KR20230048846A - Customized cloud fems system for energy saving in distributed facilities with clean room environment and method of operating the system - Google Patents

Abstract

A method of operating a customized cloud FEMS system for controlling the energy of multiple clean room sites including environmental support facilities for creating a clean room environment may include: providing a cloud FEMS networked with multiple clean room sites; collecting characteristic data and energy data for a specific period of time for each clean room site in a predetermined time unit; deriving characteristic data for each clean room site; collecting environmental data such as weather forecasts for each clean room site; and calculating an operating standard for environmental support facilities for each clean room site using the characteristic data and environmental data.

Description

A customized cloud FEMS system for energy saving in distributed workplaces equipped with a clean room environment and its operating method

The present invention relates to a customized cloud FEMS system and its operating method that can integrate and manage separated distributed workplaces and expect energy savings in operating two or more distributed workplaces equipped with a clean room environment.

Clean room refers to a space made to control environmental conditions such as dust, fine particles, temperature, humidity, static electricity, illumination, and airflow resistance to the required values. It is supplied in many industrial factories such as medicine, food, and Biohazard.

In order to maintain desired environmental conditions, a factory or workplace including a clean room basically consumes a large amount of energy. In particular, in Korea, which has emerged as a powerhouse in the high-tech semiconductor industry, semiconductor equipment and clean room equipment, which are supporting facilities, are also growing together, and the market size is gradually expanding.

Therefore, help of a factory energy management system (FEMS) is desperately needed to manage a factory or workplace including such a clean room. The energy management system installs a sensor on a device or facility that consumes energy, transmits the energy consumption information detected through the sensor to the FEMS server, and the FEMS server performs production activities while minimizing energy consumption based on the collected energy consumption information. However, it is a system that guides and manages so that there are no problems affecting quality.

Recently, the importance of systematic management is required to respond to domestic and foreign legal regulations on climate change beyond simple energy saving.

In particular, when the management entity has to operate or manage multiple workplaces rather than a single workplace, it is necessary to manage the energy of various workplaces in different regions or borders. Depending on each region or country, climate change and workplace construction It is very difficult to manage them in an integrated manner because of the diversity of construction methods, years of completion, building structures, and operating facilities.

Registered Patent No. 10-1800310 relates to "a cloud EMS system and method providing energy saving guide", which groups and classifies a plurality of buildings, guides and We provide a way to manage it.

The present invention provides a customized cloud FEMS system and a method of operating the system that can integrate and manage the energy of various distributed workplaces distributed across various regions or borders.

The present invention is a customized cloud FEMS system that can reduce the cost of building and operating an energy management system by using a cloud network to comprehensively manage distributed workplaces, thanks to the development of ICT and the development of cloud computing technology. and a method for operating the system.

According to an exemplary embodiment of the present invention for achieving the above-described objects of the present invention, operation of a customized cloud FEMS system for controlling energy of a plurality of clean room sites including environment support facilities for creating a clean room environment The method includes providing a cloud FEMS networked with a plurality of clean room sites, collecting characteristic data for each clean room site, measuring energy consumption of product production facilities and environmental support facilities for a specific period in the past for each clean room site. Collecting past energy data including energy consumption in predetermined time units, deriving characteristic data for each clean room site, collecting environmental data for each clean room site, using the characteristic data and environmental data to clean the room Calculating operating standards of environmental support facilities for each site, and operating environmental support facilities of clean room sites according to the calculated operating standards using the FEMS platform maintained for each clean room site.

According to this embodiment, characteristic data is derived for each clean room site using past energy data, but environmental data is collected for each clean room site using the weather forecast of the area to which the clean room site belongs based on the corresponding time, Using the data and environmental data, it is possible to provide in advance the operating criteria of the environmental support facilities from the corresponding time or on a daily basis.

In this embodiment, the environmental support equipment may include a refrigerator system, a pneumatic supply system, an air conditioning system, and the like, and the network may also not specify a specific type.

The cloud FEMS may include a characteristic analysis module that generates characteristic data for each clean room site and provides operating standards, and a plurality of FEMS platforms that control and monitor each clean room site according to the operating standards. The FEMS platform may include a clean room site Each driving guide module, monitoring module, and database may be included.

In addition to environmental support facilities and product production facilities, the clean room site can include local FEMS and local instruments, and the local FEMS can be in charge of communication and control with the FEMS platform. It may include a temperature sensor, a pressure sensor, a flow sensor, a power sensor, and the like for sensing operation.

Operating criteria for each clean room site may be provided in various forms. For example, in a clean room site, a refrigerator or the like requires a lot of energy to operate, and it is not easy to stop and restart the refrigerator because of its large capacity. Therefore, in this embodiment, as an operating criterion for energy saving of a clean room site, the expected number of operating units (N _s (t)) of the same facilities (eg, refrigerators) included in the environmental support facilities may be included for each hour.

The expected number of operating units per hour (N _s (t)) of the same specific facility can be calculated by the following formula.

N _s (t) = A + B * Production_kWh(t) + C * Enthalpy(t)

Here, A, B, and C are the seasonal characteristic coefficients for each clean room site, Production_kWh (t) is the energy consumption of the product production facility for the past m hours from the corresponding time (t), and Enthalpy (t) is the corresponding time ( Enthalpy information of t), and m may be selected within the range of 24 or more and 168 or less.

Seasonal characteristic coefficients A, B, and C for each clean room site can be reversely calculated from data per time unit for a specific period in the past according to a conventional coefficient calculation method.

N _s (t _p ) = A + B * Production_kWh(t _p ) + C * Enthalpy(t _p )

Here, N _s (t _p ) is the required number of operating units at the corresponding time in the past (t _p ), and Production_kWh (t _p ) is the energy consumption of the product production facility for the previous m hours from the corresponding time in the past (t _p ), Enthalpy (t _p ) is enthalpy information of a corresponding past time (t _p ), and m may be selected within the range of 24 or more and 168 or less.

Enthalpy information can be calculated by reflecting the hourly weather forecast for m hours, and the seasonal characteristic coefficients A, B, and C for each clean room site are calculated in a selected period among 1 week, 10 days, 15 days, January, and March units. can be updated accordingly.

For example, m is selected as 24, and energy consumption of product production facilities before 24 hours and enthalpy information using weather forecasts after 24 hours can be used based on the corresponding time, and the expected number of units operated every hour on the hour and real-time The required number of operating units can be calculated.

The monitoring module of the FEMS platform can calculate the real-time required number of operating units (N _{s (d)} ) of the chillers included in the environmental support facility, and the real-time required operating number (N _{s (d)} ) is the real-time measurement of the chiller system. It can be calculated by comparing the refrigeration amount (C _s ), the rated refrigeration amount (C _{s (d)} ) of the freezer system measured in real time, and the expected operating number of freezers (N _s ) per hour.

For example, the real-time required number of operating units (N _{s (d)} ) may be calculated by the following equation, and the Roundup() function may be a function that provides a natural number obtained by rounding up values in parentheses to decimal places.

In the step of collecting past energy data, a specific past period may be 1 year or more and 5 years or less, preferably, data for the past 1 year or 2 years may be used, and a specific period may be defined on a yearly basis.

In addition, the time unit for calculating the past energy data and real-time and hourly data may be one selected from 10 minutes, 15 minutes, 20 minutes, 30 minutes, 1 hour, 2 hours, and 3 hours, and the past stored energy data It can be used appropriately according to the format of

According to one exemplary embodiment of the present invention for achieving the above-described objects of the present invention, a customized cloud FEMS system includes a plurality of environment support facilities for creating a clean room environment, product production facilities, local FEMS, and local instruments. It may include a clean room site, and a cloud FEMS networked with a plurality of clean room sites, wherein the cloud FEMS generates characteristic data for each clean room site and provides operating criteria, and a characteristic analysis module for providing operating criteria, and a clean room site according to the operating criteria. It may include a plurality of FEMS platforms that each control and monitor.

The FEMS platform includes a database that stores operating criteria and characteristic data for each clean room site, an operating guide module that controls environmental support facilities according to operating criteria, and a monitoring module that monitors environmental support facilities using information from local instruments. can do.

In the customized cloud FEMS system according to this embodiment, the data stored in the FEMS platform or other databases is the past energy data including energy consumption of product production facilities and energy consumption of environmental support facilities for a specific period in the past for each clean room site. It can be stored in hourly units.

In addition, as mentioned in the above system operating method, the operating standard stored in the database may include the expected operating number (N _s (t)) of the same facility included in the environmental support facility by hour.

In the customized cloud FEMS system according to this embodiment, the data stored in the FEMS platform or other databases includes hourly weather forecast information for at least 24 to 168 hours, and the number of required operating units of refrigerators included in the environmental support facilities by hour (N _{s (d)} ) , the instantaneous refrigeration amount of the freezer system measured per hour (C _s ), the rated refrigeration amount of the freezer system measured per hour (C _{s (d)} ), and the expected operating number of freezers per hour (N _s ).

In addition, the real-time required number of operating units (N _{s (d)} ) may have the following relationship.

The customized cloud FEMS system of the present invention can integrate and manage the energy of various distributed business sites distributed across various regions or borders.

The customized cloud FEMS system of the present invention can reduce the construction cost and operating cost of an energy management system by managing distributed workplaces in an integrated manner, and can reflect environmental data such as weather forecasts to operate distributed workplaces efficiently. Alternatively, an operating schedule may be provided.

1 is a diagram for explaining a customized cloud FEMS system according to an embodiment of the present invention.
2 is a diagram for explaining a cloud FEMS and a clean room site in a customized cloud FEMS system according to an embodiment of the present invention.
3 is a diagram for explaining a method of operating a customized cloud FEMS system according to an embodiment of the present invention.
4 is a diagram for explaining environment support facilities of an n-th clean room site in a customized cloud FEMS system according to an embodiment of the present invention.
5 is a diagram comparing past data with patterns of other parameters according to outdoor temperature and outdoor enthalpy for a customized cloud FEMS system according to an embodiment of the present invention.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings, but the present invention is not limited or limited by the embodiments. For reference, in the present description, the same numbers refer to substantially the same elements, and descriptions may be made by citing contents described in other drawings under these rules, and contents determined to be obvious to those skilled in the art or repeated contents may be omitted.

1 is a diagram for explaining a customized cloud FEMS system according to an embodiment of the present invention, and FIG. 2 is a diagram for explaining a cloud FEMS and a clean room site in a customized cloud FEMS system according to an embodiment of the present invention. 3 is a diagram for explaining a method of operating a customized cloud FEMS system according to an embodiment of the present invention.

1 and 2, a plurality of clean room sites (300-1 to 300-n) and a cloud FEMS capable of transmitting and receiving information to and from each clean room site (300-1 to 300-n) through a network ( 100) may be included.

Clean room sites (300-1 to 300-n) can correspond to each distributed business site equipped with a clean room environment, and each clean room site (300-1 to 300-n) has a clean room environment support facility (300-1a). ~300-na), product production facilities (300-1b to 300-nb), local FEMS (300-1c to 300-nc), and local instruments (300-1d to 300-nd).

Clean room environment support facilities (300-1a ~ 300-na) are facilities for controlling the temperature, humidity, dust, particulates, static electricity, illumination, air pressure, etc. of the clean room. A cooling tower or the like may correspond to this.

Clean room environment support facilities (300-1a~300-na) are provided in the initial preparation process of each site, and since large-scale investment is required to replace or upgrade new facilities, it is necessary to optimize operation and improve operation energy performance as much as possible. that can be important

In addition, since the clean room environment support facilities (300-1a to 300-na) play a role in controlling the entire factory or workplace, real-time on-off control is not easy, and additional operation is relatively easy, but energy demand It is not easy to scale down operation sensitive to changes and requires a considerably long restart time, which is not desirable from the point of view of energy efficiency.

Therefore, conventionally, in operating environmental support facilities (300-1a to 300-na) that maintain clean rooms, they are operated based on the manager's experience without clear operating standards, and operated conservatively to the extent that warnings or alarms do not occur. In most cases it was.

The product production facilities 300-1b to 300-nb are actually facilities produced at each clean room site 300-1 to 300-n, and may be referred to as facilities for producing semiconductors, foods, medicines, and the like. Since product production facilities (300-1b to 300-nb) are directly involved in product production, they can be excluded from the control of this customized cloud FEMS system.

In addition, each clean room site (300-1~300-n) can be provided with local FEMS (300-1c~300-nc) and local instruments (300-1d~300-nd), and local instrumentation (300-1d~300-nd) 1d ~ 300-nd) can be a temperature sensor, flow meter, pressure gauge, etc. of clean room environment support facilities (300-1a ~ 300-na) or product production facilities (300-1b ~ 300-nb).

The cloud FEMS (100) receives necessary information from the clean room sites (300-1 to 300-n) and provides environmental support facilities (300-1a to 300-na) of each clean room site (300-1 to 300-n). may have the authority to control the To this end, the cloud FEMS 100 may include a clean room characteristic analysis module 110 and FEMS platforms 120-1 to 120-n for controlling each clean room site 300-1 to 300-n. .

Each FEMS platform (120-1 to 120-n) can store the characteristic data (120-1a to 120-na) of each clean room site (300-1 to 300-n), and this characteristic data (120-1a ~120-na) is the location of each clean room site (300-1 to 300-n), company name, year of completion, monthly electricity consumption, and environmental support that constitutes each clean room site (300-1 to 300-n). As information on facilities (300-1a~300-na), model, year of purchase, specifications, installation status of refrigerators, pumps, air conditioners, etc., and specifications and installation status of product production facilities (300-1b~300-nb) are provided. can include

And the FEMS platforms (120-1~120-n) include driving guide modules (120-1b~120-nb), monitoring modules (120-1c~120-nc) and other databases (120-1d~120-nd), etc. can include

The monitoring module (120-1c to 120-nc) of the FEMS platform (120-1 to 120-n) is a local FEMS (300-1c to 300-nc) of each clean room site (300-1 to 300-n) or The operation status of each clean room site (300-1 to 300-n) can be received in real time through the local measuring instrument (300-1d to 300-nd), and through the monitoring module (120-1c to 120-nc) Using the collected information, the driving guide modules 120-1b to 120-nb may calculate optimal operating conditions for each clean room site 300-1 to 300-n.

Other databases (120-1d~120-nd) include past operation data of clean room sites (300-1~300-n), for example, product production facilities (300- 1b ~ 300-nb) electricity consumption, electricity consumption of environmental support facilities (300-1a ~ 300-na), cold water or cooling water temperature, temperature around the factory, meteorological information such as humidity, and the like.

From these information, the clean room characteristic analysis module 110 calculates monthly/hourly electricity consumption patterns of product production facilities (300-1b to 300-nb), monthly/hourly outdoor air enthalpy patterns, and monthly/hourly environmental support facilities (300-1a to 300-nb). 300-na) can derive characteristic data of each clean room site (300-1 to 300-n), such as an operation pattern and electricity consumption pattern.

In addition, through these consumption patterns and operation patterns, it is possible to calculate the operation schedule of the environmental support facilities (300-1a to 300-na), for example, the optimal number of refrigerators operating on a monthly/hourly basis.

Referring to FIG. 3, in order to save energy in distributed workplaces equipped with a clean room environment, the characteristics of each workplace size, location, equipment specifications, and number of installations can be standardized, and the cloud FEMS 100 has multiple (n) Information may be collected from the clean room sites 300-1 to 300-n and stored in the FEMS platforms 120-1 to 120-n (S110).

In addition, the cloud FEMS 100 may collect past driving data, weather information, and energy consumption information for a certain period of time, for example, about one year (S120). As described above, such data may be stored in other databases 120-1d to 120-nd. Here, the past operation data is the electricity consumption of product production facilities (300-1b ~ 300-nb), electricity consumption of environmental support facilities (300-1a ~ 300-na), cold water or cooling water temperature, factory Weather information such as ambient temperature and humidity may be included.

The characteristic analysis module 110 of the cloud FEMS 100 may derive characteristic data of each clean room site 300-1 to 300-n from the collected data (S130), and the derivation of these characteristic data may result in product production. It may be an electricity consumption pattern of the facilities 300-1b to 300-nb, an energy consumption pattern of the environmental support facilities 300-1a to 300-na, a pattern of the number of operating refrigerators, and an outside air enthalpy pattern.

These patterns may be provided monthly or hourly, and the driving guide modules 120-1b to 120-n of the FEMS platforms 120-1 to 120-n corresponding to each clean room site 300-1 to 300-n. 120-nb) may provide a standard for controlling the environmental support facilities 300-1a to 300-na.

driving guide module

The driving guide modules 120-1b to 120-nb may be provided to FEMS platforms 120-1 to 120-n corresponding to each clean room site 300-1 to 300-n, and the environmental support facilities 300 -1a~300-na) can be controlled. For example, if the environmental support facilities (300-1a to 300-na) of the clean room site (300-1 to 300-n) include freezers, the optimal number of freezers is predicted in advance, can be compared

4 is a diagram for explaining environment support facilities of an n-th clean room site in a customized cloud FEMS system according to an embodiment of the present invention.

Referring to FIG. 4 , a chiller system may be provided in an environmental support facility (300-na) of a specific clean room site, and a plurality (s) of chillers (410-1 to 410-s) may be connected in parallel to the chiller system. can The plurality of refrigerators 410-1 to 410-s may be connected to one or more pipelines to provide cold water adjusted to a specific temperature to the product production facility 300-nb.

The temperature and flow rate of cold water supplied by the pipeline may be provided by each FEMS platform 120-n of the local FEMS 300-nc or the cloud FEMS 100, and an automatic valve or a manual valve depending on whether it is used. It is designed to provide cold water at a constant temperature through operation.

The chiller (410-1 to 410-s) is equipped with a flow sensor (412-1 to 412-s) and an outlet temperature sensor (414-1 to 414-s) on the output side, and a pump (418-s) on the input side. 1 to 418-s) and inlet temperature sensors 416-1 to 416-s. Motors of the refrigerators 410-1 to 410-s may be equipped with power sensors for detecting whether or not the refrigerators 410-1 to 410-s are operating or operating. The flow sensors 412-1 to 412-s, the inlet temperature sensors 416-1 to 416-s, the outlet temperature sensors 414-1 to 414-s, and the power sensor, etc., perform system operation according to the present embodiment. It is possible to use a sensor installed separately or installed to monitor the operation of an existing refrigerator to implement.

The cold water or medium passing through the refrigerating pipe by the operation of each pump 418-1 to 418-s is provided to each refrigerator 410-1 to 410-s, and the refrigerator 410-1 to 410-s The chilled water whose temperature is lowered by is supplied to the chilled water supply header again through the pipe, and can be supplied from the chilled water supply header to a place where cold water is needed among product production facilities 300-1b to 300-nb.

The cold water or medium that has passed through the product production facilities (300-1b to 300-nb) can be returned to the cold water return header in a state where the temperature has risen, and the returned cold water is returned to each pump (418-1 to 418-s). Through this, it is provided to the freezers 410-1 to 410-s again. A sensor for measuring temperature and pressure may be mounted on the cold water supply header, and a sensor for measuring temperature and pressure may be mounted on the cold water return header.

Local instruments such as the chiller's flow sensor (412-1 to 412-s), inlet temperature sensor (416-1 to 416-s), outlet temperature sensor (414-1 to 414-s), and power sensor are networked. ), and the network can deliver information collected from local instruments to the local FEMS (300-nc).

The local FEMS (300-nc) may transmit the collected information to the cloud FEMS (100), and conversely, it may control whether a specific device operates. The cloud FEMS 100 is connected to each clean room site 300-n through a network, and the clean room characteristic analysis module 110 predicts the refrigerator from data collected from the clean room site 300-n. It is possible to compare the number of units in operation and the number of units in operation required in real time.

The monitoring module 120-nc and the driving guide module 120-nb may use the instantaneous refrigeration amount, instantaneous power, and instantaneous load factor together to compare the real-time required and expected operating number of refrigerators.

The chillers (410-1 to 410-s) may include an automatic control circuit capable of independently controlling operating conditions, and may not be automatically adjusted independently to a small load factor when the output side flow rate of chilled water is almost zero. there is.

Relationship between load factor and efficiency of chiller system

In a chiller system in which a plurality of chillers (410-1 to 410-s) are connected in parallel, while the chillers (410-1 to 410-s) operate simultaneously, they may not always operate at their rated capacity, and may not always operate in the external environment such as the surrounding climate. Depending on the change in conditions, the amount of refrigeration to be provided may be changed from time to time.

For example, if several chillers are in operation, reducing the use of cold water in a facility using chiller utilities may reduce the instantaneous cooling capacity of the chillers, and in some cases, there may be chillers that are not actually operating.

In fact, it was found that even if the instantaneous cooling amount of cold water output from the chiller decreases in the chiller system, power consumption does not decrease as much as the reduced amount of refrigeration.

Here, the flow rate is Q (m ³ /Hr), the amount of refrigeration or frozen ton is C (RT), the power consumption is W (kW), the chilled water supply temperature is T _s (℃), and the chilled water return temperature is T _r (℃) , the density of chilled water can be defined as ρ (kg/m ³ ), the specific heat of chilled water can be defined as C _p (kcal/kg ℃), and the efficiency is η (RT/kWh), which is (freezing tons of freezer)/(of freezer can be defined as instantaneous consumption power). The instantaneous refrigeration amount or instantaneous refrigeration ton of the refrigerator can be defined as C _u by referring to Equation 1 below. For reference, Q _u is the instantaneous flow rate of each chiller.

In addition, the load factor can be defined as R (%) (instantaneous refrigeration ton of refrigerator) / (static capacity of refrigerator).

Here, R _u is the instantaneous load factor of an individual chiller, and R _s is the instantaneous load factor of the chiller system. And _Cu is the instant refrigeration ton of individual freezer, u is the individual freezer number, C _u(d) is the rated refrigeration ton of the individual freezer, C _s is the instant refrigeration ton of the freezer system, the sum of the instant refrigeration ton of the individual freezer, and C _s(d) can be defined as the rated refrigerant ton of the chiller system.

It can be seen that the chiller generally has a characteristic that its efficiency increases as the load factor increases. Therefore, when operating a chiller system composed of multiple units, operating at a high load factor may be one of energy saving measures. In order to operate at a high load factor, it may be necessary to minimize the number of operating refrigerators to correspond to the amount of refrigeration required on the load side (consumer side).

Verification of the number of operating units required in real time

Determining whether the plurality of refrigerators are operating at a high load factor can also be known in the process of calculating the real-time required operating number N _{s (d)} . According to this embodiment, the real-time required operating number (N _{s (d)} ) is the instantaneous refrigeration ton (C _s ) of the freezer system measured in real time, the rated refrigeration ton (C _{s (d)} ) and characteristics of the freezer system measured in real time It can be calculated by comparing the expected number of operating units (N _s ) using the pattern derived from the analysis module 110 .

Here, the Roundup() function may be defined as a function for obtaining a natural number obtained by rounding up the values in parentheses to less than a decimal point, and the real-time required number of operating units (N _{s (d)} ) may be calculated through the above formula.

Real-time excess/shortage operating units (N _e ) can be defined as the value obtained by subtracting the real-time required operating units from the expected operating units (=expected operating units - real-time required operating units). When N _e = 0, it can be seen that the chiller system is operating normally. However, when N _e ≥ 1, it can be defined as excessive operation of the refrigerator in the refrigerator system, and when N _e ≤ -1, it can be defined as insufficient operation of the refrigerator in the refrigerator system.

Delivery of information based on the number of operating units required in real time

According to the comparison result of the number of operating units (N _e ) in real time, the driving assistance equipment according to the present embodiment can provide various types of information.

For example, when it is determined that the freezer is operating excessively (N _e ≥ 1), if the rated refrigeration tones of the freezers in operation are not all the same, it is preferable to stop the operation of the freezers with the smallest rated refrigeration ton. This is because an unexpected cold water shortage may occur in the entire system if a refrigerator having a large rated refrigerating ton is incorrectly stopped. However, if all refrigerators in operation have the same rated refrigeration ton, it is preferable to stop the operation of the refrigerators with the lowest efficiency. For reference, the efficiency of a freezer can be defined as an instantaneous refrigeration ton for the instantaneous power consumption supplied to the freezer (C/W).

Conversely, if it is determined that the refrigerator is insufficiently operated (N _e ≤ -1), if the rated refrigerant tones of the refrigerators in operation are not all the same, it is preferable to start operation with a smaller rated refrigerant ton. Also, by operating the one with the smallest rated refrigeration ton, it is possible to prevent the load factor from falling significantly. On the other hand, if all refrigerators in operation have the same rated refrigeration ton, it is preferable to start operation with the highest efficiency.

Estimated number of units in operation for the next 24 hours using weather forecasts

The projected number of operating units (N _s ) can be influenced by the weather of the day in addition to the overall climate characteristics of the area. Therefore, in this embodiment, in order to calculate the expected number of operating units (N _s ), various methods may be attempted.

As an example, the following monthly characteristic coefficients (A, B, C) can be obtained by analyzing the correlation between the electricity consumption of production facilities for the past year and the number of operating refrigerators according to the change in outdoor air enthalpy. And, using this characteristic coefficient, the expected operating number of freezers (N _s ) for the next 24 hours is calculated every hour at clean room sites (300-1~300-n) or operation guide modules (120-1b~120-nb ) can be provided.

The expected number of operating units (N _s ) can be predicted using the characteristic coefficients of the month, the weather forecast, and the expected electricity consumption of production facilities.

(N _s )(t) = A + B * Production_kWh(t) + C * Enthalpy(t)

Here, A, B, and C are characteristic coefficients of the corresponding month, and may be calculated monthly in consideration of a climate cycle that changes every month. Of course, in order to more accurately reflect climate change, the characteristic coefficient can be defined in various ranges, such as weekly and 10-day units.

The characteristic coefficient can be calculated and updated using past data collected from each clean room site, such as the pattern of the number of refrigerators required to operate for the past year, the pattern of outdoor air enthalpy, and the pattern of electricity consumption of production facilities.

5 is a diagram comparing past data with patterns of other parameters according to outdoor temperature and outdoor enthalpy for a customized cloud FEMS system according to an embodiment of the present invention.

Referring to FIG. 5, it is possible to compare past outdoor temperature and enthalpy information with other parameters, that is, refrigeration load, power consumption, number of operating units, etc., and the monthly characteristic coefficients (A, B, C) using these past data can be derived.

In addition, on the contrary, using Production_kWh(t) and Enthalpy(t), the expected number of operating units by hour (N _s (t)) for the next 24 hours can also be calculated.

Here, Production_kWh(t) may be information on electricity consumption of production facilities 24 hours before from now, and Enthalpy(t) may be information on enthalpy of outdoor air time after 24 hours from now using a weather forecast.

The estimated number of operating units per hour (N _s ) (t) calculated in this way is determined by the environmental support facilities (300-1a) of each clean room site (300-1 to 300-n) through the operation guide modules (120-1b to 120-nb). ~300-na) can be the standard for controlling for 24 hours.

In addition, the expected number of operating units ((N _s ) (t)) for each hour is compared with the above-described real-time required operating number (N _{s (d)} (t)) to calculate the real-time excess/shortage operating unit (N _e ).

Monitoring module function

The monitoring modules (120-1c to 120-nc) of the FEMS platform (120-1 to 120-n) can be provided for each clean room site (300-1 to 300-n), and the monitoring point is the electricity consumption per ton of refrigeration. It may include information about the energy performance index of the refrigerator, chilled water temperature of the refrigerator, power consumption, and load factor.

As described above, although it has been described with reference to the preferred embodiments of the present invention, those skilled in the art will variously modify and change the present invention within the scope not departing from the spirit and scope of the present invention described in the claims below. You will understand that it can be done.

100: Cloud FEMS
300-1 to 300-n : Clean room site

Claims (19)

In the operating method of a customized cloud FEMS system for controlling the energy of a plurality of clean room sites including environmental support facilities for creating a clean room environment,
providing a cloud FEMS networked with a plurality of clean room sites;
collecting characteristic data for each clean room site;
Collecting past energy data including energy consumption of product production facilities and energy consumption of environmental support facilities for a specific period in the past for each clean room site in predetermined time units;
deriving characteristic data for each clean room site;
collecting environmental data for each clean room site;
calculating operating standards of environment support facilities for each clean room site using the characteristic data and the environmental data; and
A method of operating a customized cloud FEMS system comprising: operating the environment support facilities of the clean room site according to the calculated operating criteria using the FEMS platform maintained for each clean room site.
According to claim 1,
The FEMS platform is a method of operating a customized cloud FEMS system, characterized in that it includes a driving guide module, a monitoring module (120-1c ~ 120-nc) and a database for each clean room site.
According to claim 1,
The clean room site includes a local FEMS for communication and control with the FEMS platform and a local instrument for sensing the operation of the environment support facility and the product production facility. Method of operating a customized cloud FEMS system.
According to claim 1,
The operating standard includes the expected number of operating units (N _s (t)) by hour of the same facility included in the environmental support facility,
The expected number of operating units per hour (N _s (t)) is calculated by the following formula,
N _s (t) = A + B * Production_kWh(t) + C * Enthalpy(t)
Here, A, B, and C are seasonal characteristic coefficients for each clean room site, Production_kWh (t) is the energy consumption of product production facilities for the past m hours from the corresponding time (t), and Enthalpy (t) is the corresponding time Enthalpy information of (t), m is a method of operating a customized cloud FEMS system, characterized in that selected within the range of 24 or more and 168 or less.
According to claim 4,
The seasonal characteristic coefficients A, B, and C for each clean room site are calculated inversely by substituting the data for each time unit for a specific period in the past,
N _s (t _p ) = A + B * Production_kWh(t _p ) + C * Enthalpy(t _p )
Here, N _s (t _p ) is the required number of operating units at the corresponding time in the past (t _p ), and Production_kWh (t _p ) is the energy consumption of the product production facility for the previous m hours from the corresponding time in the past (t _p ), Enthalpy (t _p ) is enthalpy information of the past corresponding time (t _p ), and m is a method of operating a customized cloud FEMS system, characterized in that selected within the range of 24 or more and 168 or less.
According to claim 4,
The enthalpy information is a method of operating a customized cloud FEMS system, characterized in that calculated by reflecting the hourly weather forecast for the m time.
According to claim 4,
The seasonal characteristic coefficients A, B, and C for each clean room site are updated according to a period selected from among 1 week, 10 days, 15 days, January, and March. Method of operating a customized cloud FEMS system.
According to claim 4,
The monitoring module of the FEMS platform calculates the real-time required operating number of refrigerators included in the environmental support facility (N _{s (d)} ),
The real-time required operating number (N _{s (d)} ) is the instantaneous refrigeration amount (C _s ) of the freezer system measured in real time, the rated refrigeration amount (C _{s (d)} ) of the freezer system measured in real time, and the expected operating number of freezers by time (N _s ) Operating method of a customized cloud FEMS system, characterized in that calculated by comparing.
According to claim 8,
The real-time required operating number (N _{s (d)} ) is a method of operating a customized cloud FEMS system, characterized in that calculated by the following equation.

(Here, the Roundup() function is a function that provides a natural number obtained by rounding up the value in parentheses to a decimal point.)
According to claim 1,
In the step of collecting the past energy data, the specific past period is a method of operating a customized cloud FEMS system, characterized in that 1 year or more and 5 years or less.
According to claim 10,
The time unit is a method of operating a customized cloud FEMS system, characterized in that selected one of 10 minutes, 15 minutes, 20 minutes, 30 minutes, 1 hour, 2 hours and 3 hours.
In the customized cloud FEMS system,
A plurality of clean room sites including environmental support facilities for creating a clean room environment, product production facilities, local FEMS and local instruments; and
A cloud FEMS networked with a plurality of the clean room sites;
The cloud FEMS includes a characteristic analysis module generating characteristic data for each clean room site and providing operating criteria, and a plurality of FEMS platforms that respectively control and monitor the clean room sites according to the operating criteria. Cloud FEMS system.
According to claim 12,
The FEMS platform uses a database for storing the operation criteria and characteristic data for each clean room site, an operation guide module for controlling the environment support facilities according to the operation criteria, and information from the local instrument to use the environment support facilities. A customized cloud FEMS system comprising a monitoring module for monitoring.
According to claim 12,
The database stores past energy data including energy consumption of product production facilities and energy consumption of environmental support facilities for a specific period in the past for each corresponding clean room site in units of a predetermined time. .
According to claim 12,
The operating standard stored in the database includes the expected operating number (N _s (t)) of the same facility included in the environment support facility by hour.
According to claim 15,
The expected number of operating units per hour (N _s (t)) is calculated using Production_kWh (t) and Enthalpy (t), where Production_kWh (t) is the energy of the product production facility for the past m hours from the time (t) consumption, Enthalpy (t) is enthalpy information of the corresponding time (t), m is a customized cloud FEMS system, characterized in that selected within the range of 24 or more and 168 or less.
According to claim 16,
The database is a customized cloud FEMS system, characterized in that it includes hourly weather forecast for the m time.
According to claim 16,
The database includes the required operating number of refrigerators included in the environmental support facility by hour (N _{s (d)} ), the instantaneous cooling amount of the refrigerator system measured by hour (C _s ), and the rated cooling capacity of the refrigerator system measured by hour (C _{s ) (d)} ) and the expected operating number of refrigerators by hour (N _s ). A customized cloud FEMS system comprising:
According to claim 18,
The real-time required number of operating units (N _{s (d)} ) is a customized cloud FEMS system, characterized in that calculated by the following equation.

(Here, the Roundup() function is a function that provides a natural number obtained by rounding up the value in parentheses to a decimal point.)

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