KR101840738B1 - Method for predicting heating and cooling load of buildings based on actual load of air conditioning equipments - Google Patents

Abstract

The present invention relates to a method for predicting a heating and cooling load of a building based on an actual load of air conditioning equipment. The method for predicting the heating and cooling load of the building comprises the following steps: collecting data from each of the air conditioning equipment which is operated to obtain the actual load of each of the air conditioning equipment; converting the collected data into the actual load by using a conversion formula; obtaining past weather information at a time of which the air conditioning equipment is operated; calculating correction factors of a regression model equation by performing a regression analysis based on the actual load and the past weather information; obtaining future weather information; and calculating a predicted load by substituting the future weather information into the regression model equation. Accordingly, based on the actual load of the equipment, the heating and cooling load of the next day can be more accurately predicted.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for predicting heating / cooling load of a building based on actual load of an air-

The present invention relates to a method for predicting a cooling / heating load of a building.

Since a large part of the total energy demand is used for the operation of the air conditioning system, it is necessary to develop an economical and efficient operation method for the air conditioning system in order to save energy. In order to achieve efficient operation of the air conditioning system, accurate prediction of the heating and cooling load of the building should be preceded.

However, since it is not easy to predict the heating and cooling load of the building, the operation of the heating and cooling system depends mainly on the experience of the driver. As a result, in many cases, unnecessary power is consumed due to the driver's judgment error, In some cases, the supply amount is insufficient to cause inconvenience to the user.

In order to solve these problems and to operate the heating and cooling system more economically, researches on the method of predicting the heating and cooling load have been actively carried out. The inventors of the present invention have also proposed a new heating and cooling load prediction method to solve the problems of existing air-conditioning load prediction methods and have received a patent registration (Patent No. 10-1571806). This method uses weather information and building property information In the case of a building which can not obtain an input value for building characteristics, it is difficult to apply it and the accuracy of prediction is somewhat low.

Therefore, it is applicable to the building which can not obtain the input value of the building characteristics. Therefore, it is necessary to more precisely predict the building load.

KR 10-1506215 B1 KR 10-1515003 B1 KR 10-1141027 B1 KR 10-1571806 B1

Accordingly, the present invention has been made in view of the above circumstances, and an object of the present invention is to provide a method for predicting a cooling / heating load of a building so as to more accurately predict the cooling / heating load of the next day based on the actual load of the equipment.

According to another aspect of the present invention, there is provided a method for predicting a cooling / heating load of a building, the method including: collecting data from each of the air conditioners being operated to obtain an actual load of each air conditioner; Converting the collected data into an actual load using a conversion formula; Obtaining past weather information at the time of operating the air conditioning equipment; Calculating regression model equation correction coefficients by performing regression analysis based on the actual load and the past weather information; Obtaining future weather information; Calculating a prediction load by substituting the future weather information into the regression model equation; .

In addition, the regression model expression is changed for each specific time, and accordingly, the correction coefficient also varies for each specific time.

Also, the regression model equation is as follows.

(I) * (Ti-Td) + Csol (i) * Ii + Cair (i) * (hi-hd) + Cint

Here, Q (i) is an actual load value at a specific time, Cht (i) is a heat transfer load correction coefficient, Csol (i) is a solar heat load correction coefficient, Cair i is the internal load correction coefficient, Ti is the actual outdoor air temperature, Td is the indoor set temperature, Ii is the real working solar radiation, hi is the real-time enthalpy, and hd is the indoor setting enthalpy.

In another aspect of the present invention, there is provided a method for predicting a cooling / heating load of a building at a time of not cooling / heating, the method comprising: collecting data from each of the air conditioning equipment being operated, Converting the collected data into an actual load using a conversion formula; Obtaining past weather information at the time of operating the air conditioning equipment; Calculating correction coefficients of a first regression model equation by performing regression analysis based on the actual load and the past weather information; Obtaining future weather information; Computing a predicted load by substituting the future weather information into the first regression model equation; Calculating correction coefficients of a second regression model equation by performing regression analysis based on the predicted load and the weather information at the time of the predicted load; Obtaining future weather information after the prediction load; Computing future predicted loads after the predicted load by substituting future weather information after the predicted load into the second regression model equation; And the second regression model expression is a mathematical expression that does not consider the indoor set temperature Td and the indoor set enthalpy hi.

Calculating an actual predicted load considering the indoor set temperature (Td) and the indoor set enthalpy (hi) to the raw predicted load using the correction coefficient of the second regression model equation; .

The first regression model equation is as follows,

(I) * (Ti-Td) + Csol (i) * Ii + Cair (i) * (hi-hd) + Cint

Here, Q (i) is an actual load value at a specific time, Cht (i) is a heat transfer load correction coefficient, Csol (i) is a solar heat load correction coefficient, Cair i is an indoor load correction coefficient, Ti is a real working outdoor temperature, Td is an indoor set temperature, Ii is a real working solar radiation, hi is a real-time enthalpy, hd is an indoor setting enthalpy,

The second regression model equation is as follows,

C i (i) * Ii + Cair (i) * hi + Cint (i)

(I) is a raw predicted load value at a specific time, Cht (i) is a heat transfer load correction coefficient, Csol (i) is a solar radiation load correction coefficient, Cair (i) is the internal load correction coefficient, Ti is the actual ambient temperature, Ii is the actual solar radiation, and hi is the real-time enthalpy.

According to the present invention, it is possible to provide a cooling / heating load predicting method of a building so as to more accurately predict the cooling / heating load of the next day based on the actual load of the equipment.

1 is a flowchart illustrating a method of predicting a building load over time according to the present invention.
2 is a diagram showing the contents of fields corresponding to MMI sensing information and MMI measurement information.
3 is a diagram showing a load conversion formula for each demand equipment.
4 is a cooling COP curve graph according to the outdoor temperature of the electric heat pump.
5 is a heating COP curve graph according to the outdoor temperature of the electric heat pump.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the drawings, the same reference numerals are used to refer to the same elements throughout the drawings, and in which:

2 is a diagram showing the contents of fields corresponding to MMI sensing information and MMI measurement information.

3 is a diagram showing a load conversion formula for each demand equipment.

4 is a cooling COP curve graph according to the outdoor temperature of the electric heat pump.

5 is a heating COP curve graph according to the outdoor temperature of the electric heat pump. It should be noted that the same reference numerals are used to denote the same elements although they are shown in different drawings.

1 is a flowchart illustrating a method of predicting a building load over time according to the present invention. 2 is a diagram showing the contents of fields corresponding to MMI sensing information and MMI measurement information. 3 is a diagram showing a load conversion formula for each demand equipment. 4 is a cooling COP curve graph according to the outdoor temperature of the electric heat pump. 5 is a heating COP curve graph according to the outdoor temperature of the electric heat pump.

The present invention relates to a method of predicting a heating / cooling load of a building that can measure a load of an air conditioner or the like and estimate a time-dependent load of the building more accurately based on the load.

As shown in FIG. 1, the present invention roughly includes a step of obtaining an actual load of each air conditioner and a step of predicting a heating / cooling load of the building from the actual load.

The step of obtaining the actual load according to the air conditioner equipments includes the steps of collecting data from each air conditioner being operated, converting the collected data to the actual load, obtaining the weather information at the time of operating the air conditioner And storing the data in a database.

The step of predicting the heating / cooling load of the building from the actual load includes the steps of retrieving and storing the stored past data, performing the regression analysis based on the summarized data to obtain the correction coefficients of the regression model equation, And estimating the heating / cooling load of the building by time by substituting future weather information into the obtained regression model equation.

Hereinafter, each step in the method of predicting the heating / cooling load of a building of the present invention will be described in detail.

(1) Data collection step

This step is collecting data from each air conditioning equipment.

As shown in FIG. 2, each air conditioning equipment field provides MMI (Man Machine Interface) sensing information. The main fields of the MMI sensing information include sensing time, MMI equipment ID, room temperature, operation state, And the sensing time is set to year, month, day, hour, minute, and second, and the interval between sensing times can be arbitrarily set to 1 minute, 5 minutes, 10 minutes,

The MMI sensing information should be converted into MMI measurement information for use in the program. The main fields of the MMI measurement information are the measurement date, the measurement time, the equipment classification code, the equipment number, the device status, The device classification code and device number are obtained by converting the MMI device ID in the MMI mapping information table. The device status and measurement information are obtained from the device status and sensing information of the MMI sensing information, respectively, In particular, the measurement information is a value obtained by averaging the sensing information of the MMI sensing information for one hour (for example, from 9:00 to before 10:00).

In this manner, data for obtaining the actual load is collected from the MMI sensing information provided at each air conditioner site.

(2) Actual load conversion step

This step converts the collected data into the actual load using the load conversion formula.

As shown in Fig. 3, a load conversion equation is provided for various air conditioning equipments, and the actual load can be converted using an appropriate method among the load conversion equations provided according to the circumstances.

In order to obtain the actual load, η (outdoor COP value according to the outdoor temperature) should be known. To obtain η, the outdoor temperature and the COP value are measured in the graph presented by the manufacturer. I ask.

In the case of the electric heat pump indoor unit, functions are obtained by using FIG. 4 and FIG. 5, and as a result, Equation 1 is obtained for cooling and Equation 2 is obtained for heating.

[Formula 1]

η = 6.35556 - 0.0785 * Outside temperature

[Formula 2]

η = 3.98 + 0.02 * Outside temperature (when outside temperature ≥7 ℃)

η = 0.0005 * Outside temperature ^ 3 + 0.0762 * Outside temperature ^ 2 + 3.1813 (when outside temperature <7 ℃)

(3) Obtaining the past weather information

At the time of operation of the air-conditioning equipment, weather information of the time of cooling or heating is obtained by time. This weather information includes the actual ambient temperature, the actual solar radiation, and the actual enthalpy. The actual ambient temperature is provided by the Meteorological Agency and the actual enthalpy is calculated from the temperature and humidity provided by the Meteorological Agency.

The actual solar radiation is obtained by making a relational expression from hourly cloudiness, hourly relative humidity, daytime difference, etc. provided by the Korea Meteorological Administration and substituting necessary data. There are various methods for obtaining the actual solar radiation amount, for example, reference is made to the disclosure of Japanese Patent Application No. 10-1506215.

(4) Data storage step

The actual load per hour, the actual ambient temperature, the actual solar radiation, the actual enthalpy, and the like in the database.

Next, a method for predicting the heating / cooling load of the building based on the actual load at the time of cooling and heating operation will be further described.

Equation (1) indicates that the daily measurement load is the sum of the daily heat load, the daily solar load, the daily ventilation load, and the daily internal load.

Where Q is the actual load measured from the air conditioner at the time of cooling and heating operation, Qht is the heat load, Qsol is the solar load, Qair is the ventilation load, and Qint is the internal load. The heat load is a load caused by a difference between an outside temperature and an indoor set temperature, a load due to a solar irradiation, a ventilating load is a load due to ventilation, and an internal load is a load caused by heat to be.

Equation (2) indicates that the actual load per day is the sum of the actual load per time.

Here, Q is the daily actual load of Equation (1) and Q (i) is the actual load of the specific time (i). The daily actual load (Q) is equal to the sum of actual load (Q (i)) from time 0 to time 23.

Equation 3 represents the daily heat transfer load.

Here, Qht is the daily heat transfer load, Cht (i) is the heat transfer load correction coefficient, Ti is the hourly outside temperature, and Td is the room temperature.

The heat transfer load (Qht (i)) at a specific time (i) can be expressed as Qht (i) = Cht (i) * (Ti - Td).

Equation (4) represents the daily solar radiation load.

Here, Qsol denotes a daily solar radiation load, Csol (i) denotes a solar radiation load correction coefficient, and Ii denotes a solar radiation load per hour.

The solar radiation load Qsol (i) at a specific time (i) can be represented by Qsol (i) = Csol (i) * Ii.

Equation 5 represents daily ventilatory load.

Where Qair is the daily ventilation load, Cair (i) is the ventilation load correction factor, hi is the enthalpy of air per hour, and hd is the enthalpy of air at the indoor set temperature and set humidity conditions.

The ventilation load (Qair (i)) at a specific time (i) can be expressed as Qair (i) = Cair (i) * (hi - hd).

Equation (6) represents the daily internal load.

Where Qint is the daily internal load and Cint (i) is the internal load correction factor. The internal load (Qint (i)) at a specific time (i) can be expressed as Qint (i) = Cint (i).

From the equations (3), (4), (5) and (6), the actual load of the specific time (i) can be expressed by Equation (7).

The actual load value of the specific time (i) for at least 10 days prior to today, the actual running temperature (Ti), the actual running solar radiation amount (Ii), and the actual running load of the weather station, The heat transfer load correction coefficient Cht (i) of the specific time (i) and the solar radiation load correction coefficient Csol (i) are calculated through regression analysis based on the actual enthalpy hi, the indoor set temperature Td and the indoor setting enthalpy hd i), the ventilation load correction coefficient Cair (i) and the internal load correction coefficient Cint (i).

Equation 8 represents the predicted load of a specific future time.

Here, Qpred (i) is the predicted load of today's or tomorrow's specific time (i), Ti (pred) is the predicted ambient temperature of the specific time (i) , and hi (pred) represents the predicted enthalpy of the specific time (i).

Equation (8) is a predictive regression model expression reflecting the correction coefficients of the specific time (i) obtained through the regression analysis based on the actual load and the running weather information based on Equation (7). The correction coefficient values of the regression model equation of the specific time (i) are different for each time.

The predicted load (Qpred (i)) of the specific time (i) of today or tomorrow to be predicted can be calculated from the predicted outdoor temperature Ti (ped), the predicted solar radiation amount Ii (pred) It is obtained by substituting the enthalpy (hi (pred)) value. Predicted outdoor temperature, predicted solar irradiance, and predicted enthalpy can be obtained from the meteorological information provided by the meteorological office or obtained from calculations based on the meteorological information.

In the case of predicting by the cooling load or predicting by the heating load, since the load is predicted based on the cooling actual load and the heating actual load, the cooling predictor and the predicted heating load are Qpred (i).

The predicted load calculated for each specific time is summed from day 0 to 23 hour prediction as shown in equation (2) to calculate the daily prediction load.

Next, the method of predicting the heating / cooling load of the building will be described when the cooling / heating operation is not performed.

Because there is no actual load for each air conditioner at the time of no cooling and heating operation, the actual actual predicted load after the predicted load is calculated based on the predicted load obtained based on the actual load obtained at the heating and cooling operation.

To accomplish this, it is necessary to collect data from each of the air-conditioning units operated to obtain the actual load of each air-conditioning equipment, convert the collected data into actual loads using the conversion formula, Obtaining information of the present invention, obtaining a correction coefficient of a first regression model equation by performing a regression analysis based on an actual load and past weather information, obtaining a future weather information, obtaining a weather information of the future To obtain a predicted load is directly included in the present method.

Equation (9) shows that the daily predicted load is the sum of the daily heat load, the daily solar load, the daily ventilation load, and the daily internal load.

Where W is the predicted load, Wt is the heat load, Wsol is the solar load, Wair is the ventilation load, and Wint is the internal load.

Equation (10) indicates that the daily predicted load is made up of the sum of the predicted load per time.

Here, W is a predicted load of Equation (9), and W (i) is a predicted load of a specific time (i). The daily predicted load (W) is equal to the sum of the predicted load (W (i)) from time 0 to time 23.

Equation (11) represents the daily heat transfer load.

Here, Wht represents the daily heat transfer load, Cht (i) represents the heat transfer load correction coefficient, Ti represents the outside air temperature over time, and Td represents the indoor temperature.

The heat transfer load (Wht (i)) at a specific time (i) can be expressed as Wht (i) = Cht (i) * (Ti - Td).

Equation (12) represents the daily solar radiation load.

Here, Wsol denotes a daily solar radiation load, Csol (i) denotes a solar radiation load correction coefficient, and Ii denotes a solar radiation load per hour.

The solar radiation load Wsol (i) at a specific time (i) can be represented by Wsol (i) = Csol (i) * Ii.

Equation (13) represents daily ventilation load.

Here, Wair represents the daily ventilation load, Cair (i) represents the ventilation load correction factor, hi represents the enthalpy of air per hour, and hd represents the enthalpy of air at the indoor set temperature and set humidity conditions.

The ventilation load (Wair (i)) at a specific time (i) can be expressed as Wair (i) = Cair (i) * (hi - hd).

Equation (14) represents the daily internal load.

Where Wint is the daily internal load and Cint (i) is the internal load correction factor. The internal load (Wint (i)) at a specific time (i) can be expressed as Wint (i) = Cint (i).

From the equations (11), (12), (13) and (14), the predicted load of the specific time (i)

Equation 16 (regression model equation or second regression model equation) represents the original predicted load of a specific time (i).

W0 (i) is the original predicted load of the specific time (i), and equation (16) is a mathematical expression that does not consider the indoor set temperature Td and the indoor set enthalpy hi in the equation (15).

Equations (17) are derived from equations (15) and (16).

When predicting a load at a time of a change in which the heating or cooling is not performed, the load is predicted by heating during the transition period, and the load is predicted by cooling (for example, during April) (For example, in October) at which the load should be predicted. Considering this case, the value of the past original predicted load (W0 (i)) is needed to obtain the predicted load of today or tomorrow.

The predicted load value of the specific time (i) for at least 10 days prior to today, the real working outdoor temperature (Ti) and the actual working solar radiation amount (Ii), which are meteorological condition information of the weather station at the time of the predicted load, The heat transfer load correction coefficient Cht (i) of the specific time (i), the solar heat load correction coefficient Csol (i) through the regression analysis based on the running enthalpy hi, the indoor set temperature Td and the indoor setting enthalpy hd (i)), the ventilation load correction coefficient (Cair (i)) and the internal load correction coefficient (Cint (i)).

Equation (18) is a regression model expression that reflects the correction coefficients of the specific time (i) obtained through the periodic analysis based on past primitive predicted load and running weather information based on (16).

The original predicted load (WOpred (i)) of a specific time (i) of today or tomorrow to be predicted is calculated from the predicted outdoor temperature Ti (ped), the predicted solar radiation amount Ii (pred) Is obtained by substituting the predicted enthalpy (hi (pred)). Predicted outdoor temperature, predicted solar irradiance, and predicted enthalpy can be obtained from or calculated from future weather information after the predicted load provided by the meteorological office. The correction coefficient values of the regression model equation of the specific time (i) are different for each time.

Equation (19) can be derived from Equations (17) and (18).

Here, Wpred (i) is an actual predicted load of a specific time (i) of today or tomorrow.

In the case of predicting by the cooling load, the actual predicted load is Wpred (i), and the actual predicted load is -Wpred (i) when it is predicted by the heating load.

The actual predicted load calculated for each specific time is summed from day 0 to time 23 as shown in equation (10).

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit and scope of the invention will be.

Claims (6)

In a method for predicting heating / cooling load of a building,
Collecting data on each air-conditioning equipment from MMI (Man Machine Interface) sensing information provided at a site where the air-conditioning equipment is installed, in order to obtain actual load of each air-conditioning equipment;
Converting the collected data into an actual load using a conversion formula;
Obtaining past weather information at the time of operating the air conditioning equipment;
Calculating regression model equation correction coefficients by performing regression analysis based on the actual load and the past weather information;
Obtaining future weather information;
Calculating a prediction load by substituting the future weather information into the regression model equation;
And estimating the heating / cooling load of the building. The method according to claim 1,
Wherein the regression model expression is changed for each specific time, and the correction coefficient is also varied for each specific time. 3. The method of claim 2,
The regression model equation is as follows.
(I) * (Ti-Td) + Csol (i) * Ii + Cair (i) * (hi-hd) + Cint
Here, Q (i) is an actual load value at a specific time, Cht (i) is a heat transfer load correction coefficient, Csol (i) is a solar heat load correction coefficient, Cair i is the internal load correction coefficient, Ti is the actual outdoor air temperature, Td is the indoor set temperature, Ii is the real working solar radiation, hi is the real-time enthalpy, and hd is the indoor setting enthalpy. In a method for predicting a heating / cooling load of a building at a time when the heating /
Collecting data on each air-conditioning equipment from MMI (Man Machine Interface) sensing information provided at the site where the air-conditioning equipment is installed, in order to obtain the actual load of each air-conditioning equipment at the time of heating and cooling;
Converting the collected data into an actual load using a conversion formula;
Obtaining past weather information at the time of operating the air conditioning equipment;
Calculating correction coefficients of a first regression model equation by performing regression analysis based on the actual load and the past weather information;
Obtaining future weather information;
Computing a predicted load by substituting the future weather information into the first regression model equation;
Calculating correction coefficients of a second regression model equation by performing regression analysis based on the predicted load and the weather information at the time of the predicted load;
Obtaining future weather information after the prediction load;
Computing future predicted loads after the predicted load by substituting future weather information after the predicted load into the second regression model equation;
Lt; / RTI &gt;
Wherein the second regression model expression is a mathematical expression that does not consider the indoor set temperature (Td) and the indoor set enthalpy (hi). 5. The method of claim 4,
Calculating an actual predicted load considering the indoor set temperature (Td) and the indoor set enthalpy (hi) to the raw predicted load using the correction coefficient of the second regression model equation;
And estimating the heating / cooling load of the building at a time when the heating / 6. The method of claim 5,
The first regression model equation is as follows,
(I) * (Ti-Td) + Csol (i) * Ii + Cair (i) * (hi-hd) + Cint
Here, Q (i) is an actual load value at a specific time, Cht (i) is a heat transfer load correction coefficient, Csol (i) is a solar heat load correction coefficient, Cair i is an indoor load correction coefficient, Ti is a real working outdoor temperature, Td is an indoor set temperature, Ii is a real working solar radiation, hi is a real-time enthalpy, hd is an indoor setting enthalpy,
The second regression model equation is as follows,
C i (i) * Ii + Cair (i) * hi + Cint (i)
(I) is a raw predicted load value at a specific time, Cht (i) is a heat transfer load correction coefficient, Csol (i) is a solar radiation load correction coefficient, Cair (i) is an internal load correction coefficient, Ti is a real working outdoor temperature, Ii is a real working solar radiation amount, and hi is a real effect enthalpy.

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