EP2251614B1 - Air conditioning system and device for predicting building air conditioning facility power consumption amount - Google Patents

Air conditioning system and device for predicting building air conditioning facility power consumption amount Download PDF

Info

Publication number
EP2251614B1
EP2251614B1 EP09715494.2A EP09715494A EP2251614B1 EP 2251614 B1 EP2251614 B1 EP 2251614B1 EP 09715494 A EP09715494 A EP 09715494A EP 2251614 B1 EP2251614 B1 EP 2251614B1
Authority
EP
European Patent Office
Prior art keywords
air
electrical energy
consumption
electrical
building
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
EP09715494.2A
Other languages
German (de)
English (en)
French (fr)
Other versions
EP2251614A1 (en
EP2251614A4 (en
Inventor
Chuzo Ninagawa
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Mitsubishi Heavy Industries Thermal Systems Ltd
Original Assignee
Mitsubishi Heavy Industries Thermal Systems Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Mitsubishi Heavy Industries Thermal Systems Ltd filed Critical Mitsubishi Heavy Industries Thermal Systems Ltd
Publication of EP2251614A1 publication Critical patent/EP2251614A1/en
Publication of EP2251614A4 publication Critical patent/EP2251614A4/en
Application granted granted Critical
Publication of EP2251614B1 publication Critical patent/EP2251614B1/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
    • F24F11/46—Improving electric energy efficiency or saving
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
    • F24F11/46—Improving electric energy efficiency or saving
    • F24F11/47—Responding to energy costs
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2140/00—Control inputs relating to system states
    • F24F2140/60—Energy consumption

Definitions

  • Patent Citations 1 and 2 disclose air-conditioning systems in which a host computer and an air-conditioning controlling and monitoring apparatus are connected via a building-management communication network, the air-conditioning controlling and monitoring apparatus and a plurality of air conditioners are connected via an air-conditioning control network, and the air-conditioning controlling and monitoring apparatus controls the individual air conditioners on the basis of operation control instructions from the host computer.
  • This document also discloses the corresponding building-air-conditioning-equipment electrical-energy-consumption predicting method.
  • This document also discloses the corresponding computer-readable recording medium having recorded thereon a building-air-conditioning-equipment electrical-energy-consumption predicting program.
  • the electrical energy consumption relating to air-conditioning equipment occupies a large part of the electrical energy consumption of the building as a whole.
  • the base charge is automatically raised when the electrical energy consumption per defined period (e.g., per 30 minutes) exceeds a contract limit value even once. In such cases, it is necessary to constantly monitor the electrical energy consumption per defined period and to manage the operation so that the electrical energy consumption is kept within the contract limit value.
  • the statistical prediction unit predicts the electrical energy consumptions during the individual prediction target times in the prediction target period by using the actual operation data stored in the storage unit, and the electrical-energy-consumption accumulating unit calculates the cumulative electrical energy consumption of the building air-conditioning equipment during the prediction target period by using the predicted electrical energy consumptions.
  • the data specifying unit may specify actual operation data for a predetermined duration in the past belonging to the prediction target period.
  • the statistical prediction unit may calculate, for each piece of the actual operation data extracted by the data extracting unit, an electrical energy consumption of the air-conditioning equipment during the predetermined unit time, an average outside air temperature during the predetermined unit time, an average intake temperature during the predetermined unit time weighted by the capacities of indoor units, and an average temperature setting during the predetermined unit time weighted by the capacities of the indoor units, obtains a state variable vector having these items as elements for each piece of the actual operation data, and predicts the electrical energy consumptions during the individual prediction target times from an autoregressive model representing a linear combination of the state variable vectors.
  • the building-air-conditioning-equipment electrical-energy-consumption predicting apparatus may include an operating-level limiting unit that outputs a limitation signal for limiting the operation of the air conditioners installed in the building when the electrical energy consumption of the building air-conditioning equipment during the prediction target period, calculated by the electrical-energy-consumption accumulating unit, exceeds a preset target electrical energy consumption.
  • an order of priority of air conditioners whose operation is to be limited may be set in advance, and the operating-level limiting unit may limit operation in order from air conditioners with higher priority levels.
  • the operating-level limiting unit may output the limitation signal for limiting at least one of temperature settings of the air conditioners, compressor rotation speeds, and degrees of opening of electronic expansion valves provided in refrigerator pipes.
  • the building-air-conditioning-equipment electrical-energy-consumption predicting apparatus may include a data correcting unit that corrects by a predetermined amount the actual operation data used for prediction by the statistical prediction unit when the electrical energy consumption of the building air-conditioning equipment during the prediction target period, calculated by the electrical-energy-consumption accumulating unit, exceeds a preset target electrical energy consumption, the statistical prediction unit may re-predict electrical energy consumptions for the individual prediction target times by using the actual operation data corrected by the data correcting unit, and the electrical-energy-consumption accumulating unit may calculate the electrical energy consumption of the building air-conditioning equipment during the prediction target period by using the re-predicted electrical energy consumptions.
  • the data correcting unit corrects an element relating to the operating level (e.g., the weighted-average temperature setting or the like), thereby correcting the actual operation data.
  • the element corrected at this time is an element relating to the operating level, and the other elements, for example, elements relating to observed quantities, are not corrected but are maintained as they are.
  • the statistical prediction unit re-predicts the electrical energy consumptions during the individual prediction target times in the prediction target period by using the corrected actual operation data, and the cumulative electrical energy consumption during the prediction target period is re-calculated by using the re-calculated electrical energy consumptions.
  • the data correcting unit may correct by the predetermined amount at least one of temperature settings of the actual operation data and compressor rotation speeds if the compressor rotation speeds are included in the actual operation data.
  • the electrical energy consumption is restricted by restricting at least one of the temperature settings and compressor rotation speeds in the actual operation data, it is possible to alleviate discomfort for people in the building compared with the case where the air conditioners are stopped.
  • the data correcting unit may repeat correction of the actual operation data until the electrical energy consumption calculated by the electrical-energy-consumption accumulating unit comes to fall within the target electrical energy consumption.
  • the operating-level limiting unit may generate and output the limitation signal on the basis of an amount of correction by the data correcting unit when the electrical energy consumption calculated by the electrical-energy-consumption accumulating unit comes to fall within the target electrical energy consumption.
  • a second aspect of the present invention is an air-conditioning system including any one of the above building-air-conditioning-equipment electrical-energy-consumption predicting apparatuses.
  • a third aspect of the present invention is a building-air-conditioning-equipment electrical-energy-consumption predicting method of calculating the electrical energy consumption of building air-conditioning equipment during a prediction target period, including a step of specifying actual operation data to be used for predicting the cumulative electrical energy consumption of the building air-conditioning equipment during the prediction target period from a storage unit that stores actual operation data of the building air-conditioning equipment in association with the day, hour, and minute, the actual operation data including an electrical energy consumption, an outside air temperature, a room temperature, and temperature settings of individual air conditioners installed in a building, during a predetermined unit time; a step of extracting the specified actual operation data from the storage unit; a step of predicting the electrical energy consumptions during individual prediction target times in the prediction target period by using a statistical method on the basis of the extracted actual operation data; and a step of calculating the electrical energy consumption of the building air-conditioning equipment during the prediction target period by using the predicted electrical energy consumptions during the individual prediction target times and the actual operation data stored
  • a fourth aspect of the present invention is a computer-readable recording medium having recorded thereon a building-air-conditioning-equipment electrical-energy-consumption predicting program for calculating the electrical energy consumption of building air-conditioning equipment during a prediction target period, the program causing a computer to execute processing for specifying actual operation data to be used for predicting the cumulative electrical energy consumption of the building air-conditioning equipment during the prediction target period from a storage unit that stores actual operation data of the building air-conditioning equipment in association with the day, hour, and minute, the actual operation data including an electrical energy consumption, an outside air temperature, a room temperature, and temperature settings of individual air conditioners installed in a building, during a predetermined unit time; processing for extracting the specified actual operation data from the storage unit; processing for predicting the electrical energy consumptions during individual prediction target times in the prediction target period by using a statistical method on the basis of the extracted actual operation data; and processing for calculating the electrical energy consumption of the building air-conditioning equipment during the prediction target period by using the predicted electrical energy consumptions during
  • Fig. 1 is a block diagram showing the overall configuration of the air-conditioning system according to this embodiment of the present invention.
  • the air-conditioning system according to this embodiment includes a host computer 1, an air-conditioning controlling and monitoring apparatus 3 connected to the host computer 1 via a building-management communication network 2, a plurality of air conditioners 5 connected to the air-conditioning controlling and monitoring apparatus 3 via an air-conditioning control network 4, and an electrical-energy-consumption predicting apparatus 10 connected to the host computer 1 and the air-conditioning controlling and monitoring apparatus 3 via the building-management communication network 2.
  • the host computer 1 is, for example, a building management computer including a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), an input device, a display device, and so forth, and performs operational management of equipment provided in a building.
  • a building management computer including a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), an input device, a display device, and so forth, and performs operational management of equipment provided in a building.
  • a building management computer including a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), an input device, a display device, and so forth, and performs operational management of equipment provided in a building.
  • CPU Central Processing Unit
  • ROM Read Only Memory
  • RAM Random Access Memory
  • the building-management communication network 2 is a network that uses a communication protocol adopted as a standard for building management (e.g., TCP/IP), for example, a LAN (Local Area Network), an intranet, the Internet, or the like.
  • a communication protocol adopted as a standard for building management e.g., TCP/IP
  • LAN Local Area Network
  • intranet e.g., an intranet, the Internet, or the like.
  • the air-conditioning controlling and monitoring apparatus 3 is an apparatus for performing operation control and operation management of the air conditioners 5 installed in individual rooms in the building, and it monitors the operation status of the individual air conditioners 5 and provides the host computer 1 and the electrical-energy-consumption predicting apparatus 10 with operation status information representing the results of the monitoring.
  • the operation status information includes information indicating an operation type, such as cooling operation, warming operation, or operation stopped, and also includes a temperature setting, a room temperature (the temperature of air intaken by an indoor unit), an outside air temperature, an operation time, a compressor rotation speed, and so forth.
  • the air-conditioning control network 4 is a network that uses a communication protocol adopted as a standard for control and monitoring of the air conditioners 5.
  • the electrical-energy-consumption predicting apparatus 10 is, for example, an apparatus that is capable of predicting the electrical energy consumption of all the air conditioners 5 installed in the building on a per-predetermined-period basis, for example, on a per-30-minute basis.
  • Fig. 2 is a block diagram showing an example hardware configuration of the electrical-energy-consumption predicting apparatus 10 according to this embodiment.
  • the electrical-energy-consumption predicting apparatus 10 includes a computer system (computing system) that is configured of a CPU (Central Processing Unit) 21, a main storage device 22 such as a RAM (Random Access Memory), an auxiliary storage device 23 such as a ROM (Read Only Memory), an input device 24 such as a keyboard or a mouse, and an output device 25 such as a display device or a printer, and so forth. Furthermore, the electrical-energy-consumption predicting apparatus 10 includes a communication device (not shown) for exchanging data via the building-management communication network 2 with the host computer 1, the air-conditioning controlling and monitoring apparatus 3, and so forth shown in Fig. 1 .
  • a communication device not shown for exchanging data via the building-management communication network 2 with the host computer 1, the air-conditioning controlling and monitoring apparatus 3, and so forth shown in Fig. 1 .
  • the auxiliary storage device 23 stores various programs, and the CPU 21 reads the programs from the auxiliary storage device 23 into the main storage device 22 such as a RAM and executes the programs, whereby various processing is performed.
  • Fig. 3 is a functional block diagram showing an overview of functions of the electrical-energy-consumption predicting apparatus 10.
  • the electrical-energy-consumption predicting apparatus 10 includes a database (storage unit) 31, a data specifying unit 32, a data extracting unit 33, a statistical prediction unit 34, and an electrical-energy-consumption accumulating unit 35.
  • the database 31 stores actual operation data of the building air-conditioning equipment in association with the day, hour, and minute, including the electrical energy consumption, the outside air temperature, and temperature settings, compressor intake temperatures, and compressor rotation speeds of the individual air conditioners installed in the building, during a predetermined unit time. These pieces of actual operation data are created from the operation status information supplied from the air-conditioning controlling and monitoring apparatus 3, electric-energy-consumption information regarding the air-conditioning equipment in the building, supplied from an electrical-power monitoring apparatus (not shown) that monitors the power consumption of the air-conditioning equipment installed in the building, and so forth.
  • the "actual operation data” refers to a collection of various data collected in one minute, such as the electrical energy consumption, the outside air temperature, temperature settings, compressor intake temperatures, and compressor rotation speeds.
  • actual operation data composed of a collection of the above pieces of data (elements) is created on a per-minute basis, and the actual operation data is stored in the database 31 in association with the day, hour, and minute of data collection.
  • the data specifying unit 32 specifies actual operation data that is to be used for predicting the cumulative electrical energy consumption of the building air-conditioning equipment during a prediction target period. For example, as shown in Fig. 4 , when a period from 13:00 to 13:30 is specified as the prediction target period, the data specifying unit 32 specifies actual operation data of a predetermined duration in the past belonging to this prediction target period.
  • the predetermined duration in the past is a duration that can be set arbitrarily: it is set to be, for example, 5 minutes, the entire period, or the like.
  • the configuration may be such that a user can specify actual operation data from the past actual operation data stored in the database 31. In this case, the user can specify actual operation data by operating the input device 24 (see Fig. 2 ).
  • the data specifying unit 32 specifies a part or the entirety of the actual operation data for 13:00 to 13:15.
  • the data extracting unit 33 extracts a plurality of pieces of actual operation data (a time series of state vectors) specified by the data specifying unit 32 from the database 31. Thus, N pieces of actual operation data going back in the past from 13:15 are extracted from the database 31.
  • the statistical prediction unit 34 predicts the electrical energy consumptions on a prediction target day by using a statistical analysis method on the basis of the plurality of pieces of actual operation data (i.e., the time series of state vectors) extracted by the data extracting unit 33. For example, by using the N pieces of actual operation data extracted by the data extracting unit 33, for each piece of actual operation data, the statistical prediction unit 34 calculates a per-minute electrical energy consumption of the building air-conditioning equipment, a per-minute average outside air temperature, a per-minute average intake temperature weighted by the capacities of the indoor units, and a per-minute average temperature setting weighted by the capacities of the indoor units, obtains N state variable vectors having these items as elements for the individual pieces of actual operation data, and predicts the electrical energy consumption on a per-minute basis from an autoregressive model representing a linear combination of the time series of the state variable vectors.
  • the statistical prediction unit 34 generates an autoregressive model expressed by equation (1) below by using N state vectors and solves this autoregressive model by a known method, such as the minimum AIC (Akaike's Information Criterion) method, thereby predicting the electrical energy consumption on a per-minute basis during, for example, a period from 13:16 to 13:30, for which actual operation data does not exist, among 13:00 to 13:30 specified as the prediction target period.
  • a known method such as the minimum AIC (Akaike's Information Criterion) method
  • the order of the coefficient matrices is determined in accordance with the number of elements of the state vectors. That is, in this embodiment, state vectors are created by using four elements consisting of the electrical energy consumption of the building air-conditioning equipment, the average outside air temperature, a weighted-average intake temperature, and a weighted-average temperature setting, so that the coefficient matrices are 4 ⁇ 4. Furthermore, if the compressor rotation speed is added as an element, A(i) becomes 5 ⁇ 5 coefficient matrices.
  • U(k) is a k-th white noise error.
  • Whm(k) is the per-minute electrical energy consumption of the building air-conditioning equipment related to the k-th actual operation data
  • Tom(k) is the per-minute average outside air temperature related to the k-th actual operation data
  • Tam(k) is the per-minute average intake temperature (room temperature) related to the k-th actual operation data, weighted by the capacities of the indoor units
  • Tsm(k) is the per-minute average temperature setting related to the k-th actual operation data, weighted by the capacities of the indoor units.
  • weighting is performed in consideration of the capacities of the indoor units in calculating an average value.
  • the statistical prediction unit 34 obtains a matrix A(i) that minimizes U(k) in equation (1) given above, and predicts the per-minute electrical energy consumption during each prediction target time in the prediction target period, i.e., 13:16 to 13:30 in this embodiment.
  • the matrix A(i) that minimizes U(k) can be obtained by the minimum AIC method or the like.
  • N representing the number of pieces of actual operation data is also determined by solving the autoregressive model. For example, an optimal number N of pieces of actual operation data may be obtained by first solving the autoregressive model, and the above calculation may be performed again after N is determined by using the determined N pieces of actual operation data. This serves to improve the accuracy of prediction of electrical energy consumptions on the prediction target day.
  • the electrical-energy-consumption accumulating unit 35 calculates the electrical energy consumption of the building air-conditioning equipment from 13:00 to 13:30, which is the prediction target period, by using the electrical energy consumptions predicted by the statistical prediction unit 34 and the actual operation data stored in the database 31, and outputs the result of the calculation to, for example, the output device 25 (see Fig. 2 ) or the like.
  • the air-conditioning controlling and monitoring apparatus 3 controls the operation of the air conditioners 5 installed in the building, whereby operation status information of the individual air conditioners 5 is accumulated in the air-conditioning controlling and monitoring apparatus 3, and the operation status information is sent from the air-conditioning controlling and monitoring apparatus 3 to the electrical-energy-consumption predicting apparatus 10 via the building-management communication network 2. Furthermore, the electrical energy consumption of the air conditioners 5 and so forth in the building is monitored by an electrical-power monitoring apparatus (not shown), and information regarding the electrical energy consumption is sent from the electrical-power monitoring apparatus to the electrical-energy-consumption predicting apparatus 10.
  • the electrical-energy-consumption predicting apparatus 10 predicts electrical energy consumptions in the prediction target period to which the current time belongs by using the actual operation data accumulated in the database 31 on a per-minute basis.
  • the electrical-energy-consumption predicting apparatus 10 predicts the electrical energy consumption on a per-minute basis in the period of 13:16 to 13:30, for which actual operation data does not exist, and obtains the cumulative electrical energy consumption during the prediction target period by using the prediction results.
  • the data specifying unit 32 specifies N pieces for 13:15, 13:14, 13:13, ..., 13:(15-N+1), and the data extracting unit 33 extracts the specified actual operation data from the database 31.
  • the extracted N pieces of actual operation data are output to the statistical prediction unit 34.
  • the statistical prediction unit 34 creates a time series of state vectors whose elements are, for each piece of the actual operation data, the per-minute electrical energy consumption Whm of the building air-conditioning equipment, the per-minute average outside air temperature Tom, the per-minute average intake temperature Tam weighted by the capacities of the indoor units, and the per-minute average temperature setting Tsm weighted by the capacities of the indoor units.
  • the statistical prediction unit 34 sequentially performs predictive calculation of per-minute state vectors for 13:16, 13:17, ..., 13:30, thereby calculating per-minute electrical energy consumptions Whm(k) from 13:16 to 13:30, and outputs the predicted electrical energy consumptions Whm(k) in the individual prediction target times during the 15-minute period to the electrical-energy-consumption accumulating unit 35.
  • the autoregressive model i.e., the coefficient matrices A(i)
  • the electrical-energy-consumption accumulating unit 35 uses actual electrical energy consumptions stored in the database 31 as electrical energy consumptions up to 13:15 and uses the electrical energy consumptions predicted by the statistical prediction unit 34 as electrical energy consumptions on and after 13:16 and accumulates these values, thereby calculating a predicted cumulative electrical energy consumption of the building air-conditioning equipment during the prediction target period of 13:00 to 13:30.
  • the electrical-energy-consumption accumulating unit 35 outputs the calculation result and information about the electrical energy consumptions used for the calculation to the output device (see Fig. 2 ).
  • the output device for example, a monitor of a display device or the like, it is possible to report the information to the user.
  • the manner of display regarding the display device is not particularly limited.
  • the predicted electrical energy consumptions during the prediction target period may be just displayed in the form of numerical values, or may be displayed, for example, in the form of a graph as shown in Fig. 4 .
  • the predicted cumulative electrical energy consumption during the prediction target period is updated every minute, so that the latest predicted cumulative electrical energy consumption is presented to the user.
  • the air-conditioning system and the electrical-energy-consumption predicting apparatus it becomes possible to predict the cumulative electrical energy consumption on a per-predetermined-period basis, for example, on a 30-minute basis. Accordingly, the user can grasp the approximate electrical energy consumption at 30-minute intervals, and thus, for example, it becomes possible to control the operation of the building air-conditioning equipment so that the cumulative electrical energy consumption during 30 minutes does not exceed a contract electrical energy (target electrical energy consumption).
  • the configuration of the electrical-energy-consumption predicting apparatus differs from that in the first embodiment described above.
  • the data correcting unit 36 corrects, by a predetermined amount, the temperature settings of the actual operation data used by the statistical prediction unit 34 for prediction of electrical energy consumptions if the cumulative electrical energy consumption calculated by the electrical-energy-consumption accumulating unit 35 exceeds a preset target electrical energy consumption.
  • the operating-level limiting unit 37 generates a limitation signal for limiting the operation of the air conditioners 5 installed in the building on the basis of the amount of correction by the data correcting unit 36 when the cumulative electrical energy consumption calculated by the electrical-energy-consumption accumulating unit 35 comes to fall within the target electrical energy consumption, and outputs the limitation signal to the host computer 1.
  • the predicted cumulative electrical energy consumption is output to the output device 25 (see Fig. 2 ) as described earlier and is also output to the data correcting unit 36.
  • the data correcting unit 36 determines whether the predicted cumulative electrical energy consumption exceeds the target electrical energy consumption, and if it does, corrects by a predetermined amount an element relating to the operating level, for example, the temperature settings, among the actual operation data used by the statistical prediction unit 34 for prediction of electrical energy consumptions. For example, the data correcting unit 36 corrects the temperature settings of the actual operation data as in equation (2) below.
  • Tsm ′ k ⁇ i Tsm k ⁇ i + ⁇ ⁇ Tsm
  • the statistical prediction unit 34 Upon again predicting the electrical energy consumptions from 13:16 to 13:30 by using the corrected temperature settings, the statistical prediction unit 34 outputs the predicted electrical energy consumptions to the electrical-energy-consumption accumulating unit 35.
  • the electrical-energy-consumption accumulating unit 35 calculates the predicted cumulative electrical energy consumption of the air-conditioning equipment during the prediction target period by using the electrical energy consumptions during the individual prediction target times predicted on the basis of the corrected actual operation data, and outputs the predicted cumulative electrical energy consumption to the output device 25 (see Fig. 2 ) and the data correcting unit 36.
  • the data correcting unit 36 determines whether the predicted cumulative electrical energy consumption input again exceeds the target electrical energy consumption, and if the target electrical energy consumption is exceeded, further corrects the actual operation data by the predetermined amount and outputs the corrected actual operation data to the statistical prediction unit 34.
  • the statistical prediction unit 34 performs re-prediction on the basis of the corrected actual operation data
  • the electrical-energy-consumption accumulating unit 35 further calculates the predicted cumulative electrical energy consumption by using the results of prediction. Then, the correction of actual operation data is performed repeatedly until the predicted cumulative electrical energy consumption comes to fall within the target electrical energy consumption.
  • the predicted cumulative electrical energy consumption during the prediction target period is updated every minute.
  • the air-conditioning system and the electrical-energy-consumption predicting apparatus As described above, with the air-conditioning system and the electrical-energy-consumption predicting apparatus according to this embodiment, until the predicted cumulative electrical energy consumption calculated by the electrical-energy-consumption accumulating unit 35 comes to fall within the target electrical energy consumption, correction of actual operation data, statistical re-prediction using the corrected actual operation data, and re-calculation of the cumulative electrical energy consumption are performed repeatedly. Then, when the predicted cumulative electrical energy consumption comes to fall within the target electrical energy consumption, the operating-level limiting unit 37 generates a limitation signal on the basis of the amount of correction at that time, and the operation of the air conditioners 5 is restricted on the basis of the limitation signal.
  • an order of priority of the air conditioners 5 whose operation is to be limited may be set in advance so that the operating-level limiting unit 37 generates and outputs such a limitation signal that limits operation of air conditioners starting from the ones having higher priority levels.
  • the compressor rotation speeds may be corrected.
  • both the temperature settings and the compressor rotation speeds may be corrected.
  • the data correcting unit 36 corrects the compressor rotation speeds on the basis of equation (4) below. The correction of the temperature settings has been described earlier so will not be described.
  • Rcm'(k-i) is the corrected compressor rotation speed in the k-th actual operation data
  • Rcm(k-i) is the compressor rotation speed in the k-th actual operation data before the correction
  • ⁇ Rcm is the amount of correction, which is a value with which the current rotation speed is reduced by, for example, 10%.
  • the degrees of opening of electronic expansion valves provided in refrigerator pipes may be adjusted.
  • the restricted operation may be stopped, returning to normal operation. This makes it possible to control the operation of the air-conditioning equipment just within the target electrical energy consumption while reducing discomfort for users.
EP09715494.2A 2008-02-27 2009-02-26 Air conditioning system and device for predicting building air conditioning facility power consumption amount Active EP2251614B1 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2008046909A JP2009204221A (ja) 2008-02-27 2008-02-27 空調システム及びビル空調設備の消費電力量予測装置
PCT/JP2009/053501 WO2009107698A1 (ja) 2008-02-27 2009-02-26 空調システム及びビル空調設備の消費電力量予測装置

Publications (3)

Publication Number Publication Date
EP2251614A1 EP2251614A1 (en) 2010-11-17
EP2251614A4 EP2251614A4 (en) 2017-01-25
EP2251614B1 true EP2251614B1 (en) 2019-05-22

Family

ID=41016086

Family Applications (1)

Application Number Title Priority Date Filing Date
EP09715494.2A Active EP2251614B1 (en) 2008-02-27 2009-02-26 Air conditioning system and device for predicting building air conditioning facility power consumption amount

Country Status (3)

Country Link
EP (1) EP2251614B1 (ja)
JP (1) JP2009204221A (ja)
WO (1) WO2009107698A1 (ja)

Families Citing this family (20)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP5258816B2 (ja) * 2010-02-27 2013-08-07 三菱電機株式会社 空気調和機
JP5287798B2 (ja) * 2010-06-04 2013-09-11 ダイキン工業株式会社 エネルギー管理システム及び制御装置
JP5696877B2 (ja) * 2010-10-01 2015-04-08 清水建設株式会社 運転管理装置、運転管理方法、および運転管理プログラム
JP5951270B2 (ja) * 2012-01-31 2016-07-13 三菱重工業株式会社 空気調和機の消費電力量管理制御システム、サーバ装置、クライアント装置及び空気調和機の消費電力量管理制御方法
JP5895246B2 (ja) * 2012-04-26 2016-03-30 パナソニックＩｐマネジメント株式会社 制御装置および制御方法
US8996193B2 (en) 2012-08-20 2015-03-31 International Business Machines Corporation Computer room cooling control
NL2010658C2 (en) * 2013-04-18 2014-10-21 Bosch Gmbh Robert Thermostat for a hvac.
KR102123428B1 (ko) * 2013-05-15 2020-06-16 엘지전자 주식회사 공기조화기 및 공기조화기 시스템
EP3012546B1 (en) * 2013-06-17 2021-06-16 Mitsubishi Electric Corporation Air conditioning system control device and air conditioning system control method
EP3194865B1 (en) * 2014-08-14 2021-04-07 Vigilent Corporation Method and apparatus for optimizing control variables to minimize power consumption of cooling systems
JP6375923B2 (ja) * 2014-12-12 2018-08-22 オムロン株式会社 制御装置、制御方法、制御システム、プログラム、および記録媒体
CN104990213B (zh) * 2015-06-29 2017-09-15 广东美的制冷设备有限公司 一种多人环境下协同控制空调的方法及系统
JP6503305B2 (ja) * 2016-01-25 2019-04-17 株式会社日立情報通信エンジニアリング 空調制御システム、空調計画装置、及び、計画方法
CN106705381B (zh) * 2017-01-19 2019-07-23 青岛海尔空调器有限总公司 基于空调的能耗预测方法及预测装置
CN107726555A (zh) * 2017-09-21 2018-02-23 新智能源系统控制有限责任公司 一种建筑空调模型预测控制方法和装置
JP7222650B2 (ja) * 2018-10-10 2023-02-15 中部電力株式会社 空調機器の電力量予測システム及び空調機器の操作システム
WO2020198971A1 (zh) * 2019-03-29 2020-10-08 亿可能源科技（上海）有限公司 空调系统的管理方法、控制方法及系统、存储介质
CN110298476A (zh) * 2019-05-15 2019-10-01 平安科技（深圳）有限公司 能源消耗预测方法、装置、计算机设备及存储介质
KR102083583B1 (ko) * 2019-07-26 2020-03-02 주식회사 선우오토텍 소비전력 관리 기능을 갖춘 공조 시스템
CN112396763B (zh) * 2020-11-11 2022-03-18 清华大学 一种多联机空调分户计量与计费方法及装置

Family Cites Families (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH07324794A (ja) * 1994-05-30 1995-12-12 Matsushita Seiko Co Ltd 空気調和機のエネルギー消費量予測装置
JPH0942737A (ja) * 1995-07-25 1997-02-14 Matsushita Electric Ind Co Ltd 空調制御装置
JP3460901B2 (ja) * 1996-01-31 2003-10-27 株式会社エヌ・ティ・ティ ファシリティーズ 電力デマンド制御装置
JP2002142360A (ja) * 2000-10-30 2002-05-17 Daikin Ind Ltd 使用電力量制御方法およびその装置
JP2002364901A (ja) * 2001-06-07 2002-12-18 Hitachi Ltd 蓄熱式空調設備の制御システム、制御計画立案装置、制御計画立案方法、制御計画立案を実行するための記録媒体及びプログラム
JP3894892B2 (ja) * 2003-01-30 2007-03-22 東京瓦斯株式会社 コージェネレーション装置及びその制御方法
JP4010964B2 (ja) * 2003-02-26 2007-11-21 大阪瓦斯株式会社 運転制御システム
JP2004297854A (ja) * 2003-03-25 2004-10-21 Hitachi Information & Control Systems Inc デマンド監視システム
JP2005180756A (ja) * 2003-12-18 2005-07-07 Matsushita Electric Ind Co Ltd 空気調和機
JP4335731B2 (ja) 2004-03-31 2009-09-30 三菱重工業株式会社 空調制御監視装置、空調制御監視方法及びビル管理システム
JP4195676B2 (ja) 2004-04-20 2008-12-10 三菱重工業株式会社 空調監視装置およびビル管理システム
JP4410046B2 (ja) * 2004-07-12 2010-02-03 三菱電機株式会社 空調熱源設備の熱負荷予測装置および方法
JP4346584B2 (ja) * 2004-08-12 2009-10-21 三洋電機株式会社 デマンド制御装置およびプログラム
US20070203860A1 (en) * 2006-02-24 2007-08-30 Gridpoint, Inc. Energy budget manager
WO2007118128A2 (en) * 2006-04-07 2007-10-18 I-Conserve, Llc Artificial-intelligence-based energy auditing, monitoring and control

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
None *

Also Published As

Publication number Publication date
EP2251614A1 (en) 2010-11-17
JP2009204221A (ja) 2009-09-10
WO2009107698A1 (ja) 2009-09-03
EP2251614A4 (en) 2017-01-25

Similar Documents

Publication Publication Date Title
EP2251614B1 (en) Air conditioning system and device for predicting building air conditioning facility power consumption amount
EP2478303B1 (en) A system and method of modeling and monitoring an energy load
JP2009204195A (ja) 空調システム及びビル空調設備の消費電力量予測装置
JP6012868B2 (ja) 熱負荷予測装置、配信システム、熱負荷予測方法及びプログラム
JP2013142494A (ja) 空調機器制御システムおよび空調機器の制御方法
CN109074066B (zh) 优化过滤器寿命周期的方法和监测通风系统的系统
EP3862644B1 (en) Information processing device and air-conditioning system provided with same
JP6403906B2 (ja) 制御装置、空気調和システムおよび空気調和機の制御方法
US20190195525A1 (en) Method and apparatus for operating heating and cooling equipment via a network
US20170356665A1 (en) Method and apparatus for providing equipment maintenance via a network
JP5262122B2 (ja) 空調機診断装置
JP2010025475A (ja) 冷凍サイクル機器に用いられる故障診断装置
Yang et al. Toward failure mode and effect analysis for heating, ventilation and air-conditioning
JP2010038471A (ja) 空調機管理装置及び空調機管理方法
JP5635220B1 (ja) 蓄熱量予測装置、蓄熱量予測方法およびプログラム
US11514537B2 (en) Decoupled modeling methods and systems
JP4586927B2 (ja) 機器管理システム
JP2004234302A (ja) プロセス管理装置
JP4426243B2 (ja) 省エネルギー効果量算出方法および装置
JP2004138265A (ja) 消費エネルギー量算出装置及び消費エネルギー量算出方法
JP5100723B2 (ja) 消費電力量監視装置および消費電力量監視方法
US20200278130A1 (en) Operation control method, storage medium, and operation control device
JP2018106247A (ja) データ分析装置、および、データ分析方法
JP2020190767A (ja) 対象施設の電力需要予測装置
JP6438548B1 (ja) 電力デマンド予測方法及び電力デマンド予測システム

Legal Events

Date Code Title Description
PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

17P Request for examination filed

Effective date: 20100825

AK Designated contracting states

Kind code of ref document: A1

Designated state(s): AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO SE SI SK TR

AX Request for extension of the european patent

Extension state: AL BA RS

DAX Request for extension of the european patent (deleted)
RA4 Supplementary search report drawn up and despatched (corrected)

Effective date: 20161222

RIC1 Information provided on ipc code assigned before grant

Ipc: F24F 11/02 20060101AFI20161216BHEP

Ipc: F24F 11/00 20060101ALI20161216BHEP

RAP1 Party data changed (applicant data changed or rights of an application transferred)

Owner name: MITSUBISHI HEAVY INDUSTRIES THERMAL SYSTEMS, LTD.

REG Reference to a national code

Ref country code: DE

Ref legal event code: R079

Ref document number: 602009058464

Country of ref document: DE

Free format text: PREVIOUS MAIN CLASS: F24F0011020000

Ipc: F24F0011300000

GRAP Despatch of communication of intention to grant a patent

Free format text: ORIGINAL CODE: EPIDOSNIGR1

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: GRANT OF PATENT IS INTENDED

RIC1 Information provided on ipc code assigned before grant

Ipc: F24F 140/60 20180101ALI20180814BHEP

Ipc: F24F 11/62 20180101ALI20180814BHEP

Ipc: F24F 11/30 20180101AFI20180814BHEP

INTG Intention to grant announced

Effective date: 20180914

GRAJ Information related to disapproval of communication of intention to grant by the applicant or resumption of examination proceedings by the epo deleted

Free format text: ORIGINAL CODE: EPIDOSDIGR1

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: REQUEST FOR EXAMINATION WAS MADE

GRAP Despatch of communication of intention to grant a patent

Free format text: ORIGINAL CODE: EPIDOSNIGR1

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: GRANT OF PATENT IS INTENDED

INTC Intention to grant announced (deleted)
INTG Intention to grant announced

Effective date: 20190116

RIC1 Information provided on ipc code assigned before grant

Ipc: F24F 140/60 20180101ALI20180814BHEP

Ipc: F24F 11/62 20180101ALI20180814BHEP

Ipc: F24F 11/30 20180101AFI20180814BHEP

GRAS Grant fee paid

Free format text: ORIGINAL CODE: EPIDOSNIGR3

GRAA (expected) grant

Free format text: ORIGINAL CODE: 0009210

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE PATENT HAS BEEN GRANTED

AK Designated contracting states

Kind code of ref document: B1

Designated state(s): AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO SE SI SK TR

REG Reference to a national code

Ref country code: GB

Ref legal event code: FG4D

REG Reference to a national code

Ref country code: CH

Ref legal event code: EP

REG Reference to a national code

Ref country code: IE

Ref legal event code: FG4D

REG Reference to a national code

Ref country code: DE

Ref legal event code: R096

Ref document number: 602009058464

Country of ref document: DE

REG Reference to a national code

Ref country code: AT

Ref legal event code: REF

Ref document number: 1136581

Country of ref document: AT

Kind code of ref document: T

Effective date: 20190615

REG Reference to a national code

Ref country code: NL

Ref legal event code: MP

Effective date: 20190522

REG Reference to a national code

Ref country code: LT

Ref legal event code: MG4D

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: LT

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20190522

Ref country code: HR

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20190522

Ref country code: PT

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20190922

Ref country code: SE

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20190522

Ref country code: NO

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20190822

Ref country code: FI

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20190522

Ref country code: NL

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20190522

Ref country code: ES

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20190522

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: BG

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20190822

Ref country code: LV

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20190522

Ref country code: GR

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20190823

REG Reference to a national code

Ref country code: AT

Ref legal event code: MK05

Ref document number: 1136581

Country of ref document: AT

Kind code of ref document: T

Effective date: 20190522

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: SK

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20190522

Ref country code: RO

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20190522

Ref country code: AT

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20190522

Ref country code: DK

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20190522

Ref country code: EE

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20190522

Ref country code: CZ

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20190522

REG Reference to a national code

Ref country code: DE

Ref legal event code: R097

Ref document number: 602009058464

Country of ref document: DE

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: IT

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20190522

PLBE No opposition filed within time limit

Free format text: ORIGINAL CODE: 0009261

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: NO OPPOSITION FILED WITHIN TIME LIMIT

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: TR

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20190522

26N No opposition filed

Effective date: 20200225

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: PL

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20190522

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: SI

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20190522

REG Reference to a national code

Ref country code: CH

Ref legal event code: PL

REG Reference to a national code

Ref country code: BE

Ref legal event code: MM

Effective date: 20200229

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: LU

Free format text: LAPSE BECAUSE OF NON-PAYMENT OF DUE FEES

Effective date: 20200226

Ref country code: MC

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20190522

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: LI

Free format text: LAPSE BECAUSE OF NON-PAYMENT OF DUE FEES

Effective date: 20200229

Ref country code: CH

Free format text: LAPSE BECAUSE OF NON-PAYMENT OF DUE FEES

Effective date: 20200229

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: IE

Free format text: LAPSE BECAUSE OF NON-PAYMENT OF DUE FEES

Effective date: 20200226

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: BE

Free format text: LAPSE BECAUSE OF NON-PAYMENT OF DUE FEES

Effective date: 20200229

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: MT

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20190522

Ref country code: CY

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20190522

PGFP Annual fee paid to national office [announced via postgrant information from national office to epo]

Ref country code: FR

Payment date: 20220118

Year of fee payment: 14

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: MK

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20190522

Ref country code: IS

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20190922

REG Reference to a national code

Ref country code: DE

Ref legal event code: R082

Ref document number: 602009058464

Country of ref document: DE

Representative=s name: CBDL PATENTANWAELTE GBR, DE

PGFP Annual fee paid to national office [announced via postgrant information from national office to epo]

Ref country code: GB

Payment date: 20230105

Year of fee payment: 15

Ref country code: DE

Payment date: 20221230

Year of fee payment: 15