KR20150051727A - Apparatus and method for energy management by temperature - Google Patents

Abstract

The present invention relates to a method and an apparatus for temperature-dependent energy management, wherein the method comprises the steps of: receiving inputted load factor of energy and target amount of energy consumption; calculating an estimated value of a room temperature corresponding to the inputted load factor, based on a first database where a past room temperature is mapped to a past load factor; calculating a rate value for the target amount of energy consumption from the estimated value of a room temperature; setting a highest and a lowest value of the range for the target amount of energy consumption based on the rate value and the target amount of energy consumption; setting a target temperature within the preset range based on a second database where a past room temperature and a past target temperature are mapped to a past amount of energy consumption, to control a room temperature according to the target temperature, and wherein an estimated value of a room temperature and a set target temperature are corrected by using a proportional expression, thus reducing the possible occurrence of errors for the estimated value of a room temperature and a set target temperature.

Description

[0001] Apparatus and method for energy management by temperature [0002]

BACKGROUND OF THE INVENTION 1. Field of the Invention [0001] The present invention relates to a method of managing energy according to temperature, and a method of automatically saving a target temperature according to an amount of energy consumption, thereby saving energy.

As power consumption increases rapidly, there are many problems in power supply and demand. In order to solve the above problems, a building energy management system capable of efficiently managing electric power used in a building has been continuously studied.

Conventional building energy management system enables efficient energy management by predicting loads by various methods such as fuzzy linear regression analysis and neural network. In addition, the power consumption can be minimized by adjusting the proper cooling and heating temperature based on indoor and outdoor temperatures. However, until now, the proposed methods are based on mathematical expressions. Since only approximate values can be obtained, not exact values, and only statistical information of the load is provided using past statistics and weather information provided by the Korea Meteorological Administration, There is a fatal disadvantage that the coping ability is reduced.

In the prior art cited below, an empty control energy saving device based on space reservation management is introduced, and an energy saving system and method for reducing the energy consumption of a building are proposed. However, the method proposed in the above-mentioned prior art documents only blocks the energy in the space through the reservation management and can not take account of the load factor and the energy consumption amount of energy at all. Since the target power consumption can not be set and managed automatically, There is a fatal drawback.

From this point of view, it can be seen that technological measures are needed to minimize energy consumption by automatically adjusting energy consumption in consideration of load factor and energy consumption.

Japanese Patent Application Laid-Open No. 10-2007-0063441 (Jun. 27, 2007)

Therefore, a first problem to be solved by the present invention is to provide a method of managing energy according to temperature that can minimize energy consumption by automatically controlling a target temperature according to a target energy consumption amount set by a user.

A second problem to be solved by the present invention is to provide an energy management apparatus capable of minimizing energy consumption by automatically controlling a target temperature according to a target energy consumption amount set by a user.

In order to accomplish the first object of the present invention, there is provided a method of controlling an energy saving method, Deriving a room temperature predicted value corresponding to the input load factor based on a first database in which a past load factor and a past room temperature are mapped; Deriving a ratio value for the target energy consumption amount from the room temperature predicted value; Setting a minimum value and a maximum value which are ranges for the target energy consumption amount based on the ratio value and the target energy consumption amount; And controlling the room temperature according to the target temperature by setting a target temperature for the set range based on the past room temperature and the past target temperature and the past energy consumption amount based on the second database, The predicted room temperature and the set target temperature are corrected using a proportional formula so as to reduce the probability of occurrence of an error with respect to the predicted room temperature and the set target temperature.

According to an embodiment of the present invention, the first database is constructed by mapping the date, the outside temperature, the humidity, the lighting apparatus, the personnel, and the room temperature as time factors for the load factor of energy. Can be an energy management method.

The step of deriving the predicted room temperature may include the steps of sequentially checking the degree of similarity between the input load factor and a past load factor forming the first database; Comparing the values derived through the examination to derive a past load factor including a result having the maximum similarity; And deriving the past room temperature mapped with the past load factor including the calculated result value having the maximum degree of similarity as the room temperature predicted value.

According to another embodiment of the present invention, a unit of data on outside temperature, humidity, lighting equipment, and personnel, which are variables of the load factor, is integrated through standardization of data scales that normalize each variable of the load factor As shown in FIG.

According to another embodiment of the present invention, the second database may be a temperature-dependent energy management method, wherein the room temperature, the target temperature, and the target energy consumption amount are mapped.

According to another embodiment of the present invention, the setting of the target temperature for the set range is performed by querying the second database with the minimum value and the maximum value, which are ranges for the target energy consumption amount, The selected target temperature is set to the target temperature for the set range by selecting the most recently used target temperature from among the derived minimum and maximum values and the mapped target temperature, Energy management method.

According to another embodiment of the present invention, the setting of the target temperature for the set range is performed by querying the second database with the minimum value and the maximum value, which are ranges for the target energy consumption amount, The average value of the minimum value and the minimum value is derived, and the degree of similarity between the derived average value and the target energy consumption amount forming the second database is sequentially checked, and the values derived through the inspection By deriving the target energy consumption amount including the result having the maximum similarity degree, and deriving a target temperature that is mapped with the target energy consumption amount including the result value having the maximum derived degree of similarity, Setting the temperature to the target temperature for the set range It can be an energy management method depending on the temperature to be gained.

According to another embodiment of the present invention, unit integration is performed on a room temperature, a target temperature, and a target energy consumption, which are variables forming the second database, through standardization of data scales that normalize each variable of the second database The temperature of the fuel cell can be reduced.

According to another embodiment of the present invention, setting the target temperature for the set range may be performed by setting a target temperature range that is a total of the target energy consumption amount, which is a sum of the target energy consumption amounts per section, So that the fixed target temperature can be set.

According to another embodiment of the present invention, the step of controlling the room temperature according to the target temperature may include the step of continuously decreasing the difference value between the set target temperature and the room temperature, the difference between the set target temperature and the room temperature And switching the cooling / heating unit to an off state when the absolute value of the temperature change is less than or equal to a predetermined set value and the time before reaching the set target temperature is equal to or less than a predetermined time .

In order to achieve the second object of the present invention, there is provided an energy saving apparatus comprising: an input unit for inputting a load factor of energy and a target energy consumption amount; Deriving a predicted room temperature value corresponding to the input load factor based on a first database in which a past load factor and a past room temperature are mapped to derive a ratio value with respect to the target energy consumption amount, Based on the past room temperature and the past target temperature and the past energy consumption amount, based on the target energy consumption amount and the target energy consumption amount, A target temperature setting unit for setting a target temperature for the heating unit; And a controller for controlling the room temperature according to the target temperature, wherein the predicted room temperature and the set target temperature are calculated using a proportional formula so as to reduce the probability of occurrence of an error with respect to the predicted room temperature and the set target temperature, The energy management device is characterized in that the energy management device performs the correction.

According to an embodiment of the present invention, the processing unit sequentially checks the degree of similarity between the input load factor and the past load factors forming the first database, compares the derived values with each other, Deriving a past load factor including a result value having a maximum similarity degree, deriving a past room temperature mapped with a past load factor including a result value having the maximum value of the derived similarity as the predicted room temperature value , And integrating units of outside temperature, humidity, lighting equipment, and personnel, which are variables of the load factor, through standardization of the data scale standardizing each variable of the load factor.

In addition, the processing unit may be configured to query the second database from the minimum value to the maximum value in the range of the target energy consumption amount, and when a value coinciding with the minimum value to the maximum value is derived, And the selected target temperature is set to the target temperature for the set range by selecting the most recently used target temperature.

According to another embodiment of the present invention, the processing unit may query the second database for the minimum value and the maximum value in the range for the target energy consumption amount, and if the value matching the minimum value and the maximum value is not derived, The average value of the maximum value is derived, and the similarity between the derived average value and the target energy consumption amount forming the second database is sequentially checked, and the values derived through the inspection are compared, And deriving the target temperature mapped with the target energy consumption amount including the resultant value having the maximum degree of similarity derived thereby to calculate the derived target temperature as the target temperature for the set range , And normalizing each variable of the second database Data measure may be an energy management apparatus which comprises an integrated unit for the room temperature variable to form the second database, the target temperature, and the target energy consumption through standardization.

According to the present invention, there is an effect that the energy consumption can be minimized by automatically controlling the target temperature according to the target energy consumption amount set by the user.

Also, the similarity degree search is performed on the big data stored in the database to derive the result having the maximum similarity degree, thereby obtaining the accurate data necessary for the energy management method according to the temperature.

Further, by correcting the data acquired through the database by using the proportional expression, there is an effect of reducing the possibility of error occurrence.

1 is a flowchart illustrating an energy management method according to an embodiment of the present invention.
FIG. 2 is a graph showing a predicted room temperature according to another embodiment of the present invention.
FIG. 3 is a diagram illustrating a procedure for calculating an area ratio from a room temperature predicted value according to another embodiment of the present invention.
FIG. 4 is a diagram illustrating a similarity search result according to another embodiment of the present invention.
FIG. 5 is a graph illustrating a target temperature according to an energy consumption amount range allocated according to another embodiment of the present invention.
FIG. 6 is a diagram for setting a fixed target temperature in accordance with an allocated energy consumption range according to another embodiment of the present invention.
FIG. 7 is a graph showing the amount of energy saved in the air conditioner according to another embodiment of the present invention.
8 is a block diagram illustrating an energy management apparatus according to an embodiment of the present invention.

Prior to describing the embodiments of the present invention, the technical means adopted by the embodiments of the present invention will be introduced to solve the problems occurring in the existing energy management system.

As power consumption increases, a variety of energy management systems have been proposed, but the amount of energy consumed by the building has not been taken into consideration, and the user has not been able to control the amount of energy automatically.

Therefore, the embodiments of the present invention provide energy consumption information according to the target temperature setting by setting the target temperature to be maintained in the building using the big data and the weather station data stored in the database, In order to provide a technological means to automatically adjust the temperature of the liquid.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. In the following description and the accompanying drawings, detailed description of well-known functions or constructions that may obscure the subject matter of the present invention will be omitted. It should be noted that the same constituent elements are denoted by the same reference numerals as possible throughout the drawings.

FIG. 1 is a flowchart illustrating an energy management method according to an embodiment of the present invention. Referring to FIG. 1, a load factor and a target energy consumption amount for energy are input, and the first load factor and the past room temperature are mapped Calculating a room temperature predicted value corresponding to the input load factor on the basis of the room temperature, deriving a ratio value with respect to the target energy consumption amount from the room temperature predicted value, and calculating, based on the ratio value and the target energy consumption amount, By setting the target temperature for the set range based on the past room temperature and the past target temperature and the past energy consumption amount mapped to the target energy consumption amount, And controlling the indoor temperature according to the temperature, wherein the predicted room temperature and the set target temperature The predicted room temperature value and the set target temperature are corrected using a proportional formula so as to reduce the possibility of occurrence of an error with respect to the room temperature.

More specifically, in step S110, a load factor for energy and a target energy consumption amount are input. Here, the load factors for the energy may be factors influencing the temperature inside the building such as the outside temperature of the building, the humidity in the building, the number of lighting devices in the building, the number of people in the building, and the indoor temperature in the building. The target energy consumption amount may be an amount of energy consumption according to the temperature control in the building, and the user may be able to arbitrarily set it according to the situation.

In step S120, a room temperature predicted value corresponding to the input load factor is derived based on a first database in which a past load factor and a past room temperature are mapped.

More specifically, in order to minimize the error of the predicted room temperature in the building and maintain the room temperature in accordance with the target energy consumption, the indoor temperature of the building can be predicted based on data reflecting the characteristics of the building.

Table 1 described below can be a part of a long-term database for predicting the energy consumption of a building. The energy load factor and the room temperature in the building according to the energy load factor are stored in the first database . Here, the first database will be described in detail with reference to Table 1 below.

The first database may be constructed by mapping the date, the outside temperature, the humidity, the lighting device, the personnel, and the room temperature as time factors for the energy as a load factor. That is, as a load factor for the energy, the outdoor temperature, the lighting equipment, and the number of people in a space can be set and stored, and the first database can serve as a basis for predicting future room temperature. Here, although the first database is composed of outside temperature, humidity, lighting device, and personnel only as a load factor for energy, it is possible to additionally set various load factors affecting the temperature in the building.

In order to derive the room temperature predicted value, the degree of similarity between the input load factor and the past load factor forming the first database is sequentially checked, and the values derived through the inspection are compared with each other, And the past room temperature mapped with the past load factor including the result value having the maximum value of the derived degree of similarity can be derived as the room temperature predicted value have.

이고,

까지 총 n개의 부하 요인과 상기 부하요인에 따른 실내온도가 상기 제 1 데이터베이스에 저장될 수 있으며, 상기 부하요인은

로 표현할 수 있다. 또한, t시점의 예상되는 부하요인들의 예측 값은

이며, 상기 t시점의 부하요인은

로 표현할 때, 제 1 데이터베이스에서 입력된 부하요인에 따른 가장 유사한 데이터를 찾기 위해 유사 검색을 하며, 유클리디안 거리 공식을 이용하여 유사도를 검사하는 것은 다음과 같이 표현할 수 있다.More specifically, the degree of similarity can be retrieved using the Euclidean distance formula to derive the room temperature predicted value. Here, the above Euclidean distance formula is a load factor

ego,

A total of n load factors and the room temperature according to the load factor can be stored in the first database,

. Also, the predicted value of expected load factors at time t

, And the load factor at the time point t

, The similarity search is performed to find the most similar data according to the load factor inputted from the first database and the similarity is checked using the Euclidean distance formula can be expressed as follows.

Equation 1, which is the Euclidean distance formula, is a method widely used for checking the similarity in the information retrieval theory, and the similarity may be higher when the result of the Euclidean distance formula is lower.

(32.8, 18, 8, 48)로 표현할 수 있다. 여기서, 제 1 데이터베이스에서 에너지에 대한 부하요인을 외기온도, 습도, 조명기기, 인원만을 이용하지만 실제로는 이 외에 다양한 부하요인을 추가로 설정할 수 있다.Also, the temperature of the data satisfying the minimum distance calculated through Equation (1) can be used as a predicted value. For example, in the case of Table 1, the load factor is expressed as outside temperature, humidity, lighting equipment, personnel, and the load factor is expressed as F (outside temperature, humidity, , The outside temperature was 32.8 degrees, the humidity was 18%, the lighting devices were turned on and the number of people in the room was 48

(32.8, 18, 8, 48). Here, the load factor of energy in the first database is used only in the outside temperature, the humidity, the lighting equipment, and the personnel, but in addition, various load factors can be additionally set.

On the other hand, through standardization of the data scale standardizing each variable of the load factor, it is possible to unitize the variables of the load factor such as the outside temperature, the humidity, the lighting device, and the personnel.

(30.8, 20, 6, 0)이라고 할 때, 표 2의 제 1 데이터베이스에 저장된 각 두 경우에 대해 유클리디안 거리 공식을 이용하여 유사도를 검사한 경우 아래와 같을 수 있다.More specifically, the load factors stored in the first database may be numbers with different units. Therefore, when calculating the stored value of the original of the first database, it can affect the checking of the similarity between the two data because the unit is not integrated. For example, if the predicted value at a future time t is

(30.8, 20, 6, 0), the similarity of each of the two cases stored in the first database of Table 2 may be determined using the Euclidean distance formula.

100.0002 can be derived when the similarity is retrieved by applying Equation (1) to the data at 11:00 on August 1, 2012 in Table 2, and the data of 12:00 on November 5, 1 is applied to search for similarity, 15 can be derived. Here, the predicted temperature at the time t of the future according to Equation (1) may be 18.1 degrees. Considering the similarity between the two cases in Table 2, the room temperature can be more influenced by the outside temperature than the personnel. Therefore, the data at 11:00 on August 1, 2012, when there is little difference in outside temperature, should most effectively reflect the current situation. However, as a result of calculation using the Euclidian distance formula, the data at 12:00 on November 5, 2012 will be similar to the situation in the future t because the data on November 5, 2012 has a smaller value. Can result. The above result is a problem that occurs because the units are not integrated between the load factors. Therefore, the load factor unit can be standardized and integrated to generate the above problem. Here, the standardization is the most common standardized form, and each variable can be converted into a standard score such as a Z score.

가 각각의 부하요인에 대한 표준편차일 때, 다음과 같이 표현할 수 있다.For example, assuming that the population is large enough, and standardizing the first database in Table 1, the standardization of the load factor is that X is a variable for each load factor, m is the average for each load factor,

Is the standard deviation of each load factor.

Here, Equation (2) is a formula used for obtaining a standard score in statistics, and values derived by calculating Equation (2) are referred to as a standard score, a standard value, a Z value, and a Z score.

Therefore, the load factors of the first database of Table 1 can be normalized through Equation (2) as shown in Table 3.

Now, similar data can be retrieved using Equation (1) for the load factor input based on the standardized first database of Table 3. The data containing the most significant value of the search result is the closest data and may be the value searched for by similarity search. For example, if the outside temperature is 30 degrees, the humidity is 20%, the number of bulbs is 10, and the number of people is 25 as a load factor for the future time t, standardization is performed through Equation 2, The load factor for the time point is retrieved from the first database which is standardized through Equation 1, and the search result can be expressed as shown in Table 4. < tb > < TABLE >

Here, since the degree of similarity is 0 on the data of 12 o'clock on August 15, 2012, the room temperature with respect to the load factor at the input time point t can be predicted to be 26.5 degrees.

On the other hand, when the outside temperature is 30 degrees, the humidity is 21%, the number of bulbs is 6, and the number of people is 20 at a time point of the future, standardization of the load factor inputted through Equation 2 is performed, The load factor for the time point is searched for the euclidean similarity in the first database which is standardized through Equation (1), and the search result can be expressed as shown in Table 5.

Here, since the value of the similarity degree search result of 0 is not found, an accurate value can not be found in the first database. Therefore, data having the smallest similarity value can be used as the closest data. In Table 5, since the degree of similarity of data at 12:00 on August 1, 2012 is the smallest at 1.4785, it becomes the first in the ranking, so the predicted room temperature value for the input load factor can be predicted to 26 degrees.

의 외기온도를 이용하여 값을 보정할 수 있다. 이하에서 표 6은 표 5에서 유사도가 최소인 랭킹 1위 값인

과 상기 입력받은 미래의 t시점의 부하요인인

의 외기온도와 실내온도를 테이블로 표현한 것일 수 있다.In Equation 1, as the derived value approaches 0, the degree of similarity is high. However, since the similarity degree of the Euclidian distance is 1.4785 at 12:00 on August 1, 2012 in Table 5, It is possible to perform the room temperature correction using the proportional formula for the room temperature. Here, the proportional expression can be corrected using the outside temperature and the room temperature stored in the first database. That is, the proportional formula can be used in order of influencing the room temperature among the load factors, and in consideration of the fact that the outside air temperature has the greatest influence on the room temperature, in order to correct to the most accurate value,

It is possible to correct the value using the outside temperature of the outdoor unit. Table 6 below shows the ranking value of ranking

And the load factors of the future t

The outside temperature and the room temperature of the outdoor unit can be expressed in a table.

의 외기온도가 y이고, 랭킹 1의 실내온도가 z이며, 비례식을 통한 보정된 실내온도가 X라고 할 때, 상기 비례식은 다음과 같이 표현할 수 있다.Here, the outside temperature of Ranking 1 in Table 6 is r,

The indoor temperature of Rank 1 is z, and the corrected indoor temperature through proportional expression is X, the proportional expression can be expressed as follows.

In addition, it can be corrected by using the degree of similarity, which is the Euclidean distance between Ranking 1 and Ranking 2, and the room temperature in Table 5. In other words, the correction can be performed using the similarity and the room temperature, which are the Euclidean distances derived from Equation 1, and the values can be corrected using Ranking 1 and Ranking 2 in Table 5 to make the similarity close to zero have. Table 7 below is a table showing the Euclidean distance and the room temperature of Ranking 1 and Ranking 2 in Table 5. [

For example, in Table 7, the room temperature difference between Ranking 1 and Ranking 2 is 0.5 degrees, and the Euclidean distance difference is 0.9361, so that the Euclidean distance is 0.9361 when the room temperature is changed by 0.5 degrees. Therefore, the Euclidean distance can be estimated every time the room temperature is increased by 0.1 degree, and since the similarity to be sought is 0, when the Euclidian distance is increased from 0 to the Euclidean distance of Ranking 1, the room temperature is 26.0 Will increase or decrease by X in the figure. Where X is the correction value, y is the Euclidian distance each time the room temperature is increased by n, and z is the value obtained by subtracting 0, the similarity to be sought, from the Euclidean distance at the top of the ranking, Can be expressed together.

와 표 5의 랭킹 1위인

을 비교하며, 상기 비교는 다음과 같이 표현할 수 있다.Thereafter, it is possible to decide whether to add or subtract the X value derived from the equation (4), and to determine whether the X value is to be added or subtracted,

And the top ranking in Table 5

, And the comparison can be expressed as follows.

의 외기온도를 판단의 근거로 사용할 수 있다. 또한, 표 5의 랭킹 1의 외기온도가 미래 t 시점의 외기온도보다 클 경우 예측된 t 시점의 실내온도는 표 5의 랭킹 1과 같이 저장된 실내온도에 비해 작으므로, 수학식 5를 적용할 수 있다.Here, Temp1 is the outside air temperature value of the first ranking in Table 5, Temp2 is the outside air temperature value at the time point of future t input, and Temp3 is the predicted room temperature value. Equations (5) and (6) can be a method for accurately correcting the approximate search ranking, which is the first rank of the similarity search, when the matching data can not be found from the first database through Equation (1). Therefore, considering that the room temperature is influenced by the greatest weight on the outside temperature, the outside temperature of Ranking 1 in Table 5,

Can be used as a basis for judgment. If the outside temperature of Rank 1 of Table 5 is larger than the outside temperature of the future time t, the predicted room temperature at time t is smaller than the stored room temperature as shown in Table 5, have.

On the other hand, if the outside temperature of Ranking 1 in Table 5 is smaller than the outside temperature of the future t-time, the predicted room temperature at time t can be calculated by Equation 6 according to the stored room temperature as shown in Table 5 have.

Alternatively, the similarity degree search may use a vector space model that can determine information search and similarity rank instead of the Euclidean distance formula, and the vector space model can be expressed as follows.

는 부하요인이고,

까지 총 n개의 부하요인이 있으며,

으로 표현할 수 있다. 또한,

는 t시점의 부하요인이고,

까지 총 n개의 부하요인이 있으며,

으로 표현할 수 있다. 또한, n은 부하요인의 개수일 때, 상기 벡터 공간 모델을 이용하는 검색이며, 상기 벡터 공간 모델 공식은 정보검색론에서 유사도를 검사하기 위해서 많이 활용하는 방법이다. here,

Is a load factor,

There are a total of n load factors,

. Also,

Is a load factor at time t,

There are a total of n load factors,

. Also, n is a search using the vector space model when the number of load factors is a number, and the vector space model formula is a method widely used for checking the similarity in information search theory.

Therefore, as the cosine value of Equation (7) approaches 0, the degree of similarity becomes lower. When the cosine value of Equation (7) approaches to 1, the degree of similarity increases. The results are shown in Table 8 below.

Here, since the degree of similarity at 12:00 on August 15, 2012 is closest to 1, the room temperature with respect to the load factor at the time point t of the input future can be predicted to be 26.5 degrees.

과 입력받은 미래의 t 지점의 부하요인인

의 외기온도와 실내온도를 테이블로 표현한 것이다.The value derived from Equation (7) can be corrected to a more precise value by the data value correction using the proportional expression. Table 9 below shows the ranking value of the ranking

And the load factor of the future t point input

The outside temperature and the room temperature.

의 외기온도 값이 같기 때문에 수학식 3의 비례식을 이용할 필요가 없으며, 표 9의 경우는 수학식 8의 비례식을 통해 보정할 수 있다.Here, the outdoor temperature value of Rank 1

It is not necessary to use the proportional expression of Equation (3), and in the case of Table 9, it can be corrected through the proportional expression of Equation (8).

Table 10 below is a table representing the first and second highest similarities and room temperatures according to the degree of similarity in Table 8 derived through Equation (7).

For example, in Table 10, the room temperature difference between Ranking 1 and Ranking 2 is 7.1 degrees, and the similarity difference is 0.0149, so that the similarity is 0.0149 when the room temperature is changed by 7.1 degrees. Therefore, it is possible to estimate the room temperature that increases every 0.0001, and since the similarity to be sought is 1, the room temperature can be increased or decreased by X at 26.5 degrees when the similarity is increased by 1 at 0.9991 . Here, X is a correction value, y is an increase value of the room temperature every time the degree of similarity is increased by n, and z is a value obtained by subtracting the degree of similarity with respect to ranking 1 in Table 10 from the degree of similarity 1 to be searched. Can be expressed together.

Therefore, when Equation (8) is applied to Table 10, it can be seen that when the room temperature value is changed by 0.4293 degrees, the similarity value increases from 0.9991 to 1. Therefore, it can be seen that the correction value X is 0.4293. Then, it is necessary to decide whether to add or subtract the correction value X obtained according to the external load factor, and it can be determined through Equations (5) and (6).

Now, step S120 and subsequent steps will be described again.

Before describing step S130, it is possible to automatically control the room temperature through the target temperature setting of the cooling / heating unit according to the target energy consumption based on the room temperature predicted in step S120, and the target energy consumption amount is used during the target period And the target energy consumption amount allows the user to specify an error. The target time unit is divided into three time units because the outside air temperature received from the meteorological office used in predicting the room temperature is 3 hours, and the target energy consumption per section is set to the target temperature of the air conditioner in units of time The energy consumption amount corresponding to each of the three-hour unit periods is allocated to the room temperature, the room temperature is a temperature recorded at a specific point in time in the past, and the target temperature is assumed to be the desired temperature set in the air conditioner at the specific point in time.

In step S130, a ratio value with respect to the target energy consumption amount is derived from the room temperature predicted value.

More specifically, in order to match the energy consumption of the building to the target energy consumption, the target energy consumption may be allocated according to the period divided into the period units. In other words, the target energy consumption can be allocated for every three hour unit. The target energy consumption amount for each section may be the estimated area of the room temperature.

FIG. 2 is a graph showing the predicted room temperature according to another embodiment of the present invention, in which the room temperature 21 predicted in step S120 is displayed in units of three hours. In step S130, the graph of FIG. 2 is divided into sections according to intervals to allocate the target energy consumption amount for each section, and the divided graph may be as shown in FIG. 3 below.

FIG. 3 is a diagram illustrating a process of calculating an area ratio from a room temperature predicted value according to another embodiment of the present invention. In FIG. 3, a room temperature graph predicted to be allocated to a target energy consumption amount per section is divided into sections have. The area of the graph of FIG. 3 for each section can be derived through a trapezoidal formula. Here, when the total area obtained by summing the area per section in the graph of FIG. 3 is T and the area for S1 which is a period from 0:00 to 3:00 is Sn, the ratio value to the target energy consumption for S1 is Can be expressed together.

Then, the ratio of all the sections to the graph of FIG. 3 can be obtained as shown in Table 11 below.

In step S140, a minimum value and a maximum value, which are ranges for the target energy consumption amount, are set based on the ratio value and the target energy consumption amount.

라 할 때, 목표 에너지 소비량의 범위는 다음과 같이 표현할 수 있다.More specifically, the target energy consumption amount range for each section can be set according to the target energy consumption amount based on the ratio value derived in step S130. That is, the minimum value of the energy consumption per section and the maximum value of the energy consumption per section can be obtained. Herein, the energy consumption amount per the minimum interval is a ratio by interval to satisfy the minimum target energy consumption amount, and can be expressed as Min (Sn). Also, the maximum energy consumption per section is expressed as Max (Sn), which is the amount of power set for each section to satisfy the maximum target energy consumption. In other words, the target energy consumption per section should be included in the minimum value or the maximum value. At this time, some error due to decimal point calculation can be tolerated. Here, the target energy consumption amount is denoted by E, and an error with respect to the target energy consumption amount is denoted by

, The range of the target energy consumption amount can be expressed as follows.

The minimum and maximum values, which are the ranges of the target energy consumption according to the ratios in Table 11 through Equation (10), can be respectively obtained as shown in Table 12 below.

The indoor temperature is controlled according to the target temperature by setting the target temperature for the set range based on the past room temperature and the past target temperature and the past energy consumption amount mapped in the step S150.

More specifically, the target temperature, which can be consumed according to the target energy consumption amount per allocated section in step S140, can be searched in the second database in order according to the section. Here, the second database may be constructed by mapping the past room temperature, the target temperature, and the target energy consumption amount, and the target energy consumption amount may be the amount of energy consumed from the past room temperature to the target temperature. Also, the past room temperature, the target temperature, and the target energy consumption amount for the building can be stored in a large amount in the second database, and a specific time point can be referred to as t, have.

In order to set the starting target temperature for the S1 section of Table 12, which is the first section, Table 12, it is possible to query the second database in consideration of the set range for the S1 section of Table 12, and to derive the query result of Table 13 below .

That is, when a value matching the minimum value and the maximum value is derived by querying the second database from the minimum value to the maximum value in the range of the target energy consumption amount, the most recently used one of the target temperatures mapped with the derived minimum value and the maximum value The selected target temperature can be set as the target temperature for the set range. Table 14 shows that the target temperature of 18 degrees has been used most recently and can be selected as the target temperature. Here, it can be assumed that 18 degrees has been used as the target temperature most often. Also, in Table 14, if there are two or more results satisfying the query, the user can arbitrarily select according to the power consumption.

On the other hand, if the second database is inquired of the minimum value and the maximum value in the range of the target energy consumption amount, and if a value matching the minimum value and the maximum value is not derived, an average value is calculated for the minimum value and the maximum value, Sequentially comparing the average value with the target energy consumption amount forming the second database and comparing the values derived through the inspection to derive the target energy consumption amount including the result having the maximum similarity, The derived target temperature can be set as the target temperature for the set range by deriving the target temperature mapped with the target energy consumption amount including the resultant value having the maximum derived degree of similarity.

Here, the second database used for the search may be as shown in Table 15 below.

라 할 때, 상기 표준화는 수학식 2를 통해 할 수 있으며, 표준화된 제 2 데이터베이스는 이하에서 표 16으로 표현할 수 있다.Standardization of each parameter of the second database of Table 15 can standardize the unit of temperature, target temperature, and target energy consumption, which are variables that form the second database, through standardization of the data scale. Here, E is the average of the items forming Table 15, and the standard deviation

, The standardization can be performed through Equation (2), and the standardized second database can be expressed in Table 16 below.

The detailed description of the similarity search using the Euclidean distance calculation and the vector formula corresponds to the detailed description of Equations (1) and (7) in the step S120, and detailed explanations of Equations (1) and .

The target temperature derived from Equations (1) and (7) can be more precisely used through the correction. Here, the correction will be described in detail with reference to FIG. 4 below.

FIG. 4 is a graphical illustration of the result of similarity search according to another embodiment of the present invention. By correcting the set target temperature by using a proportional formula, the possibility of an error with respect to the set target temperature can be reduced.

More specifically, the proportional equation using the ratio of the area can be used by using the proportional relation between the amount of power and the area as a correction method. 4, the left-hand trapezoidal model 401 includes a start indoor temperature 402, an end indoor temperature 403 start target temperature 404, an end target temperature 405, and a power amount 406, In a trapezoidal shape. The trapezoidal model 411 on the right side shows the start indoor temperature 412, the end indoor temperature 413, the start target temperature 414, the end target temperature 415, and the electric energy 416, It may be shaped in a trapezoidal shape. Here, A is the width of the trapezoidal model 401 having the rank of the first degree of similarity search result, Ap is the amount of power for the first rank of the similarity degree search result ( 406), the start indoor temperature 412 of the right trapezoidal shape 411 is Ba, the end room temperature 413 is Bb, the start target temperature 414 is Bc, (415) is x, the proportional expression can be expressed as follows.

Through the correction of Equation (11), the temperature of the entire target period can be finally set as shown in FIG. 5 below.

On the other hand, the fixed target temperature can be set in accordance with the range of energy consumption per allocated section. Here, the fixed target temperature is described below with reference to FIG.

FIG. 6 is a diagram illustrating a fixed target temperature setting according to an allocated energy consumption amount range according to another embodiment of the present invention. The total target energy consumption amount, which is a sum of all the target energy consumption amounts per section, The fixed target temperature can be set by being included in the total target energy usage amount range which is a total value.

라 하며, 고정목표온도(81)를 X라 할 때, 상기 고정목표온도는 다음과 같이 표현할 수 있다.More specifically, the fixed target temperature can be set such that the sum of the energy usage amounts of the respective sections falls within the total energy usage range. That is, the target energy consumption amount is E, and the error with respect to the target energy consumption amount is

And the fixed target temperature 81 is X, the fixed target temperature can be expressed as follows.

Further, the range of the target energy consumption amount can be derived from the following equation (10). Therefore, the X value as the fixed target temperature 81 satisfying the equations (10) and (12) can be derived by querying the second database.

Thereafter, when the predetermined condition is satisfied, the energy can be controlled by automatically switching the cooling / heating unit to the off state. That is, in order to improve the existing method of turning off the cooling / heating unit when the temperature becomes lower than the conventional temperature, the cooling / heating unit can be automatically turned off before the room temperature reaches the target temperature.

Here, the predetermined condition is that when the difference between the target temperature and the room temperature is decreasing first, secondly, when the absolute value of the difference between the room temperature and the target temperature is equal to or less than the value arbitrarily set by the user, And the time before the time reaches the target temperature may be less than the time set by the user arbitrarily. Accordingly, when the temperature is switched to the off state when all the above conditions are satisfied, the temperature is automatically adjusted to the target temperature due to the cold and hot air from the cooling / heating unit and the temperature of the outside air.

7 is a diagram showing the amount of energy savings Cooling & Heating according to another embodiment of the invention, G ₁ is the outdoor air temperature graph, G ₂ is room temperature graph, G ₃ is the target temperature graph, S _n is automatically turned off The amount of energy saved by the system.

For example, when the absolute value of the difference between the room temperature and the target temperature is 0.3 and the time before the time that the second condition reaches the target temperature is set to 15 minutes, In the room temperature graph, the difference between the target temperature and the room temperature is gradually reduced. As a result, the temperature of the room temperature gradually decreases at 18 o C to reach the target temperature. Thereafter, since the room temperature has reached the target temperature at 18:30, the air conditioner 61 can be automatically turned off. Here, the difference of 0.3 degrees set at the room temperature and the target temperature 15 minutes before the 15 minutes set at 18:30 is 0.3, and the cooler can be turned off because it is less than the temperature difference set by the user. S ₁ 62 may be the amount of energy saved when the air conditioner 61 is automatically turned off and the method of obtaining S ₁ 62 may be based on the assumption that the predicted energy consumption, The amount of energy consumed to be consumed up to the time when the energy consumption is estimated. It can be confirmed that the radiator 62 saves energy in the same manner as the radiator 62.

FIG. 8 is a block diagram illustrating an energy management apparatus according to an embodiment of the present invention. The energy management apparatus 70 includes a configuration corresponding to each process of FIG. 1 described above. Therefore, in order to avoid duplication of explanation, the function is outlined mainly in the detailed configuration of the system.

The input unit 71 receives a load factor for energy and a target energy consumption amount.

A processing unit (72) deriving a predicted room temperature value corresponding to the input load factor based on a first database in which a past load factor and a past room temperature are mapped to derive a ratio value to the target energy consumption amount, And sets a minimum value and a maximum value as a range for the target energy consumption amount based on a ratio value and the target energy consumption amount, Set the target temperature for the range.

The control unit 73 controls the room temperature according to the target temperature so as to compensate the room temperature predicted value and the set target temperature by using a proportional formula so as to reduce the possibility of error with respect to the predicted room temperature and the set target temperature (70).

The processing unit 72 sequentially checks the degree of similarity between the input load factor and the past load factors forming the first database, and compares the values derived through the inspection, Deriving a past load factor including a result of past load factors including a result value having the maximum value of the degree of similarity as the estimated room temperature, Standardization of data standardizing each variable can integrate the variables of the load factors such as outside temperature, humidity, lighting equipment, and personnel.

In addition, the processing unit 72 may inquire of the second database the minimum to maximum values in the range of the target energy consumption amount, and when a value that matches the minimum value to the maximum value is derived, By selecting the most recently used target temperature among the temperatures, the selected target temperature can be set as the target temperature for the set range.

In addition, the processing unit 72 queries the second database for the minimum value and the maximum value in the range of the target energy consumption amount, and when the value matching the minimum value and the maximum value is not derived, the processing unit 72 calculates the average value for the minimum value and the maximum value And sequentially comparing the derived average value with the target energy consumption amount forming the second database, comparing the derived values, and comparing the derived values with the target value And deriving the target temperature mapped with the target energy consumption amount including the resultant value having the maximum degree of similarity derived thereby to set the derived target temperature as the target temperature for the set range, Through the standardization of data scales that standardize each variable in the second database Group may incorporate units for the variables of room temperature, the target temperature, and the target energy consumption for forming the second database.

Also, since the actual room temperature and the predicted room temperature may be different from each other, the sensor 74 senses the actual temperature every hour and inputs a value to reduce the error range with respect to the target energy consumption set by applying the real-time temperature.

According to the embodiments of the present invention described above, the target temperature can be set using the database and the weather station data storing the energy consumption according to the load factors reflecting the characteristics of one building, and the energy consumption amount information can be provided accordingly. In addition, the target temperature can be automatically adjusted by the amount of building energy consumption desired by the user, and the energy consumption predicted through the big data of the database is more sophisticated than the existing prediction method, and the stored energy is used to meet the target energy amount set by the user So that efficient energy management can be achieved. Furthermore, by automatically turning off the cooling / heating unit before the target temperature is reached, the unnecessary energy consumption is reduced, thereby achieving excellent energy saving.

Meanwhile, the embodiments of the present invention can be embodied as computer readable codes on a computer readable recording medium. A computer-readable recording medium includes all kinds of recording apparatuses in which data that can be read by a computer system is stored.

Examples of the computer-readable recording medium include a ROM, a RAM, a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device and the like, and also a carrier wave (for example, transmission via the Internet) . In addition, the computer-readable recording medium may be distributed over network-connected computer systems so that computer readable codes can be stored and executed in a distributed manner. In addition, functional programs, codes, and code segments for implementing the present invention can be easily deduced by programmers skilled in the art to which the present invention belongs.

The present invention has been described above with reference to various embodiments. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the disclosed embodiments should be considered in an illustrative rather than a restrictive sense. The scope of the present invention is defined by the appended claims rather than by the foregoing description, and all differences within the scope of equivalents thereof should be construed as being included in the present invention.

70: Energy management device
71:
72:
73:
74: Sensor

Claims (14)

Receiving a load factor for energy and a target energy consumption amount;
Deriving a room temperature predicted value corresponding to the input load factor based on a first database in which a past load factor and a past room temperature are mapped;
Deriving a ratio value for the target energy consumption amount from the room temperature predicted value;
Setting a minimum value and a maximum value which are ranges for the target energy consumption amount based on the ratio value and the target energy consumption amount; And
And controlling a room temperature according to the target temperature by setting a target temperature for the set range based on a past database and a second database to which a past target temperature and a past energy consumption are mapped,
Wherein the room temperature predicted value and the set target temperature are corrected using a proportional formula so as to reduce the probability of occurrence of an error with respect to the predicted room temperature and the set target temperature. The method according to claim 1,
Wherein the first database comprises:
A temperature, an outside temperature, a humidity, an illuminating device, a person, and an indoor temperature are mapped on a time basis as a load factor for the energy. The method according to claim 1,
Wherein deriving the predicted room temperature comprises:
Sequentially checking the degree of similarity between the input load factor and a past load factor forming the first database;
Comparing the values derived through the examination to derive a past load factor including a result having the maximum similarity; And
And deriving, as the room temperature prediction value, a past room temperature mapped with a past load factor including a result value having the maximum degree of similarity thus derived. The method of claim 3,
Wherein the units of the outside temperature, the humidity, the lighting equipment, and the personnel, which are variables of the load factors, are integrated through standardization of the data factors standardizing each variable of the load factors. The method according to claim 1,
Wherein the second database comprises:
A room temperature, a target temperature, and a target energy consumption are mapped and constructed. The method according to claim 1,
Setting the target temperature for the set range includes:
When a value matching the minimum value to the maximum value is derived by querying the second database for a minimum value or a maximum value in the range of the target energy consumption amount,
Wherein the selected target temperature is set as a target temperature for the set range by selecting the derived minimum value and the maximum value and the most recently used target temperature among the mapped target temperatures. The method according to claim 1,
Setting the target temperature for the set range includes:
When a value matching the minimum value to the maximum value is not derived by querying the second database for the minimum value and the maximum value in the range of the target energy consumption amount,
Deriving an average value of the minimum value and the maximum value,
Sequentially examining the degree of similarity between the derived average value and the target energy consumption amount forming the second database,
Comparing the values derived through the inspection to derive the target energy consumption amount including a result having the maximum similarity,
And deriving the target temperature mapped with the target energy consumption amount including the resultant value having the maximum degree of similarity thus derived, thereby setting the derived target temperature to the target temperature for the set range. How to manage. 8. The method of claim 7,
And unit integration is performed for the room temperature, the target temperature, and the target energy consumption, which are the variables forming the second database, through standardization of the data scale standardizing each variable of the second database . The method according to claim 1,
Setting the target temperature for the set range includes:
The fixed target temperature is set by setting the sum of the target energy consumption amount, which is a sum of all the target energy consumption amounts per section, to be included in the total target energy usage amount range which is a sum of the energy consumption amount ranges per section . The method according to claim 1,
Wherein the step of controlling the indoor temperature according to the target temperature comprises:
Wherein the difference between the set target temperature and the room temperature is continuously decreased and the absolute value of the difference between the set target temperature and the room temperature is changed to a predetermined set value or less, And switching the cooling / heating unit to an off state when the predetermined time is equal to or less than a predetermined time. An input unit for receiving a load factor for energy and a target energy consumption amount;
Deriving a predicted room temperature value corresponding to the input load factor based on a first database in which a past load factor and a past room temperature are mapped to derive a ratio value with respect to the target energy consumption amount, Based on the past room temperature and the past target temperature and the past energy consumption amount, based on the target energy consumption amount and the target energy consumption amount, A target temperature setting unit for setting a target temperature for the heating unit; And
And a controller for controlling the room temperature according to the target temperature,
Wherein the controller corrects the room temperature predicted value and the set target temperature by using a proportional formula so as to reduce the probability of occurrence of an error with respect to the predicted room temperature and the set target temperature. 12. The method of claim 11,
Wherein,
Sequentially examining the similarity between the input load factor and past load factors forming the first database, comparing the values derived through the inspection, and comparing past derived load factors including the result with the maximum similarity And deriving a past room temperature mapped with a past load factor including a result value having the maximum degree of similarity as the predicted room temperature value,
And integrating units of outdoor temperature, humidity, lighting equipment, and personnel, which are variables of the load factor, through standardization of data scales that normalize each variable of the load factors. 12. The method of claim 11,
Wherein,
When a value matching the minimum value to the maximum value is derived by querying the second database for a minimum value or a maximum value in the range of the target energy consumption amount,
And sets the selected target temperature to the target temperature for the set range by selecting the most recently used target temperature among the derived minimum and maximum values and the mapped target temperature. 12. The method of claim 11,
Wherein,
When a value matching the minimum value to the maximum value is not derived by querying the second database for the minimum value and the maximum value in the range of the target energy consumption amount,
The average value of the minimum value and the maximum value is derived, and the similarity between the derived average value and the target energy consumption amount forming the second database is sequentially checked, and the values derived through the inspection are compared, Deriving the target energy consumption amount including a result value that is maximum and deriving a target temperature that is mapped with the target energy consumption amount including the resultant value in which the derived degree of similarity is the maximum, To the target temperature,
And unit integrates the room temperature, the target temperature, and the target energy consumption, which are variables that form the second database, through standardization of data scales that normalize each variable of the second database.

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