KR101966532B1 - System and method for a building energy optimization based on dynamic user setting and prediction of indoor environment parameter - Google Patents

Abstract

The present invention relates to a system and method for optimizing indoor environment parameters and a building energy optimization system based on dynamic user settings. The present invention relates to a sensor for sensing temperature, illuminance and air quality in a building and outputting indoor environment parameters. The indoor environment parameter predictor which predicts the indoor environment parameters and outputs the predicted indoor environment parameters. The minimum / maximum values for the temperature, the illuminance, and the air quality of each of the plurality of users by receiving the minimum / maximum setting information of the indoor environment parameters for each user. A user-specific environment parameter setting unit that calculates a dynamic user set point of an indoor environment parameter by unifying, and receives the predicted indoor environment parameter and a dynamic user set point of the indoor environment parameter, and optimizes the indoor environment parameter by a rule-based algorithm. Optimum indoor environment wave A rule-based optimizer for outputting a parameter, an integrated comfort index calculator for calculating an integrated comfort index based on the optimized indoor environment parameter and a predetermined comfort index calculation formula, and a difference between the predicted indoor environment parameter and the optimal indoor environment parameter It characterized in that it comprises a purge controller for calculating the required amount of power for controlling the temperature, illuminance, air quality in the building based on. By this arrangement, it is possible to reduce energy consumption while providing a comfortable indoor environment for the user by reducing the power required by the optimized environmental parameters.

Description

SYSTEM AND METHOD FOR A BUILDING ENERGY OPTIMIZATION BASED ON DYNAMIC USER SETTING AND PREDICTION OF INDOOR ENVIRONMENT PARAMETER}

The present invention relates to a building energy optimization system and method, and more particularly, to a building energy optimization system and method that can optimize the indoor environment parameters, including temperature, illuminance, air quality, efficiency of building energy consumption.

Recent rapid advances in IT and other high-tech technologies are accelerating the trend of automation and intelligence of energy facilities in buildings, such as securing a pleasant environment, securing business functions and functions necessary for business, and in this regard, buildings that utilize IT for building management. Various systems such as BAS (Building Automation System), Intelligent Building System (IBS) and Building Energy Management System (BEMS) are being introduced.

As one of these systems, the Building Energy Management System (BEMS) minimizes energy consumption through detailed energy usage monitoring and intelligent control of all energy devices such as office equipment and information equipment as well as energy facilities in buildings. It is a technology that guarantees a pleasant and economical environment based on the condition detection and environmental information in buildings.

However, most building energy management systems currently use only raw data such as temperature, humidity, light intensity, dust concentration, etc. to control indoor environment uniformly. There is a limit.

Therefore, there is a demand for a technology capable of minimizing energy consumption in a building while providing a comfortable indoor environment according to user's haptic information in a building or a smart home.

In addition, existing building energy management systems can increase energy consumption by taking into account the current temperature, humidity, light intensity, air quality, etc., without considering changes in the indoor environment (eg, temperature increase / decrease).

On the other hand, in the building energy management system, one user is considered to be indoors, and the heating and cooling device is controlled in consideration of a single indoor environmental parameter. However, in a smart home, it is important to provide a comfortable indoor environment for all family members. Therefore, it is required for everyone in the smart home to set a comfortable user set point.

Accordingly, an object of the present invention is to develop an indoor environment setting method of a device such as air conditioning and the like by predicting indoor environment parameters and rule-based optimization, thereby improving a user's comfort index and consuming less power. To provide.

In addition, an object of the present invention is to develop an environment setting method that synthesizes a plurality of indoor environment needs of a plurality of dynamic users with respect to the indoor environment parameters to calculate the optimal indoor environment parameters to improve the overall satisfaction of the plurality of users with respect to the building environment. It is to provide a building energy optimization system and method that can be increased.

여기에서,

,

,

는 온도, 조도 및 공기질 파라미터간의 충돌을 회피하기 위해 정의된 인수로서,

의 관계식에 의해 0과 1 사이의 값을 갖고,

는 센싱된 실제 온도와 상기 최적화된 온도 파라미터와의 차이값,

은 센싱된 실제 조도와 상기 최적화된 조도 파라미터와의 차이값,

는 센싱된 실제 공기질과 상기 최적화된 공기질 파라미터와의 차이값을 나타내며,

,

,

, 은 사용자에 의해 설정된 온도, 조도, 공기질 파라미터를 나타내고, 수학식 1에 적용되는 사용자 설정 온도, 조도, 공기질 파라미터는, 복수의 사용자의 설정값들을 평균 기반, MAX-MIN 기반, MIN-MAX 기반 중 하나로 단일화 한 값인 것을 특징으로 한다.According to a first aspect of the present invention for achieving the above object, in the building energy optimization system based on the prediction of indoor environment parameters and dynamic user setting, the indoor environment parameters are sensed by sensing temperature, illuminance and air quality in the building, respectively. A sensing unit including a plurality of sensors for outputting a plurality of sensors, an indoor environment parameter predictor for predicting the indoor environment parameters by using a prediction algorithm, and outputting predicted indoor environment parameters, and inputting minimum / maximum setting information of indoor environment parameters Input unit for inputting the minimum / maximum setting information of a plurality of user's indoor environment parameters from the input unit to calculate the dynamic user setting range of the indoor environment parameter by unifying the minimum / maximum values for each of the plurality of users' temperature, illuminance and air quality User-specific environment parameter setting unit, the prediction room Inputs the environment parameters and dynamic user set points of the indoor environment parameters and optimizes the indoor environment parameters by a rule-based algorithm to output optimized indoor environment parameters including optimized temperature parameters, optimized illuminance parameters, and optimized air quality parameters. A rule-based optimizer configured to calculate an integrated comfort index based on the optimized indoor environment parameter and a predetermined comfort index calculation formula, and a building based on a difference between the predicted indoor environment parameter and the optimal indoor environment parameter. A fuzzy controller for calculating the required amount of power for controlling the temperature, illuminance, and air quality in the interior; Power calculating A control agent, an actual power consumption calculator configured to calculate actual power consumption based on the calculated required power amount and the adjusted power amount, and a plurality of actuators for controlling the operation of the energy facilities in the building according to the calculated actual power consumption; The integrated comfort index calculator calculates an integrated comfort index based on the optimized indoor environment parameter and a preset comfort index (Equation 1).
(Equation 1)

From here,

,

,

Is a defined factor to avoid collisions between temperature, light intensity and air quality parameters.

Has a value between 0 and 1,

Is the difference between the sensed actual temperature and the optimized temperature parameter,

Is a difference between the sensed actual illuminance and the optimized illuminance parameter,

Represents the difference between the sensed actual air quality and the optimized air quality parameters,

,

,

, Represents temperature, illuminance and air quality parameters set by the user, and the user set temperature, illuminance and air quality parameters applied to Equation 1 are based on a plurality of user set values based on average, MAX-MIN, and MIN-MAX. It is characterized in that the value is unified with one.

Here, the prediction algorithm preferably includes a Kalman filter.

The rule-based algorithm is preferably such that the predicted indoor environment parameter and the dynamic user setting range have a minimum difference, whereby the required power for controlling temperature, illuminance and air quality can be significantly reduced.

Preferably, the user-specific environment parameter setting unit calculates a dynamic user setting range of the indoor environment parameter by one of average-based setting, maximum-minimum based setting, and minimum-maximum based setting.

The power control agent preferably controls the actuator of the energy equipment in the building in advance according to the temperature, illuminance and air quality values of the predicted indoor environment parameter output from the indoor environment parameter predictor.

여기에서,

,

,

는 온도, 조도 및 공기질 파라미터간의 충돌을 회피하기 위해 정의된 인수로서,

의 관계식에 의해 0과 1 사이의 값을 갖고,

는 센싱된 실제 온도와 상기 최적화된 온도 파라미터와의 차이값,

은 센싱된 실제 조도와 상기 최적화된 조도 파라미터와의 차이값,

는 센싱된 실제 공기질과 상기 최적화된 공기질 파라미터와의 차이값을 나타내며,

,

,

, 은 사용자에 의해 설정된 온도, 조도, 공기질 파라미터를 나타내고, 수학식 1에 적용되는 사용자 설정 온도, 조도, 공기질 파라미터는, 복수의 사용자의 설정값들을 평균 기반, MAX-MIN 기반, MIN-MAX 기반 중 하나로 단일화 한 값인 것을 특징으로 한다.According to a second aspect of the present invention for achieving the above object, in a method of optimizing indoor environment parameters and optimizing building energy based on dynamic user setting, sensing an indoor environment parameter including temperature, illuminance and air quality in a building Making; Predicting the indoor environment parameters with a prediction algorithm to generate predicted indoor environment parameters; Calculating the dynamic user setting range of the indoor environment parameters by receiving the minimum / maximum setting information of the indoor environment parameters for each user by unifying the minimum / maximum values for each of temperature, illuminance and air quality of the plurality of users; Optimized indoor environment including optimized temperature parameter, optimized illuminance parameter, and optimized air quality parameter by receiving the predicted indoor environment parameter and dynamic user setting range of the indoor environment parameter by optimizing the indoor environment parameter by a rule-based algorithm. Generating an environment parameter; Calculating an integrated comfort index based on the optimized indoor environment parameter and a predetermined comfort index calculation formula; Calculating an amount of power required to control temperature, illuminance and air quality in the building based on the difference between the predicted indoor environment parameter and the optimal indoor environment parameter; Calculating an adjusted amount of power for controlling temperature, illuminance and air quality based on the optimum indoor environment parameter, the calculated integrated comfort index and available power in the building; Calculating actual power consumption based on the calculated required power amount and the adjusted power amount; And supplying the calculated actual power consumption to a plurality of actuators for controlling the operation of the energy facilities in the building, wherein the integrated comfort index calculator comprises an optimized indoor environment parameter and a predetermined comfort index calculation formula (mathematical formula) Calculate the integrated comfort index based on
(Equation 1)

From here,

,

,

Is a defined factor to avoid collisions between temperature, light intensity and air quality parameters.

Has a value between 0 and 1,

Is the difference between the sensed actual temperature and the optimized temperature parameter,

Is a difference between the sensed actual illuminance and the optimized illuminance parameter,

Represents the difference between the sensed actual air quality and the optimized air quality parameters,

,

,

, Represents temperature, illuminance and air quality parameters set by the user, and the user set temperature, illuminance and air quality parameters applied to Equation 1 are based on a plurality of user set values based on average, MAX-MIN, and MIN-MAX. It is characterized in that the value is unified with one.

Here, the prediction algorithm preferably includes a Kalman filter.

The rule-based algorithm is preferably such that the predicted indoor environment parameter and the dynamic user setting range have a minimum difference, whereby the required power for controlling temperature, illuminance and air quality can be significantly reduced.

The calculating of the dynamic user setting range may include unifying the maximum / minimum of each indoor environment parameter of temperature, illuminance and air quality by one of an average-based setting, a maximum-minimum base setting, and a minimum-maximum base setting. It is desirable to calculate the dynamic user setting range of the parameter.

In addition, before the step of calculating the dynamic user set point of the indoor environmental parameters, it is preferable to include the step of controlling the actuator of the energy equipment in the building in advance in accordance with the temperature, illuminance, air quality values of the predicted indoor environment parameters. Do.

With this configuration, in order to reduce the actual power consumption through indoor environment parameter prediction, the environmental parameters of temperature, illuminance and air quality detected by the sensor are predicted and rule-based optimization to calculate the comfort index and the required power by the optimized environmental parameters. There is provided a building energy optimization system and method that can reduce energy consumption while at the same time providing a comfortable indoor environment for a user. In addition, it is possible to increase the overall satisfaction of the plurality of users in a short time by calculating the comfort index based on the dynamic user environment parameter setting input by the plurality of users in the building environment, and it is possible to consume less power than before.

1 is a diagram illustrating a concept of optimizing building energy according to the present invention;
2 is a block diagram of a building energy optimization system based on prediction and dynamic user setting of indoor environment parameters according to the present invention;
3 is a block diagram illustrating an energy optimization step in the building energy optimization system of FIG. 2;
4 is a conceptual explanatory diagram for calculating a dynamic user point based on an average, MAX-MIN, and MIN-MAX environment parameters set by a plurality of users according to the present invention;
5 is a flowchart of a rule-based optimization method of indoor environment parameters applied to the present invention;
6 is a graph comparing power consumption when temperature control is performed based on prediction of indoor environment parameters, non-prediction, average-based multi-user environment parameters, MAX-MIN-based, MIN-MAX-based, rule-based optimization,
7 is a graph comparing power consumption when illuminance control is performed based on the present invention when predicting indoor environment parameters, when not predicting, based on average of multiple user environment parameters, based on MAX-MIN, based on MIN-MAX, and rules based optimization,
8 is a graph comparing power consumption when air quality control is performed based on the present invention when predicting indoor environment parameters, unpredicting, average-based multi-user environment parameters, MAX-MIN-based, MIN-MAX-based, rule-based optimization,
9 is a graph comparing comfort indexes when optimizing multi-user indoor environment parameters according to the present invention;
10 to 12 is a view for explaining a method for calculating the amount of power required to control the temperature, illuminance, air quality in the building in the present invention,
13 is a flowchart of a method for optimizing indoor environment parameters and optimizing building energy based on dynamic user setting according to the present invention.

Advantages and features of the present invention, and methods of achieving the same will be described through embodiments described below in detail with the accompanying drawings. However, the present invention is not limited to the embodiments described herein and may be embodied in other forms. However, the embodiments are provided to explain in detail enough to easily implement the technical idea of the present invention to those skilled in the art.

In the drawings, embodiments of the invention are not limited to the specific forms shown, and are exaggerated for clarity. In addition, parts denoted by the same reference numerals throughout the specification represent the same components. The expression "and / or" is used herein to mean including at least one of the components listed before and after. In addition, the singular also includes the plural unless specifically stated otherwise in the text. Also, as used herein, components, steps, operations, and elements referred to as "comprising" or "comprising" refer to the presence or addition of one or more other components, steps, operations, elements, and devices.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the following description of the present invention, if it is determined that the detailed description of the related known function or configuration may unnecessarily obscure the subject matter of the present invention, the detailed description thereof will be omitted.

The present invention predicts indoor environment information, which is a value of sensing temperature, illuminance, and air quality in a building, using a Kalman filter, and uses a predicted indoor environment information and a plurality of user-specific indoor environment parameter set points. Improve energy efficiency by optimizing environmental parameters.

In addition, the present invention obtains future indoor environment information (temperature, humidity, illuminance, air quality) by using the prediction technology to control the heating and cooling devices (heaters, fans, air conditioners, etc.) in advance to increase energy efficiency. At this time, the indoor environment control parameters are optimized using a rule-based optimization algorithm to increase energy efficiency.

In addition, in the present invention, the building energy optimization that can be adjusted to the optimal indoor environment to satisfy a variety of user needs by using an indoor demand environment parameter setting algorithm that can satisfy the indoor environment information of a plurality of users in the building Configure the system and method.

Prior to describing the present invention, the basic concepts of the present invention will be briefly described as follows.

1 is a view for explaining the basic concept of the present invention.

First, referring to FIG. 1A, a comfortable index is calculated using raw sensing data of temperature, illuminance, and air quality, and the indoor environment is comfortable for a user when the indoor environment is controlled based on the calculated comfort index. In order to provide high energy consumption, HVAC (Heating / Ventilation / Air-Conditioning) systems such as air-conditioning units and fans must be operated. In other words, the comfort index and energy consumption felt by the user are in a trade off relation.

To this end, the present invention calculates the integrated comfort index by optimizing the environmental parameters of temperature, illuminance and air quality to minimize energy consumption, as shown in FIG. 1 (b), and based on the calculated integrated comfort index and predicted power. By intelligently controlling the power of the energy equipment in the building, it is possible to minimize the energy consumption while providing a comfortable indoor environment to the user.

2 is a block diagram of a building energy optimization system based on prediction and dynamic user setting of indoor environment parameters according to the present invention.

Referring to FIG. 2, the building energy optimization system 1 according to an embodiment of the present invention includes a sensing unit 10 including a temperature sensor, an illuminance sensor, and an air quality sensor, and a plurality of users of the temperature and illuminance and the sensor. A user input unit 11 for inputting preferred indoor environment parameter values for each user, an indoor environment parameter prediction unit 12 for outputting an indoor environment parameter prediction value using a predetermined prediction algorithm, and an average based on indoor environment parameters set by a plurality of users Dynamic parameter output unit 13 for calculating a dynamic user set point by unifying a plurality of minimum / maximum ranges based on MIN-MAX and MIN-MAX, and dynamic parameter setter 13 Rule-based optimization of the predicted indoor environment parameters output from the user set point and the indoor environment parameter predictor 12 by a rule-based algorithm. The redundancy unit 14, the integrated comfort index calculator 14 that calculates the integrated comfort index based on the optimized indoor environment parameter and the predetermined comfort index calculation formula, and the building based on the difference between the predicted indoor environment parameter and the optimal indoor environment parameter. Fuzzy controller 15 for calculating the required amount of power for controlling the temperature, illuminance and air quality in the interior, and an adjusted amount of power for controlling temperature, illuminance and air quality based on the optimal indoor environment parameters, the calculated integrated comfort index and the available power in the building. The power control agent 17 for calculating the power consumption, the power consumption calculation unit 18 for calculating the actual power consumption based on the calculated required power amount and the adjusted power amount, controlling the operation of the energy equipment in the building according to the calculated actual power consumption It is comprised including the several actuator 30. As shown in FIG.

The sensing unit 10 includes a temperature sensor, an illuminance sensor, and an air quality sensor, and senses and outputs environmental parameters such as temperature, illuminance, and air quality in a building.

The indoor environment parameter predictor 12 calculates a predicted indoor environment parameter value based on a previous indoor environment parameter value using a Kalman filter.

Here, the Kalman filter is an algorithm used to estimate a new result by removing noise included in data by using old and new measurement data, and using a recursive operation using past and present data ( Recursive data processing is used to track the optimal value of a linear system. Since the method of predicting the indoor environment parameter by the Kalman filter is well-known technology, detailed description thereof will be omitted.

The indoor environment parameter predicting unit 12 provides the predicted indoor environment parameter value to the power control agent 17, so that the power control agent 17 matches the temperature, illuminance, and air quality values of the predicted indoor environment parameter in advance with the energy in the building. It is possible to control devices (actuators) such as air conditioning and heating of facilities.

The user-specific environment parameter setting unit 13 receives a minimum / maximum range of indoor environment parameters preferred for temperature, illuminance and air quality input from a plurality of users through the user input unit 11. The indoor environment parameters set by the plurality of users have minimum and maximum values for each of temperature, illuminance and air quality.

The method of setting a single indoor environment parameter using a plurality of user setting values for each indoor environment parameter of temperature, illuminance, and air quality is based on a method of setting a user set point based on average, Max-Min. Based user set point setting method, and user set point setting method based on Min-Max.

In the following, using the minimum / maximum values set by a plurality of users for indoor environment parameters, the minimum / maximum range unified based on average, MAX-MIN, and MIN-MAX (hereinafter referred to as 'multiple dynamic user set points') Will be described.

4 is a conceptual explanatory diagram for calculating a dynamic user set point based on an average, MAX-MIN, and MIN-MAX environment parameters set by a plurality of users according to the present invention.

For example, if there are three users in a building, there are minimum and maximum values set for temperature (T), roughness (L), and air quality (A) for each user.

According to the average (AVG) base, as shown in Table 1, the minimum values set by the three users for the temperature is calculated as an average value (Tmin = AVG [60, 63, 66]), and the maximum values are calculated as an average value (Tmax). = AVG [80, 78, 69]). The average value is calculated in the same manner for the illuminance and the air quality.

According to the MAX-MIN base, as shown in Table 2, the maximum value among the minimum values set by the three users for the temperature is taken as the lower set value (Tmin = Max [60, 63, 66]), Among the maximum values set for the temperature, the minimum value is taken as the upper set value (Tmax = Min [80, 78, 69]). Thus, the dynamic user set point (min / max) of the MAX-MIN based temperature is 66/69. On the other hand, the lower set value and the higher set value are taken in the same manner for the illuminance and the air quality.

Based on MIN-MAX, as shown in Table 3, the minimum value among the minimum values set by the three users for the temperature is taken as the lower set value (Tmin = Min [60, 63, 66]), Of the maximum values set for temperature, take the maximum value as the upper set point (Tmax = MAX [80, 78, 69]). Thus, the dynamic user set point (minimum / maximum) of the minimum (MIN-MAX) based temperature is 60/80. On the other hand, the lower set value and the higher set value are taken in the same manner for the illuminance and the air quality.

As can be seen in the comparison table of Table 4 below, when optimizing the indoor environmental parameters based on Min-Max, it can be seen that the power consumption for controlling temperature, illuminance and air quality is the smallest.

Dynamic user settings Temperature Roughness Air quality Total power Average base
Power Consumption 74.60748 205.059 63.86507 343.5316 Max-Min based
Power Consumption 85.00414 210.0556 63.86507 358.9249 Min-Max base
Power Consumption 63.60321 200.0592 63.86507 327.5275

Next, the rule-based optimizer 14 of FIG. 2 optimizes the sensed environmental parameters using a rule-based algorithm. This will be described later with reference to FIG. 5.

According to the present invention, the rule based optimizer 14 optimizes using a rule based optimization algorithm such that the sensed temperature, illuminance and air quality parameters have a minimum difference from the user set parameter (dynamic user set point).

That is, the rule-based optimizer 14 optimizes temperature, illuminance and air quality parameters so as to minimize energy consumption while satisfying user requirements according to indoor environment parameters set by the user.

FIG. 5 is a diagram for describing a process of optimizing indoor environment parameters using a rule-based optimization algorithm according to the present invention. The rule-based optimization process described below is performed for each of temperature, illuminance and air quality, and will be described briefly.

As shown in FIG. 5, a user in a building inputs a maximum / minimum range (dynamic user set point) of temperature, illuminance and air quality for a comfortable state into the rule-based optimizer 14 (S1). The predicted indoor environment parameter value is input to the rule-based optimizer 14 (S2). It is determined whether the predicted parameters predicting the current temperature parameter, the illuminance parameter, and the air quality parameter are each in a comfortable area having a minimum / maximum range of the dynamic user set point (S3).

Here, the user comfort zone has a maximum value and a minimum value of the dynamic user set point set using indoor environment parameters input by a plurality of users.

As a result of the determination in step S3, if the current temperature parameter, illuminance parameter, and air quality parameter value are each in the comfort zone, the current temperature, illuminance, and air quality are respectively used as optimal setting values (S4).

As a result of the determination in step S3, if the current temperature parameter, illuminance parameter and air quality parameter value are not in the comfort zone, it is determined whether each parameter of the current temperature, illuminance and air quality is smaller than the minimum value of the dynamic user set point (S5). . If the determination result is smaller than the minimum value, the currently predicted temperature, illuminance, and air quality are changed to the optimal set value (S6), and the minimum value of the currently predicted parameter is treated as the optimal set value (S7).

On the other hand, if it is determined in step S5 that each parameter of the current temperature, illuminance and air quality is greater than the minimum value of the dynamic user set point, it is determined whether each parameter of the current temperature, illuminance and air quality is greater than the maximum value of the dynamic user set point. (S8). If the determination result of step S8 is greater than the maximum value of the dynamic user set point, the currently predicted temperature, illuminance and air quality are changed to the optimum set value (S9), and the maximum value of the dynamic user set point is treated as the optimum set value (S10). ).

On the other hand, when rule-based optimization of the above-described indoor environment parameters, power consumption required to control temperature, illuminance, and air quality, power consumption when optimizing indoor environment parameters based on the existing GA, and indoor environment based on GIA When optimizing the parameters, the power consumption comparison table is as follows.

Temperature Roughness Air quality Total power Rule-based
Power Consumption 494.907 1052.34 365.663 1903.91 GA based
Power Consumption 614.214 1135.18 372.416 2121.81 IGA based
Power Consumption 834.593 1261.57 419.65 2515.82

As can be seen from the comparison table of Table 5 above, when optimizing indoor environmental parameters on a rule basis, power consumption required to control temperature, illuminance and air quality is optimized based on conventional GA-based and GIA-based indoor environmental parameters. It can be seen that smaller than the case.

FIG. 6 is a graph comparing power consumption when temperature control is performed based on the present invention when predicting indoor environment parameters, unpredicting, average-based multi-user environment parameters, MAX-MIN-based, MIN-MAX-based, and rule-based optimization. According to the present invention, when the indoor environment parameter prediction, non-prediction, the average power of the multi-user environment parameters, MAX-MIN-based, MIN-MAX-based, rule-based optimization, power consumption comparison graph when controlling, Figure 8 Therefore, it is a graph comparing power consumption when controlling the air quality by predicting indoor environment parameters, unpredicting, multi-user environment average, MAX-MIN, MIN-MAX, and rule-based optimization.

According to the graphs of FIG. 6 to FIG. 8, it can be seen that power consumption is less when the indoor environment parameters are predicted and then optimized using the predicted indoor environment parameters when controlling temperature, illuminance and air quality. .

Referring to Tables 6 to 8 below, in the case of using the dynamic user setting environment parameter calculated based on Min-Max based on a plurality of indoor environment parameters, the total amount of power consumption required for controlling temperature, illuminance and air quality is the smallest. Able to know.

In addition, referring to Tables 6 to 8 below, when the indoor environment parameters are optimized using the predicted indoor environment parameters along with the dynamic user setting environment parameters calculated based on the Min-Max dynamic user setting indoor environment parameters, And it turns out that the total amount of power consumption required at the time of air quality control is the smallest compared with the case where an unpredicted indoor environment parameter is used.

ABS calculation  Temperature Roughness Air quality Total amount Estimated Power Consumption 73.09653 210.0001 63.8465 346.9401 Unpredicted Power Consumption 74.60748 210.0556 63.8472 348.5103

When calculating Max-Min Temperature Roughness Air quality Total amount Estimated Power Consumption 84.39 219 63.8262 368.21 Unpredicted Power Consumption 85 220 63.8271 368.88

When calculating Min-Max Temperature Roughness Air quality Total amount Estimated Power Consumption 61.51 200.008 63.864 325.38 Unpredicted Power Consumption 63.6 200.059 63.865 327.52

Next, the integrated comfort index calculator 15 of FIG. 2 calculates the integrated comfort index based on the optimized indoor environment parameter and the predetermined comfort index calculation formula. The method for calculating the integrated comfort index will be described later.

Here, the integrated comfort index (comfort) can be calculated by the following equation (1).

,

,

는 온도, 조도 및 공기질 파라미터간의 충돌을 회피하기 위해 정의된 인수로,

의 관계식에 의해 0과 1 사이의 값을 갖는다.

는 센싱된 실제 온도와 최적화된 온도 파라미터와의 차이값,

은 센싱된 실제 조도와 최적화된 조도 파라미터와의 차이값,

는 센싱된 실제 공기질과 최적화된 공기질 파라미터와의 차이값을 나타내며,

,

,

, 은 사용자에 의해 설정된 온도, 조도, 공기질 파라미터를 나타낸다.From here,

,

,

Is a defined parameter to avoid collisions between temperature, light intensity and air quality parameters.

It has a value between 0 and 1 by the relation of.

Is the difference between the actual sensed temperature and the optimized temperature parameter,

Is the difference between the sensed actual illuminance and the optimized illuminance parameter,

Represents the difference between the actual air quality sensed and the optimized air quality parameters.

,

,

, Represents the temperature, illuminance and air quality parameters set by the user.

Herein, the user setting temperature, illuminance, and air quality parameters applied to Equation 1 input values obtained by unifying a plurality of user set values based on average, MAX-MIN, and MIN-MAX.

9 is a comfort index comparison table output from the comfort index calculator 15 in the case of average-based, MAX-MIN-based, MIN-MAX-based, and rule-based optimization of multiple user environment parameters. The comfort index has a value between 0 and 1. In the case of unifying multiple user environment parameters based on MAX-MIN, the user comfort index starts at 0.967 and reaches 1 at 18:00. In the case of unifying multiple user environment parameters on an average basis, the user comfort index starts at 0.979 and reaches 1 at 14:00. When unifying multiple user environment parameters on a MIN-MAX basis, the user comfort index starts at 0.98 and reaches 1 at 13:00. In other words, it can be seen that when the multi-user environment parameter is unified based on average and MAX-MIN, it is started with higher comfort index and reaches the maximum comfort index in a faster time than when unified based on MIN-MAX. .

Next, the fuzzy controller 16 of FIG. 2 is a power amount (P _T , P _L , P _A ) required to control the temperature, illuminance, and air quality in the building based on the difference between the sensed environment parameter and the optimized environment parameter. This will be calculated and described in more detail with reference to FIGS. 10 to 12 as follows. The output of the purge controller 16 is required power for each of temperature, illuminance and air quality. The output of the purge controller 16 is then input to the power control agent 17 and the power consumption calculator 18.

10 to 12 are diagrams for explaining that the fuzzy controller 16 of the present invention calculates the amount of power required to control temperature, illuminance and air quality in a building.

)을 계산하고, 제1 차이값(

)의 현재값과 이전값의 차이인 제2 차이값(

)을 계산한다.First, a method of calculating the required amount of power P _T for controlling the temperature in a building will be described. As shown in FIGS. 10A and 10B, the actual temperature sensed and the optimized temperature parameter are compared. The first difference, which is the difference

), The first difference (

Is the difference between the current value and the previous value of

Calculate

,

)은 삼각 소속 함수의 분포를 갖는다.Here, the first and second difference value (

,

) Has a distribution of trigonometric membership functions.

,

)을 기초로 건물 내의 온도를 제어하기 위한 요구 전력(P_T)을 산출하면 도 11의 (c)에 도시된 바와 같이 삼각 소속 함수의 분포를 갖는 요구 전력(P_T)을 얻을 수 있으며, 이를 표로 나타내면 다음의 표 9와 같다.These first and second difference values (

,

By calculating the required power (P _T ) for controlling the temperature in the building based on), as shown in (c) of FIG. 11, the required power P _T having a distribution of trigonometric membership functions can be obtained. The table is shown in Table 9 below.

)와 오차(

: 온도에 대한 규칙 기반 최적화의 최적 파라미터와 실제 환경 파라미터인 센서 온도[0, 1] 사이의 오차)가 온도에 대한 퍼지 컨트롤러(16)의 입력이다. NB, NM, NS, ZE, PS, PM 및 PB라는 용어는 음수 대, 음수 중, 음수 소, O, 양수 소, 양수 중, 양수 대를 나타낸다. In Table 9, the change in error between the optimum parameter and the actual environmental parameter

) And error (

The difference between the optimal parameter of the rule-based optimization for temperature and the sensor temperature [0, 1], which is the actual environmental parameter, is the input of the fuzzy controller 16 to the temperature. The terms NB, NM, NS, ZE, PS, PM, and PB denote negative bands, negative numbers, negative numbers, O, positive numbers, positive numbers, and positive numbers.

)를 기초로 건물 내의 조도를 제어하기 위한 요구 전력(P_L)을 산출하면 삼각 소속 함수의 분포를 갖는 요구 전력(P_L)을 얻을 수 있으며, 이를 표로 나타내면 다음의 표 10과 같다.In the same manner as above, the difference between the sensed actual illuminance and the optimized illuminance parameter as shown in FIG.

), And if the obtained required power (P _L) having a distribution of a triangular membership function based to calculate the required power (P _L) for controlling the illumination in the building, it indicates tabulated as follows in Table 10.

The input of the fuzzy controller 16 to the illuminance is the error (input error) between the optimal parameter and the actual environmental illuminance parameter. The input membership functions in Table 10 and FIG. 10 are for errors that are inputs. If the illuminance error is High Small, the required output power is OHS (OLittle). If the illuminance error is Medium Small (MS), the output power is OMS. If the illuminance error is Basic Small (BS), the output power is OBS.

)를 기초로 건물 내의 공기질을 제어하기 위한 요구 전력(P_A)을 산출하면 삼각 소속 함수의 분포를 갖는 요구 전력(P_A)을 얻을 수 있으며, 이를 표로 나타내면 다음의 표 11과 같다.Also, in the same manner as above, the difference between the actual air quality sensed as shown in FIG. 12 and the optimized air quality parameter (

By calculating the required power (P _A ) for controlling the air quality in the building based on), it is possible to obtain the required power (P _A ) having a distribution of triangular membership function, which is shown in Table 11 below.

The input of the purge controller 16 to the air quality is the error of the optimized air quality parameter and the actual environmental air quality parameter. When the input error is low, the required power as the output is OFF. When the input error is 'OK', the required power as the output is ON. If the input error is LH, the required power output is OLH. If the input error is MH, the output power demand is OMH. If the input error is HIGH, the output power demand is OHIGH.

On the other hand, the power control agent 17 at the output of the purge controller 16 receives the required power calculated for each of the temperature, illuminance and air quality from the purge controller 16, and calculates the total required power amount. The power control agent 17 compares the total required power amount with the supply power of the power supply unit 20. As a result of the comparison, if the total required power amount is smaller than the supply power of the power supply unit 20, the power control agent 17 maintains the required power amount and the total required power amount. If the power supply is larger than the power supply of the power supply unit 20, the required power amount is adjusted and output.

The power consumption calculator 18 at the output of the power control agent 17 calculates the actual power consumption based on the amount of adjustment power and the required power output from the power control agent 17. The power consumption calculation unit 18 compares the adjusted power amount with the required power amount and sets and outputs the actual power consumption as the adjusted power amount when the adjusted power amount is smaller than the required power amount. Set and print.

Next, the plurality of actuators 30 of FIG. 2 control the operation of the energy installation in the building according to the actual power consumption.

By the above-described prediction of indoor environment parameters and the construction of a building energy optimization system based on dynamic user setting, it is possible to minimize energy consumption while providing a comfortable indoor environment to a plurality of users in the building.

FIG. 13 illustrates a building energy optimization process based on prediction of indoor environment parameters, a plurality of user settings, and rule-based optimization according to the present invention. As shown in FIG. 13, the sensing unit 10 senses indoor environment parameters including temperature, illuminance, and air quality (T1). The indoor environment parameter predictor 12 calculates a predicted indoor environment parameter for the temperature, illuminance, and air quality of the sensed indoor environment parameter (T2). Actuators such as air conditioning and heating devices of the energy installation in the building are controlled in advance in accordance with the temperature, illuminance and air quality values of the predicted indoor environmental parameters (T3).

It is determined whether the user mode is a dynamic user setting mode that sets a maximum / minimum of a plurality of user-specific indoor environment parameters (T4). As a result of the determination, in the dynamic user setting mode, the user-specific environment parameter setting unit 13 sets the minimum / maximum points for the indoor environment parameters (temperature, illuminance, air quality) of the plurality of users on the average basis (ABS) and MAX-MIN, respectively. The dynamic user set point (minimum / maximum value) of the indoor environment parameter is calculated by unifying by one of the base and MIN-MAX based methods (T5).

The rule-based optimizer 14 receives the values of the indoor environment parameters predicted by the indoor environment parameter predictor 12 and the maximum / minimum values of the dynamic user set points for the calculated environmental parameters of temperature, illuminance and air quality. By optimizing the indoor environment parameters (T6).

The integrated comfort index calculator 15 calculates the integrated comfort index based on the optimized indoor environment parameter and the predetermined comfort index calculation formula (T7).

The purge controller 16 calculates a required amount of power PT, PL, and PA for controlling the temperature, illuminance, and air quality in the building based on the difference between the sensed environment parameter and the optimized environment parameter (T8).

The power control agent 17 receives the required power calculated for each of the temperature, illuminance and air quality from the purge controller 16 and calculates the total required power amount. Then, the adjusted amount of power for controlling temperature, illuminance and air quality is calculated based on the total required amount of power, the integrated comfort index, and the available power in the building (power of the power supply) (T9).

The power consumption calculation unit 18 calculates the actual power consumption based on the amount of adjustment power and the required power output from the power control agent 17 (T10).

The plurality of actuators 30 controls the operation of the energy installation in the building according to the actual power consumption (T11).

As described above, in order to reduce the actual power consumption through indoor environmental parameter prediction, the environmental index of temperature, illuminance, and air quality detected by the sensor is predicted and rule-based optimization to calculate the comfort index, and the required power is determined by the optimized environmental parameter. It is possible to reduce energy consumption while providing a comfortable indoor environment for the user.

As described above, the present invention predicts the indoor environmental parameters of temperature, illuminance and air quality, calculates a new integrated comfort index based on a plurality of user set values, and based on the calculated integrated comfort index and predicted indoor environment parameters. Control the operation of energy equipment in buildings.

Therefore, according to the present invention, it is possible to provide a comfortable indoor environment and at the same time minimize the energy consumption. Through this, control considering the pleasant indoor environment and energy efficiency in the architectural engineering field combining various IT as well as IBS / BIM / BEMS. It is effective to provide a system.

 So far, the present invention has been described based on the preferred embodiments. However, embodiments of the present invention is provided to more fully describe the present invention to those skilled in the art, the scope of the present invention is not limited to the above embodiments, various other Of course, the shape can be modified.

10: sensing unit 10a: temperature sensor
10b: Ambient light sensor 10c: Air quality sensor
12: indoor environment parameter estimation unit 13: dynamic user setting environment parameters
14: rule-based optimization unit 15: comfort index calculation unit
16: fuzzy controller 17: power control agent
18: power consumption calculator 20: power supply
30: Smart Home Actuator

Claims (10)

In the building energy optimization system based on the prediction of indoor environment parameters and the dynamic user setting,
A sensing unit including a plurality of sensors configured to sense temperature, illuminance, and air quality in a building, respectively, and output indoor environment parameters;
An indoor environment parameter predictor configured to predict the indoor environment parameter by using a prediction algorithm and output a predicted indoor environment parameter;
Input unit for inputting the minimum / maximum setting information of the indoor environment parameters,
The user inputs the minimum / maximum setting information of the indoor environment parameters for each user from the input unit and calculates the dynamic user setting point of the indoor environment parameters by unifying the minimum / maximum values for each of the plurality of users' temperature, illuminance and air quality. Environment parameter setting unit,
An optimized indoor environment including an optimized temperature parameter, an optimized illuminance parameter, and an optimized air quality parameter by receiving the predicted indoor environment parameter and a dynamic user set point of the indoor environment parameter by optimizing the indoor environment parameter by a rule-based algorithm. Rule-based optimizer for outputting parameters,
An integrated comfort index calculation unit configured to calculate an integrated comfort index based on the optimized indoor environment parameter and a predetermined comfort index calculation formula;
A fuzzy controller for calculating an amount of power required to control temperature, illuminance and air quality in a building based on a difference between the predicted indoor environment parameter and the optimized indoor environment parameter;
A power control agent for calculating an adjusted amount of power for controlling temperature, illuminance and air quality based on the optimized indoor environment parameter, the calculated integrated comfort index and available power in the building,
An actual power consumption calculator configured to calculate actual power consumption based on the calculated required power amount and the adjusted power amount;
A plurality of actuators for controlling the operation of the energy installation in the building according to the calculated actual power consumption,
The integrated comfort index calculator calculates an integrated comfort index based on the optimized indoor environment parameter and a preset comfort index (Equation 1),
(Equation 1)

From here,

,

,

Is a defined factor to avoid collisions between temperature, light intensity and air quality parameters.

Has a value between 0 and 1,

Is the difference between the sensed actual temperature and the optimized temperature parameter,

Is a difference between the sensed actual illuminance and the optimized illuminance parameter,

Represents the difference between the sensed actual air quality and the optimized air quality parameters,

,

,

, Represents temperature, illuminance and air quality parameters set by the user,
The user set temperature, illuminance and air quality parameters applied to Equation 1 are values obtained by unifying a plurality of user set values into one of an average, a MAX-MIN-based, and a MIN-MAX-based. User-based building energy optimization system. The method of claim 1,
Wherein said prediction algorithm comprises a Kalman filter. Building energy optimization system based on prediction and dynamic user setting of indoor environmental parameters. The method of claim 1,
The rule-based algorithm is such that the predicted indoor environment parameter and the dynamic user set point has a minimum difference, the building energy optimization system based on the prediction and dynamic user setting of the indoor environment parameter. The method of claim 1,
The user-specific environment parameter setting unit calculates a dynamic user set point of the indoor environment parameter by one of an average base setting, a maximum-minimum base setting, and a minimum-maximum base setting. Based building energy optimization system. The method of claim 1,
The power control agent controls the actuator of the energy equipment in the building in advance according to the temperature, illuminance and air quality values of the predicted indoor environment parameter output from the indoor environment parameter predictor. User-based building energy optimization system. In the building energy optimization method based on the prediction of indoor environment parameters and dynamic user setting,
Sensing indoor environmental parameters including temperature, illumination and air quality in the building;
Predicting the indoor environment parameters with a prediction algorithm to generate predicted indoor environment parameters;
Calculating the dynamic user set point of the indoor environment parameter by receiving the minimum / maximum setting information of the indoor environment parameters for each user by unifying the minimum / maximum values for each of temperature, illuminance and air quality of the plurality of users;
An optimized indoor environment including an optimized temperature parameter, an optimized illuminance parameter, and an optimized air quality parameter by receiving the predicted indoor environment parameter and a dynamic user set point of the indoor environment parameter by optimizing the indoor environment parameter by a rule-based algorithm. Generating a parameter;
Calculating an integrated comfort index based on the optimized indoor environment parameter and a predetermined comfort index calculation formula;
Calculating an amount of power required to control temperature, illuminance and air quality in the building based on the difference between the predicted indoor environment parameter and the optimized indoor environment parameter;
Calculating an adjusted amount of power for controlling temperature, illuminance and air quality based on the optimized indoor environment parameter, the calculated integrated comfort index and available power in the building;
Calculating actual power consumption based on the calculated required power amount and the adjusted power amount;
Supplying the calculated actual power consumption to a plurality of actuators for controlling the operation of the energy installation in the building,
The integrated comfort index calculator calculates an integrated comfort index based on the optimized indoor environment parameter and a predetermined comfort index calculation formula (Equation 1),
(Equation 1)

From here,

,

,

Is a defined factor to avoid collisions between temperature, light intensity and air quality parameters.

Has a value between 0 and 1,

Is the difference between the sensed actual temperature and the optimized temperature parameter,

Is a difference between the sensed actual illuminance and the optimized illuminance parameter,

Represents the difference between the sensed actual air quality and the optimized air quality parameters,

,

,

, Represents temperature, illuminance and air quality parameters set by the user,
The user setting temperature, illuminance, and air quality parameters applied to Equation 1 may be a value obtained by unifying a plurality of user setting values into one of average, MAX-MIN, and MIN-MAX based. Dynamic user settings based building energy optimization method. The method of claim 6,
The prediction algorithm includes a Kalman filter, the building energy optimization method based on the prediction of the indoor environment parameters and dynamic user settings. The method of claim 7, wherein
The rule-based algorithm is such that the predicted indoor environment parameter and the dynamic user set point to have a minimum difference, building energy optimization method based on the prediction and dynamic user settings of the indoor environment parameter. The method of claim 8,
Computing the dynamic user set point,
A dynamic user set point of the indoor environment parameter is calculated by unifying the maximum / minimum of each indoor environment parameter of temperature, illuminance and air quality by one of the average base setting, the maximum-minimum base setting, and the minimum-maximum base setting. , Building energy optimization method based on the prediction of indoor environmental parameters and dynamic user settings. The method of claim 6,
Prior to calculating a dynamic user set point of the indoor environment parameter, controlling the actuator of the energy installation in the building in advance according to the temperature, illuminance and air quality values of the predicted indoor environment parameter. Building energy optimization method based on prediction of environmental parameters and dynamic user setting.

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