KR102667747B1 - Customized cloud fems system for energy saving in distributed facilities with clean room environment and method of operating the system - Google Patents

Abstract

The operation method of a customized cloud FEMS system for controlling the energy of multiple clean room sites including environmental support facilities for creating a clean room environment provides a cloud FEMS networked with multiple clean room sites, and clean room sites Characteristic data and energy data for a specific past period are collected in predetermined time units, characteristic data is derived for each clean room site, environmental data such as weather forecasts are collected for each clean room site, and characteristic data and environmental data are collected. Using this method, operating standards for environmental support facilities can be calculated for each clean room site.

Description

Customized cloud FEMS system and method of operating the system for energy saving in distributed workplaces with clean room environments {CUSTOMIZED CLOUD FEMS SYSTEM FOR ENERGY SAVING IN DISTRIBUTED FACILITIES WITH CLEAN ROOM ENVIRONMENT AND METHOD OF OPERATING THE SYSTEM}

The present invention relates to a customized cloud FEMS system and its operating method that can integrate management of separated distributed workplaces and expect energy saving effects when operating two or more distributed workplaces equipped with a clean room environment.

A clean room refers to a space created to control environmental conditions such as indoor dust, particulates, temperature, humidity, static electricity, illuminance, and air current pressure to the required values, and is used in the semiconductor industry, electronic devices, precision machinery, optical machinery, etc. It is distributed in many industrial plants such as medicine, food, and biohazard.

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

Therefore, the help of an energy management system (FEMS; Factory Energy Management System) is urgently needed to manage factories or workplaces including such clean rooms. The energy management system installs sensors on devices or facilities that consume energy, transmits the energy consumption information detected through the sensor to the FEMS server, and the FEMS server conducts production activities while minimizing energy consumption based on the collected energy consumption information. However, it is a system that guides and manages products so that there are no problems affecting quality.

Recently, energy issues go beyond simple energy savings, and the importance of systematic management is required to respond to domestic and international legal regulations on climate change.

In particular, in cases where the management entity must operate or manage multiple workplaces rather than a single workplace, it is necessary to manage energy at various workplaces across regions or borders. Depending on each region or country, climate change and workplace construction factors may need to be managed. Because construction methods, completion years, building structures, operating facilities, etc. are diverse, it is very difficult to manage them in an integrated manner.

Registered Patent No. 10-1800310 relates to “Cloud EMS system and method providing energy saving guide,” which groups and classifies multiple buildings and uses an energy prediction model to guide and guide buildings in the same group under the same conditions. We provide management methods.

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

Thanks to the development of ICT and cloud computing technology, the present invention is a customized cloud FEMS system that can reduce the construction and operating costs of the energy management system by integrated management of distributed business sites using a cloud network. and provides a method of operating the system.

According to an exemplary embodiment of the present invention for achieving the above-described objectives of the present invention, operation of a customized cloud FEMS system for controlling the energy of a plurality of clean room sites including environmental support equipment for creating a clean room environment. The method includes providing a cloud FEMS server connected to a network with a plurality of clean room sites, collecting characteristic data for each clean room site, energy consumption of product production facilities and environmental support facilities for each clean room site during a specific period in the past. A step of collecting past energy data including energy consumption in predetermined time units, a step of deriving characteristic data for each clean room site, a step of collecting environmental data for each clean room site, and a step of collecting environmental data for each clean room site. A step of calculating the operating standards of environmental support facilities for each room site, and a step of operating the environmental support facilities of the clean room site according to the calculated operating standards using the FEMS platform maintained for each clean room site can be provided.

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 relevant time, and the characteristics Data and environmental data can be used to provide operating standards for environmental support facilities in advance from that time or on a daily basis.

In this embodiment, the environmental support equipment may include a refrigeration system, a pneumatic supply system, an air conditioning system, etc., and the network may also not be of a specific type.

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

In addition to environmental support equipment and product production equipment, the clean room site may include local FEMS and local instrumentation. The local FEMS may be responsible for communication and control with the FEMS platform, and the local instrumentation may be responsible for the control of environmental support equipment and product production equipment. It may include a temperature sensor, pressure sensor, flow sensor, power sensor, etc. for sensing operation.

Operating standards for each clean room site may be provided in various forms. For example, in a clean room site, refrigerators require a lot of energy to operate, and because their capacity is large, operations such as stopping and restarting are not easy. Therefore, in this embodiment, the operating standard for energy saving at a clean room site may include the expected number of hours in operation (N _s (t)) of the same equipment (ex. refrigerator) included in the environmental support equipment.

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

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

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

The seasonal characteristic coefficients A, B, and C for each clean room site can be inversely calculated from the data for each time unit for a specific period in the past according to the 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 number of units required to operate at the relevant time (t _p ) in the past, and Production_kWh (t _p ) is the energy consumption of the product production facility for the previous m hours from the relevant time (t _p ) in the past, Enthalpy(t _p ) is enthalpy information for the corresponding time (t _p ) in the past, and m can be selected within the range of 24 to 168.

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 selected periods of 1 week, 10 days, 15 days, January, and March. It can be updated accordingly.

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

The monitoring module of the FEMS platform can calculate the real-time required operating number (N _s(d) ) of refrigerators included in environmental support facilities, and the real-time required operating number (N _s(d) ) is the instantaneous value of the refrigerator system measured in real time. It can be calculated by comparing the refrigeration amount (C _s ), the rated refrigeration amount of the freezer system measured in real time (C _s(d) ), and the expected number of refrigerators operating per hour (N _s ).

As an example, the real-time required operating number (N _s(d) ) can be calculated by the following equation, and the Roundup() function can be a function that provides a natural number in which the value in parentheses is rounded up to the decimal point.

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

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

According to an exemplary embodiment of the present invention for achieving the above-described objectives of the present invention, a customized cloud FEMS system includes a plurality of environmental support facilities, product production facilities, local FEMS, and local measuring instruments for creating a clean room environment. It may include a clean room site, and a cloud FEMS server connected to a network with a plurality of clean room sites. The cloud FEMS server generates characteristic data for each clean room site and performs clean cleaning according to the characteristic analysis module and operating standards that generates characteristic data for each clean room site and provides operating standards. It may include multiple FEMS control platforms that each control and monitor room sites.

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

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

In addition, as mentioned in the system operation method, the operation criteria stored in the database may include the expected number of hours in operation (N _s (t)) of the same equipment included in the environmental support equipment.

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

Additionally, the real-time required operating number (N _s(d) ) may have the following relationship.

The customized cloud FEMS system of the present invention can comprehensively 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 and operating costs of the energy management system by managing distributed business sites in an integrated manner, and provides operating standards to efficiently operate distributed business sites by reflecting environmental data such as weather forecasts. Alternatively, an operation schedule can be provided.

1 is a diagram for explaining a customized cloud FEMS system according to an embodiment of the present invention.
Figure 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.
Figure 3 is a diagram for explaining the operation method of a customized cloud FEMS system according to an embodiment of the present invention.
Figure 4 is a diagram illustrating the environmental support facilities of the nth clean room site among the customized cloud FEMS systems according to an embodiment of the present invention.
Figure 5 is a diagram comparing past data with patterns of other parameters according to outside air temperature and outside air 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 this description, the same numbers refer to substantially the same elements, and under these rules, the description can be made by citing the content shown in other drawings, and content that is judged to be obvious to those skilled in the art or that is repeated can be omitted.

Figure 1 is a diagram for explaining a customized cloud FEMS system according to an embodiment of the present invention, and Figure 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. , Figure 3 is a diagram for explaining the operation method of a customized cloud FEMS system according to an embodiment of the present invention.

Referring to Figures 1 and 2, a plurality of clean room sites (300-1 to 300-n) and a cloud that is a server capable of transmitting and receiving information to and from each clean room site (300-1 to 300-n) through a network. May include FEMS (100).

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

Clean room environment support equipment (300-1a ~ 300-na) is equipment for controlling the temperature, humidity, dust, particulates, static electricity, illuminance, and internal pressure of airflow in the clean room. Examples include refrigerators, cold water pumps, cooling water pumps, etc. This may include cooling towers, etc.

Clean room environment support equipment (300-1a~300-na) is provided during the initial preparation process of each site. Since large-scale investment is required to replace or upgrade to new equipment, it is recommended to optimize operation and improve operation energy performance as much as possible. may be important.

In addition, since the clean room environment support equipment (300-1a~300-na) is responsible for controlling the entire factory or business site, it is not easy to control it on and off in real time. Additional operation is relatively easy, but energy demand is high. It is not easy to scale back operation due to sensitivity to changes, and it is also undesirable in terms of energy efficiency because it requires a fairly long restart time.

Therefore, in the past, when operating environmental support equipment (300-1a~300-na) that maintains a clean room, it is operated based on the manager's experience without clear operating standards, and is operated conservatively to the extent that no warning or alarm occurs. In most cases, it did.

Product production facilities (300-1b ~ 300-nb) are actually production facilities at each clean room site (300-1 ~ 300-n), and can be said to be facilities that produce semiconductors, food, pharmaceuticals, etc. Since product production facilities (300-1b~300-nb) are directly involved in product production, they may be excluded from the control of this customized cloud FEMS system.

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

Cloud FEMS (100) receives necessary information from 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). You may have the authority to control . To this end, the cloud FEMS (100) may include a clean room characterization module (110) and a FEMS platform (120-1 to 120-n) for controlling each clean room site (300-1 to 300-n). .

Each FEMS platform (120-1~120-n) can store characteristic data (120-1a~120-na) of each clean room site (300-1~300-n), and this characteristic data (120-1a ~120-na) is the location of each clean room site (300-1~300-n), company name, completion year, monthly electricity consumption, and environmental support that constitutes each clean room site (300-1~300-n). Equipment information (300-1a~300-na) includes the model, purchase year, specifications, and installation status of refrigerators, pumps, and air conditioners, as well as the specifications and installation status of product production equipment (300-1b~300-nb). It can be included.

And the FEMS platform (120-1~120-n) includes driving guide module (120-1b~120-nb), monitoring module (120-1c~120-nc), and other databases (120-1d~120-nd). may include.

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

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

From this information, the clean room characteristic analysis module 110 determines the electricity consumption pattern of product production facilities (300-1b~300-nb) by month/hour, the enthalpy pattern of outdoor air by month/hour, and the environmental support facility (300-1a~) by month/hour. Characteristic data of each clean room site (300-1 to 300-n), such as the operation pattern and electricity consumption pattern of 300-na), can be derived.

In addition, through these consumption patterns and operation patterns, the operation schedule of environmental support facilities (300-1a ~ 300-na), for example, the optimal number of refrigerators to operate, can be calculated by month/hour.

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

Additionally, 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, past operation data includes 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, and factory It may include weather information such as surrounding temperature and humidity.

The characteristic analysis module 110 of the cloud FEMS 100 can derive characteristic data for each clean room site (300-1 to 300-n) from the collected data (S130), and the derivation of these characteristic data is performed during product production. This may be the electricity consumption pattern of facilities (300-1b~300-nb), the energy consumption pattern of environmental support facilities (300-1a~300-na), the number of refrigerators operating, the outdoor air enthalpy pattern, etc.

These patterns can be provided separately by month or hour, and can be provided through the operation guide module (120-1b~) of the FEMS platform (120-1~120-n) corresponding to each clean room site (300-1~300-n). 120-nb) can provide a standard for controlling environmental support facilities (300-1a to 300-na).

driving guide module

The operation guide module (120-1b~120-nb) can be provided to the corresponding FEMS platform (120-1~120-n) for each clean room site (300-1~300-n), and the environmental support facility (300-120-n) It can function to control -1a~300-na). For example, if the environmental support facilities (300-1a~300-na) of the clean room site (300-1~300-n) include refrigerators, the optimal number of refrigerators to be operated is predicted in advance and this is compared with the real-time required operating number. You can compare.

Figure 4 is a diagram illustrating the environmental support facilities of the nth clean room site among the customized cloud FEMS systems according to an embodiment of the present invention.

Referring to FIG. 4, a refrigerator system may be provided in the environmental support facility (300-na) of a specific clean room site, and a plurality of refrigerators (s) (410-1 to 410-s) may be connected in parallel to the refrigerator system. You can. A plurality of refrigerators (410-1 to 410-s) can 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 can be provided by each FEMS platform (120-n) of the local FEMS (300-nc) or cloud FEMS (100), and can be provided by 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 refrigerator (410-1~410-s) is equipped with a flow sensor (412-1~412-s) and an outlet temperature sensor (414-1~414-s) on the output side, and a pump (418-s) on the input side. 1~418-s) and inlet temperature sensors (416-1~416-s) can be installed. The motor of the refrigerator (410-1 to 410-s) may be equipped with a power sensor to detect whether or not the refrigerator (410-1 to 410-s) is operating or in an operating state. Flow sensors (412-1 to 412-s), inlet temperature sensors (416-1 to 416-s), outlet temperature sensors (414-1 to 414-s), and power sensors operate the system according to this embodiment. For implementation, sensors installed separately or installed to monitor the operation of existing refrigerators can be used.

Cold water or medium passing through the refrigeration 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 cold water whose temperature has been lowered is supplied back to the cold water supply header through the piping, and can be provided from the cold water supply header to places where cold water is needed among the product production facilities (300-1b to 300-nb).

Cold water or media that has passed through the product production facilities (300-1b~300-nb) can be returned to the cold water return header with an increased temperature, and the returned cold water can be returned to each pump (418-1~418-s). It is provided back to the freezer (410-1~410-s). The cold water supply header may be equipped with a sensor to measure temperature and pressure, and the cold water return header may also be equipped with a sensor to measure temperature and pressure.

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

The local FEMS (300-nc) can transmit the collected information to the cloud FEMS (100) and, conversely, can 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 freezer from the data collected from the clean room site 300-n. You can compare the number of units in operation with the number of units required to operate in real time.

The monitoring module (120-nc) and the operation guide module (120-nb) may use the instantaneous refrigeration amount, instantaneous power, instantaneous load rate, etc. together to compare the real-time required operation number of refrigerators with the expected operation number.

The refrigerators (410-1 to 410-s) may include an automatic control circuit that can independently control the operating conditions, and when the flow rate of cold water on the output side is little, the load ratio can be independently adjusted automatically to a small state. there is.

Relationship between load factor and efficiency of refrigeration system

In a freezer system where multiple freezers (410-1 to 410-s) are connected in parallel, the freezers (410-1 to 410-s) operate simultaneously and may not always operate at their rated capacity, and may not always operate at their rated capacity, and may not operate at their rated capacity due to external factors such as the surrounding climate, etc. The amount of refrigeration provided may change at any time depending on changes in conditions.

For example, if several chillers are operating, reducing the amount of chilled water used in a facility using chiller utilities may reduce the instantaneous freezing 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 refrigeration amount of cold water output from the refrigeration system decreases, power consumption does not decrease by the amount of refrigeration. In order to understand this more easily, it is necessary to adjust the load factor and system efficiency.

Here, the flow rate is Q (m ³ /Hr), the refrigeration volume or refrigeration ton is C (RT), the power consumption is W (kW), the cold water supply temperature is T _s (℃), and the cold water return temperature is T _r (℃). , the cold water density can be defined as ρ (kg/m ³ ), the specific heat of cold water can be defined as C _p (kcal/kg·°C), and the efficiency can be defined as η (RT/kWh) (instantaneous refrigeration tons of the freezer)/(cooler's It can be defined as instantaneous consumption power). The instantaneous freezing amount or instantaneous freezing ton of the refrigerator is C _u and can be defined with reference to Equation 1 below. For reference, Q _u is the instantaneous flow rate of the individual refrigerator.

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

Here, R _u is the instantaneous load rate of the individual refrigerator, and R _s is the instantaneous load rate of the refrigerator system. And C _u is the instantaneous refrigeration ton of the individual freezer, u is the individual freezer number, C _u(d) is the rated refrigeration ton of the individual freezer, C _s is the instantaneous refrigeration ton of the freezer system, the sum of the instantaneous refrigeration tons of the individual freezers, and C _s(d) can be defined as the rated refrigeration ton of the refrigerator system.

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

Verification of required operating number in real time

Determining whether a plurality of refrigerators are operating at a high load rate can also be done 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) ) of the freezer system measured in real time, and the characteristics. 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 can be defined as a function that calculates a natural number by rounding the value in parentheses to the decimal point, and the real-time required operating number (N _s(d) ) can be calculated through the above equation.

The real-time excess or shortfall operating number (N _e ) can be defined as the value obtained by subtracting the real-time required operating number from the expected operating number (=expected operating number - real-time required operating number). When N _e = 0, it can be seen that the refrigerator 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 underoperation of the refrigerator in the refrigerator system.

Information delivery based on real-time required number of operating units

Depending on the results of comparison of the real-time over/under operating numbers (N _e ), the driving assistance equipment according to this embodiment can provide various information.

For example, if it is determined that the refrigerator is operating excessively (N _e ≥ 1), and if the rated refrigeration tons of the operating refrigerators are not all the same, it is desirable to stop operation starting from the one with the smaller rated refrigeration tons. This is because if a refrigerator with a large rated refrigeration ton is stopped incorrectly, an unexpected cold water shortage may occur in the overall system. However, if the rated refrigeration tons of all operating refrigerators are the same, it is desirable to stop operation starting from the one with the lowest efficiency. For reference, the efficiency of a refrigerator can be defined as the instantaneous refrigeration ton relative to the instantaneous power consumption supplied to the refrigerator (C/W).

Conversely, when it is determined that the refrigerator is operating insufficiently (N _e ≤ -1), if the rated refrigeration tons of the operating refrigerators are not all the same, it is desirable to start operation from the one with the smaller rated refrigeration ton. This is because, by operating the one with the smallest rated refrigeration ton, the load factor can be prevented from falling significantly. On the other hand, if the rated refrigeration tons of the operating refrigerators are all the same, it is desirable to start operation from the one with the highest efficiency.

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

The expected number of units to be operated (N _s ) may be influenced by the weather of the day in addition to the overall climate characteristics of the region. Therefore, in this embodiment, various methods can be tried to calculate the expected number of units in operation (N _s ).

As an example, the monthly characteristic coefficients (A, B, C) below can be obtained through correlation analysis between the electricity consumption of production facilities over the past year and the number of refrigerators operating according to changes in external air enthalpy. And by using this characteristic coefficient, the expected number of refrigerators (N _s ) in operation for the next 24 hours can be calculated on an hourly basis at the clean room site (300-1 to 300-n) or operation guide module (120-1b to 120-nb). ) can be provided.

The expected number of units in operation (N _s ) can be predicted using the month's characteristic coefficients, weather forecast, and expected production facility electricity consumption information.

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

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

Characteristic coefficients can be calculated and updated using historical data collected at each clean room site, such as the pattern of the number of refrigerators required to operate, the pattern of outdoor air enthalpy, and the electricity consumption pattern of production facilities over the past year.

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

Referring to Figure 5, past outdoor temperature and enthalpy information can be compared with other parameters, such as refrigeration load, power consumption, number of operating units, etc., and using these past data, monthly characteristic coefficients (A, B, C) can be calculated. can be calculated.

Also, conversely, the expected number of hours in operation (N _s (t)) for the next 24 hours can also be calculated using Production_kWh(t) and Enthalpy(t).

Here, Production_kWh(t) may be information on electricity consumption of production facilities 24 hours prior to the present, and Enthalpy(t) may be enthalpy information for each hour of outdoor air 24 hours from the present using weather forecasts.

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

In addition, the expected number of operating units per hour ((N _s )(t)) can be compared with the above-mentioned real-time required operating number of units (N _s(d) (t)) to calculate the real-time excess or insufficient operating number of units (N _e ).

Monitoring module function

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

As described above, although the present invention has been described with reference to preferred embodiments, those skilled in the art may make various modifications and changes to the present invention without departing from the spirit and scope of the present invention as set forth in the claims below. You will understand that you can do it.

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

Claims (19)

In the operation 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 server networked with a plurality of clean room sites;
Here, the cloud FEMS server includes a characteristic analysis module and a plurality of FEMS control platforms,
The cloud FEMS server collecting past energy data including energy consumption of product production facilities and energy consumption of environmental support facilities for a specific period of time for each clean room site in predetermined time units;
The characteristic analysis module of the cloud FEMS server deriving characteristic data for each clean room site;
The characteristic analysis module collecting environmental data for each clean room site;
calculating, by the characteristic analysis module, an operation standard for environmental support facilities for each clean room site using the characteristic data and the environmental data; and
A step wherein the FEMS control platform maintained for each clean room site operates the environmental support facilities of the clean room site according to the calculated operating standards,
The operating standards include the expected number of hours in operation (N _s (t)) of the same equipment included in the environmental support equipment,
The expected number of operating units per hour (N _s (t)) is calculated by the following equation,
N _s (t) = A + B * Production_kWh(t) + C * Enthalpy(t)
Here, A, B, C are the seasonal characteristic coefficients for each clean room site, Production_kWh(t) is the energy consumption of the product production facility at the relevant time (t), and Enthalpy(t) is the enthalpy at the relevant time (t). It is information,
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 number of units required to operate at the relevant time in the past (t _p ), Production_kWh(t _p ) is the energy consumption of the product production facility at the relevant time in the past (t _p ), and Enthalpy(t _p ) is the A method of operating a customized cloud FEMS system characterized by enthalpy information of the past time (t _p ).
According to paragraph 1,
The FEMS control 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 paragraph 1,
The clean room site is a method of operating a customized cloud FEMS system, characterized in that it includes a local FEMS for communication and control with the FEMS control platform and a local measuring device for sensing the operation of the environmental support equipment and the product production equipment. .
delete delete According to paragraph 1,
A method of operating a customized cloud FEMS system, characterized in that the enthalpy information is calculated by reflecting hourly weather forecasts.
According to paragraph 1,
The seasonal characteristic coefficients A, B, and C for each clean room site are updated according to a selected period of 1 week, 10 days, 15 days, January, or March. Method of operating a customized cloud FEMS system.
According to paragraph 1,
The monitoring module of the FEMS control platform calculates the real-time required operating number (N _s(d) ) of the refrigerator included in the environmental support facility,
The number of units required to be operated in real time (N _s(d) ) is the instantaneous refrigeration amount of the freezer system measured in real time (C _s ), the rated refrigeration amount of the freezer system measured in real time (C _s(d) ), and the number of units expected to be operated per hour. Operation method of a customized cloud FEMS system, characterized in that it is calculated by comparing (N _s ).
According to clause 8,
A method of operating a customized cloud FEMS system, characterized in that the real-time required operating number (N _s(d) ) is calculated by the following equation.

(Here, the Roundup() function is a function that provides a natural number with the value in parentheses rounded up to the decimal point.)
According to paragraph 1,
A method of operating a customized cloud FEMS system, characterized in that in the step of collecting the past energy data, the past specific period is 1 year or more and 5 years or less.
According to clause 10,
A method of operating a customized cloud FEMS system, characterized in that the time unit is one selected from 10 minutes, 15 minutes, 20 minutes, 30 minutes, 1 hour, 2 hours, and 3 hours.
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