating building energy performance 100.
The system for measuring and evaluating building energy performance 100 includes a building energy performance evaluation unit 200 and an input/output unit 130.
The building energy performance evaluation unit 200 includes a measurement variable setting module 210, a measurement data analysis module 220, a regression model generating module 230, a statistical analysis module 240, and an energy saving analysis module 250.
An input/output unit 140 receives data which has been output from the building management servers 110, 120, and may output or transmit data which has been output from the energy saving analysis module 250 to outside.
Each of the modules 210, 220, 230, 240, and 250 may include a memory to perform a corresponding function. A processor (not shown) included in the building energy performance evaluation unit 200 may be configured in an outside of each module or a processor and a memory may be integrated and included in an inside of each module.
First, an energy retrofit of building can be defined as a set of all actions for energy saving of building including replacing low performance windows, heat insulating materials of wall and equipment system (chiller, boiler, pump, etc.) of existing building or improving automatic control operation method, etc.
In this regard, until now, operation of a building management server A 110 (building automation system) of existing buildings has been focused on simple operation and monitoring, and in case of a building management server B 120 (building energy management system), there is a limitation in energy saving management of facility manager, so it is insufficient to verify energy saving effect and to improve performance through efficient energy management, actually, even when using good energy saving equipment or materials.
In order to objectively measure and verify the energy saving effect through energy retrofit of building, it is important to perform the building energy performance evaluation considering change of outerair temperature which has a major influence on cooling energy or heating energy consumption of building before and after energy retrofit.
Building energy is largely divided into cooling or heating energy, which is influenced by outerair temperature, and base energy (appliance and lighting energy) and other energy, which are hardly influenced by outerair temperature.
Such building energy (dependent variable) can be expressed as a simple regression model, which is described by outerair temperature (independent variable). If relation between energy consumption and outerair temperature is explained by only one model, there is no change point in the outerair temperature, and if it is explained by two models, there is one change point (represented by two models based on a specific fiducial outerair temperature).
Therefore, setting of the change point is an important parameter that determines the accuracy and reliability of the building energy regression model.
A system for measuring and evaluating building energy performance 100 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 past to present, from the building management servers A and B
110, 120 and setting building energy and an outerair temperature as variables; a measurement data analysis module 220 for receiving the variables, related to occasions before and after energy retrofit for saving the building energy, from the measurement variable setting module 210 and analyzing a correlation between the building energy and first and second outerair temperatures; a regression model generating module 230 for using (plotting) the variables related to the occasion before energy retrofit, which have been output from the measurement data analysis module 220, generating a building energy regression model with a change point of the first outerair temperature, and applying the second outerair temperature, related to the occasion 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 a difference between a predicted amount of energy consumption, which is one of data resulting from statistical analysis by the statistical analysis module 240, and an actual amount of energy consumption, which has been output from the building management servers A and B 110, 120, thereby dataanalyzing the amount of energy saving.
Here, the first outerair temperature and the second outerair temperature generally have different data profiles but may have exceptionally the same data profile.
The building energy performance evaluation unit 200 registers one or more monitoring and control points corresponding to facility generating the building energy before analyzing the correlation, and then collects the measurement data using any one or more of a method of configuring a work tree type or a method of installing an individual energy meter for the facility.
Based on the performance evaluation range and variables according to the energy retrofit, measurement data, which is related to building energy and outerair temperature before and after energy retrofit using the building management server 110 and/or 120, and/or a separate energy monitoring system, is collected and analyzed.
The collection of the measurement data is performed by calling the monitoring and control point of the existing automatic control system or by registering the additional installed monitoring system in the monitoring and control point of performance evaluation and constructing the work tree, and then, it is analyzed using a graph (e.g.
Time series plot, XY scatter plot) showing trend of and correlation between the building energy (dependent variable) and outerair temperature (independent variable) before and after energy retrofit based on the working period of energy retrofit which is separately registered.
In this case, the energy metering that is installed separately performs registering an energy retrofit work including the recorded information such as a work name and a working period before and after the energy retrofit is performed.
Analysis of the correlation uses a time series plot or an XY scatter plot.
The building energy regression model is any one or more 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 shape of the graph and a number of the change points according to graph analysis of measured data.
In a method for searching a change point, virtual change points are sequentially selected from a minimum value to a maximum value of population and a factor in which sum of residual (MSE) for left side data and right side data is minimum is searched using a least square method with each virtual change point as center.
A left side regression model and a right side regression model of explored factor are = =
analyzed thereby to set final change point (an intersection point of two regression models).
Statistical analysis computes any one or more of a number of population data, a number of change points of a regression model, coordinates of change point of a regression model, slope of left side or right side regression model, or type of a regression model, and reliability analysis analyzes any one or more of R2, AdjR2, RMSE or CV_RMSE.
with regard to the amount of energy saving, use conditions of outerair temperatures before and after execution of energy retrofit which is comprised of building energy saving actions including improvements in heat insulating material, equipment or automatic control operation method, are adjusted to be same, thereby, the amount of energy saving is identified based on actual difference before and after execution of energy retrofit.
Referring to FIG. 2, a driving method of a system for measuring and evaluating building energy performance 100 according to an embodiment of the present invention uses the system for measuring and evaluating building energy performance 100 of FIG. 1, and it is understood that functions and steps to perform the measuring building energy performance and the driving method according to the present invention are the same in the system of FIG. 1 and the method of FIG. 2.
A method for measuring and evaluating building energy performance of a system for measuring and evaluating building energy performance 100 using the building management server 110, 120 includes steps of setting an energy retrofit performance evaluation range and variables S100, collecting and analyzing measurement data before and after energy retrofit S200, generating a building energy regression model and reference model considering change point S300, conducting a statistical analysis and a reliability analysis for building energy regression model and reference model S400, and analyzing building energy saving effect before and after energy retrofit S500.
Each step may be performed by the building energy performance evaluation unit or each module.
The step S100 of setting an energy retrofit performance evaluation range and variables receives measurement data, which includes a measurement history from past to present, from the building management servers A and B 110, 120 and measures energy use.
The performance evaluation range according to the energy retrofit is basically set considering the collection method and range of measurement data according to the type and scope of the energy saving factor applied to the target building.
In addition, the dependent variable of the building energy regression model for evaluating the energy performance before and after energy retrofit is classified by energy source (electricity or gas energy, etc.) or building energy use purpose (cooling, heating, lighting, hot water supply, equipment, etc.) and the outerair temperature is set as an independent variable.
In the step S200 of collecting and analyzing measurement data, the variables are set and measurement data of the variables before and after energy retrofit for building energy saving is inputted, and the correlation between the building energy and outerair temperature is analyzed.
Referring to FIG. 3, based on the performance evaluation range and variables according to the energy retrofit, measurement data, which is related to building energy and outerair temperature before and after energy retrofit using the BAS 110 and/or a separate energy monitoring system, is collected and analyzed.
The collection of the measurement data is performed by calling the monitoring and control point of the existing automatic control system (S110) or by registering the additional installed monitoring system (S140) in the monitoring and control point of performance evaluation (S120) and constructing the work tree (S130), and then, it is analyzed using a graph (e.g. Time series plot, XY scatter plot) showing trend of and correlation between the building energy (dependent variable) and outerair temperature (independent variable) before and after energy retrofit based on the working period of energy retrofit which is separately registered (S200).
In this case, the energy metering that is installed separately (S140) performs registering an energy retrofit work including the recorded information such as a work name and a working period (S160) before and after the energy retrofit is performed (S150) in the step (S200).
For example, in order to save the electric energy of a target building during the summer coolingup period, in case of replacing two existing aged turbo chillers (turbo freezing machines) with a high efficiency turbo chiller and evaluating the cooling energy (electricity) saving effect, it is necessary to check first for operating conditions (operation time, etc.) related to the two existing chillers and monitoring and control points related to power consumption of the chillers, which have been measured by existing automatic control (or energy management system), and register all of them as monitoring and control points for performance evaluation.
However, if power consumption related to one chiller is not measured, a separate energy meter (electricity meter) can be installed and it is registered in a 'Monitoring and Control point of Performance evaluation'.
In addition, registered monitoring and control points for each chiller generate a virtual =
monitoring and control point by some computing operations (e.g. the monitoring and control points of power consumption of a chiller 1 and a chiller 2 are summed together so it is reconstructed into one monitoring and control point as 'a chiller power consumption') and thereby construct a 'work tree' of performance evaluation.
In addition, the data measured for a certain period of time before and after energy retrofit based on registered work (a work name and a working period, etc.) related to the energy retrofit replacing the chiller is indicated in graph with correlation between the dependent variable (a chiller power consumption) and the independent variable (outerair temperature).
In the step S300 of generating a reference model, the correlation is analyzed by using the measurement data of the variables before energy retrofit, a building energy regression model is created in view of the change point of outerair temperature, and a reference model is generated by applying the outerair temperature after energy retrofit to the regression model.
In other words, a building energy regression model in view of the change point is created based on the measurement data before energy retrofit, and a reference model (baseline) is created by applying the outerair temperature after the energy retrofit to the regression model before energy retrofit.
In the present invention, the building energy regression model is a function (graphically representable) of the cooling/heating energy (amount of electricity or gas consumption) with respect to the outerair temperature before the energy retrofit, while the reference model (baseline), which is applying the outerair temperature after energy retrofit to the regression model before energy retrofit, can be defined as a predictive regression model of the cooling/heating energy (amount of electricity or gas consumption) with respect to the outerair temperature after energy retrofit.
In other words, it is possible to calculate (evaluate) the amount of energy savings after retrofit, by creating a reference model after retrofit (baseline energy model) based on a regression model before retrofit (Baseyear energy model), and by comparing it with amount of energy consumption after retrofit.
A method for creating a building energy regression model (or reference model) considering the change point of the present invention is characterized by further including, first, setting a type of building energy regression model and a number of change points and second, searching for change point of the building energy regression model.
Referring to FIG. 4, the outerair temperature is set as an independent variable in the population, and the cooling or heating energy is set as a dependent variable (S200).
When selecting a building energy regression model depending on a graph of population (5393a), 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).
The number of change points (or turning point) of the outerair temperature of the regression model is set (S393c), and it is determined whether the number of change points is 0 (S393d), in response, when it is not 0, it is determined whether the number of change points is 1 (S393e), in response, when it is 1, the one change point is searched (S3930, and statistical analysis (S400) and reliability analysis (S400a) for building energy regression model are performed.
In this instance, if the model of step S393b is the average model or the simple t regression model, proceed to step S400, and if the number of change points is 0 in step S393d, proceed to step S400.
In step S393e, when the number of change points is not 1, it is determined whether the number of change points is 2 (S393i), in response, when it is not 2, return to step S393c, and when it is 2, search two change points (S393j) and proceed to step S400.
Referring to FIG. 5, a process for searching a change point of a building energy regression model or a reference model is classified according to the number of change points of regression model which is set.
First, in case of an average model and a simple regression model, which are having a zero (0) change point, it immediately performs statistical analysis of the regression model (or the average model) without going through a change point search process, separately.
Second, in case of a regression model having one (1) change point, virtual change points are sequentially selected from a minimum outerair temperature (second data) to a maximum outerair temperature ((n2)th data) of population and a factor (outerair temperature) in which sum of residual (MSE) for left side data and right side data is minimum is searched using a least square method with each virtual change point as center.
And a left side regression model and a right side regression model of explored factor are analyzed thereby to set final change point (an intersection point of two regression models).
Third, in case of a regression model having two (2) change points, the process further includes a step of setting a virtual equilibrium point (outerair temperature) for distinguishing the two change points, and the subsequent method for searching a change point is the same as the process for searching one (1) change point.
Referring to FIG. 5, a method for searching the change point sets the number of =
change points (S340) first, and then when it is 1, the one change point is searched (S370), and the process starts with `i (data) = 2' (S371).
It is determined whether `i <n' (S372), if yes, perform data setting to left [x(1),,x(i)][y(1),,y(i)] and right [x(i+1),,x(n)][y(i+1),,y(n)] (5373).
In the regression model, sum of MSE of left data and right data is calculated using the least square method `MSE(i1)=MSE(L)+MSE(R)' (S374), then if 1 = i + 1' (S375), search a factor i at which the MES(i) becomes a minimum `K=arg min MSE(i)' (S376), perform a data setting to left [x(1),,x(k)] [y(1),,y(k)] and right [x(k+1),,x(n)][y(k+1),,y(n)]
(S377), calculate intercepts and slopes of left regression model and right regression model (S378), determine a change point by performing analysis on left and right regression model (S379), and perform statistical analysis of the regression model (S379a).
In this instance, if the number of the change points of step S340 is 0, proceed to step S379a, and if the number of change points is 2, search the two change points (S390), set a virtual equilibrium point (outerair temperature) for distinguishing the two change points (S390a), perform a data setting to left change point [x(1),,x(m1)][y(1),,y(m1)] and right change point [x(m+1),,x(n)][y(m+1),,y(n)] (S390b), and return to a In the step S372, if it is not `i <n', proceed to step S376.
In the step S400 of conducting a statistical analysis and a reliability analysis, the building energy regression model and reference model are analyzed.
The number of population data, the number of change points of a regression model, coordinates of change point of a regression model, slope of left side or right side regression model, or type of a regression model, and so on are derived through statistical analysis of the building energy regression model (or reference model) including searched change point, and R2, AdjR2, RMSE, CV RMSE, etc. are analyzed to verify the reliability of the regression model.
FIG. 6 and Table 1 show examples of heating energy regression model and statistical analysis of the present invention.
Table 1 Left side model Result value of Right side model Result value of statistical analysis statistical analysis 49.0000 N 51 yintercept 20.1249 yintercept 8.5862 Slope 2.0195 Slope 0 RMSE 0.9326 RMSE 0.9589 R2 0.9905 R2 0 AdjR2 0.9903 AdjR2 0 CVRMSE 4.1246 CVRMSE 11.1675 Change point (CP) 5.7137 The step S250 of analyzing the energy saving amount performs a statistical analysis and a reliability analysis, and the energy saving effect before and after energy retrofit is quantitatively analyzed by comparing the difference between the predicted amount of energy consumption of the reference model and an actual amount of energy consumption.
A building energy reference model is created by applying the outerair temperature after energy retrofit to the created building energy regression model before energy retrofit, and the difference between it and an actual measured amount of energy consumption after energy retrofit is calculated to analyze the energy saving effect (energy saving amount and energy saving rate).
or CV_RMSE.
With regard to the amount of energy saving, use conditions of the first and second outerair temperatures before and after execution of energy retrofit which is comprised of building energy saving actions including a replacement of building heat insulating material, an improvement in equipment or automatic control operation method, are adjusted to be same, thereby, the amount of energy saving is identified based on comparison with actual amount of the building energy consumption.
Also, in order to achieve the above objects, a method for driving 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 following steps:
receiving, by a measurement variable setting module 210, measurement data, which includes a measurement history, from the building management servers A and B 110, 120 and setting building energy and an outerair temperature as variables; receiving, by a measurement data analysis module 220, the variables, related to occasions before and after energy retrofit for saving the building energy, from the measurement variable setting module 210 and analyzing a correlation between the building energy and first and second outerair temperatures using an analysis graph showing trend of and correlation between the building energy and outerair temperature before and after energy retrofit; plotting, by a regression model generating module 230, the variables related to the occasion before energy retrofit, which have been output from the measurement data analysis module 220, generating a building energy regression model with a change point of the first outerair temperature, and applying the second outerair temperature, related to the occasion after energy retrofit, to the building energy regression model, thereby generating a reference model;
statistically analyzing, by a statistical analysis module 240, 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 comparing, by an energy saving analysis module 250, a difference between a predicted amount of energy consumption, which is one of data resulting from statistical analysis by the statistical analysis module 240, and an actual amount of energy consumption, which has been output from the building management servers A and B 110, 120, thereby extracting an amount of energy saving.
The method further comprises a step wherein the system for measuring and evaluating building energy performance registers one or more monitoring and control points corresponding to facility generating the building energy before analyzing the correlation, and then collects the measurement data using any one of a work tree type configuration method or an individual energy metering installation method for the facility.
The method further comprises a step wherein the analysis graph according to the correlation uses a time series plot or an XY scatter plot.
The method further comprises a step wherein the building energy regression model is set as at least any 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 shape of the analysis graph and a number of the change points searched from the analysis graph.
In the method, searching for change point of the analysis graph is to sequentially select a virtual change point from a minimum value to a maximum value of the = =
measurement data and to search a factor in which sum of residual (Mean Squared Error, MSE) for left side data and right side data is minimum using a least square method with each virtual change point as center.
The method further comprises steps wherein the statistical analysis comprises:
calculating and deriving any one of a number of the measurement data, a number of the change points of the building energy regression model, coordinates of the change point of the building energy regression model, slope of the left side model or right side model of the building energy regression model, or type of the building energy regression model; and conducting a reliability analysis on at least any one of R2, AdjR2, RMSE or CV_RMSE.
The method further comprises a step wherein with regard to the amount of energy saving, use conditions of the first and second outerair temperatures before and after execution of energy retrofit which is comprised of building energy saving actions including a replacement of building heat insulating material, an improvement in equipment or automatic control operation method, are adjusted to be same, thereby, the amount of energy saving is identified based on comparison with actual amount of the building energy consumption.
ADVANTAGEOUS EFFECTS
A system for measuring and evaluating building energy performance and method for driving same of the present invention can verify the energy saving effect when using good energy saving devices or materials and improve the performance through efficient energy management.
A system for measuring and evaluating building energy performance and method for = =
driving same of the present invention relates to a method for evaluating building energy performance considering a change in outerair temperature that has a major influence on building energy consumption before and after energy retrofit of building based on measurement data of building management server (A and/or B), and it can secure the reliability of the energy saving effect through the energy retrofit of the existing building, thereby can guarantee the performance.
In addition, when a system for measuring and evaluating building energy performance and method for driving same of the present invention is applied to an existing building management server (A and/or B) of building, it is possible to minimize an installation cost of a separate monitoring sensor for collecting measurement data of before/after energy retrofit. Even after the energy retrofit, it can quickly and objectively analyze the energy saving effect through continuous commissioning and provide it to users, thereby able to prevent imprudent equipment replacement and able to efficient building energy management.
BRIEF DESCRIPTION OF DRAWINGS
FIG. 1 is a block diagram of a system for measuring and evaluating building energy performance according to one embodiment of the present invention.
FIG. 2 is a flow chart showing a method for driving a performance evaluation of a system for measuring and evaluating building energy performance according to FIG. 1.
FIG. 3 is a specific operation flow chart of measurement data analysis step (S200) according to FIG. 2.
FIG. 4 is an operation flow chart illustrating a reference model generating step (S300) and statistical analysis step (S400) according to FIG. 2.
. .
FIG. 5 is a specific operation flow chart of a method of searching for a change point in a reference model generating step (S300) according to FIG. 2.
FIG. 6 is an exemplary drawing illustrating a graph of heating energy change point model of a system for measuring and evaluating building energy performance and method for driving same according to one embodiment of the present invention.
DETAILED DESCRIPTION OF THE EMBODIMENTS
Hereinafter, one embodiment of the present invention will be described.
FIG. 1 is a block diagram of a system for measuring and evaluating building energy performance according to one embodiment of the present invention, FIG. 2 is a flow chart showing a method for driving a performance evaluation of a system for measuring and evaluating building energy performance according to FIG. 1, FIG. 3 is a specific operation flow chart of measurement data analysis step (S200) according to FIG. 2, FIG.
4 is an operation flow chart illustrating a reference model generating step (S300) and statistical analysis step (S400) according to FIG. 2, FIG. 5 is a specific operation flow chart of a method of searching for a change point in a reference model generating step (S300) according to FIG. 2, and FIG. 6 is an exemplary drawing illustrating a graph of heating energy change point model of a system for measuring and evaluating building energy performance and method for driving same according to one embodiment of the present invention.
Referring to FIG. 1, a building management server according to an embodiment of the present invention includes a server A 110 (a building automatic system or BAS) and/or a server B 120 (a building energy management system or BEMS), and it is installed separately from a system for measuring and evaluating building energy performance 100.
The system for measuring and evaluating building energy performance 100 includes a building energy performance evaluation unit 200 and an input/output unit 130.
The building energy performance evaluation unit 200 includes a measurement variable setting module 210, a measurement data analysis module 220, a regression model generating module 230, a statistical analysis module 240, and an energy saving analysis module 250.
An input/output unit 140 receives data which has been output from the building management servers 110, 120, and may output or transmit data which has been output from the energy saving analysis module 250 to outside.
Each of the modules 210, 220, 230, 240, and 250 may include a memory to perform a corresponding function. A processor (not shown) included in the building energy performance evaluation unit 200 may be configured in an outside of each module or a processor and a memory may be integrated and included in an inside of each module.
First, an energy retrofit of building can be defined as a set of all actions for energy saving of building including replacing low performance windows, heat insulating materials of wall and equipment system (chiller, boiler, pump, etc.) of existing building or improving automatic control operation method, etc.
In this regard, until now, operation of a building management server A 110 (building automation system) of existing buildings has been focused on simple operation and monitoring, and in case of a building management server B 120 (building energy management system), there is a limitation in energy saving management of facility manager, so it is insufficient to verify energy saving effect and to improve performance through efficient energy management, actually, even when using good energy saving equipment or materials.
In order to objectively measure and verify the energy saving effect through energy retrofit of building, it is important to perform the building energy performance evaluation considering change of outerair temperature which has a major influence on cooling energy or heating energy consumption of building before and after energy retrofit.
Building energy is largely divided into cooling or heating energy, which is influenced by outerair temperature, and base energy (appliance and lighting energy) and other energy, which are hardly influenced by outerair temperature.
Such building energy (dependent variable) can be expressed as a simple regression model, which is described by outerair temperature (independent variable). If relation between energy consumption and outerair temperature is explained by only one model, there is no change point in the outerair temperature, and if it is explained by two models, there is one change point (represented by two models based on a specific fiducial outerair temperature).
Therefore, setting of the change point is an important parameter that determines the accuracy and reliability of the building energy regression model.
A system for measuring and evaluating building energy performance 100 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 past to present, from the building management servers A and B
110, 120 and setting building energy and an outerair temperature as variables; a measurement data analysis module 220 for receiving the variables, related to occasions before and after energy retrofit for saving the building energy, from the measurement variable setting module 210 and analyzing a correlation between the building energy and first and second outerair temperatures; a regression model generating module 230 for using (plotting) the variables related to the occasion before energy retrofit, which have been output from the measurement data analysis module 220, generating a building energy regression model with a change point of the first outerair temperature, and applying the second outerair temperature, related to the occasion 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 a difference between a predicted amount of energy consumption, which is one of data resulting from statistical analysis by the statistical analysis module 240, and an actual amount of energy consumption, which has been output from the building management servers A and B 110, 120, thereby dataanalyzing the amount of energy saving.
Here, the first outerair temperature and the second outerair temperature generally have different data profiles but may have exceptionally the same data profile.
The building energy performance evaluation unit 200 registers one or more monitoring and control points corresponding to facility generating the building energy before analyzing the correlation, and then collects the measurement data using any one or more of a method of configuring a work tree type or a method of installing an individual energy meter for the facility.
Based on the performance evaluation range and variables according to the energy retrofit, measurement data, which is related to building energy and outerair temperature before and after energy retrofit using the building management server 110 and/or 120, and/or a separate energy monitoring system, is collected and analyzed.
The collection of the measurement data is performed by calling the monitoring and control point of the existing automatic control system or by registering the additional installed monitoring system in the monitoring and control point of performance evaluation and constructing the work tree, and then, it is analyzed using a graph (e.g.
Time series plot, XY scatter plot) showing trend of and correlation between the building energy (dependent variable) and outerair temperature (independent variable) before and after energy retrofit based on the working period of energy retrofit which is separately registered.
In this case, the energy metering that is installed separately performs registering an energy retrofit work including the recorded information such as a work name and a working period before and after the energy retrofit is performed.
Analysis of the correlation uses a time series plot or an XY scatter plot.
The building energy regression model is any one or more 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 shape of the graph and a number of the change points according to graph analysis of measured data.
In a method for searching a change point, virtual change points are sequentially selected from a minimum value to a maximum value of population and a factor in which sum of residual (MSE) for left side data and right side data is minimum is searched using a least square method with each virtual change point as center.
A left side regression model and a right side regression model of explored factor are = =
analyzed thereby to set final change point (an intersection point of two regression models).
Statistical analysis computes any one or more of a number of population data, a number of change points of a regression model, coordinates of change point of a regression model, slope of left side or right side regression model, or type of a regression model, and reliability analysis analyzes any one or more of R2, AdjR2, RMSE or CV_RMSE.
with regard to the amount of energy saving, use conditions of outerair temperatures before and after execution of energy retrofit which is comprised of building energy saving actions including improvements in heat insulating material, equipment or automatic control operation method, are adjusted to be same, thereby, the amount of energy saving is identified based on actual difference before and after execution of energy retrofit.
Referring to FIG. 2, a driving method of a system for measuring and evaluating building energy performance 100 according to an embodiment of the present invention uses the system for measuring and evaluating building energy performance 100 of FIG. 1, and it is understood that functions and steps to perform the measuring building energy performance and the driving method according to the present invention are the same in the system of FIG. 1 and the method of FIG. 2.
A method for measuring and evaluating building energy performance of a system for measuring and evaluating building energy performance 100 using the building management server 110, 120 includes steps of setting an energy retrofit performance evaluation range and variables S100, collecting and analyzing measurement data before and after energy retrofit S200, generating a building energy regression model and reference model considering change point S300, conducting a statistical analysis and a reliability analysis for building energy regression model and reference model S400, and analyzing building energy saving effect before and after energy retrofit S500.
Each step may be performed by the building energy performance evaluation unit or each module.
The step S100 of setting an energy retrofit performance evaluation range and variables receives measurement data, which includes a measurement history from past to present, from the building management servers A and B 110, 120 and measures energy use.
The performance evaluation range according to the energy retrofit is basically set considering the collection method and range of measurement data according to the type and scope of the energy saving factor applied to the target building.
In addition, the dependent variable of the building energy regression model for evaluating the energy performance before and after energy retrofit is classified by energy source (electricity or gas energy, etc.) or building energy use purpose (cooling, heating, lighting, hot water supply, equipment, etc.) and the outerair temperature is set as an independent variable.
In the step S200 of collecting and analyzing measurement data, the variables are set and measurement data of the variables before and after energy retrofit for building energy saving is inputted, and the correlation between the building energy and outerair temperature is analyzed.
Referring to FIG. 3, based on the performance evaluation range and variables according to the energy retrofit, measurement data, which is related to building energy and outerair temperature before and after energy retrofit using the BAS 110 and/or a separate energy monitoring system, is collected and analyzed.
The collection of the measurement data is performed by calling the monitoring and control point of the existing automatic control system (S110) or by registering the additional installed monitoring system (S140) in the monitoring and control point of performance evaluation (S120) and constructing the work tree (S130), and then, it is analyzed using a graph (e.g. Time series plot, XY scatter plot) showing trend of and correlation between the building energy (dependent variable) and outerair temperature (independent variable) before and after energy retrofit based on the working period of energy retrofit which is separately registered (S200).
In this case, the energy metering that is installed separately (S140) performs registering an energy retrofit work including the recorded information such as a work name and a working period (S160) before and after the energy retrofit is performed (S150) in the step (S200).
For example, in order to save the electric energy of a target building during the summer coolingup period, in case of replacing two existing aged turbo chillers (turbo freezing machines) with a high efficiency turbo chiller and evaluating the cooling energy (electricity) saving effect, it is necessary to check first for operating conditions (operation time, etc.) related to the two existing chillers and monitoring and control points related to power consumption of the chillers, which have been measured by existing automatic control (or energy management system), and register all of them as monitoring and control points for performance evaluation.
However, if power consumption related to one chiller is not measured, a separate energy meter (electricity meter) can be installed and it is registered in a 'Monitoring and Control point of Performance evaluation'.
In addition, registered monitoring and control points for each chiller generate a virtual =
monitoring and control point by some computing operations (e.g. the monitoring and control points of power consumption of a chiller 1 and a chiller 2 are summed together so it is reconstructed into one monitoring and control point as 'a chiller power consumption') and thereby construct a 'work tree' of performance evaluation.
In addition, the data measured for a certain period of time before and after energy retrofit based on registered work (a work name and a working period, etc.) related to the energy retrofit replacing the chiller is indicated in graph with correlation between the dependent variable (a chiller power consumption) and the independent variable (outerair temperature).
In the step S300 of generating a reference model, the correlation is analyzed by using the measurement data of the variables before energy retrofit, a building energy regression model is created in view of the change point of outerair temperature, and a reference model is generated by applying the outerair temperature after energy retrofit to the regression model.
In other words, a building energy regression model in view of the change point is created based on the measurement data before energy retrofit, and a reference model (baseline) is created by applying the outerair temperature after the energy retrofit to the regression model before energy retrofit.
In the present invention, the building energy regression model is a function (graphically representable) of the cooling/heating energy (amount of electricity or gas consumption) with respect to the outerair temperature before the energy retrofit, while the reference model (baseline), which is applying the outerair temperature after energy retrofit to the regression model before energy retrofit, can be defined as a predictive regression model of the cooling/heating energy (amount of electricity or gas consumption) with respect to the outerair temperature after energy retrofit.
In other words, it is possible to calculate (evaluate) the amount of energy savings after retrofit, by creating a reference model after retrofit (baseline energy model) based on a regression model before retrofit (Baseyear energy model), and by comparing it with amount of energy consumption after retrofit.
A method for creating a building energy regression model (or reference model) considering the change point of the present invention is characterized by further including, first, setting a type of building energy regression model and a number of change points and second, searching for change point of the building energy regression model.
Referring to FIG. 4, the outerair temperature is set as an independent variable in the population, and the cooling or heating energy is set as a dependent variable (S200).
When selecting a building energy regression model depending on a graph of population (5393a), 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).
The number of change points (or turning point) of the outerair temperature of the regression model is set (S393c), and it is determined whether the number of change points is 0 (S393d), in response, when it is not 0, it is determined whether the number of change points is 1 (S393e), in response, when it is 1, the one change point is searched (S3930, and statistical analysis (S400) and reliability analysis (S400a) for building energy regression model are performed.
In this instance, if the model of step S393b is the average model or the simple t regression model, proceed to step S400, and if the number of change points is 0 in step S393d, proceed to step S400.
In step S393e, when the number of change points is not 1, it is determined whether the number of change points is 2 (S393i), in response, when it is not 2, return to step S393c, and when it is 2, search two change points (S393j) and proceed to step S400.
Referring to FIG. 5, a process for searching a change point of a building energy regression model or a reference model is classified according to the number of change points of regression model which is set.
First, in case of an average model and a simple regression model, which are having a zero (0) change point, it immediately performs statistical analysis of the regression model (or the average model) without going through a change point search process, separately.
Second, in case of a regression model having one (1) change point, virtual change points are sequentially selected from a minimum outerair temperature (second data) to a maximum outerair temperature ((n2)th data) of population and a factor (outerair temperature) in which sum of residual (MSE) for left side data and right side data is minimum is searched using a least square method with each virtual change point as center.
And a left side regression model and a right side regression model of explored factor are analyzed thereby to set final change point (an intersection point of two regression models).
Third, in case of a regression model having two (2) change points, the process further includes a step of setting a virtual equilibrium point (outerair temperature) for distinguishing the two change points, and the subsequent method for searching a change point is the same as the process for searching one (1) change point.
Referring to FIG. 5, a method for searching the change point sets the number of =
change points (S340) first, and then when it is 1, the one change point is searched (S370), and the process starts with `i (data) = 2' (S371).
It is determined whether `i <n' (S372), if yes, perform data setting to left [x(1),,x(i)][y(1),,y(i)] and right [x(i+1),,x(n)][y(i+1),,y(n)] (5373).
In the regression model, sum of MSE of left data and right data is calculated using the least square method `MSE(i1)=MSE(L)+MSE(R)' (S374), then if 1 = i + 1' (S375), search a factor i at which the MES(i) becomes a minimum `K=arg min MSE(i)' (S376), perform a data setting to left [x(1),,x(k)] [y(1),,y(k)] and right [x(k+1),,x(n)][y(k+1),,y(n)]
(S377), calculate intercepts and slopes of left regression model and right regression model (S378), determine a change point by performing analysis on left and right regression model (S379), and perform statistical analysis of the regression model (S379a).
In this instance, if the number of the change points of step S340 is 0, proceed to step S379a, and if the number of change points is 2, search the two change points (S390), set a virtual equilibrium point (outerair temperature) for distinguishing the two change points (S390a), perform a data setting to left change point [x(1),,x(m1)][y(1),,y(m1)] and right change point [x(m+1),,x(n)][y(m+1),,y(n)] (S390b), and return to a In the step S372, if it is not `i <n', proceed to step S376.
In the step S400 of conducting a statistical analysis and a reliability analysis, the building energy regression model and reference model are analyzed.
The number of population data, the number of change points of a regression model, coordinates of change point of a regression model, slope of left side or right side regression model, or type of a regression model, and so on are derived through statistical analysis of the building energy regression model (or reference model) including searched change point, and R2, AdjR2, RMSE, CV RMSE, etc. are analyzed to verify the reliability of the regression model.
FIG. 6 and Table 1 show examples of heating energy regression model and statistical analysis of the present invention.
Table 1 Left side model Result value of Right side model Result value of statistical analysis statistical analysis 49.0000 N 51 yintercept 20.1249 yintercept 8.5862 Slope 2.0195 Slope 0 RMSE 0.9326 RMSE 0.9589 R2 0.9905 R2 0 AdjR2 0.9903 AdjR2 0 CVRMSE 4.1246 CVRMSE 11.1675 Change point (CP) 5.7137 The step S250 of analyzing the energy saving amount performs a statistical analysis and a reliability analysis, and the energy saving effect before and after energy retrofit is quantitatively analyzed by comparing the difference between the predicted amount of energy consumption of the reference model and an actual amount of energy consumption.
A building energy reference model is created by applying the outerair temperature after energy retrofit to the created building energy regression model before energy retrofit, and the difference between it and an actual measured amount of energy consumption after energy retrofit is calculated to analyze the energy saving effect (energy saving amount and energy saving rate).
Claims (14)
1. A system for measuring and evaluating building energy performance conducting measurement and evaluation of performance regarding building energy using building management servers A and B 110, 120, and the system comprising:
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 outerair temperature as variables;
a measurement data analysis module 220 for receiving the variables, related to occasions before and after energy retrofit for saving the building energy, from the measurement variable setting module 210 and analyzing a correlation between the building energy and first and second outerair temperatures using an analysis graph showing trend of, and correlation between the building energy and outerair temperature before and after energy retrofit;
a regression model generating module 230 for plotting the variables related to the occasion before energy retrofit, which have been output from the measurement data analysis module 220, generating a building energy regression model with a change point of the first outerair temperature, and applying the second outerair temperature, related to the occasion 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 a difference between a predicted amount of energy consumption, which is one of data resulting from statistical analysis by the statistical analysis module 240, and an actual amount of energy consumption, which has been output from the building management servers A and B 110, 120, thereby extracting an amount of energy saving.
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 outerair temperature as variables;
a measurement data analysis module 220 for receiving the variables, related to occasions before and after energy retrofit for saving the building energy, from the measurement variable setting module 210 and analyzing a correlation between the building energy and first and second outerair temperatures using an analysis graph showing trend of, and correlation between the building energy and outerair temperature before and after energy retrofit;
a regression model generating module 230 for plotting the variables related to the occasion before energy retrofit, which have been output from the measurement data analysis module 220, generating a building energy regression model with a change point of the first outerair temperature, and applying the second outerair temperature, related to the occasion 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 a difference between a predicted amount of energy consumption, which is one of data resulting from statistical analysis by the statistical analysis module 240, and an actual amount of energy consumption, which has been output from the building management servers A and B 110, 120, thereby extracting an amount of energy saving.
2. The system for measuring and evaluating building energy performance according to claim 1, which registers one or more monitoring and control points corresponding to facility generating the building energy before analyzing the correlation, and then collects the measurement data using any one of a work tree type configuration method or an individual energy metering installation method for the facility.
3. The system for measuring and evaluating building energy performance according to claim 1, wherein the analysis graph according to the correlation uses a time series plot or an XY scatter plot.
4. The system for measuring and evaluating building energy performance according to claim 1, wherein the building energy regression model is at least any 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 shape of the analysis graph and a number of the change points searched from the analysis graph.
5. The system for measuring and evaluating building energy performance according to claim 4, wherein searching for change point of the analysis graph is to sequentially select a virtual change point from a minimum value to a maximum value of the measurement data and to search a factor in which sum of residual (Mean Squared Error, MSE) for left side data and right side data is minimum using a least square method with each virtual change point as center.
6. The system for measuring and evaluating building energy performance according to claim 1, wherein the statistical analysis is to calculate and derive any one of a number of the measurement data, a number of the change points of the building energy regression model, coordinates of the change point of the building energy regression model, slope of the left side model or right side model of the building energy regression model, or type of the building energy regression model, and to conduct a reliability analysis on at least any one of R2, AdjR2, RMSE or CV_RMSE.
7. The system for measuring and evaluating building energy performance according to claim 1, wherein with regard to the amount of energy saving, use conditions of the first and second outerair temperatures before and after execution of energy retrofit which is comprised of building energy saving actions including a replacement of building heat insulating material, an improvement in equipment or automatic control operation method, are adjusted to be same, thereby, the amount of energy saving is identified based on comparison with actual amount of the building energy consumption.
8. A method for driving a system for measuring and evaluating building energy performance conducting measurement and evaluation of performance regarding building energy using building management servers A and B 110, 120, and the method comprising steps of:
receiving, by a measurement variable setting module 210, measurement data, which includes a measurement history, from the building management servers A and B
110, 120 and setting building energy and an outerair temperature as variables;
receiving, by a measurement data analysis module 220, the variables, related to occasions before and after energy retrofit for saving the building energy, from the measurement variable setting module 210 and analyzing a correlation between the building energy and first and second outerair temperatures using an analysis graph showing trend of, and correlation between the building energy and outerair temperature before and after energy retrofit;
plotting, by a regression model generating module 230, the variables related to the occasion before energy retrofit, which have been output from the measurement data analysis module 220, generating a building energy regression model with a change point of the first outerair temperature, and applying the second outerair temperature, related to the occasion after energy retrofit, to the building energy regression model, thereby generating a reference model;
statistically analyzing, by a statistical analysis module 240, 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 comparing, by an energy saving analysis module 250, a difference between a predicted amount of energy consumption, which is one of data resulting from statistical analysis by the statistical analysis module 240, and an actual amount of energy consumption, which has been output from the building management servers A and B 110, 120, thereby extracting an amount of energy saving.
receiving, by a measurement variable setting module 210, measurement data, which includes a measurement history, from the building management servers A and B
110, 120 and setting building energy and an outerair temperature as variables;
receiving, by a measurement data analysis module 220, the variables, related to occasions before and after energy retrofit for saving the building energy, from the measurement variable setting module 210 and analyzing a correlation between the building energy and first and second outerair temperatures using an analysis graph showing trend of, and correlation between the building energy and outerair temperature before and after energy retrofit;
plotting, by a regression model generating module 230, the variables related to the occasion before energy retrofit, which have been output from the measurement data analysis module 220, generating a building energy regression model with a change point of the first outerair temperature, and applying the second outerair temperature, related to the occasion after energy retrofit, to the building energy regression model, thereby generating a reference model;
statistically analyzing, by a statistical analysis module 240, 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 comparing, by an energy saving analysis module 250, a difference between a predicted amount of energy consumption, which is one of data resulting from statistical analysis by the statistical analysis module 240, and an actual amount of energy consumption, which has been output from the building management servers A and B 110, 120, thereby extracting an amount of energy saving.
9. The method for driving a system for measuring and evaluating building energy performance according to claim 8, further comprising a step wherein the system for measuring and evaluating building energy performance registers one or more monitoring and control points corresponding to facility generating the building energy before analyzing the correlation, and then collects the measurement data using any one of a work tree type configuration method or an individual energy metering installation method for the facility.
10. The method for driving a system for measuring and evaluating building energy performance according to claim 8, further comprising a step wherein the analysis graph according to the correlation uses a time series plot or an XY scatter plot.
11. The method for driving a system for measuring and evaluating building energy performance according to claim 8, further comprising a step wherein the building energy regression model is set as at least any 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 shape of the analysis graph and a number of the change points searched from the analysis graph.
12. The method for driving a system for measuring and evaluating building energy performance according to claim 11, wherein searching for change point of the analysis graph is to sequentially select a virtual change point from a minimum value to a maximum value of the measurement data and to search a factor in which sum of residual (Mean Squared Error, MSE) for left side data and right side data is minimum using a least square method with each virtual change point as center.
13. The method for driving a system for measuring and evaluating building energy performance according to claim 8, wherein the statistical analysis comprises further steps of:
calculating and deriving any one of a number of the measurement data, a number of the change points of the building energy regression model, coordinates of the change point of the building energy regression model, slope of the left side model or right side model of the building energy regression model, or type of the building energy regression model; and conducting a reliability analysis on at least any one of R2, AdjR2, RMSE or CV RMSE.
calculating and deriving any one of a number of the measurement data, a number of the change points of the building energy regression model, coordinates of the change point of the building energy regression model, slope of the left side model or right side model of the building energy regression model, or type of the building energy regression model; and conducting a reliability analysis on at least any one of R2, AdjR2, RMSE or CV RMSE.
14. The method for driving a system for measuring and evaluating building energy performance according to claim 8, further comprising a step wherein with regard to the amount of energy saving, use conditions of the first and second outerair temperatures before and after execution of energy retrofit which is comprised of building energy saving actions including a replacement of building heat insulating material, an improvement in equipment or automatic control operation method, are adjusted to be same, thereby, the amount of energy saving is identified based on comparison with actual amount of the building energy consumption.
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