WO2023234617A1 - Système et procédé de gestion d'énergie permettant de mettre en correspondance un consommateur et un fournisseur sur la base d'une analyse quantitative de données d'énergie d'un équipement de consommateur - Google Patents

Système et procédé de gestion d'énergie permettant de mettre en correspondance un consommateur et un fournisseur sur la base d'une analyse quantitative de données d'énergie d'un équipement de consommateur Download PDF

Info

Publication number
WO2023234617A1
WO2023234617A1 PCT/KR2023/006968 KR2023006968W WO2023234617A1 WO 2023234617 A1 WO2023234617 A1 WO 2023234617A1 KR 2023006968 W KR2023006968 W KR 2023006968W WO 2023234617 A1 WO2023234617 A1 WO 2023234617A1
Authority
WO
WIPO (PCT)
Prior art keywords
equipment
consumer
energy
supplier
time
Prior art date
Application number
PCT/KR2023/006968
Other languages
English (en)
Korean (ko)
Inventor
안영호
조선건
Original Assignee
주식회사 레티그리드
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
Priority claimed from KR1020220155866A external-priority patent/KR20230166844A/ko
Application filed by 주식회사 레티그리드 filed Critical 주식회사 레티그리드
Publication of WO2023234617A1 publication Critical patent/WO2023234617A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/08—Auctions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply

Definitions

  • the present invention relates to an energy management system, and more specifically, to an energy management system and method capable of matching consumers and suppliers based on quantitative energy data analysis of consumer facilities.
  • FEMS Factory Energy Management System
  • the reliability of energy efficiency is very low, and energy efficiency diagnosis results may vary by hundreds of percent or more depending on the personal capabilities of the consultant.
  • the conventional FEMS lacks a matching system between consumers such as factories that require energy efficiency and suppliers who supply equipment such as compressors, compressors, extruders, motors, and inverters used in factories. , there is an inconvenience in that consumers have to contact suppliers directly to introduce equipment for energy efficiency.
  • the present invention quantitatively analyzes the facility operation rate and operation pattern based on the consumer's measured energy data, matches the facility consumer and the supplier providing the facility, and facilitates the sharing of facility-related information and the facility purchase process between the consumer and supplier.
  • the purpose is to provide an energy management system and method that allows convenient performance.
  • the present invention is intended to provide an energy management system and method that can provide optimal energy efficiency services by quantitatively analyzing the facility operation rate and operation pattern of the consumer's equipment using artificial intelligence based on the consumer's actual energy data. .
  • An energy management system includes a supplier equipment information receiving unit configured to receive, from a plurality of supplier terminals, plural supplier equipment information related to a plurality of different supplier equipment provided by a plurality of suppliers; a consumer equipment information receiving unit configured to receive consumer equipment information regarding consumer equipment being used by a consumer and first actual energy data for the consumer equipment from at least one consumer terminal; and a consumer equipment operation information analysis unit configured to analyze the operation rate and operation pattern per unit time of the consumer equipment based on the first measured energy data regarding the consumer equipment.
  • the energy management system includes an energy efficiency analysis unit configured to analyze supplier equipment related to energy efficiency of the consumer equipment based on the operation rate and operation pattern of the consumer equipment; a supplier equipment information transmission unit configured to transmit equipment information of at least one first supplier equipment related to energy efficiency of the consumer equipment and the analyzed energy efficiency information to the consumer terminal; and a supplier equipment supply request unit configured to request supply of the second supplier equipment to the supplier terminal when a second supplier equipment is selected among the one or more first supplier equipment by the consumer terminal.
  • the energy efficiency analysis unit determines the operation time, non-operation time, and operation end time of the consumer equipment based on the actual energy measurement over time of the first actual energy data, analyzes the operation pattern, and calculates the operation time per unit time.
  • the operation rate can be calculated according to the ratio.
  • the energy efficiency analysis unit weights the operation rate to the difference between the first actual operation time energy measurement value generated during the operation time among the first actual energy data and the second operation time energy predicted value generated during the operation time of the supplier facility. Predict operation time energy efficiency information based on the value; Among the first actual energy data, the difference value between the first non-operation time energy measured value generated during the non-operation time and the second non-operation time energy predicted value generated during the non-operation time of the supplier facility is the difference value between the non-operation time per unit time. Predict non-operational time energy efficiency information based on a weighted value of the time ratio; And, it may be configured to generate energy efficiency information of the consumer facility based on the operation time energy efficiency information and the non-operation time energy efficiency information.
  • the consumer equipment information receiving unit may be configured to receive second actual energy data related to the second supplier equipment from the consumer terminal after the consumer equipment is replaced with the second supplier equipment.
  • the energy management system calculates an actual energy efficiency measurement value by comparing the first actual energy data and the second actual energy data, and calculates the cost of energy efficiency service based on the actual energy efficiency measurement value. It may further include a settlement unit that calculates.
  • An energy management method includes the steps of (A) receiving, by a supplier equipment information receiving unit, plural supplier equipment information related to a plurality of different supplier equipment provided by a plurality of suppliers from a plurality of supplier terminals; (B) receiving, by a consumer equipment information receiving unit, consumer equipment information about the consumer equipment being used by the consumer and first actual energy data about the consumer equipment from at least one consumer terminal; and (C) analyzing, by the consumer equipment operation information analysis unit, the operation rate and operation pattern per unit time of the consumer equipment based on the first measured energy data regarding the consumer equipment.
  • the energy management method includes the steps of (D) analyzing, by an energy efficiency analysis unit, supplier equipment related to energy efficiency of the consumer equipment, based on the operation rate and the operation pattern of the consumer equipment; (E) transmitting, by a supplier equipment information transmission unit, equipment information of at least one first supplier equipment related to energy efficiency of the consumer equipment and the analyzed energy efficiency information to the consumer terminal; and (F) requesting, by the supplier equipment supply request unit, the supply of the second supplier equipment to the supplier terminal when the second supplier equipment is selected among the one or more first supplier equipment by the consumer terminal. It can be included.
  • the step (C) is (C-1) analyzing the type of consumer equipment based on the first measured energy data by a first artificial intelligence model, and using a second artificial intelligence corresponding to the analyzed type of consumer equipment. determining a model; and (C-2) analyzing the first measured energy data using the second artificial intelligence model to analyze the operation rate and operation pattern per unit time of the consumer equipment.
  • the step (C-1) includes extracting a representative waveform of the first measured energy data; Clustering is performed on the representative waveform by comparing the representative waveform with a plurality of set reference waveforms based on dynamic time warping by the first artificial intelligence model based on GMM, and the representative waveform and the reference Classifying control schemes of the consumer equipment by analyzing distance distribution between waveforms and determining similarity between waveforms; And it may include determining the second artificial intelligence model corresponding to the type of the consumer equipment based on the control scheme of the consumer equipment.
  • the control scheme includes at least two of various control schemes, including an intake air volume control method, a loading-unloading control method, an automatic dual control method, a variable internal space control method, a variable speed control method, and a blow-off control method. can do.
  • a computer-readable non-transitory recording medium on which a program for executing the energy management method is recorded is provided.
  • the equipment operation rate and operation pattern are quantitatively analyzed based on the consumer's measured energy data, the equipment consumer and the supplier supplying the equipment are matched with each other, and equipment-related information is shared between the consumer and the supplier.
  • An energy management system and method are provided to facilitate the process of purchasing equipment.
  • optimal energy efficiency services can be provided by quantitatively analyzing the facility operation rate and operation pattern of the consumer's equipment using artificial intelligence based on the consumer's actual energy data.
  • FIG. 1 is a configuration diagram of an energy management system according to an embodiment of the present invention.
  • FIG. 2 is a configuration diagram of an energy management server constituting an energy management system according to an embodiment of the present invention.
  • Figure 3 is a configuration diagram of a consumer facility operation information analysis unit and an energy efficiency analysis unit that constitute an energy management system according to an embodiment of the present invention.
  • FIG. 4 is a flowchart showing an energy management method according to an embodiment of the present invention.
  • Figure 5 is an exemplary diagram of first actual energy data of consumer equipment to explain the operation pattern analysis process of the consumer equipment operation information analysis unit constituting the energy management system according to an embodiment of the present invention.
  • Figure 6 is a flowchart for explaining the settlement process of the energy management method according to an embodiment of the present invention.
  • Figure 7 is a diagram for explaining the settlement process of the energy management method according to an embodiment of the present invention, and is an example of second actual energy data of consumer equipment after being replaced with supplier equipment.
  • Figure 8 shows examples of representative waveforms extracted from first measured energy data according to an embodiment of the present invention.
  • Figure 9 is a diagram showing the clustering results of representative waveforms extracted according to an embodiment of the present invention.
  • Figure 10 is a conceptual diagram showing a method for calculating similarity between waveforms.
  • ' ⁇ module' and ' ⁇ unit' are units that process at least one function or operation, and may refer to, for example, hardware components such as software, FPGA, or one or more processors.
  • the energy management system 10 includes a plurality of consumer terminals 100, a plurality of supplier terminals 200, and an energy management server 300.
  • Consumer terminals 110, 120, and 130 are consumer terminals of factories or buildings, and are configured to request energy-related services such as energy efficiency of facilities, energy monitoring, supplier information search, and supplier matching from the energy management server 300. It can be.
  • the supplier terminals 210, 220, and 230 are supplier terminals that supply various equipment used by consumers such as factories and buildings. They provide equipment-related information provided by the supplier to the energy management server 300 and search for consumer information. , can be configured to request energy-related services such as consumer matching.
  • the energy management server 300 matches consumers and suppliers to improve the energy efficiency of the equipment used by the consumer, and provides energy efficiency services such as allowing the consumer to search for supplier equipment information or the supplier to search for consumer equipment information. .
  • the energy management server 300 may receive consumer equipment information related to the consumer equipment 140 used by each consumer in a factory, building, etc. from a plurality of consumer terminals 100.
  • Consumer equipment information may include equipment information such as specifications, type, and manufacturing year of the consumer equipment 140, and energy data measured for the equipment.
  • Consumer equipment 140 may include, but is not limited to, a compressor, compressor, motor, inverter, pump, or extruder.
  • the energy management server 300 is provided as a cloud energy management server to reduce data collection and communication costs, and provides cloud-based data from the meter 150 of the consumer terminal 100 connected to the consumer facility 140. You can collect consumer equipment information and supplier equipment information in other ways.
  • the energy management server 300 may receive supplier equipment information related to the supplier equipment supplied by each supplier from the plurality of supply terminals 200.
  • Supplier equipment information may include equipment information such as specifications, type, manufacturing year, rated voltage/current when in operation, and power consumption when not in operation.
  • FIG. 2 is a configuration diagram of an energy management server constituting an energy management system according to an embodiment of the present invention.
  • the energy management server 300 includes a supplier equipment information receiving unit 310, a consumer equipment information receiving unit 320, a consumer equipment operation information analysis unit 330, an energy efficiency analysis unit 340, It may include a supplier equipment information transmission unit 350, a supplier equipment supply request unit 360, a settlement unit 370, and a control unit 380.
  • the supplier equipment information receiving unit 310 may be configured to receive, from the plurality of supplier terminals 200, a plurality of supplier equipment information related to a plurality of different supplier equipment provided by a plurality of suppliers.
  • the consumer equipment information receiving unit 320 may be configured to receive consumer equipment information about the consumer equipment 140 being used by the consumer and first actual energy data about the consumer equipment from at least one consumer terminal 100.
  • the consumer equipment operation information analysis unit 330 may be configured to analyze the operation rate and operation pattern per unit time of the consumer equipment based on the first measured energy data regarding the consumer equipment.
  • the energy efficiency analysis unit 340 may be configured to analyze supplier equipment related to energy efficiency of the consumer equipment 140, based on the operation rate and operation pattern of the consumer equipment 140.
  • the supplier equipment information transmission unit 350 may be configured to transmit equipment information of one or more first supplier equipment related to energy efficiency of the consumer equipment 140 and the analyzed energy efficiency information to the consumer terminal 100.
  • the supplier equipment supply request unit 360 may be configured to request supply of the second supplier equipment to the supplier terminal when the second supplier equipment is selected among one or more first supplier equipment by the consumer terminal 100.
  • the settlement unit 370 can calculate the actual energy efficiency measurement value based on the operation rate and operation pattern of the first actual energy data collected for the consumer equipment, and calculate the cost according to the energy efficiency service based on the actual energy efficiency measurement value. there is.
  • the control unit 380 may include a processor that controls each component of the energy management server 300 and executes a program to provide services such as consumer-supplier matching, consumer/supplier information search, and equipment trading.
  • FIG 3 is a configuration diagram of a consumer facility operation information analysis unit and an energy efficiency analysis unit that constitute an energy management system according to an embodiment of the present invention.
  • the consumer equipment operation information analysis unit 330 may include an operation pattern analysis unit 332 and an operation rate calculation unit 334.
  • the operation pattern analysis unit 332 determines the operation time, non-operation time, and operation end time of the consumer equipment based on the time-dependent energy measurement value of the first actual energy data collected for the consumer equipment 140 and operates it. Patterns can be analyzed.
  • the operation rate calculation unit 334 may calculate the operation rate according to the ratio of operation time per unit time.
  • the energy efficiency analysis unit 340 may include an operation time energy efficiency prediction unit 342, a non-operation time energy efficiency prediction unit 344, and an energy efficiency information generation unit 346.
  • the operation time energy efficiency prediction unit 342 includes a first actual operation time energy measurement value generated during the operation time among the first actual energy data collected for the consumer facility 140 and a second operation time energy measurement value generated during the operation time of the supplier facility. Uptime energy efficiency information can be predicted based on the difference between the time energy forecast values and the operating rate weighted.
  • the non-operational time energy efficiency prediction unit 344 determines the first non-operational energy measurement value generated during non-operational time among the first actual energy data collected for the customer equipment 140 and the first non-operational energy efficiency value generated during non-operational time of the supplier equipment.
  • Non-downtime energy efficiency information can be predicted based on a value obtained by weighting the ratio of downtime per unit time to the difference between the second downtime energy prediction values.
  • the energy efficiency information generation unit 346 may generate energy efficiency information for the consumer facility 140 based on the operation time energy efficiency information and the non-operation time energy efficiency information.
  • the consumer equipment information receiver 320 may receive second actual energy data related to the second supplier equipment from the consumer terminal 100 after the consumer equipment 140 is replaced with the second supplier equipment.
  • the settlement unit 370 uses first actual energy data collected for the consumer equipment 140 before replacement and second actual energy data collected for the second supplier equipment installed on the consumer side as a replacement for the consumer equipment 140. By comparison, the actual energy efficiency measurement value can be calculated, and the cost of the energy efficiency service can be calculated based on the calculated energy efficiency measurement value.
  • FIG 4 is a flowchart showing an energy management method according to an embodiment of the present invention. 1 to 4, first, the supplier facility information receiving unit 310 of the energy management server 300 receives multiple provider facility information related to multiple different provider facilities provided by multiple suppliers. It can be received from the terminal 200 (S11).
  • the consumer equipment information receiving unit 320 of the energy management server 300 receives consumer equipment information about the consumer equipment 140 being used by the consumer and first actual energy data about the consumer equipment from at least one consumer terminal 100. You can do it (S12, S13).
  • the consumer equipment operation information analysis unit 330 of the energy management server 300 may analyze the operation rate and operation pattern per unit time of the consumer equipment 140 based on the first actual energy data regarding the consumer equipment (S14). .
  • Figure 5 is an exemplary diagram of first actual energy data of consumer equipment to explain the operation pattern analysis process of the consumer equipment operation information analysis unit constituting the energy management system according to an embodiment of the present invention.
  • the operation pattern analysis unit 332 of the consumer equipment operation information analysis unit 330 performs energy measurement over time of the first actual energy data 20 collected for the consumer equipment 140. Based on the value, the operation pattern can be analyzed by determining the operation time (T1), non-operation time (T2), and operation end time (T3) of the consumer facility 140.
  • the operation rate calculation unit 334 of the consumer facility operation information analysis unit 330 may calculate the operation rate according to the ratio of operation time (T1) per unit time (T0).
  • the amount of power of the consumer equipment 140 is generated as much as the operation power (P1). Due to the motor idling of the consumer equipment 140 during the non-operating time (T2), an amount of power equal to the non-operating power (P2) is generated in the consumer equipment 140, which causes unnecessary power consumption.
  • the energy efficiency analysis unit 340 of the energy management server 300 may analyze supplier equipment related to energy efficiency of the consumer equipment 140 based on the operation rate and operation pattern of the consumer equipment 140 (S15).
  • the operation time energy efficiency prediction unit 342 of the energy efficiency analysis unit 340 measures the first operation time energy generated at the operation time T1 among the first actual energy data 20 collected for the consumer equipment 140.
  • the operation time energy efficiency information can be predicted based on a value obtained by weighting the operation rate to the difference value between the value P1 and the second operation time energy prediction value generated during the operation time of the supplier equipment 200.
  • the non-operation time energy efficiency prediction unit 344 of the energy efficiency analysis unit 340 determines the first non-operation occurring during the non-operation time (T2) among the first actual energy data 20 collected for the consumer equipment 140.
  • Downtime energy efficiency information can be predicted based on the difference between the actual time energy measurement value and the second downtime energy predicted value generated during the downtime of the supplier facility 200, weighted by the ratio of downtime per unit time. You can.
  • the energy efficiency information generation unit 346 reports to the operation time energy efficiency prediction unit 342.
  • Energy efficiency information of the consumer facility 140 can be generated based on the operation time energy efficiency information calculated by and the non-operation time energy efficiency information predicted by the non-operation time energy efficiency prediction unit 344.
  • the energy efficiency analysis unit 340 may generate energy efficiency information by reflecting the energy efficiency information based on seasonal deviation (change). For example, in summer, additional energy may be generated for cooling the compressor, and by correcting data measured in winter, etc., energy efficiency information for summer, etc. can be predicted.
  • the supplier equipment information transmission unit 350 of the energy management server 300 transmits equipment information of one or more first supplier equipment related to energy efficiency of the consumer equipment 140 and the analyzed energy efficiency information to the consumer terminal 100. You can do it (S16).
  • the supplier equipment supply request unit 360 of the energy management server 300 may request the supply of the second supplier equipment to the supplier terminal. There are (S17, S18, S19).
  • Figure 6 is a flowchart for explaining the settlement process of the energy management method according to an embodiment of the present invention.
  • the consumer equipment information receiver 320 may receive the second actual energy data related to the second supplier equipment from the consumer terminal 100 (S21, S22). .
  • the settlement unit 370 uses first actual energy data collected for the consumer equipment 140 before replacement and second actual energy data collected for the second supplier equipment installed on the consumer side as a replacement for the consumer equipment 140. Compare and calculate actual energy efficiency measurements, calculate the cost of energy efficiency services based on the calculated actual energy efficiency measurements, transmit evaluation information on supplier equipment to the supplier terminal, and send ROI-based settlement information to the consumer terminal ( 100) can be transmitted (S23, S24, S25).
  • Figure 7 is a diagram for explaining the settlement process of the energy management method according to an embodiment of the present invention, and is an example of second actual energy data of consumer equipment after being replaced with supplier equipment.
  • FSD Fixed Speed Drive
  • VSD Very Speed Drive
  • the power amount of the consumer's equipment may be reduced compared to before the equipment replacement.
  • the operation pattern analysis unit 332 of the consumer equipment operation information analysis unit 330 performs actual energy measurements over time of the second actual energy data 30 collected for the replaced consumer equipment 140. Based on the value, the operation pattern can be analyzed by determining the operation time (T1), non-operation time (T2), and operation end time (T3) of the consumer facility 140.
  • the operation rate calculation unit 334 of the consumer facility operation information analysis unit 330 may calculate the operation rate according to the ratio of operation time (T1) per unit time (T0).
  • the amount of power of the consumer equipment 140 is generated during the operating time (T1) by the second operating power (P3), which is reduced from the first operating power (P1) before the equipment replacement.
  • the second non-operating power (P4) generated due to the idling of the motor of the consumer equipment 140 during the non-operating time (T2) is significantly reduced compared to the non-operating power (P2) before replacement.
  • the energy usage of the customer facility (140) can be reduced by the amount of operating energy saved (40) during the operating time (T1), and the energy consumption of the consumer facility (140) can be reduced by the amount of non-operating energy saved (50) during the non-operating time (T2). Energy usage can be reduced.
  • the energy efficiency analysis unit 340 of the energy management server 300 may analyze supplier equipment related to energy efficiency of the consumer equipment 140 based on the operation rate and operation pattern of the consumer equipment 140 (S15).
  • the operation time energy efficiency prediction unit 342 of the energy efficiency analysis unit 340 measures the first operation time energy generated at the operation time T1 among the first actual energy data 20 collected for the consumer equipment 140.
  • the operation time energy efficiency information can be predicted based on a value obtained by weighting the operation rate to the difference value between the value P1 and the second operation time energy prediction value generated during the operation time of the supplier equipment 200.
  • the non-operation time energy efficiency prediction unit 344 of the energy efficiency analysis unit 340 determines the first non-operation occurring during the non-operation time (T2) among the first actual energy data 20 collected for the consumer equipment 140.
  • Downtime energy efficiency information can be predicted based on the difference between the actual time energy measurement value and the second downtime energy predicted value generated during the downtime of the supplier facility 200, weighted by the ratio of downtime per unit time. You can.
  • the energy efficiency information generation unit 346 reports to the operation time energy efficiency prediction unit 342.
  • Energy efficiency information of the consumer facility 140 can be generated based on the operation time energy efficiency information calculated by and the non-operation time energy efficiency information predicted by the non-operation time energy efficiency prediction unit 344.
  • the energy management system and method receives first actual energy data about the consumer equipment in use by the consumer from at least one consumer terminal related to the consumer, and provides information about the consumer equipment to improve the energy efficiency of the consumer equipment. Based on the first actual energy data, the operation rate and operation pattern per unit time of consumer equipment can be analyzed.
  • the operation rate and operation pattern of consumer equipment include a process of analyzing the type of consumer equipment based on first actual energy data using a first artificial intelligence model and determining a second artificial intelligence model corresponding to the type of consumer equipment.
  • the operation rate and operation pattern per unit time of consumer equipment can be analyzed by analyzing the first actual energy data using a second artificial intelligence model determined according to the type of consumer equipment analyzed.
  • by determining different artificial intelligence models according to the type of consumer equipment and analyzing the operation rate and operation pattern per unit time of consumer equipment it is possible to provide services for energy efficiency according to the type of consumer equipment. .
  • the energy management server 300 may receive first actual energy data about the consumer equipment 140 that the consumer is using in a factory, building, etc. from the consumer terminal 100 related to the consumer. .
  • the energy management server 300 automatically analyzes the type of the corresponding consumer equipment 140 based on the first actual energy data obtained through remote collection using the first artificial intelligence model and the second artificial intelligence model, and Operation rates and operation patterns can be analyzed.
  • the energy management server 300 analyzes the operation rate and operation pattern per unit time for the consumer equipment 140 using the first artificial intelligence model and the second artificial intelligence model, Even if consumer facility information related to (140) is not separately provided, the optimal energy efficiency plan can be provided by accurately analyzing the operation rate and operation pattern per unit time for the consumer facility (140).
  • the energy management server 300 analyzes the type of consumer equipment 140 based on the first actual energy data using the first artificial intelligence model, and derives an accurate operation rate and operation pattern per unit time for the consumer equipment 140. To this end, a second artificial intelligence model corresponding to the type of the analyzed consumer equipment 140 can be determined.
  • the second artificial intelligence model is an artificial intelligence model for analyzing the operation rate and operation pattern per unit time of the consumer equipment 140 from the first measured energy data related to the consumer equipment 140, and data processing/ It may include analysis algorithms and various parameters for data processing/analysis.
  • the energy management server 300 can analyze the first actual energy data using the second artificial intelligence model to analyze the operation rate and operation pattern per unit time of consumer equipment.
  • the first artificial intelligence model may be an artificial intelligence model based on GMM (Gaussian mixture model).
  • the first artificial intelligence model extracts a representative waveform of the first measured energy data, compares the representative waveform with a plurality of reference waveforms set by a GMM-based artificial intelligence model, and performs clustering on the representative waveform.
  • the control scheme of the consumer equipment 140 can be classified.
  • the first artificial intelligence model first normalizes the first measured energy data to have a set normalization range, for example, 0 to 1, and calculates the first measured energy based on set standards related to consumer equipment such as compressor capacity. Outliers can be removed from data.
  • the first artificial intelligence model applies a time window with a set time section to the first measured energy data, and slides the time window along the time axis to search for a time section for extracting a representative waveform, while changing the time window within the time window.
  • the number of waveform peaks in a section can be calculated.
  • the first artificial intelligence model performs the first actual measurement when the number of waveform peaks corresponding to the time window satisfies a preset peak number range (e.g., 3 to 4, or 4 to 5 or less) Among the energy data, data in the time section corresponding to the time window that satisfies the set number of waveform peaks can be extracted as a representative waveform.
  • a preset peak number range e.g. 3 to 4, or 4 to 5 or less
  • the first artificial intelligence model is, for example, when the current harmonic distortion rate deviates from the preset current harmonic distortion rate reference range (e.g., 20% or more), or the voltage harmonic distortion rate deviates from the preset voltage harmonic distortion rate reference range. (e.g., 5% or more), the corresponding noise data can be removed.
  • the preset current harmonic distortion rate reference range e.g. 20% or more
  • the voltage harmonic distortion rate deviates from the preset voltage harmonic distortion rate reference range. (e.g., 5% or more)
  • the corresponding noise data can be removed.
  • Figure 8 shows examples of representative waveforms extracted from first measured energy data according to an embodiment of the present invention.
  • Figure 9 is a diagram showing the clustering results of representative waveforms extracted according to an embodiment of the present invention.
  • Figure 10 is a conceptual diagram showing a method for calculating similarity between waveforms.
  • the first artificial intelligence model is based on dynamic time warping by a GMM-based artificial intelligence model, representative waveforms and techniques extracted from the first measured energy data.
  • the control scheme of the consumer equipment 140 is determined by analyzing the distance distribution between the set reference waveforms (statistically processed waveforms of previously acquired energy data for various equipment) (Shapelet s 1 , s 2 ) and determining the similarity between the waveforms. Can be classified.
  • the reference waveform (Shapelet s 1 , s 2 ) may be a basic waveform extracted from actual energy data collected for equipment such as a specific compressor.
  • the types of consumer equipment are classified into four types (Class 1, 2, 3, and 4).
  • the horizontal and vertical axes of Figure 9 are the distance d(x, s 1 ) between the representative waveform extracted for consumer equipment and the first reference waveform (Shapelet s 1 ), respectively, and the representative waveform and second reference waveform extracted for consumer equipment.
  • the distance from (Shapelet s 2 ) is d(x, s 2 ).
  • the type of consumer equipment 140 corresponding to the representative waveform for example, the control scheme, can be determined by analyzing the similarity between the two waveforms at a high level using a kernel trick.
  • the similarity between waveforms will be analyzed using a Dynamic Time Warping Matching algorithm based on an encoder-decoder supervised learning algorithm or a Gaussian mixture model unsupervised learning algorithm, as illustrated in Figure 10.
  • Euclidean matching algorithms, etc. may be used.
  • the control scheme of the consumer equipment 140 is, for example, an intake air volume control method (inlet modulation method), a loading/unloading control method, an automatic dual control method, and an internal space (volume).
  • -off control may include at least two or more of various control schemes, and among these various control schemes, one or a plurality of control schemes corresponding to the consumer equipment 140 may be determined.
  • consumer equipment 140 may include one or more compressors.
  • the energy management server 300 can measure the power usage of the consumer equipment 140 based on the unit time operation rate and operation pattern of the compressor corresponding to the consumer equipment 140 using the second artificial intelligence model.
  • consumer equipment information may include actually measured energy data (first measured energy data) for the consumer equipment 140 .
  • the consumer equipment information may further include equipment information such as specifications, type, and manufacturing year of the consumer equipment 140, in addition to actual energy data.
  • the energy management server 300 When equipment information of the consumer equipment 140 is additionally provided along with the actual energy data of the consumer equipment 140, the energy management server 300 provides consumer equipment ( By identifying the type of 140), energy efficiency information of the corresponding consumer facility 140 can be analyzed.
  • the energy management server 300 manages the consumer equipment 140 for which the equipment information of the consumer equipment 140 is not provided using the above-described first artificial intelligence model and the second artificial intelligence model. Energy efficiency information can be analyzed by identifying the type.
  • the consumer equipment operation information analysis unit 330 may be configured to analyze the operation rate and operation pattern per unit time of the consumer equipment based on the first actual energy data regarding the consumer equipment 140.
  • the consumer equipment operation information analysis unit 330 analyzes the type of consumer equipment 140 based on the first actual energy data using the first artificial intelligence model, and analyzes the type of consumer equipment 140 corresponding to the analyzed type of consumer equipment 140. You can decide on an artificial intelligence model.
  • the first artificial intelligence model for analyzing the type of consumer equipment 140 may be an artificial intelligence model based on GMM (Gaussian mixture model).
  • the input data of the first artificial intelligence model may be actual energy data of consumer equipment, and the output data may be the type of consumer equipment.
  • the first artificial intelligence model is a GMM-based artificial intelligence model, as well as supervised learning artificial intelligence models such as SVM (Support Vector Machine), LSTM (Long Short-Term Memory), and LSTM-CNN/RNN (LSTM Convolutional/Recurrent Neural Network). , It can also be implemented with unsupervised learning artificial intelligence models such as Variational Autoencoder, Adversarial Autoencoder, and MAD-GAN (Multivariate Anomaly Detection with Generative Adversarial Network).
  • SVM Serial Vector Machine
  • LSTM Long Short-Term Memory
  • LSTM-CNN/RNN LSTM Convolutional/Recurrent Neural Network
  • unsupervised learning artificial intelligence models such as Variational Autoencoder, Adversarial Autoencoder, and MAD-GAN (Multivariate Anomaly Detection with Generative Adversarial Network).
  • the first artificial intelligence model of the consumer facility operation information analysis unit 330 extracts a representative waveform of the first actual energy data, and compares the extracted representative waveform with a number of set reference waveforms and a GMM-based artificial intelligence model to represent the representative waveform. By performing clustering on the waveform, the control scheme of the consumer equipment can be classified.
  • the first artificial intelligence model is a representative waveform extracted from actual energy data of consumer equipment based on dynamic time warping by a GMM-based artificial intelligence model and collected by various types of consumer equipment, respectively.
  • the control scheme of consumer equipment can be classified.
  • the consumer equipment operation information analysis unit 330 analyzes the first actual energy data using a second artificial intelligence model determined according to the type of the analyzed consumer equipment 140 to determine the unit time operation rate and operation pattern of the consumer equipment 140. It can be configured to analyze.
  • the second artificial intelligence model of the consumer equipment operation information analysis unit 330 may be pre-trained (supervised learning or unsupervised learning) based on learning data corresponding to the type of consumer equipment 140.
  • the input data of the second artificial intelligence model is the actual energy data of the consumer's equipment, and the output data is the operation rate and operation pattern per unit time of the equipment.
  • the unit time operation rate and/or operation pattern of the consumer equipment 140 analyzed by the consumer equipment operation information analysis unit 330 is used for prediction of energy usage or power generation, energy efficiency analysis, energy use pattern analysis, energy optimization scheduling analysis, etc. It can be.
  • the usage pattern, operation pattern, energy usage, energy saving amount, energy efficiency ROI, etc. of consumer equipment 140 can be determined with high accuracy of about 95% or more (based on TPR and MAPE). It can be analyzed or predicted.
  • the energy efficiency analysis unit 340 is based on the operation rate and operation pattern of the consumer equipment 140 analyzed by the first artificial intelligence model and the second artificial intelligence model of the consumer equipment operation information analysis unit 330, consumer equipment ( 140) can be configured to analyze supplier facilities related to energy efficiency.
  • the devices, methods, and components described in the embodiments may include, for example, a processor, a controller, an Arithmetic Logic Unit (ALU), a Digital Signal Processor, a microcomputer, and a Field Programmable Gate (FPGA). It may be implemented using one or more general-purpose computers or special-purpose computers, such as an array, PLU (Programmable Logic Unit), microprocessor, or any other device that can execute and respond to instructions.
  • ALU Arithmetic Logic Unit
  • FPGA Field Programmable Gate
  • the processing device may execute an operating system and one or more software applications that run on the operating system. Additionally, a processing device may access, store, manipulate, process, and generate data in response to the execution of software. For ease of understanding, a single processing device may be described as being used; however, those skilled in the art will understand that a processing device may include multiple processing elements and/or multiple types of processing elements. You will understand that it can be included.
  • a processing device may include a plurality of processors or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are also possible.
  • Software may include a computer program, code, instructions, or a combination of one or more of these, and may configure a processing unit to operate as desired, or to process independently or collectively. You can command the device.
  • Software and/or data may be used on any type of machine, component, physical device, virtual equipment, computer storage medium or device to be interpreted by or to provide instructions or data to a processing device. It can be embodied in . Software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.
  • the method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium.
  • Computer-readable media may include program instructions, data files, data structures, etc., singly or in combination.
  • Program instructions recorded on the medium may be specially designed and configured for the embodiment or may be known and available to those skilled in the art of computer software.
  • Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CDROMs and DVDs, and ROM, RAM, and flash memory.
  • the hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Economics (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Strategic Management (AREA)
  • Marketing (AREA)
  • Finance (AREA)
  • General Business, Economics & Management (AREA)
  • Accounting & Taxation (AREA)
  • Human Resources & Organizations (AREA)
  • Development Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Tourism & Hospitality (AREA)
  • Biomedical Technology (AREA)
  • Mathematical Physics (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Software Systems (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Primary Health Care (AREA)
  • Water Supply & Treatment (AREA)
  • Educational Administration (AREA)
  • Public Health (AREA)
  • Game Theory and Decision Science (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

Sont divulgués ici un système et un procédé de gestion d'énergie qui analysent quantitativement des taux d'utilisation et des motifs d'utilisation d'un équipement afin de mettre en correspondance un consommateur d'équipement et un fournisseur de l'équipement sur la base de données réelles de mesure d'énergie du consommateur, et assurent l'interface entre le consommateur et le fournisseur pour faciliter le partage d'informations relatives à un équipement et un processus d'achat d'équipement. Le système de gestion d'énergie selon la présente invention comprend : une unité d'analyse d'informations d'utilisation d'équipement de consommateur pour analyser le taux d'utilisation et le motif d'utilisation par unité de temps d'un équipement de consommateur sur la base de premières données réelles de mesure d'énergie concernant l'équipement de consommateur ; une unité d'analyse d'efficacité énergétique pour analyser un premier équipement de fournisseur en relation avec l'efficacité énergétique de l'équipement de consommateur sur la base du taux d'utilisation et du motif d'utilisation de l'équipement de consommateur ; une unité de transmission d'informations d'équipement de fournisseur pour transmettre des informations d'équipement concernant le premier équipement de fournisseur et des informations d'efficacité énergétique à un terminal de consommateur ; et une unité de demande de fourniture d'équipement de fournisseur pour demander à un terminal de fournisseur de fournir un second équipement de fournisseur sélectionné par le premier équipement de fournisseur.
PCT/KR2023/006968 2022-05-30 2023-05-23 Système et procédé de gestion d'énergie permettant de mettre en correspondance un consommateur et un fournisseur sur la base d'une analyse quantitative de données d'énergie d'un équipement de consommateur WO2023234617A1 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
KR20220066319 2022-05-30
KR10-2022-0066319 2022-05-30
KR10-2022-0155866 2022-11-18
KR1020220155866A KR20230166844A (ko) 2022-05-30 2022-11-18 수요자 설비에 대한 정량적 에너지 데이터 분석을 기초로 수요자-공급자 매칭 가능한 에너지 관리 시스템 및 방법

Publications (1)

Publication Number Publication Date
WO2023234617A1 true WO2023234617A1 (fr) 2023-12-07

Family

ID=89025359

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/KR2023/006968 WO2023234617A1 (fr) 2022-05-30 2023-05-23 Système et procédé de gestion d'énergie permettant de mettre en correspondance un consommateur et un fournisseur sur la base d'une analyse quantitative de données d'énergie d'un équipement de consommateur

Country Status (1)

Country Link
WO (1) WO2023234617A1 (fr)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080172312A1 (en) * 2006-09-25 2008-07-17 Andreas Joanni Synesiou System and method for resource management
JP2012170285A (ja) * 2011-02-16 2012-09-06 Toshiba Corp ホームエネルギーマネジメントシステム
JP2013106374A (ja) * 2011-11-10 2013-05-30 Shimizu Corp 需要電力制御装置および需要電力制御方法
WO2015108041A1 (fr) * 2014-01-14 2015-07-23 京セラ株式会社 Dispositif et procédé de gestion d'énergie
KR20160112313A (ko) * 2015-03-18 2016-09-28 신라대학교 산학협력단 온실가스 수급 및 공급 모니터링을 통한 에너지 관리 시스템

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080172312A1 (en) * 2006-09-25 2008-07-17 Andreas Joanni Synesiou System and method for resource management
JP2012170285A (ja) * 2011-02-16 2012-09-06 Toshiba Corp ホームエネルギーマネジメントシステム
JP2013106374A (ja) * 2011-11-10 2013-05-30 Shimizu Corp 需要電力制御装置および需要電力制御方法
WO2015108041A1 (fr) * 2014-01-14 2015-07-23 京セラ株式会社 Dispositif et procédé de gestion d'énergie
KR20160112313A (ko) * 2015-03-18 2016-09-28 신라대학교 산학협력단 온실가스 수급 및 공급 모니터링을 통한 에너지 관리 시스템

Similar Documents

Publication Publication Date Title
US20210241544A1 (en) Platform for analyzing health of heavy electric machine and analysis method using the same
US20240142543A1 (en) Remaining-Life and Time-To-Failure Predictions of Power Assets
US10409231B2 (en) Methods and apparatuses for utilizing adaptive predictive algorithms and determining when to use the adaptive predictive algorithms for virtual metrology
WO2018221948A1 (fr) Procédé de gestion de processus au moyen d'une analyse de mégadonnées
WO2022163956A1 (fr) Procédé et système à base d'intelligence artificielle pour prédire la demande au niveau d'un magasin
CN112330034A (zh) 故障预测方法及系统
KR20200074825A (ko) 패턴 태깅 기술 기반 비정상 전력 데이터 판별 방법 및 시스템
CN112070353A (zh) 一种精确检测数据中心能效的方法及系统
Hartmann et al. Suspicious electric consumption detection based on multi-profiling using live machine learning
CN117931583B (zh) 一种设备集群运行状态预测方法、电子设备及存储介质
Karunakaran et al. Toward evolving dispatching rules for dynamic job shop scheduling under uncertainty
CN118176467A (zh) 用于监视技术安装中的资产的状况的系统、装置和方法
WO2023234617A1 (fr) Système et procédé de gestion d'énergie permettant de mettre en correspondance un consommateur et un fournisseur sur la base d'une analyse quantitative de données d'énergie d'un équipement de consommateur
JP7226542B2 (ja) 時系列データ処理方法、時系列データ処理装置、プログラム
WO2024090662A1 (fr) Système et procédé de service d'efficacité énergétique basés sur une analyse de motif d'utilisation d'équipement basée sur une intelligence artificielle
WO2023234616A1 (fr) Système et procédé de gestion d'énergie de type location de compteur d'énergie mobile
CN110765486B (zh) 一种资产故障识别方法
US20220230028A1 (en) Determination method, non-transitory computer-readable storage medium, and information processing device
Mebratu et al. AI assisted fan failure prediction using workload fingerprinting
KR20230077994A (ko) 제조 설비의 고장 예측 시스템
KR20230166844A (ko) 수요자 설비에 대한 정량적 에너지 데이터 분석을 기초로 수요자-공급자 매칭 가능한 에너지 관리 시스템 및 방법
WO2023200131A1 (fr) Système et procédé de dérivation de score d'état basés sur un tenseur
KR20230166845A (ko) 모바일 에너지 계측기 임대형 에너지 관리 시스템 및 방법
KR102617618B1 (ko) 산업용 공장의 고효율 설비 교체를 위한 인공지능 기반 의사 결정 서비스 제공 장치, 방법 및 시스템
US20240320688A1 (en) Estimating system and estimating method for energy-saving and emission-reduction of energy-consumption device

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 23816279

Country of ref document: EP

Kind code of ref document: A1