WO2019198122A1 - Energy efficiency evaluation device - Google Patents

Energy efficiency evaluation device Download PDF

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Publication number
WO2019198122A1
WO2019198122A1 PCT/JP2018/014882 JP2018014882W WO2019198122A1 WO 2019198122 A1 WO2019198122 A1 WO 2019198122A1 JP 2018014882 W JP2018014882 W JP 2018014882W WO 2019198122 A1 WO2019198122 A1 WO 2019198122A1
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evaluation
unit
threshold value
evaluation threshold
calculation unit
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PCT/JP2018/014882
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French (fr)
Japanese (ja)
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貴之 井對
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三菱電機株式会社
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Priority to KR1020207027599A priority Critical patent/KR102267660B1/en
Priority to PCT/JP2018/014882 priority patent/WO2019198122A1/en
Priority to JP2020512949A priority patent/JP6771692B2/en
Priority to CN201880091962.9A priority patent/CN111971701A/en
Priority to DE112018007234.7T priority patent/DE112018007234T5/en
Publication of WO2019198122A1 publication Critical patent/WO2019198122A1/en

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    • 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
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • 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
    • G06Q10/063—Operations research, analysis or management
    • 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
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • 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
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • 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
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • G06Q10/06375—Prediction of business process outcome or impact based on a proposed change
    • 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
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • 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
    • G06Q50/04—Manufacturing
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/80—Management or planning
    • Y02P90/82—Energy audits or management systems therefor

Definitions

  • This invention relates to an energy efficiency evaluation apparatus for evaluating energy intensity.
  • Patent Document 1 discloses a technique for evaluating energy intensity.
  • Patent Document 1 evaluates the energy intensity by comparing the current energy intensity with a threshold value created based on past performance values.
  • the threshold value is a fixed value.
  • the present invention has been made to solve the above-described problems, and an object of the present invention is to provide an energy efficiency evaluation apparatus that can evaluate energy intensity.
  • the energy efficiency evaluation apparatus includes an energy intensity calculation unit that calculates an energy intensity based on a production amount of a product and an energy consumption amount necessary for producing the product of the production amount, The median value of energy intensity is calculated for each time zone that is earlier than the unit evaluation period, and this calculated median is set as the evaluation threshold value for evaluating the energy intensity in each time zone.
  • a calculation unit and an evaluation unit that evaluates the energy intensity for each time zone using the evaluation threshold value calculated for each time zone by the evaluation threshold value calculation unit are provided.
  • the energy intensity can be evaluated.
  • FIG. FIG. 1 is a block diagram showing the configuration of the energy efficiency evaluation apparatus according to the first embodiment.
  • the energy efficiency evaluation apparatus according to Embodiment 1 includes an energy intensity calculation unit 11, an energy intensity storage unit 12, an evaluation threshold value calculation unit 13, and an evaluation unit 14.
  • the energy intensity calculation unit 11 receives a product production amount A and an energy consumption amount E required to produce a product with the production amount A. Then, the energy intensity calculation unit 11 calculates the energy intensity EC by dividing the energy consumption E by the production A at regular time intervals, and stores the calculated energy intensity EC as the energy intensity storage. Output to the unit 12 and the evaluation unit 14.
  • FIG. 2 is a flowchart showing the operation of the energy intensity calculation unit 11.
  • step ST ⁇ b> 11 the energy intensity calculation unit 11 divides the energy consumption E by the production A to calculate the energy intensity EC.
  • step ST12 the energy intensity calculation unit 11 outputs the calculated energy intensity EC to the energy intensity storage unit 12 and the evaluation unit 14.
  • the energy intensity unit storage unit 12 sequentially stores the energy intensity units EC input from the energy intensity unit calculation unit 11, and accumulates the stored energy intensity unit past data DE.
  • the evaluation threshold value calculation unit 13 sets a window whose display range is the range of the past few days of the evaluation target period for the energy unit past data DE stored in the energy unit storage unit 12. Next, the evaluation threshold value calculation unit 13 calculates the median value of the energy intensity unit EC within the window display range of the energy intensity unit past data DE, and sets the calculated median value as the evaluation threshold value S.
  • the evaluation threshold value S is a value for evaluating the energy intensity unit EC, and is a target value of the energy intensity unit EC set for each unit time zone within the window display range. Then, the evaluation threshold value calculation unit 13 outputs the calculated evaluation threshold value S toward the evaluation unit 14.
  • the evaluation target period is a period from the present to the time when a predetermined period has elapsed, and the energy intensity unit EC continues to be evaluated at regular time intervals within the evaluation target period. For this reason, the window display range set before the evaluation target period follows the evaluation target period, and shifts with the passage of time.
  • the evaluation threshold value calculation unit 13 first calculates the energy intensity unit EC in units of one day in order to simplify the calculation process. After the median value is calculated, the median value whose value is located at the center among the median values calculated for each day may be set as the evaluation threshold value S.
  • FIG. 3 is a flowchart showing the operation of the evaluation threshold value calculation unit 13.
  • the evaluation threshold value calculation unit 13 displays the energy consumption unit past data DE stored in the energy consumption unit storage unit 12 in the past few days in the evaluation target period. Set the window to be in the range.
  • the evaluation threshold value calculation unit 13 calculates the median value of the energy intensity unit EC within the window display range of the energy intensity unit past data DE, and sets the calculated median value as the evaluation threshold value S.
  • step ST23 the evaluation threshold value calculation unit 13 outputs the calculated evaluation threshold value S to the evaluation unit 14.
  • the evaluation unit 14 performs correction such as constant multiplication or constant addition on the evaluation threshold S input from the evaluation threshold calculation unit 13 using the evaluation threshold correction parameter P1. Then, the evaluation unit 14 uses the corrected evaluation threshold value S as an evaluation standard for the energy intensity unit EC input from the energy intensity unit calculation unit 11, and outputs the evaluation result R to the outside. That is, the evaluation unit 14 determines the quality of the energy efficiency by evaluating the energy intensity unit EC using the corrected evaluation threshold value S.
  • FIG. 4 is a flowchart showing the operation of the evaluation unit 14.
  • step ST31 the evaluation unit 14 uses the evaluation threshold correction parameter P1 for the evaluation threshold S input from the evaluation threshold calculation unit 13 to correct a constant multiple or a constant addition. Then, the correction result is set as a new evaluation threshold value S.
  • step ST32 the evaluation unit 14 determines whether or not the energy basic unit EC is larger than the corrected evaluation threshold value S.
  • the process proceeds to step ST33.
  • the evaluation unit 14 determines that the energy intensity EC is not greater than the corrected evaluation threshold S, the process proceeds to step ST34.
  • step ST33 the evaluation unit 14 determines that the efficiency of consumed energy is defective.
  • step ST34 the evaluation unit 14 determines that the efficiency of consumed energy is good.
  • step ST35 the evaluation unit 14 outputs the evaluation result R.
  • the energy efficiency evaluation apparatus includes the energy intensity calculation unit 11 that calculates the energy intensity EC, and the energy intensity EC calculated from a period before the evaluation period of the energy intensity EC.
  • the evaluation threshold value calculation unit 13 that sets the median value of the evaluation threshold value S and the evaluation value unit 14 that evaluates the energy intensity EC using the evaluation threshold value S calculated by the evaluation threshold value calculation unit 13 are provided. Thereby, the energy efficiency evaluation apparatus can evaluate the energy intensity unit EC.
  • the evaluation threshold value calculation unit 13 calculates the median value of the energy intensity unit EC in units of one day, and thereafter, for each day. Of the calculated medians, the median whose magnitude is located at the center is set as the evaluation threshold S. Thereby, the energy efficiency evaluation apparatus can simplify the arithmetic processing in the evaluation threshold value calculation unit 13.
  • FIG. 5 is a block diagram illustrating a configuration of the energy efficiency evaluation apparatus according to the second embodiment.
  • the energy efficiency evaluation apparatus according to Embodiment 2 includes an energy intensity calculation unit 11, an energy intensity storage unit 12, an evaluation threshold calculation unit 13A, an evaluation unit 14A, an evaluation threshold accumulation unit 21, Temperature data storage unit 22, temperature representative value calculation unit 23, temperature representative value storage unit 24, production volume data storage unit 25, production volume representative value calculation unit 26, production volume representative value storage unit 27, evaluation threshold learning unit 28, and The evaluation threshold value prediction unit 29 is provided.
  • the energy efficiency evaluation apparatus according to Embodiment 2 can input the temperature T, and the temperature T changes according to seasonal fluctuations.
  • the energy intensity calculation unit 11 and the energy intensity storage unit 12 constituting the energy efficiency evaluation device according to the second embodiment are the energy intensity calculation unit 11 and the energy intensity evaluation unit 11 constituting the energy efficiency evaluation device according to the first embodiment. It has the same function as that of the energy intensity storage unit 12. For this reason, in the energy efficiency evaluation apparatus which concerns on Embodiment 2, it abbreviate
  • the evaluation threshold value calculation unit 13A sets a window whose display range is the range of the past several days of the evaluation target period for the energy intensity unit past data DE stored in the energy intensity unit storage unit 12. Next, the evaluation threshold value calculation unit 13A calculates the median value of the energy intensity unit EC within the window display range of the energy intensity unit past data DE, and sets the calculated median value as the evaluation threshold value S. Then, the evaluation threshold value calculation unit 13A outputs the calculated evaluation threshold value S toward the evaluation threshold value accumulation unit 21.
  • the evaluation threshold value calculation unit 13A first determines the energy intensity unit EC in units of one day in order to simplify the calculation process. After the median value is calculated, the median value having the value located at the center among the plurality of median values calculated every day may be set as the evaluation threshold value S.
  • FIG. 6 is a flowchart showing the operation of the evaluation threshold value calculation unit 13A.
  • the evaluation threshold value calculation unit 13A compares the energy intensity unit past data DE stored in the energy intensity unit storage unit 12 with the display range of the past several days of the evaluation target period. Sets the window that will be the range.
  • step ST42 the evaluation threshold value calculation unit 13A calculates the median value of the energy intensity unit EC within the window display range of the energy intensity unit past data DE, and sets the calculated median value as the evaluation threshold value S.
  • step ST43 the evaluation threshold value calculation unit 13A outputs the calculated evaluation threshold value S to the evaluation threshold value accumulation unit 21.
  • the evaluation threshold accumulation unit 21 sequentially stores and accumulates the evaluation threshold S input from the evaluation threshold calculation unit 13A.
  • the temperature data storage unit 22 sequentially stores the temperature data regarding the input temperature T, and stores the stored temperature data as the temperature past data DT.
  • the temperature representative value calculation unit 23 extracts the past temperature data DT corresponding to each unit time zone within the window display range from the past temperature data DT stored in the temperature data storage unit 22. And the temperature representative value calculation part 23 calculates temperature representative value CT based on the extracted temperature past data DT.
  • the temperature representative value CT for example, any one of the maximum value, the minimum value, the average value, the median value, and the like of the temperature T in the window display range is set.
  • FIG. 7 is a flowchart showing the operation of the temperature representative value calculation unit 23.
  • step ST51 the temperature representative value calculation unit 23 extracts the temperature past data DT corresponding to each unit time zone in the window display range from the temperature data storage unit 22. And the temperature representative value calculation part 23 calculates temperature representative value CT based on the extracted temperature past data DT.
  • step ST52 the temperature representative value calculation unit 23 outputs the calculated temperature representative value CT to the temperature representative value storage unit 24.
  • the temperature representative value accumulation unit 24 sequentially stores and accumulates the temperature representative value CT input from the temperature representative value calculation unit 23.
  • the production volume data accumulation unit 25 sequentially stores the production volume data related to the input production volume A, and accumulates the stored production volume data as production volume past data DA.
  • the production amount representative value calculation unit 26 extracts the past production amount data DA corresponding to each unit time zone within the window display range from the past production amount data DA stored in the production amount data storage unit 25. Then, the production quantity representative value calculation unit 26 calculates the production quantity representative value CA based on the extracted production quantity past data DA.
  • the production amount representative value CA for example, any one of the maximum value, the minimum value, the average value, the median value, and the like of the production amount A within the window display range is set.
  • FIG. 8 is a flowchart showing the operation of the production volume representative value calculation unit 26.
  • step ST61 the production amount representative value calculation unit 26 extracts the past production amount data DA corresponding to each unit time zone in the window display range from the production amount data storage unit 25. Then, the production quantity representative value calculation unit 26 calculates the production quantity representative value CA based on the extracted production quantity past data DA.
  • step ST62 the production quantity representative value calculation unit 26 outputs the calculated production quantity representative value CA to the production quantity representative value storage unit 27.
  • the production quantity representative value accumulation unit 27 sequentially stores and accumulates the production quantity representative value CA input from the production quantity representative value calculation unit 26.
  • the evaluation threshold learning unit 28 includes information on the evaluation threshold S stored in the evaluation threshold storage unit 21, information on the temperature representative value CT stored in the temperature representative value storage unit 24, and the production amount representative value storage unit 27.
  • the evaluation threshold value learning data GS In the state where the information related to the production representative value CA stored in is linked to each past window display range that has shifted with the passage of time, the evaluation threshold value learning data GS, the temperature representative value learning data GT, And it is acquired as production amount representative value learning data GA.
  • the evaluation threshold value learning unit 28 selects a set that can be adopted as learning data based on the data acceptance / rejection information V from a plurality of sets associated with each past window display range that has shifted with the passage of time.
  • the learning data GS, GT, and GA are selected in a state in which the learning data GS, GT, and GA are linked to each past window display range that has shifted with time.
  • the evaluation threshold value learning unit 28 generates, by learning, an evaluation threshold value prediction parameter P2 used when predicting the evaluation threshold value, based on the selected set of learning data GS, GT, GA. Subsequently, the evaluation threshold value learning unit 28 outputs the generated evaluation threshold value prediction parameter P2 to the evaluation threshold value prediction unit 29.
  • the data acceptance / rejection information V is, for example, information that excludes a set of learning data GS, GT, GA associated with a period in which the skill level of the producer is generally low.
  • the evaluation threshold value learning unit 28 uses the data acceptance / rejection information V to exclude the non-target sets before generating the evaluation threshold value prediction parameter P2, thereby generating the evaluation threshold value prediction parameter P2 generated. Reliability can be improved.
  • FIG. 9 is a flowchart showing the operation of the evaluation threshold value learning unit 28.
  • the evaluation threshold value learning unit 28 learns each set of learning data GS, GT, GA associated with each past window display range that has shifted with the passage of time. Whether or not the data can be adopted is determined based on the data acceptance / rejection information V. Subsequently, the evaluation threshold value learning unit 28 selects a set that can be adopted as learning data in a state where the learning data GS, GT, and GA are attached to each past window display range string that has shifted with time. .
  • step ST72 the evaluation threshold value learning unit 28 generates an evaluation threshold value prediction parameter P2 by learning using a set of learning data GS, GT, GA selected as learning data.
  • step ST73 the evaluation threshold value learning unit 28 outputs the generated evaluation threshold value prediction parameter P2 toward the evaluation threshold value prediction unit 29.
  • the evaluation threshold value prediction unit 29 Based on the evaluation threshold value prediction parameter P2, the temperature T, and the production amount A input from the evaluation threshold value learning unit 28, the evaluation threshold value prediction unit 29 sets the energy intensity unit EC in each unit time zone within the window display range. An evaluation threshold is predicted. Then, the evaluation threshold value prediction unit 29 outputs the predicted value S ′ thus predicted toward the evaluation unit 14A.
  • FIG. 10 is a flowchart showing the operation of the evaluation threshold value prediction unit 29.
  • the evaluation threshold value prediction unit 29 predicts an evaluation threshold value based on the evaluation threshold value prediction parameter P2, the temperature T, and the production amount A.
  • step ST82 the evaluation threshold value prediction unit 29 outputs the predicted value S ′ predicted to the evaluation unit 14A.
  • the evaluation unit 14A corrects the prediction value S ′ input from the evaluation threshold value prediction unit 29, such as constant multiplication or constant addition, using the evaluation threshold value correction parameter P1. Then, the evaluation unit 14A uses the corrected predicted value S ′ as an evaluation standard for the energy intensity unit EC input from the energy intensity unit calculation unit 11, and outputs the evaluation result R to the outside. That is, the evaluation unit 14A evaluates the energy intensity unit EC using the corrected predicted value S ′, thereby determining the quality of the energy efficiency.
  • FIG. 11 is a flowchart showing the operation of the evaluation unit 14A.
  • step ST91 the evaluation unit 14A uses the evaluation threshold correction parameter P1 for the prediction value S ′ input from the evaluation threshold prediction unit 29, and performs constant multiplication, constant addition, or the like. After the correction, the correction result is set as a new predicted value S ′.
  • step ST92 the evaluation unit 14A determines whether or not the energy basic unit EC is larger than the corrected predicted value S ′. If the evaluation unit 14A determines that the energy basic unit EC is larger than the corrected predicted value S ′, the process proceeds to step ST93. On the other hand, if the evaluation unit 14A determines that the energy basic unit EC is not larger than the corrected predicted value S ′, the process proceeds to step ST94.
  • step ST93 the evaluation unit 14A determines that the efficiency of consumed energy is defective.
  • step ST94 the evaluation unit 14A determines that the efficiency of consumed energy is good.
  • step ST95 the evaluation unit 14A outputs the evaluation result R.
  • the energy efficiency evaluation apparatus generates an evaluation threshold value prediction parameter P2 by learning for each group associated with each past window display range that has shifted with time. Based on the learning unit 28, the evaluation threshold prediction parameter P2, the temperature T and the production amount A corresponding thereto, the evaluation threshold prediction unit 29 that predicts the predicted value S ′, and the window using the predicted value S ′ And an evaluation unit 14A that evaluates the energy basic unit EC for each unit time zone within the display range. Thereby, the energy efficiency evaluation apparatus can evaluate the energy intensity unit EC in consideration of seasonal variation.
  • the evaluation threshold value learning unit 28 generates an evaluation threshold value prediction parameter P2 based on the data acceptance / rejection information V from a plurality of sets associated with each past window display range that has shifted with the passage of time. Select the group to be used at times. Thereby, the energy efficiency evaluation apparatus can improve the reliability of the evaluation threshold value prediction parameter P2 generated by the evaluation threshold value learning unit 28 by learning.
  • the energy efficiency evaluation apparatus employs a configuration in which the evaluation unit evaluates the energy intensity by using the evaluation threshold calculated by the evaluation threshold value calculation unit, so that the energy intensity can be evaluated. Suitable for energy efficiency evaluation equipment for evaluating basic units.

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Abstract

This energy efficiency evaluation device is provided with: an energy consumption rate calculation unit (11) which calculates an energy consumption rate (EC) on the basis of a production volume (A) of a product and the amount (E) of energy consumption required to achieve this production volume (A) of the product; an evaluation threshold value calculation unit (13) which calculates, for each past time zone before a time period for which the energy consumption rate (EC) is to be evaluated, the median value of energy consumption rate (EC) values obtained during the time zone, and sets this calculated median value as an evaluation threshold value (S) for evaluating the energy consumption rate (EC) for the time zone; and an evaluation unit (14) which evaluates the energy consumption rate (EC) for each time zone using the evaluation threshold value (S) for the time zone as calculated by the evaluation threshold value calculation unit (13).

Description

エネルギー効率評価装置Energy efficiency evaluation device
　この発明は、エネルギー原単位を評価するエネルギー効率評価装置に関する。 This invention relates to an energy efficiency evaluation apparatus for evaluating energy intensity.
　近年、製品を生産する生産現場においては、省エネルギー対策を採ることが常に求められており、例えば、消費エネルギー量を可視化して、生産状況を評価する試みが行われている。その評価指標としては、消費エネルギー量を生産量で除算した、エネルギー原単位が広く用いられている。そこで、特許文献１には、エネルギー原単位を評価する技術が開示されている。 In recent years, in production sites where products are produced, it has always been required to take energy saving measures. For example, attempts have been made to visualize the amount of energy consumed and evaluate the production status. As an evaluation index, an energy unit obtained by dividing the consumed energy amount by the production amount is widely used. Therefore, Patent Document 1 discloses a technique for evaluating energy intensity.
特開２０１４－７８１１１号公報JP 2014-78111 A
　特許文献１に開示された技術は、現在のエネルギー原単位を、過去の実績値に基づいて作成した閾値と対比させることにより、そのエネルギー原単位を評価するものである。ここで、上記閾値は、固定値となっている。このように、固定閾値をエネルギー原単位の評価に使用した場合には、当該エネルギー原単位を適切に評価することができないおそれがある。 The technique disclosed in Patent Document 1 evaluates the energy intensity by comparing the current energy intensity with a threshold value created based on past performance values. Here, the threshold value is a fixed value. Thus, when a fixed threshold value is used for evaluation of energy intensity, there is a possibility that the energy intensity cannot be appropriately evaluated.
　この発明は、上記のような課題を解決するためになされたもので、エネルギー原単位を評価することができるエネルギー効率評価装置を提供することを目的とする。 The present invention has been made to solve the above-described problems, and an object of the present invention is to provide an energy efficiency evaluation apparatus that can evaluate energy intensity.
　この発明に係るエネルギー効率評価装置は、製品の生産量と、生産量の製品を生産するために必要な消費エネルギー量とに基づいて、エネルギー原単位を算出するエネルギー原単位算出部と、エネルギー原単位の評価対象期間よりも過去の時間帯ごとに、エネルギー原単位の中央値を算出し、この算出した中央値を、各時間帯におけるエネルギー原単位を評価するための評価閾値と設定する評価閾値算出部と、評価閾値算出部が時間帯ごとに算出した評価閾値を用いて、当該時間帯ごとにエネルギー原単位を評価する評価部とを備えることを特徴とするものである。 The energy efficiency evaluation apparatus according to the present invention includes an energy intensity calculation unit that calculates an energy intensity based on a production amount of a product and an energy consumption amount necessary for producing the product of the production amount, The median value of energy intensity is calculated for each time zone that is earlier than the unit evaluation period, and this calculated median is set as the evaluation threshold value for evaluating the energy intensity in each time zone. A calculation unit and an evaluation unit that evaluates the energy intensity for each time zone using the evaluation threshold value calculated for each time zone by the evaluation threshold value calculation unit are provided.
　この発明によれば、エネルギー原単位を評価することができる。 According to this invention, the energy intensity can be evaluated.
この発明の実施の形態１に係るエネルギー効率評価装置の構成を示すブロック図である。It is a block diagram which shows the structure of the energy efficiency evaluation apparatus which concerns on Embodiment 1 of this invention. エネルギー原単位算出部の動作を示すフローチャートである。It is a flowchart which shows operation | movement of an energy basic unit calculation part. 評価閾値算出部の動作を示すフローチャートである。It is a flowchart which shows operation | movement of an evaluation threshold value calculation part. 評価部の動作を示すフローチャートである。It is a flowchart which shows operation | movement of an evaluation part. この発明の実施の形態２に係るエネルギー効率評価装置の構成を示すブロック図である。It is a block diagram which shows the structure of the energy efficiency evaluation apparatus which concerns on Embodiment 2 of this invention. 評価閾値算出部の動作を示すフローチャートである。It is a flowchart which shows operation | movement of an evaluation threshold value calculation part. 気温代表値算出部の動作を示すフローチャートである。It is a flowchart which shows operation | movement of a temperature representative value calculation part. 生産量代表値算出部の動作を示すフローチャートである。It is a flowchart which shows operation | movement of a production amount representative value calculation part. 評価閾値学習部の動作を示すフローチャートである。It is a flowchart which shows operation | movement of an evaluation threshold value learning part. 評価閾値予測部の動作を示すフローチャートである。It is a flowchart which shows operation | movement of an evaluation threshold value estimation part. 評価部の動作を示すフローチャートである。It is a flowchart which shows operation | movement of an evaluation part.
　以下、この発明をより詳細に説明するために、この発明を実施するための形態について、添付の図面に従って説明する。 Hereinafter, in order to explain the present invention in more detail, modes for carrying out the present invention will be described with reference to the accompanying drawings.
実施の形態１．
　図１は、実施の形態１に係るエネルギー効率評価装置の構成を示すブロック図である。この図１に示すように、実施の形態１に係るエネルギー効率評価装置は、エネルギー原単位算出部１１、エネルギー原単位蓄積部１２、評価閾値算出部１３、及び、評価部１４を備えている。 Embodiment 1 FIG.
FIG. 1 is a block diagram showing the configuration of the energy efficiency evaluation apparatus according to the first embodiment. As shown in FIG. 1, the energy efficiency evaluation apparatus according to Embodiment 1 includes an energy intensity calculation unit 11, an energy intensity storage unit 12, an evaluation threshold value calculation unit 13, and an evaluation unit 14.
　エネルギー原単位算出部１１は、製品の生産量Ａと、その生産量Ａの製品を生産するために必要な消費エネルギー量Ｅとが入力される。そして、エネルギー原単位算出部１１は、一定の時間間隔で、消費エネルギー量Ｅを生産量Ａで除算して、エネルギー原単位ＥＣを算出し、この算出したエネルギー原単位ＥＣを、エネルギー原単位蓄積部１２及び評価部１４に向けて出力する。 The energy intensity calculation unit 11 receives a product production amount A and an energy consumption amount E required to produce a product with the production amount A. Then, the energy intensity calculation unit 11 calculates the energy intensity EC by dividing the energy consumption E by the production A at regular time intervals, and stores the calculated energy intensity EC as the energy intensity storage. Output to the unit 12 and the evaluation unit 14.
　図２は、エネルギー原単位算出部１１の動作を示すフローチャートである。 FIG. 2 is a flowchart showing the operation of the energy intensity calculation unit 11.
　図２に示すように、ステップＳＴ１１において、エネルギー原単位算出部１１は、消費エネルギー量Ｅを生産量Ａで除算して、エネルギー原単位ＥＣを算出する。 As shown in FIG. 2, in step ST <b> 11, the energy intensity calculation unit 11 divides the energy consumption E by the production A to calculate the energy intensity EC.
　ステップＳＴ１２において、エネルギー原単位算出部１１は、算出したエネルギー原単位ＥＣを、エネルギー原単位蓄積部１２及び評価部１４に向けて出力する。 In step ST12, the energy intensity calculation unit 11 outputs the calculated energy intensity EC to the energy intensity storage unit 12 and the evaluation unit 14.
　エネルギー原単位蓄積部１２は、エネルギー原単位算出部１１から入力されたエネルギー原単位ＥＣを順次格納し、格納したそれらをエネルギー原単位過去データＤＥとして蓄積する。 The energy intensity unit storage unit 12 sequentially stores the energy intensity units EC input from the energy intensity unit calculation unit 11, and accumulates the stored energy intensity unit past data DE.
　評価閾値算出部１３は、エネルギー原単位蓄積部１２に格納されているエネルギー原単位過去データＤＥに対して、表示範囲が評価対象期間の直近過去数日間の範囲となるウィンドウを設定する。次いで、評価閾値算出部１３は、エネルギー原単位過去データＤＥのウィンドウ表示範囲内におけるエネルギー原単位ＥＣの中央値を算出し、この算出した中央値を、評価閾値Ｓと設定する。この評価閾値Ｓは、エネルギー原単位ＥＣを評価するための値であって、ウィンドウ表示範囲内における単位時間帯ごとに設定される、エネルギー原単位ＥＣの目標値となっている。そして、評価閾値算出部１３は、算出した評価閾値Ｓを評価部１４に向けて出力する。 The evaluation threshold value calculation unit 13 sets a window whose display range is the range of the past few days of the evaluation target period for the energy unit past data DE stored in the energy unit storage unit 12. Next, the evaluation threshold value calculation unit 13 calculates the median value of the energy intensity unit EC within the window display range of the energy intensity unit past data DE, and sets the calculated median value as the evaluation threshold value S. The evaluation threshold value S is a value for evaluating the energy intensity unit EC, and is a target value of the energy intensity unit EC set for each unit time zone within the window display range. Then, the evaluation threshold value calculation unit 13 outputs the calculated evaluation threshold value S toward the evaluation unit 14.
　ここで、上記評価対象期間は、現在から所定期間経過した時点までの期間となっており、エネルギー原単位ＥＣは、その評価対象期間内において、一定の時間間隔で、評価され続ける。このため、評価対象期間よりも過去に設定されたウィンドウ表示範囲は、評価対象期間に追従し、時間の経過と共にずれていく。 Here, the evaluation target period is a period from the present to the time when a predetermined period has elapsed, and the energy intensity unit EC continues to be evaluated at regular time intervals within the evaluation target period. For this reason, the window display range set before the evaluation target period follows the evaluation target period, and shifts with the passage of time.
　但し、評価閾値算出部１３は、エネルギー原単位過去データＤＥのウィンドウ表示範囲が複数日に跨る場合には、演算処理の簡素化を図るために、先ず、一日単位で、エネルギー原単位ＥＣの中央値を算出した後、その一日ごとに算出した複数の中央値のうち、値の大きさが中央に位置する中央値を、評価閾値Ｓと設定しても構わない。 However, when the window display range of the energy intensity unit past data DE extends over a plurality of days, the evaluation threshold value calculation unit 13 first calculates the energy intensity unit EC in units of one day in order to simplify the calculation process. After the median value is calculated, the median value whose value is located at the center among the median values calculated for each day may be set as the evaluation threshold value S.
　図３は、評価閾値算出部１３の動作を示すフローチャートである。 FIG. 3 is a flowchart showing the operation of the evaluation threshold value calculation unit 13.
　図３に示すように、ステップＳＴ２１において、評価閾値算出部１３は、エネルギー原単位蓄積部１２に格納されているエネルギー原単位過去データＤＥに対して、表示範囲が評価対象期間の直近過去数日間の範囲となるウィンドウを設定する。 As shown in FIG. 3, in step ST21, the evaluation threshold value calculation unit 13 displays the energy consumption unit past data DE stored in the energy consumption unit storage unit 12 in the past few days in the evaluation target period. Set the window to be in the range.
　ステップＳＴ２２において、評価閾値算出部１３は、エネルギー原単位過去データＤＥのウィンドウ表示範囲内におけるエネルギー原単位ＥＣの中央値を算出し、この算出した中央値を、評価閾値Ｓと設定する。 In step ST22, the evaluation threshold value calculation unit 13 calculates the median value of the energy intensity unit EC within the window display range of the energy intensity unit past data DE, and sets the calculated median value as the evaluation threshold value S.
　ステップＳＴ２３において、評価閾値算出部１３は、算出した評価閾値Ｓを評価部１４に向けて出力する。 In step ST23, the evaluation threshold value calculation unit 13 outputs the calculated evaluation threshold value S to the evaluation unit 14.
　評価部１４は、評価閾値算出部１３から入力された評価閾値Ｓに対して、評価閾値補正用パラメータＰ１を用いて、定数倍または定数加算等の補正を行う。そして、評価部１４は、その補正した評価閾値Ｓを、エネルギー原単位算出部１１から入力されたエネルギー原単位ＥＣに対する評価基準とし、その評価結果Ｒを外部に向けて出力する。即ち、評価部１４は、エネルギー原単位ＥＣを、補正した評価閾値Ｓを用いて評価することにより、そのエネルギー効率の良否を判定する。 The evaluation unit 14 performs correction such as constant multiplication or constant addition on the evaluation threshold S input from the evaluation threshold calculation unit 13 using the evaluation threshold correction parameter P1. Then, the evaluation unit 14 uses the corrected evaluation threshold value S as an evaluation standard for the energy intensity unit EC input from the energy intensity unit calculation unit 11, and outputs the evaluation result R to the outside. That is, the evaluation unit 14 determines the quality of the energy efficiency by evaluating the energy intensity unit EC using the corrected evaluation threshold value S.
　図４は、評価部１４の動作を示すフローチャートである。 FIG. 4 is a flowchart showing the operation of the evaluation unit 14.
　図４に示すように、ステップＳＴ３１において、評価部１４は、評価閾値算出部１３から入力された評価閾値Ｓに対して、評価閾値補正用パラメータＰ１を用いて、定数倍または定数加算等の補正を行った後、その補正結果を新たな評価閾値Ｓとする。 As shown in FIG. 4, in step ST31, the evaluation unit 14 uses the evaluation threshold correction parameter P1 for the evaluation threshold S input from the evaluation threshold calculation unit 13 to correct a constant multiple or a constant addition. Then, the correction result is set as a new evaluation threshold value S.
　ステップＳＴ３２において、評価部１４は、エネルギー原単位ＥＣが、補正した評価閾値Ｓよりも大きいか否かを判定する。ここで、評価部１４が、エネルギー原単位ＥＣを、補正した評価閾値Ｓよりも大きいと判定した場合には、その処理はステップＳＴ３３に進む。一方、評価部１４が、エネルギー原単位ＥＣを、補正した評価閾値Ｓよりも大きくないと判定した場合には、その処理はステップＳＴ３４に進む。 In step ST32, the evaluation unit 14 determines whether or not the energy basic unit EC is larger than the corrected evaluation threshold value S. Here, when the evaluation unit 14 determines that the energy intensity EC is larger than the corrected evaluation threshold S, the process proceeds to step ST33. On the other hand, if the evaluation unit 14 determines that the energy intensity EC is not greater than the corrected evaluation threshold S, the process proceeds to step ST34.
　ステップＳＴ３３において、評価部１４は、消費したエネルギーの効率が、不良であると判定する。 In step ST33, the evaluation unit 14 determines that the efficiency of consumed energy is defective.
　一方、ステップＳＴ３４において、評価部１４は、消費したエネルギーの効率が、良好であると判定する。 On the other hand, in step ST34, the evaluation unit 14 determines that the efficiency of consumed energy is good.
　ステップＳＴ３５において、評価部１４は、評価結果Ｒを出力する。 In step ST35, the evaluation unit 14 outputs the evaluation result R.
　以上より、実施の形態１に係るエネルギー効率評価装置は、エネルギー原単位ＥＣを算出するエネルギー原単位算出部１１と、エネルギー原単位ＥＣの評価対象期間よりも過去の期間から算出したエネルギー原単位ＥＣの中央値を、評価閾値Ｓと設定する評価閾値算出部１３と、評価閾値算出部１３が算出した評価閾値Ｓを用いて、エネルギー原単位ＥＣを評価する評価部１４とを備えている。これにより、エネルギー効率評価装置は、エネルギー原単位ＥＣを評価することができる。 As described above, the energy efficiency evaluation apparatus according to Embodiment 1 includes the energy intensity calculation unit 11 that calculates the energy intensity EC, and the energy intensity EC calculated from a period before the evaluation period of the energy intensity EC. The evaluation threshold value calculation unit 13 that sets the median value of the evaluation threshold value S and the evaluation value unit 14 that evaluates the energy intensity EC using the evaluation threshold value S calculated by the evaluation threshold value calculation unit 13 are provided. Thereby, the energy efficiency evaluation apparatus can evaluate the energy intensity unit EC.
　また、評価閾値算出部１３は、エネルギー原単位過去データＤＥのウィンドウ表示範囲が複数日に跨る場合には、一日単位で、エネルギー原単位ＥＣの中央値を算出した後、その一日ごとに算出した複数の中央値のうち、値の大きさが中央に位置する中央値を、評価閾値Ｓと設定する。これにより、エネルギー効率評価装置は、評価閾値算出部１３における演算処理の簡素化を図ることができる。 In addition, when the window display range of the energy intensity unit past data DE extends over a plurality of days, the evaluation threshold value calculation unit 13 calculates the median value of the energy intensity unit EC in units of one day, and thereafter, for each day. Of the calculated medians, the median whose magnitude is located at the center is set as the evaluation threshold S. Thereby, the energy efficiency evaluation apparatus can simplify the arithmetic processing in the evaluation threshold value calculation unit 13.
実施の形態２
　図５は、実施の形態２に係るエネルギー効率評価装置の構成を示すブロック図である。この図５に示すように、実施の形態２に係るエネルギー効率評価装置は、エネルギー原単位算出部１１、エネルギー原単位蓄積部１２、評価閾値算出部１３Ａ、評価部１４Ａ、評価閾値蓄積部２１、気温データ蓄積部２２、気温代表値算出部２３、気温代表値蓄積部２４、生産量データ蓄積部２５、生産量代表値算出部２６、生産量代表値蓄積部２７、評価閾値学習部２８、及び、評価閾値予測部２９を備えている。また、実施の形態２に係るエネルギー効率評価装置は、気温Ｔを入力可能としており、その気温Ｔは、季節の変動に応じて変化するものである。 Embodiment 2
FIG. 5 is a block diagram illustrating a configuration of the energy efficiency evaluation apparatus according to the second embodiment. As shown in FIG. 5, the energy efficiency evaluation apparatus according to Embodiment 2 includes an energy intensity calculation unit 11, an energy intensity storage unit 12, an evaluation threshold calculation unit 13A, an evaluation unit 14A, an evaluation threshold accumulation unit 21, Temperature data storage unit 22, temperature representative value calculation unit 23, temperature representative value storage unit 24, production volume data storage unit 25, production volume representative value calculation unit 26, production volume representative value storage unit 27, evaluation threshold learning unit 28, and The evaluation threshold value prediction unit 29 is provided. In addition, the energy efficiency evaluation apparatus according to Embodiment 2 can input the temperature T, and the temperature T changes according to seasonal fluctuations.
　なお、実施の形態２に係るエネルギー効率評価装置を構成するエネルギー原単位算出部１１及びエネルギー原単位蓄積部１２は、実施の形態１に係るエネルギー効率評価装置を構成するエネルギー原単位算出部１１及びエネルギー原単位蓄積部１２の機能と同じ機能を有している。このため、実施の形態２に係るエネルギー効率評価装置においては、エネルギー原単位算出部１１及びエネルギー原単位蓄積部１２の詳細については省略する。 The energy intensity calculation unit 11 and the energy intensity storage unit 12 constituting the energy efficiency evaluation device according to the second embodiment are the energy intensity calculation unit 11 and the energy intensity evaluation unit 11 constituting the energy efficiency evaluation device according to the first embodiment. It has the same function as that of the energy intensity storage unit 12. For this reason, in the energy efficiency evaluation apparatus which concerns on Embodiment 2, it abbreviate | omits about the detail of the energy basic unit calculation part 11 and the energy basic unit storage part 12. FIG.
　評価閾値算出部１３Ａは、エネルギー原単位蓄積部１２に格納されているエネルギー原単位過去データＤＥに対して、表示範囲が評価対象期間の過去数日間の範囲となるウィンドウを設定する。次いで、評価閾値算出部１３Ａは、エネルギー原単位過去データＤＥのウィンドウ表示範囲内におけるエネルギー原単位ＥＣの中央値を算出し、この算出した中央値を、評価閾値Ｓと設定する。そして、評価閾値算出部１３Ａは、算出した評価閾値Ｓを評価閾値蓄積部２１に向けて出力する。 The evaluation threshold value calculation unit 13A sets a window whose display range is the range of the past several days of the evaluation target period for the energy intensity unit past data DE stored in the energy intensity unit storage unit 12. Next, the evaluation threshold value calculation unit 13A calculates the median value of the energy intensity unit EC within the window display range of the energy intensity unit past data DE, and sets the calculated median value as the evaluation threshold value S. Then, the evaluation threshold value calculation unit 13A outputs the calculated evaluation threshold value S toward the evaluation threshold value accumulation unit 21.
　但し、評価閾値算出部１３Ａは、エネルギー原単位過去データＤＥのウィンドウ表示範囲が複数日に跨る場合には、演算処理の簡素化を図るために、先ず、一日単位で、エネルギー原単位ＥＣの中央値を算出した後、その一日ごとに算出した複数の中央値のうち、値の大きさが中央に位置する中央値を、評価閾値Ｓと設定しても構わない。 However, when the window display range of the energy intensity unit past data DE extends over a plurality of days, the evaluation threshold value calculation unit 13A first determines the energy intensity unit EC in units of one day in order to simplify the calculation process. After the median value is calculated, the median value having the value located at the center among the plurality of median values calculated every day may be set as the evaluation threshold value S.
　図６は、評価閾値算出部１３Ａの動作を示すフローチャートである。 FIG. 6 is a flowchart showing the operation of the evaluation threshold value calculation unit 13A.
　図６に示すように、ステップＳＴ４１において、評価閾値算出部１３Ａは、エネルギー原単位蓄積部１２に格納されているエネルギー原単位過去データＤＥに対して、表示範囲が評価対象期間の過去数日間の範囲となるウィンドウを設定する。 As shown in FIG. 6, in step ST41, the evaluation threshold value calculation unit 13A compares the energy intensity unit past data DE stored in the energy intensity unit storage unit 12 with the display range of the past several days of the evaluation target period. Sets the window that will be the range.
　ステップＳＴ４２において、評価閾値算出部１３Ａは、エネルギー原単位過去データＤＥのウィンドウ表示範囲内におけるエネルギー原単位ＥＣの中央値を算出し、この算出した中央値を、評価閾値Ｓと設定する。 In step ST42, the evaluation threshold value calculation unit 13A calculates the median value of the energy intensity unit EC within the window display range of the energy intensity unit past data DE, and sets the calculated median value as the evaluation threshold value S.
　ステップＳＴ４３において、評価閾値算出部１３Ａは、算出した評価閾値Ｓを評価閾値蓄積部２１に向けて出力する。 In step ST43, the evaluation threshold value calculation unit 13A outputs the calculated evaluation threshold value S to the evaluation threshold value accumulation unit 21.
　評価閾値蓄積部２１は、評価閾値算出部１３Ａから入力された評価閾値Ｓを、順次格納して蓄積する。 The evaluation threshold accumulation unit 21 sequentially stores and accumulates the evaluation threshold S input from the evaluation threshold calculation unit 13A.
　気温データ蓄積部２２は、入力された気温Ｔに関する気温データを順次格納し、これら格納した気温データを、気温過去データＤＴとして蓄積する。 The temperature data storage unit 22 sequentially stores the temperature data regarding the input temperature T, and stores the stored temperature data as the temperature past data DT.
　気温代表値算出部２３は、気温データ蓄積部２２に格納されている気温過去データＤＴの中から、ウィンドウ表示範囲内の各単位時間帯に対応する気温過去データＤＴを抽出する。そして、気温代表値算出部２３は、その抽出した気温過去データＤＴに基づいて、気温代表値ＣＴを算出する。気温代表値ＣＴとしては、例えば、ウィンドウ表示範囲内における気温Ｔの最大値、最小値、平均値、及び、中央値等のうち、いずれか１つの値とする。 The temperature representative value calculation unit 23 extracts the past temperature data DT corresponding to each unit time zone within the window display range from the past temperature data DT stored in the temperature data storage unit 22. And the temperature representative value calculation part 23 calculates temperature representative value CT based on the extracted temperature past data DT. As the temperature representative value CT, for example, any one of the maximum value, the minimum value, the average value, the median value, and the like of the temperature T in the window display range is set.
　図７は、気温代表値算出部２３の動作を示すフローチャートである。 FIG. 7 is a flowchart showing the operation of the temperature representative value calculation unit 23.
　図７に示すように、ステップＳＴ５１において、気温代表値算出部２３は、気温データ蓄積部２２内から、ウィンドウ表示範囲内の各単位時間帯に対応する気温過去データＤＴを抽出する。そして、気温代表値算出部２３は、その抽出した気温過去データＤＴに基づいて、気温代表値ＣＴを算出する。 As shown in FIG. 7, in step ST51, the temperature representative value calculation unit 23 extracts the temperature past data DT corresponding to each unit time zone in the window display range from the temperature data storage unit 22. And the temperature representative value calculation part 23 calculates temperature representative value CT based on the extracted temperature past data DT.
　ステップＳＴ５２において、気温代表値算出部２３は、算出した気温代表値ＣＴを気温代表値蓄積部２４に向けて出力する。 In step ST52, the temperature representative value calculation unit 23 outputs the calculated temperature representative value CT to the temperature representative value storage unit 24.
　気温代表値蓄積部２４は、気温代表値算出部２３から入力された気温代表値ＣＴを、順次格納して蓄積する。 The temperature representative value accumulation unit 24 sequentially stores and accumulates the temperature representative value CT input from the temperature representative value calculation unit 23.
　生産量データ蓄積部２５は、入力された生産量Ａに関する生産量データを順次格納し、これら格納した生産量データを、生産量過去データＤＡとして蓄積する。 The production volume data accumulation unit 25 sequentially stores the production volume data related to the input production volume A, and accumulates the stored production volume data as production volume past data DA.
　生産量代表値算出部２６は、生産量データ蓄積部２５に格納されている生産量過去データＤＡの中から、ウィンドウ表示範囲内の各単位時間帯に対応する生産量過去データＤＡを抽出する。そして、生産量代表値算出部２６は、その抽出した生産量過去データＤＡに基づいて、生産量代表値ＣＡを算出する。生産量代表値ＣＡとしては、例えば、ウィンドウ表示範囲内における生産量Ａの最大値、最小値、平均値、及び、中央値等のうち、いずれか１つの値とする。 The production amount representative value calculation unit 26 extracts the past production amount data DA corresponding to each unit time zone within the window display range from the past production amount data DA stored in the production amount data storage unit 25. Then, the production quantity representative value calculation unit 26 calculates the production quantity representative value CA based on the extracted production quantity past data DA. As the production amount representative value CA, for example, any one of the maximum value, the minimum value, the average value, the median value, and the like of the production amount A within the window display range is set.
　図８は、生産量代表値算出部２６の動作を示すフローチャートである。 FIG. 8 is a flowchart showing the operation of the production volume representative value calculation unit 26.
　図８に示すように、ステップＳＴ６１において、生産量代表値算出部２６は、生産量データ蓄積部２５内から、ウィンドウ表示範囲内の各単位時間帯に対応する生産量過去データＤＡを抽出する。そして、生産量代表値算出部２６は、その抽出した生産量過去データＤＡに基づいて、生産量代表値ＣＡを算出する。 As shown in FIG. 8, in step ST61, the production amount representative value calculation unit 26 extracts the past production amount data DA corresponding to each unit time zone in the window display range from the production amount data storage unit 25. Then, the production quantity representative value calculation unit 26 calculates the production quantity representative value CA based on the extracted production quantity past data DA.
　ステップＳＴ６２において、生産量代表値算出部２６は、算出した生産量代表値ＣＡを生産量代表値蓄積部２７に向けて出力する。 In step ST62, the production quantity representative value calculation unit 26 outputs the calculated production quantity representative value CA to the production quantity representative value storage unit 27.
　生産量代表値蓄積部２７は、生産量代表値算出部２６から入力された生産量代表値ＣＡを、順次格納して蓄積する。 The production quantity representative value accumulation unit 27 sequentially stores and accumulates the production quantity representative value CA input from the production quantity representative value calculation unit 26.
　評価閾値学習部２８は、評価閾値蓄積部２１に格納されている評価閾値Ｓに関する情報、気温代表値蓄積部２４に格納されている気温代表値ＣＴに関する情報、及び、生産量代表値蓄積部２７に格納されている生産量代表値ＣＡに関する情報を、時間の経過と共にずれていった過去のウィンドウ表示範囲ごとに紐付けした状態で、評価閾値学習用データＧＳ、気温代表値学習用データＧＴ、及び、生産量代表値学習用データＧＡとして取得する。 The evaluation threshold learning unit 28 includes information on the evaluation threshold S stored in the evaluation threshold storage unit 21, information on the temperature representative value CT stored in the temperature representative value storage unit 24, and the production amount representative value storage unit 27. In the state where the information related to the production representative value CA stored in is linked to each past window display range that has shifted with the passage of time, the evaluation threshold value learning data GS, the temperature representative value learning data GT, And it is acquired as production amount representative value learning data GA.
　次いで、評価閾値学習部２８は、時間の経過と共にずれていった過去のウィンドウ表示範囲ごとに紐付けした複数組の中から、データ採否情報Ｖに基づいて、学習用データとして採用できる組を、学習用データＧＳ，ＧＴ，ＧＡを時間の経過と共にずれていった過去のウィンドウ表示範囲ごとに紐付けした状態で、選別する。 Next, the evaluation threshold value learning unit 28 selects a set that can be adopted as learning data based on the data acceptance / rejection information V from a plurality of sets associated with each past window display range that has shifted with the passage of time. The learning data GS, GT, and GA are selected in a state in which the learning data GS, GT, and GA are linked to each past window display range that has shifted with time.
　そして、評価閾値学習部２８は、選別した組の学習用データＧＳ，ＧＴ，ＧＡに基づいて、評価閾値を予測するときに使用する評価閾値予測用パラメータＰ２を、学習により生成する。続いて、評価閾値学習部２８は、その生成した評価閾値予測用パラメータＰ２を評価閾値予測部２９向けて出力する。 The evaluation threshold value learning unit 28 generates, by learning, an evaluation threshold value prediction parameter P2 used when predicting the evaluation threshold value, based on the selected set of learning data GS, GT, GA. Subsequently, the evaluation threshold value learning unit 28 outputs the generated evaluation threshold value prediction parameter P2 to the evaluation threshold value prediction unit 29.
　データ採否情報Ｖは、例えば、生産者の熟練度が全体的に低くなるような期間に紐付けられた組の学習用データＧＳ，ＧＴ，ＧＡを、対象外とする情報となっている。このように、評価閾値学習部２８は、評価閾値予測用パラメータＰ２を生成する前に、データ採否情報Ｖを用いて、対象外となる組を排除することによって、生成した評価閾値予測用パラメータＰ２の信頼性を向上させることができる。 The data acceptance / rejection information V is, for example, information that excludes a set of learning data GS, GT, GA associated with a period in which the skill level of the producer is generally low. In this manner, the evaluation threshold value learning unit 28 uses the data acceptance / rejection information V to exclude the non-target sets before generating the evaluation threshold value prediction parameter P2, thereby generating the evaluation threshold value prediction parameter P2 generated. Reliability can be improved.
　図９は、評価閾値学習部２８の動作を示すフローチャートである。 FIG. 9 is a flowchart showing the operation of the evaluation threshold value learning unit 28.
　図９に示すように、ステップＳＴ７１において、評価閾値学習部２８は、時間の経過と共にずれていった過去のウィンドウ表示範囲ごとに紐付けした各組の学習用データＧＳ，ＧＴ，ＧＡを、学習用データとして採用できるか否かを、データ採否情報Ｖに基づいて判定する。続いて、評価閾値学習部２８は、学習データとして採用できる組を、学習用データＧＳ，ＧＴ，ＧＡを時間の経過と共にずれていった過去のウィンドウ表示範囲紐ごとに付けした状態で、選別する。 As shown in FIG. 9, in step ST71, the evaluation threshold value learning unit 28 learns each set of learning data GS, GT, GA associated with each past window display range that has shifted with the passage of time. Whether or not the data can be adopted is determined based on the data acceptance / rejection information V. Subsequently, the evaluation threshold value learning unit 28 selects a set that can be adopted as learning data in a state where the learning data GS, GT, and GA are attached to each past window display range string that has shifted with time. .
　ステップＳＴ７２において、評価閾値学習部２８は、学習データとして選別した組の学習用データＧＳ，ＧＴ，ＧＡを用いて、評価閾値予測用パラメータＰ２を、学習により生成する。 In step ST72, the evaluation threshold value learning unit 28 generates an evaluation threshold value prediction parameter P2 by learning using a set of learning data GS, GT, GA selected as learning data.
　ステップＳＴ７３において、評価閾値学習部２８は、生成した評価閾値予測用パラメータＰ２を評価閾値予測部２９に向けて出力する。 In step ST73, the evaluation threshold value learning unit 28 outputs the generated evaluation threshold value prediction parameter P2 toward the evaluation threshold value prediction unit 29.
　評価閾値予測部２９は、評価閾値学習部２８から入力された評価閾値予測用パラメータＰ２、気温Ｔ、及び、生産量Ａに基づいて、ウィンドウ表示範囲内の各単位時間帯におけるエネルギー原単位ＥＣの評価閾値を、予測する。そして、評価閾値予測部２９は、その予測した予測値Ｓ´を評価部１４Ａに向けて出力する。 Based on the evaluation threshold value prediction parameter P2, the temperature T, and the production amount A input from the evaluation threshold value learning unit 28, the evaluation threshold value prediction unit 29 sets the energy intensity unit EC in each unit time zone within the window display range. An evaluation threshold is predicted. Then, the evaluation threshold value prediction unit 29 outputs the predicted value S ′ thus predicted toward the evaluation unit 14A.
　図１０は、評価閾値予測部２９の動作を示すフローチャートである。 FIG. 10 is a flowchart showing the operation of the evaluation threshold value prediction unit 29.
　図１０に示すように、ステップＳＴ８１において、評価閾値予測部２９は、評価閾値予測用パラメータＰ２、気温Ｔ、及び、生産量Ａに基づいて、評価閾値を予測する。 10, in step ST81, the evaluation threshold value prediction unit 29 predicts an evaluation threshold value based on the evaluation threshold value prediction parameter P2, the temperature T, and the production amount A.
　ステップＳＴ８２において、評価閾値予測部２９は、予測した予測値Ｓ´を評価部１４Ａに向けて出力する。 In step ST82, the evaluation threshold value prediction unit 29 outputs the predicted value S ′ predicted to the evaluation unit 14A.
　評価部１４Ａは、評価閾値予測部２９から入力された予測値Ｓ´に対して、評価閾値補正用パラメータＰ１を用いて、定数倍または定数加算等の補正を行う。そして、評価部１４Ａは、その補正した予測値Ｓ´を、エネルギー原単位算出部１１から入力されたエネルギー原単位ＥＣに対する評価基準とし、その評価結果Ｒを外部に向けて出力する。即ち、評価部１４Ａは、エネルギー原単位ＥＣを、補正した予測値Ｓ´を用いて評価することにより、そのエネルギー効率の良否を判定する。 The evaluation unit 14A corrects the prediction value S ′ input from the evaluation threshold value prediction unit 29, such as constant multiplication or constant addition, using the evaluation threshold value correction parameter P1. Then, the evaluation unit 14A uses the corrected predicted value S ′ as an evaluation standard for the energy intensity unit EC input from the energy intensity unit calculation unit 11, and outputs the evaluation result R to the outside. That is, the evaluation unit 14A evaluates the energy intensity unit EC using the corrected predicted value S ′, thereby determining the quality of the energy efficiency.
　図１１は、評価部１４Ａの動作を示すフローチャートである。 FIG. 11 is a flowchart showing the operation of the evaluation unit 14A.
　図１１に示すように、ステップＳＴ９１において、評価部１４Ａは、評価閾値予測部２９から入力された予測値Ｓ´に対して、評価閾値補正用パラメータＰ１を用いて、定数倍または定数加算等の補正を行った後、その補正結果を新たな予測値Ｓ´とする。 As shown in FIG. 11, in step ST91, the evaluation unit 14A uses the evaluation threshold correction parameter P1 for the prediction value S ′ input from the evaluation threshold prediction unit 29, and performs constant multiplication, constant addition, or the like. After the correction, the correction result is set as a new predicted value S ′.
　ステップＳＴ９２において、評価部１４Ａは、エネルギー原単位ＥＣが、補正した予測値Ｓ´よりも大きいか否かを判定する。ここで、評価部１４Ａが、エネルギー原単位ＥＣを、補正した予測値Ｓ´よりも大きいと判定した場合には、その処理はステップＳＴ９３に進む。一方、評価部１４Ａが、エネルギー原単位ＥＣを、補正した予測値Ｓ´よりも大きくないと判定した場合には、その処理はステップＳＴ９４に進む。 In step ST92, the evaluation unit 14A determines whether or not the energy basic unit EC is larger than the corrected predicted value S ′. If the evaluation unit 14A determines that the energy basic unit EC is larger than the corrected predicted value S ′, the process proceeds to step ST93. On the other hand, if the evaluation unit 14A determines that the energy basic unit EC is not larger than the corrected predicted value S ′, the process proceeds to step ST94.
　ステップＳＴ９３において、評価部１４Ａは、消費したエネルギーの効率が、不良であると判定する。 In step ST93, the evaluation unit 14A determines that the efficiency of consumed energy is defective.
　一方、ステップＳＴ９４において、評価部１４Ａは、消費したエネルギーの効率が、良好であると判定する。 On the other hand, in step ST94, the evaluation unit 14A determines that the efficiency of consumed energy is good.
　ステップＳＴ９５において、評価部１４Ａは、評価結果Ｒを出力する。 In step ST95, the evaluation unit 14A outputs the evaluation result R.
　以上より、実施の形態２に係るエネルギー効率評価装置は、時間の経過と共にずれていった過去のウィンドウ表示範囲ごとに紐付けした組ごとに、評価閾値予測用パラメータＰ２を学習により生成する評価閾値学習部２８と、評価閾値予測用パラメータＰ２と、これに対応する気温Ｔ及び生産量Ａとに基づいて、予測値Ｓ´を予測する評価閾値予測部２９と、予測値Ｓ´を用いてウィンドウ表示範囲内における単位時間帯ごとにエネルギー原単位ＥＣを評価する評価部１４Ａとを備えている。これにより、エネルギー効率評価装置は、季節変動を考慮して、エネルギー原単位ＥＣを評価することができる。 As described above, the energy efficiency evaluation apparatus according to the second embodiment generates an evaluation threshold value prediction parameter P2 by learning for each group associated with each past window display range that has shifted with time. Based on the learning unit 28, the evaluation threshold prediction parameter P2, the temperature T and the production amount A corresponding thereto, the evaluation threshold prediction unit 29 that predicts the predicted value S ′, and the window using the predicted value S ′ And an evaluation unit 14A that evaluates the energy basic unit EC for each unit time zone within the display range. Thereby, the energy efficiency evaluation apparatus can evaluate the energy intensity unit EC in consideration of seasonal variation.
　また、評価閾値学習部２８は、時間の経過と共にずれていった過去のウィンドウ表示範囲ごとに紐付けした複数組の中から、データ採否情報Ｖに基づいて、評価閾値予測用パラメータＰ２を生成するときに使用する組を、選別する。これにより、エネルギー効率評価装置は、評価閾値学習部２８が学習により生成した評価閾値予測用パラメータＰ２の信頼性を向上させることができる。 Further, the evaluation threshold value learning unit 28 generates an evaluation threshold value prediction parameter P2 based on the data acceptance / rejection information V from a plurality of sets associated with each past window display range that has shifted with the passage of time. Select the group to be used at times. Thereby, the energy efficiency evaluation apparatus can improve the reliability of the evaluation threshold value prediction parameter P2 generated by the evaluation threshold value learning unit 28 by learning.
　なお、本願発明は、その発明の範囲内において、各実施の形態の自由な組み合わせ、あるいは、各実施の形態における任意の構成要素の変形、もしくは、各実施の形態における任意の構成要素の省略が可能である。 In the present invention, within the scope of the invention, a free combination of each embodiment, a modification of any component in each embodiment, or omission of any component in each embodiment is possible. Is possible.
　この発明に係るエネルギー効率評価装置は、評価閾値算出部が算出した評価閾値を用いて、評価部がエネルギー原単位を評価する構成を採用することで、エネルギー原単位を評価することができ、エネルギー原単位を評価するためのエネルギー効率評価装置に適している。 The energy efficiency evaluation apparatus according to the present invention employs a configuration in which the evaluation unit evaluates the energy intensity by using the evaluation threshold calculated by the evaluation threshold value calculation unit, so that the energy intensity can be evaluated. Suitable for energy efficiency evaluation equipment for evaluating basic units.
　１１　エネルギー原単位算出部、１２　エネルギー原単位蓄積部、１３，１３Ａ　評価閾値算出部、１４，１４Ａ　評価部、２１　評価閾値蓄積部、２２　気温データ蓄積部、２３　気温代表値算出部、２４　気温代表値蓄積部、２５　生産量データ蓄積部、２６　生産量代表値算出部、２７　生産量代表値蓄積部、２８　評価閾値学習部、２９　評価閾値予測部、Ａ　生産量、Ｅ　消費エネルギー量、ＥＣ　エネルギー原単位、Ｔ　気温、ＤＥ　エネルギー原単位過去データ、ＤＴ　気温過去データ、ＤＡ　生産量過去データ、Ｓ　評価閾値、Ｓ´　予測値、ＣＴ　気温代表値、ＣＡ　生産量代表値、ＧＳ　評価閾値学習用データ、ＧＴ　気温代表値学習用データ、ＧＡ　生産量代表値学習用データ、Ｖ　データ採否情報、Ｐ１　評価閾値補正用パラメータ、Ｐ２　評価閾値予測用パラメータ、Ｒ　評価結果。 11 Energy intensity unit calculation unit, 12 Energy intensity unit storage unit, 13, 13A Evaluation threshold value calculation unit, 14, 14A evaluation unit, 21 Evaluation threshold value storage unit, 22 Temperature data storage unit, 23 Temperature representative value calculation unit, 24 Temperature representative Value accumulation unit, 25 production volume data accumulation unit, 26 production volume representative value calculation unit, 27 production volume typical value accumulation unit, 28 evaluation threshold learning unit, 29 evaluation threshold prediction unit, A production volume, E consumption energy amount, EC energy Basic unit, T temperature, DE energy basic unit past data, DT past temperature data, DA past production data, S evaluation threshold, S 'predicted value, CT temperature representative value, CA production representative value, GS evaluation threshold learning data , GT temperature representative value learning data, GA production amount representative value learning data, V data acceptance / rejection information, P1 evaluation threshold Correction parameters, P2 evaluation threshold prediction parameter, R evaluation results.

Claims (4)

  1. 　製品の生産量と、前記生産量の前記製品を生産するために必要な消費エネルギー量とに基づいて、エネルギー原単位を算出するエネルギー原単位算出部と、
    　前記エネルギー原単位の評価対象期間よりも過去の時間帯ごとに、前記エネルギー原単位の中央値を算出し、この算出した前記中央値を、各時間帯における前記エネルギー原単位を評価するための評価閾値と設定する評価閾値算出部と、
    　前記評価閾値算出部が前記時間帯ごとに算出した前記評価閾値を用いて、当該時間帯ごとに前記エネルギー原単位を評価する評価部とを備える
    　ことを特徴とするエネルギー効率評価装置。     An energy intensity calculation unit for calculating an energy intensity based on the production volume of the product and the amount of energy consumed to produce the product of the production volume;
    The median of the energy intensity is calculated for each time zone that is earlier than the evaluation target period of the energy intensity, and the calculated median is evaluated for evaluating the energy intensity in each time zone. An evaluation threshold value calculation unit to be set as a threshold value;
    An energy efficiency evaluation apparatus comprising: an evaluation unit that evaluates the energy intensity for each time period using the evaluation threshold value calculated for each time period by the evaluation threshold value calculation unit.
  2. 　前記各時間帯の気温に基づいて、当該時間帯ごとに気温代表値を算出する気温代表値算出部と、
    　前記各時間帯の生産量に基づいて、当該時間帯ごとに生産量代表値を算出する生産量代表値算出部と、
    　前記評価閾値算出部が算出した前記評価閾値、前記気温代表値算出部が算出した前記気温代表値、及び、前記生産量代表値算出部が算出した前記生産量代表値を紐付けし、この紐付けした前記評価閾値、前記気温代表値、及び、前記生産量代表値に基づいて、前記評価閾値を予測するときに使用する予測用パラメータを学習により生成する評価閾値学習部と、
    　前記評価閾値学習部が生成した予測用パラメータ、気温、及び、生産量に基づいて、前記評価閾値を予測するための予測値を予測する評価閾値予測部とを備え、
    　前記評価部は、
    　前記評価閾値予測部が前記時間帯ごとに予測した前記予測値を用いて、当該時間帯ごとに前記エネルギー原単位を評価する
    　ことを特徴とする請求項１記載のエネルギー効率評価装置。     Based on the temperature of each time zone, a temperature representative value calculation unit that calculates a temperature representative value for each time zone,
    Based on the production volume of each time zone, a production volume representative value calculation unit that calculates a production volume representative value for each time zone; and
    The evaluation threshold value calculated by the evaluation threshold value calculation unit, the temperature representative value calculated by the temperature representative value calculation unit, and the production amount representative value calculated by the production amount representative value calculation unit are associated with each other. An evaluation threshold value learning unit that generates, by learning, a prediction parameter to be used when predicting the evaluation threshold value based on the attached evaluation threshold value, the temperature representative value, and the production amount representative value;
    An evaluation threshold prediction unit that predicts a prediction value for predicting the evaluation threshold, based on the prediction parameter generated by the evaluation threshold learning unit, the temperature, and the production amount;
    The evaluation unit is
    The energy efficiency evaluation apparatus according to claim 1, wherein the energy intensity is evaluated for each time period using the predicted value predicted by the evaluation threshold prediction unit for each time period.
  3. 　前記評価閾値学習部は、
    　紐付けした複数組の中から、採否情報に基づいて、前記予測用パラメータを生成するときに使用する組を、選別する
    　ことを特徴とする請求項２記載のエネルギー効率評価装置。     The evaluation threshold value learning unit
    The energy efficiency evaluation apparatus according to claim 2, wherein a group to be used when generating the prediction parameter is selected from a plurality of linked groups based on acceptance / rejection information.
  4. 　前記評価閾値算出部は、
    　前記評価対象期間の過去の期間が複数日に跨る場合には、一日単位で、前記エネルギー原単位の中央値を算出した後、その一日ごとに算出した複数の中央値のうち、値の大きさが中央に位置する中央値を、前記評価閾値と設定する
    　ことを特徴とする請求項１から請求項３のうちのいずれか１項記載のエネルギー効率評価装置。     The evaluation threshold value calculation unit
    When the past period of the evaluation target period spans a plurality of days, after calculating the median of the energy intensity per day, among the plurality of median values calculated every day, The energy efficiency evaluation apparatus according to any one of claims 1 to 3, wherein a median whose size is located at the center is set as the evaluation threshold value.
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005292726A (en) * 2004-04-05 2005-10-20 Yamaha Corp Sound environment detecting device and program
JP2014078111A (en) * 2012-10-10 2014-05-01 Yokogawa Electric Corp Energy saving operation evaluation system
JP2016122416A (en) * 2014-12-25 2016-07-07 オムロン株式会社 Management device, display device, control method of management device, control program, and recording medium

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102009017959B4 (en) * 2009-04-20 2019-10-31 Limón GmbH Method and device for assessing the energy efficiency of an industrial plant
DE102014109949B4 (en) * 2014-07-16 2018-06-28 Horst Zacharias Computer-implemented method for the evaluation of an energy-efficient operation of a building-technical system
KR101687816B1 (en) * 2015-10-05 2016-12-20 (주)에스아이티 Method and apparatus of evlauating energy efficiency
JP2018045280A (en) * 2016-09-12 2018-03-22 株式会社東芝 Energy monitoring apparatus, energy monitoring method, and program

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005292726A (en) * 2004-04-05 2005-10-20 Yamaha Corp Sound environment detecting device and program
JP2014078111A (en) * 2012-10-10 2014-05-01 Yokogawa Electric Corp Energy saving operation evaluation system
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