KR102585859B1 - The Building management system and method based on the analysis of big data - Google Patents

Abstract

A big data analysis-based building management system according to an embodiment of the present invention includes first data including environmental conditions outside the building, second data including user information inside the building, and first data including environmental conditions inside the building. 3A data collection unit that collects data, an environmental goal setting unit that sets the environmental goal of the building, and a device that obtains and analyzes the first data to the third data and adjusts the environmental conditions to achieve the set environmental goal. When supplying power, a power consumption prediction unit that predicts the expected power consumption by the device, and a power consumption prediction unit that predicts the expected power consumption to be supplied to the device from the building's general power supply source and the building's reserve power supply source, respectively, according to the predicted power consumption. It may include a supply power setting unit that sets the amount of supplied power, and a supply power control unit that controls the power supply to supply power to the device from the general power supply source and the reserve power supply source according to the set amount of supplied power.

Description

Building management system and method based on big data analysis {The Building management system and method based on the analysis of big data}

The present invention relates to a building management system and method based on big data analysis, which can minimize building energy consumption and provide a comfortable indoor environment by using big data analysis results regarding building internal and external environmental conditions. It relates to building management systems and methods.

As construction technology develops, buildings continue to be modernized, and various facilities located within the building also continue to develop. As building facilities are modernized, control systems that automatically control facilities such as power, lighting, and disaster prevention installed in buildings are also spreading, and the development of building management systems that manage these control systems is also actively progressing. This building management system has the function of automatically turning on/off facilities or equipment in the building, or automatically controlling them according to set values at a predetermined time. However, this building management system automatically controls facilities in the building based on predetermined settings without considering the distribution of users in the building, which may result in unnecessary energy waste and provide an uncomfortable environment for some users in the building. . Therefore, there is a need for the development of a building management system that can reduce building energy consumption and increase convenience for users in the building.

Republic of Korea Patent No. 10-2269581

The present invention aims to provide a big data analysis-based building management system and method that can minimize building energy consumption.

Additionally, the present invention provides a big data analysis-based building management system and method that can maintain a comfortable indoor environment inside a building.

In addition, the present invention provides a big data analysis-based building management system and method that can automatically adjust the indoor environment according to the building's internal and external environmental conditions.

In addition, the present invention provides a big data analysis-based building management system and method that can automatically adjust the indoor environment according to user environmental conditions inside the building.

In order to achieve the above object, a big data analysis-based building management system according to an embodiment of the present invention includes first data including environmental conditions outside the building, second data including user information inside the building, and the building. a data collection unit that collects third data including internal environmental conditions; An environmental goal setting unit that sets environmental goals for the building; a power consumption prediction unit that obtains and analyzes the first data to the third data to predict expected power consumption by the device when supplying power to the device that adjusts the environmental conditions to meet the set environmental goal; a supply power setting unit that sets the amount of power to be supplied to the device from the building's general power supply source and the building's reserve power supply source according to the predicted expected power consumption; and a power supply control unit that controls power supply to supply power to the device from the general power supply source and the spare power supply source according to the set amount of power supply.

In addition, in the big data analysis-based building management system according to an embodiment of the present invention, the supply power setting unit compares the predicted expected power consumption with the power supply capacity of the general power supply source of the building, and as a result of the comparison, the predicted expected power is obtained. If the power consumption is more than the preset first ratio of the power supply capacity, the amount of power to be supplied from the general power supply source is set as the first supply power amount to a value reflecting the first ratio compared to the total power supply, and the reserve power is set as the first power supply amount. The amount of power to be supplied from the supply source is set as the second supply power amount to a value reflecting the second ratio excluding the first ratio compared to the total supply amount, and the supply power control unit supplies the first power supply to the device from the general power supply source. Power supply may be controlled to supply power according to the amount of power, and to supply power from the reserve power source to the device according to the second amount of power supplied.

In addition, in the big data analysis-based building management system according to an embodiment of the present invention, the first data is temperature, humidity, precipitation, illuminance, sun position, fine dust, wind strength and wind direction, and the second data is The number, gender and age of people entering the building, and the third data include temperature, humidity, illuminance and air quality inside the space, and the device may include an air conditioner, a humidifier, a lighting device and an air purifier. .

In addition, in the big data analysis-based building management system according to an embodiment of the present invention, the data collection unit, the environmental target setting unit, the power consumption prediction unit, the supply power setting unit, and the supply power control unit each day have at least three or more After classifying into sections, it can be processed independently according to the classified sections.

In addition, in the big data analysis-based building management system according to an embodiment of the present invention, the reserve power supply source is located outside the building and may include an energy storage device that stores energy generated by the external environment of the building. there is.

In order to achieve the above object, the big data analysis-based building management method according to an embodiment of the present invention includes first data including environmental conditions outside the building, second data including user information inside the building, and the building. collecting third data including internal environmental conditions; Setting environmental goals for the building; obtaining and analyzing the first to third data to predict expected power consumption by the device when supplying power to the device that adjusts the environmental conditions to meet the set environmental goal; Setting the amount of power to be supplied to the device from the general power supply of the building and the reserve power supply of the building according to the predicted power consumption; And it may include controlling power supply to supply power to the device from the general power supply source and the spare power supply source according to the set amount of power supply.

The big data analysis-based building management system and method according to an embodiment of the present invention can minimize building energy consumption.

Additionally, the big data analysis-based building management system and method according to an embodiment of the present invention can maintain a comfortable indoor environment inside the building.

In addition, the big data analysis-based building management system and method according to an embodiment of the present invention can automatically adjust the indoor environment according to environmental conditions inside and outside the building.

In addition, the big data analysis-based building management system and method according to an embodiment of the present invention can automatically adjust the indoor environment according to user environmental conditions inside the building.

Figure 1 is a diagram showing a big data analysis-based building management system according to an embodiment of the present invention.
Figure 2 is a diagram for explaining measurement of environmental conditions outside a building according to an embodiment of the present invention.
Figure 3 is a diagram for explaining user measurement inside a building according to an embodiment of the present invention.
Figure 4 is a diagram for explaining measurement of users staying in a separately partitioned space inside a building according to an embodiment of the present invention.
Figure 5 is a diagram for explaining measurement of environmental conditions inside a building according to an embodiment of the present invention.
Figure 6 is a diagram for explaining prediction of environmental conditions inside a building space according to an embodiment of the present invention.
Figure 7 is a diagram for explaining the operation of the power consumption prediction unit according to the first embodiment of the present invention.
Figure 8 is a diagram for explaining the operation of the power consumption prediction unit according to the second embodiment of the present invention.
Figure 9 is a diagram for explaining the operation of the supply power setting unit according to an embodiment of the present invention.
Figure 10 is a diagram for explaining a big data analysis-based building management method according to an embodiment of the present invention.

Since the present invention can make various changes and have various embodiments, specific embodiments will be illustrated in the drawings and described in detail.

However, this is not intended to limit the present invention to specific embodiments, and should be understood to include all changes, equivalents, and substitutes included in the spirit and technical scope of the present invention.

The terms used in this application are only used to describe specific embodiments and are not intended to limit the invention. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this application, terms such as “comprise” or “have” are intended to designate the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, but are not intended to indicate the presence of one or more other features. It should be understood that this does not exclude in advance the possibility of the existence or addition of elements, numbers, steps, operations, components, parts, or combinations thereof.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the technical field to which the present invention pertains. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and should not be interpreted in an ideal or excessively formal sense unless explicitly defined in the present application. No.

Hereinafter, preferred embodiments of the present invention will be described in more detail with reference to the attached drawings. In order to facilitate overall understanding when describing the present invention, the same reference numerals are used for the same components in the drawings, and duplicate descriptions for the same components are omitted.

Figure 1 is a diagram showing a big data analysis-based building management system according to an embodiment of the present invention. As shown in Figure 1, the big data analysis-based building management system 100 according to an embodiment of the present invention includes a data collection unit 110, an environmental goal setting unit 120, a power consumption prediction unit 130, and a supply It may include a power setting unit 140 and a power supply control unit 150. Hereinafter, the operation of the big data analysis building management system 100 according to an embodiment of the present invention will be examined with reference to FIG. 1.

The data collection unit 110 can collect various data regarding environmental conditions and user conditions inside and outside the building. More specifically, first data including environmental conditions outside the building, second data including user information inside the building, and third data including environmental conditions inside the building may be collected.

Figure 2 is a diagram for explaining measurement of environmental conditions outside a building according to an embodiment of the present invention. As shown in FIG. 2, according to the big data analysis-based building management system 100 according to an embodiment of the present invention, various measuring devices 111 for measuring environmental conditions can be installed outside the building. Any device that can measure specific environmental conditions can be used as a measuring device, and its type is not particularly limited. For example, all kinds of sensors, measuring instruments, etc. can be used as measuring devices. Additionally, temperature, humidity, precipitation, fine dust, sunlight, wind, etc. measured by various measuring devices can be managed as first data. In an embodiment of the present invention, the value measured by the measuring device can be used as the first data, but data and/or forecast data used by authorized organizations such as the Korea Meteorological Administration can also be used as the first data.

Figure 3 is a diagram for explaining user measurement inside a building according to an embodiment of the present invention. As shown in FIG. 3, a device 112 that measures and analyzes people entering the building may be located at the building entrance. These devices can be used to measure information about people entering a building, including the number, gender, and age of people entering the building, and the measured values can be managed as secondary data. Sensors and/or cameras may be used as devices that measure user entry, and these sensors and/or cameras may be used to measure all information about people entering the building, such as the number, time, gender, and age of people entering the building. Information can be measured and managed.

FIG. 4 is a diagram illustrating measurement of users staying in a separately partitioned space inside a building according to an embodiment of the present invention. As shown in Figure 4, the inside of the building 113 is divided into a number of spaces (A, B, C, D, E, F), and each space has the number, gender, and age of people entering the space. There are devices (113a, 113b, 113c, 113d, 113e, 113f, 113g) that can measure information about people entering the space, and the values measured through each of these devices can be managed as secondary data. there is. Multiple spaces (A, B, C, D, E, F) can be divided and partitioned by floor, and devices (113a, 113b, 113c, 113d, 113e) that can measure information about people entering the space , 113f, 113g), various sensors and/or cameras may be used.

The interior of a building may include various facilities that affect the environmental conditions of the building, including the location and operation of ventilation fans inside the building, the location and openness of doors, the location and size of windows, and whether they are open or closed, and the location and covering of window blinds. Equipment information including availability can be managed as additional data of third party data. Information about various facilities can be obtained through separate sensors and/or cameras, or through facility information installed through a layout map inside the building and cameras that observe these facilities.

Figure 5 is a diagram for explaining measurement of environmental conditions inside a building according to an embodiment of the present invention. As shown in Figure 5, various measuring devices for measuring environmental conditions inside the building may be installed in each of the multiple spaces inside the building, and the temperature and humidity of each space inside the building measured by these measuring devices. , environmental conditions inside the building, such as precipitation, fine dust, sunlight, wind, etc., can be managed with third party data. And, the data collection unit 110 can collect these first to third data.

The big data analysis-based building management system 100 according to an embodiment of the present invention obtains the first data to the third data collected by the data collection unit 110 and analyzes the correlation to determine the environmental conditions inside the building space. When no separate power is supplied to the device that controls the environment, each environmental condition inside the space can be predicted. Due to environmental conditions inside and outside the building (temperature, humidity, precipitation, fine dust, sunlight, wind, etc.), the environmental conditions of the building's interior space can change even if the building does not supply any power. For example, environmental conditions outside the building such as temperature, precipitation, position of the sun, etc., the number of people entering and exiting the building, the number of people staying in each space inside the building, the time these people stay in the space, and the number of people located inside the building. The temperature, humidity, illumination, and air quality of each space inside the building may vary depending on the location of the ventilator, the operating time of the ventilator, and the location, size, and openness of the windows located in each space of the building. In addition, in the big data analysis-based building management system 100 according to an embodiment of the present invention, different weights are assigned to the first and second data, and the correlation with the third data is analyzed to determine the Environmental conditions can be predicted. Therefore, the environmental conditions of the building can be known without any power being supplied inside the building.

Figure 6 is a diagram for explaining prediction of environmental conditions inside a building space according to an embodiment of the present invention. As shown in Figure 6, the external environmental conditions of spaces A, B, C, and D are temperature (30℃), wind (east wind 2.3m/s), humidity (60%), precipitation (3mm), and building user conditions. When the number of people entering and leaving (100 people in the morning, 200 people in the afternoon) is the same, and the space user conditions and internal facility conditions are different as shown in Figure 6, the space A, space B, space C, and space D as additional data are Spatial environmental conditions can be predicted. In this way, the big data analysis-based building management system 100 according to an embodiment of the present invention can predict environmental conditions for each space inside the building, so when the inside of the building is divided into 20 spaces, each of the 20 spaces Environmental conditions can be predicted. Additionally, environmental conditions inside the building can also be predicted.

The environmental goal setting unit 120 can set environmental goals for the interior of the building. Additionally, the environmental goal setting unit 120 can also set environmental goals for the space inside the building. In general, a building can be divided into a number of physically separated spaces, and environmental goals for these spaces may need to be set individually. For example, environmental goals can be set differently for an office that employees use all the time and a break room that employees use only during specific times. Accordingly, the environmental goal setting unit 120 can collect information about the entire space located inside the building, identify the environmental conditions required for this space, and then set different environmental goals for each space.

Here, the environmental conditions include environmental conditions that can be adjusted through equipment control located in the building, and may include temperature, humidity, illuminance, and air quality, and the environmental target setting unit 120 determines the temperature and humidity of the building. , target conditions for illuminance and air quality can be set. In addition, environmental goals for each building space can be set the same for all, or can be set differently for several groups. Therefore, the environmental goal setting unit 120 can set environmental goals inside the building space according to the characteristics of the building and the characteristics of the space inside the building. At this time, environmental goals can be set differently depending on the month, day of the week, and public holiday, and the day can be divided into at least 3 or more sections and set differently for each section. For example, different environmental goals can be set depending on morning work hours, lunch hours, and afternoon work hours.

The power consumption prediction unit 130 is a device that obtains and analyzes the first to third data, and adjusts the environmental conditions inside the building so that the environmental conditions of the building meet the environmental target set by the environmental target setting unit 120. When powering, you can predict the expected power consumption by these devices.

Figure 7 is a diagram for explaining the operation of the power consumption prediction unit according to the first embodiment of the present invention. Hereinafter, with reference to FIG. 7, the operation of the power consumption prediction unit 130 can be examined. As shown in FIG. 7, the power consumption prediction unit 130 uses first data (external environmental conditions, 700), second data (building user conditions, 710), and third data (internal environmental conditions, 720) in a table. After managing in the form, the target environmental condition 730 and the device 740 for controlling the environmental condition can be matched to this table. In addition, through correlation analysis of each data, it is possible to predict the expected power consumption by the device 740 to maintain the building environment as the target environment. For example, based on the correlation between the first data and the third data, the power supply amount and supply time that must be supplied to the device 740 to increase the temperature of the building by 1°C and humidity by 1% are calculated, and then The expected power consumption by this device 740 can be predicted.

Figure 8 is a diagram for explaining the operation of the power consumption prediction unit according to the second embodiment of the present invention. As shown in Figure 8, space A, space B, space C, and space D are the big data analysis-based building management system 100 according to an embodiment of the present invention. The internal environmental conditions 800 and the environmental goal setting unit ( The target environmental conditions 810 set in 120) can be managed in table form. Additionally, in order to set the environmental conditions of each space to a target value, the power consumed by the device that controls the environmental conditions can be predicted. For example, if the air conditioner must be operated for 3 hours, the air purifier for 8 hours, and the humidifier for 3 hours to create the target environmental conditions 810, considering the power consumption and operation time of each device, each environmental condition control device and The power consumption of the entire environmental condition control device can be predicted. In this way, the power consumption prediction unit 130 first calculates the operation time of each environmental condition control device, and then reflects the calculated operation time in the hourly power consumption information of each environmental condition control device to predict the expected power consumption. .

The supply power setting unit 140 determines the amount of power that should be supplied from the building's general power supply source and the building's reserve power source to the device that controls environmental conditions inside the building, respectively, according to the expected power consumption predicted by the power consumption prediction unit 130. can be set. According to the big data analysis-based building management system 100 according to an embodiment of the present invention, it is located inside the building and uses a general power supply source and a reserve power supply source as a source of power supplied to a device that controls the environmental conditions of the building. You can. The supply power setting unit 140 can distribute the amount of supply power required to supply the expected power consumption predicted by the power consumption prediction unit 130 and set the amount of supply power to be supplied from the general power supply source and the reserve power supply source, respectively. For example, if the expected power consumption is 15 kWh, the supply power setting unit 140 may set the amount of power to be supplied from the general power supply source to 10 kWh and the amount of power to be supplied from the reserve power supply source to 5 kWh. At this time, the supply power setting unit 140 may express the amount of supply power to be supplied from each of the general power supply source and the reserve power source as a ratio to the total amount of power supply, or determine the amount of power to be supplied from the general power supply source and use the remainder as reserve power. It can also be set to the amount of power supplied by the source.

The supply power control unit 150 can control the power supply to supply power to a device that adjusts the environmental conditions of the building from the general power supply source and the reserve power supply source according to the amount of supply power set in the supply power setting unit 140. . Environmental conditions inside and outside the building of the big data analysis-based building management system 100 according to an embodiment of the present invention may be different every year, every month, every day, every week, every day, and every hour. Therefore, the data collection unit 110, the environmental target setting unit 120, the power consumption prediction unit 130, the supply power setting unit 140, and the supply power control unit 150 have at least 3 days of the month, day of the week, public holidays, and one day. It can be classified according to the above sections and processed independently according to the classified section. For example, a day can be divided into three or more sections, such as morning (08:00 ~ 12:00), afternoon (12:00 ~ 17:00), and night (17:00 ~ 20:00), and then divided into months. After classifying by day of the week and public holiday, it can be processed independently according to the classified section. In the embodiment of the present invention, a day is classified into 3 sections, but this is not necessarily limited, and the number is not particularly limited as long as it can be classified into 3 or more sections.

Figure 9 is a diagram for explaining the operation of the supply power setting unit according to an embodiment of the present invention. Hereinafter, the operation of the supply power setting unit 140 will be examined in detail with reference to FIG. 9.

The supply power setting unit 140 classifies the expected power consumption predicted by the power consumption prediction unit 130 into at least three sections, and then compares the expected power consumption for each section with the power supply capacity of the general power supply source. Depending on the comparison result, the amount of power supplied from the general power supply source and the reserve power supply source can be set differently. More specifically, the supply power setting unit 140 compares the expected power consumption with the power supply capacity of a general power supply source, and as a result of the comparison, in sections (section 1 and section 3) where the expected power consumption is less than 90% of the power supply capacity, The amount of power supplied from the general power supply source is set to 100% (section 1 (16 kWh) and section 3 (18 kWh)), and in the section (section 2) where the expected power consumption is less than 90% of the power supply capacity, the general power supply source The amount of power supplied from can be set to 90% of the expected power consumption (section 2 (19.8kWh)), and the amount supplied from the reserve power source can be set to 10% of the expected power consumption (section 2 (0.2kWh)). . Here, the 90% ratio may be set differently depending on the power supply and demand situation. In addition, the power supply ratio of the general power supply source and the reserve power source can be set differently depending on the result of comparing the expected power consumption and the power supply capacity of the general power supply source.

In this way, in sections where the expected stored power is greater than the power supply capacity of the general power supply source, the amount of power supplied from the reserve power supply source may be expanded, and in sections where the expected stored power is small, the amount of power supplied from the reserve power source may not be supplied. That is, the supply power setting unit 140 may set the supply power supplied from the reserve power source differently through comparison of the expected power consumption and power supply capacity.

As in the embodiment of the present invention, if the amount of power supplied from a general power supply source is determined based on the expected power consumption, the general power supply source can be stably used even in sections where the expected power consumption is large, so power can be managed efficiently. .

Here, the reserve power supply source may include an energy storage device that, when located outside the building, stores energy generated by the environment surrounding the building. Here, the Energy Storage System (ESS) stores electrical energy produced by the external environment and allows the energy to be used when power is needed. The big data analysis-based building management system 100 according to an embodiment of the present invention may be equipped with an energy storage device outside the building, and the power stored in this energy storage device can be used when necessary. A solar energy storage device may be used in embodiments of the present invention, but is not necessarily limited thereto. In addition, in the big data analysis-based building management system 100 according to an embodiment of the present invention, first data such as temperature, humidity, precipitation, fine dust, sunlight, wind, etc. outside the building and electricity substantially stored in the energy storage device By analyzing the relationship between energy, the expected storage power stored in the energy storage device can be predicted. In addition, the power supply amount of the reserve power supply source can be predicted through the predicted expected storage power, and this can be used to adjust the messenger supply amount of the general power supply source and the reserve power supply source.

Figure 10 is a diagram for explaining a big data analysis-based building management method according to an embodiment of the present invention. Hereinafter, with reference to FIGS. 1 to 10, we will look at the operation of the big data analysis-based building management method according to an embodiment of the present invention.

In step 1000, first to third data are collected. More specifically, first data including environmental conditions outside the building, second data including user information inside the building, and third data including environmental conditions inside the building can be collected.

In step 1100, environmental goals for the building are set. Additionally, environmental goals required by users can be set for each building space. At this time, environmental goals can be set differently depending on the month, day of the week, and public holiday, and a day can also be divided into multiple sections and set differently for each section. For example, different environmental goals can be set depending on morning work hours, lunch hours, and afternoon work hours.

In step 1200, the first to third data are obtained and analyzed to predict the expected power consumption by the device when supplying power to the device that adjusts environmental conditions to meet the set environmental goal of the building. More specifically, with reference to FIGS. 7 and 8 , in order to set the environmental conditions of each space of a building to a target value, the power consumed by the device for controlling the environmental conditions can be predicted.

In step 1300, the amount of power to be supplied to the device from the building's general power supply source and the building's reserve power supply source is set according to the predicted expected power consumption. In an embodiment of the present invention, the expected power consumption and the supply capacity of the general power supply source can be compared, and the amount of power supplied by the general power supply source and the reserve power supply source can be set according to the comparison result. In addition, the amount of power supplied is divided into three or more sections such as morning (08:00 ~ 12:00), afternoon (12:00 ~ 17:00), and night (17:00 ~ 20:00), and each section is divided into three or more sections. You can set it differently. More specifically, the expected power consumption is compared with the power supply capacity of the building's general power supply source, and if the predicted expected power consumption as a result of the comparison is more than the preset first ratio (90%) of the power supply capacity, the general power supply source The amount of power to be supplied from the reserve power source is set as the first power supply amount reflecting the first ratio (90%) to the total power supply, and the amount of power to be supplied from the reserve power supply source is set to the first ratio (90%) to the total power supply. A value reflecting the remaining second ratio (10%) can be set as the second supply power amount.

In step 1400, power supply is controlled so that power is supplied to the device from the general power supply source and the reserve power supply source according to the set supply power amount. Therefore, building energy consumption can be minimized and a comfortable indoor environment can be provided.

Embodiments of the invention may be implemented in a computer-implemented method or as a non-transitory computer-readable medium on which computer-executable instructions are recorded. When the computer-readable instructions are executed by a processor, the computer-readable instructions may perform a method according to at least one aspect of the present invention.

As described above, embodiments of the present invention are disclosed in the drawings and specification. Although specific terms are used here, they are used only for the purpose of describing the present invention and are not used to limit the meaning or scope of the present invention described in the claims. Therefore, those skilled in the art will understand that various modifications and equivalent other embodiments are possible therefrom. Therefore, the true technical protection scope of the present invention should be determined by the technical spirit of the attached patent claims.

100: Big data analysis-based building management system
110: Data collection department
120: Environmental goal setting department
130: Power consumption prediction unit
140: Supply power setting unit
150: Supply power control unit

Claims (6)

In a big data analysis-based building management system,
First data containing information about the temperature, humidity, precipitation, illuminance, sun position, fine dust, wind strength, and wind direction outside the building where the system is located, and the system can be accessed by entering the building where the system is located. Data that collects secondary data, which includes information about the number, gender and age of people staying inside the building where the system is located, and third data, which includes information about temperature, humidity, illuminance and air quality inside the building where the system is located. collection department;
An environmental goal setting unit that sets environmental goals for the building where the system is located;
The first data to the third data are managed in the form of a table, and a device including an air conditioner, a humidifier, a lighting device, and an air purifier that adjusts environmental conditions to meet the set environmental goals, and the set environmental goals are stored in the table. After matching, a power consumption prediction unit that predicts the expected power consumption of the device when supplying power to the device to achieve the set target environment through correlation analysis of the first data to the third data. ;
The predicted expected power consumption is compared with the power supply capacity of the general power supply source of the building where the system is located, and if the predicted expected power consumption is more than a preset first ratio of the power supply capacity as a result of the comparison, the general power supply capacity is compared. The amount of power to be supplied from the power supply source is set as the first supply power amount to a value that reflects the first ratio compared to the total power supply, and the amount of power to be supplied from the reserve power supply source is set to the remaining amount excluding the first ratio compared to the total power supply. 2A supply power setting unit that sets a value reflecting the ratio as the second supply power amount; and
A supply power control unit that controls the power supply so that the general power supply source supplies power to the device according to the first amount of power supplied, and the reserve power supply source supplies power to the device according to the second amount of power supplied; ,
The data collection unit, the environmental target setting unit, the power consumption prediction unit, the supply power setting unit, and the supply power control unit classify the month, day of the week, public holiday, and day into at least three sections, and then A big data analysis-based building management system characterized by independent processing. delete delete delete The method of claim 1, wherein the spare power supply source is
A big data analysis-based building management system that is located outside the building and includes an energy storage device that stores energy generated by the external environment of the building. In the big data analysis-based building management method of the big data analysis-based building management system,
First data containing information about the temperature, humidity, precipitation, illuminance, sun position, fine dust, wind strength, and wind direction outside the building where the system is located, and the system can be accessed by entering the building where the system is located. Collecting second data including information on the number, gender, and age of people staying inside the building where the system is located, and third data including information on temperature, humidity, illumination, and air quality inside the building where the system is located. ;
Setting environmental goals for the building where the system is located;
The first data to the third data are managed in the form of a table, and a device including an air conditioner, a humidifier, a lighting device, and an air purifier that adjusts environmental conditions to meet the set environmental goals, and the set environmental goals are stored in the table. After matching, predicting expected power consumption by the device when supplying power to the device to achieve the set target environment through correlation analysis of the first data to the third data;
Compare the predicted expected power consumption with the power supply capacity of the general power supply source of the building, and if the predicted expected power consumption is more than a preset first ratio of the power supply capacity as a result of the comparison, the general power supply source The amount of power to be supplied is set as the first power supply amount reflecting the first ratio to the total power supply, and the amount of power to be supplied from the reserve power supply source is set to the second ratio excluding the first ratio to the total power supply. Setting the reflected value as the second supply power amount; and
Comprising the step of controlling power supply so that the general power supply source supplies power to the device according to the first power supply amount, and the spare power supply source supplies power to the device according to the second power supply amount,
All of the above steps are a big data analysis-based building management method of a big data analysis-based building management system, characterized in that the month, day of the week, public holiday, and day are classified into at least three sections and then independently processed according to the classified sections. .