Electricity consumption analysis method and electricity consumption analysis system

Abstract

To provide a power consumption device identification method capable of identifying a device consuming power while reducing installation work and cost with a simple configuration for processing, and a power consumption device identification system.SOLUTION: A power consumption device identification method in an en embodiment includes: a step of generating a frequency distribution B7 representing the relationship between power amount and the number of hours by a power analysis system from electricity usage; a step of distinguishing a frequency distribution B7 where the electric energy is larger than the boundary value V as frequency distribution of product manufacturing time B71, and a frequency distribution B7 where the electric energy is less than the boundary value V is set as frequency distribution of a non-production time B72; a step of identifying a device consuming power from frequency distribution B72 during non-production hours.SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] This disclosure relates to a method and system for identifying power-consuming equipment, which are among multiple pieces of equipment installed in a factory that consume electricity. [Background technology]

[0002] Japanese Patent Publication No. 2012-168018 describes a power consumption management system. The power consumption management system comprises multiple power connection devices, which are smart power strips connected to each of multiple office automation equipment in an office; a relay device that collects power values ​​from the multiple power connection devices; and an information processing device, which is an energy management server that can communicate with the relay device.

[0003] The information processing device comprises a communication unit, a user storage unit that stores information about users of power-connected devices, and an evaluation unit that evaluates power values. The communication unit acquires detected values, which are power values ​​associated with the plug connections of multiple electrical connection devices. The user storage unit stores user identification information, which identifies users, in association with each electrical connection device. If the evaluation unit confirms that the power value based on the detected value exceeds a predetermined allowable range, it sets a message based on the user identification information to the user of the electrical connection device in which the detected value was found, urging them to improve their power consumption. [Prior art documents] [Patent Documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2012-168018 [Overview of the Initiative] [Problems that the invention aims to solve]

[0005] Incidentally, in factories that manufacture products, it is expected that the number of devices that use electricity will be very large. Therefore, if power connection devices are connected to each of the multiple devices, as in the power usage management system mentioned above, the system configuration may become complex. In other words, since power meters must be installed for each piece of equipment, problems may arise in terms of the effort and cost of installing power meters. Furthermore, the power usage management system mentioned above requires processing to store user identification information in association with each power connection device, and processing to set up messages that encourage improvement in power usage, so problems may arise in terms of the complexity of the processing configuration.

[0006] This disclosure aims to provide a method and system for identifying power-consuming equipment that can reduce installation effort and costs, simplify the configuration for processing, and identify equipment that is consuming power. [Means for solving the problem]

[0007] [1] The power consumption equipment identification method relating to this disclosure is provided for each group of equipment that uses electricity in a factory that manufactures a product, and is a power consumption equipment identification method that uses a power measuring instrument to measure the amount of electricity used by the group of equipment, and a power analysis system to analyze the amount of electricity used measured by the power measuring instrument to identify equipment that is consuming electricity. The power consumption equipment identification method comprises the steps of: generating a frequency distribution from the amount of electricity used by the power analysis system that shows the relationship between the amount of electricity and the number of times it occurs; classifying the frequency distribution in which the amount of electricity is greater than a boundary value as the frequency distribution of the product's manufacturing time, and classifying the frequency distribution in which the amount of electricity is less than or equal to the boundary value as the frequency distribution of the product's non-manufacturing time; and identifying equipment that is consuming electricity from the frequency distribution of non-manufacturing time.

[0008] In this method for identifying power-consuming equipment, power meters are installed in each group of equipment, including multiple devices, to measure power consumption. The power consumption measured by the power meters is then analyzed by a power analysis system. Since a power meter is installed for each group of equipment, the number of power meters can be reduced relative to the number of devices, thus reducing the effort and cost of installing power meters. Consequently, the configuration for identifying power-consuming equipment can be simplified. The power analysis system generates a frequency distribution showing the relationship between power consumption and the number of times it occurs, based on the power consumption measured by the power meters. By generating a frequency distribution, it is easy to understand how much power is being used and how often. In addition, in factories, the power consumption of equipment is greater during manufacturing time than during non-manufacturing time. Therefore, in this method for identifying power-consuming equipment, the frequency distribution is divided into two parts: the frequency distribution for manufacturing time where the power consumption is greater than a boundary value, and the frequency distribution for non-manufacturing time where the power consumption is less than or equal to the boundary value. Furthermore, for multiple pieces of equipment in a factory, it is easier to identify which equipment is consuming power from the frequency distribution of non-manufacturing times than from the frequency distribution of manufacturing times. In many cases, reducing the power consumption of equipment consuming power during non-manufacturing times is more effective from an energy-saving perspective than reducing the power consumption of equipment consuming power during manufacturing times. Therefore, this method for identifying power-consuming equipment identifies equipment consuming power from the frequency distribution of non-manufacturing times. As a result, equipment consuming power during non-manufacturing times can be reliably identified, and energy-saving measures can be implemented on the identified equipment to more effectively promote energy conservation.

[0009] [2] In [1] above, the step of identifying the equipment may include a step of analyzing the power usage of the equipment to be identified from the waveform of the frequency distribution of non-manufacturing time. If the waveform of the frequency distribution showing the relationship between the amount of power and the number of times it occurs is steep, it can be seen that the equipment that consumes power is one that starts and stops infrequently or one that starts and stops regularly at a period of an integer fraction of the sampling period, and if the waveform is smooth, it can be seen that the equipment that consumes power is one that starts and stops frequently. As mentioned above, by analyzing the power usage of the equipment from the waveform, the equipment can be identified more reliably.

[0010] [3] In [1] or [2] above, the method for identifying power-consuming equipment may include a step of setting a boundary value for the amount of power at which the cumulative number of occurrence times when moving from the maximum to the minimum value of the power amount obtained as a frequency distribution is less than or equal to a time threshold. In the classification step described above, the frequency distribution of power amounts greater than the boundary value may be set as the frequency distribution of manufacturing time, and the frequency distribution of power amounts less than or equal to the boundary value may be set as the frequency distribution of non-manufacturing time. In this case, the boundary value described above can be set using the cumulative number of occurrence times when moving from the maximum to the minimum value of the power amount. Then, by using this boundary value, the frequency distribution of manufacturing time and the frequency distribution of non-manufacturing time can be classified, so that the classification of manufacturing time and non-manufacturing time can be performed with higher accuracy.

[0011] [4] In any of the above [1] to [3], the method for identifying power-consuming equipment may include a step of calculating the manufacturing time from the frequency distribution generated in the step of generating the frequency distribution. In this case, the manufacturing time of the product can be determined from the frequency distribution.

[0012] [5] The power consumption equipment identification system relating to this disclosure is provided for each group of equipment that uses electricity in a factory where a product is manufactured, and comprises a power meter for measuring the power consumption of the group of equipment, and a power analysis system for analyzing the power consumption measured by the power meter. The power analysis system comprises a frequency distribution generation unit for generating a frequency distribution showing the relationship between the amount of power and the number of times it occurs from the amount of power consumption, a time setting unit for classifying the frequency distribution where the amount of power is greater than a boundary value as the frequency distribution of the product's manufacturing time, and the frequency distribution where the amount of power is less than or equal to the boundary value as the frequency distribution of the product's non-manufacturing time, and an equipment identification unit for identifying equipment that is consuming power from the frequency distribution of non-manufacturing time.

[0013] This power consumption equipment identification system is equipped with a power meter for each group of equipment, including multiple pieces of equipment in a factory. Therefore, the number of power meters can be reduced relative to the number of pieces of equipment, thereby reducing the effort and cost of installing power meters. Furthermore, the configuration for the process of identifying equipment that consumes power can be simplified. This power consumption equipment identification system includes a power analysis system that analyzes the measured power usage, and the power analysis system has a frequency distribution generation unit that generates a frequency distribution showing the relationship between the amount of power and the number of times it occurs. Therefore, similar to the power consumption equipment identification method described above, it is possible to easily understand how much power is being used and how often. The power analysis system also includes a time setting unit, which divides the frequency distribution into manufacturing time frequency distributions where the amount of power is greater than a boundary value and non-manufacturing time frequency distributions where the amount of power is less than or equal to the boundary value. The power analysis system then includes an equipment identification unit, which identifies the equipment consuming power from the non-manufacturing time frequency distribution. Therefore, similar to the power-consuming equipment identification method described above, it is possible to reliably identify equipment that consumes power during non-manufacturing hours, and by implementing energy-saving measures on the identified equipment, energy conservation can be promoted more effectively. [Effects of the Invention]

[0014] According to the present disclosure, it is possible to reduce the labor and cost of installation, simplify the configuration for processing, and identify devices that consume power.

Brief Description of the Drawings

[0015] [Figure 1] It is a block diagram showing an example of the configuration of a power consumption device identification system according to an embodiment. [Figure 2] It is a schematic graph of a frequency distribution showing the relationship between the amount of power and the number of occurrence hours. [Figure 3] It is a graph schematically showing the relationship between time and the amount of power consumed by a device. [Figure 4] It is an exemplary graph of a frequency distribution showing the relationship between the amount of power and the number of occurrence hours. [Figure 5] It is a graph showing another example of a frequency distribution. [Figure 6] It is a graph for explaining the boundary value between the frequency distribution of manufacturing time and the frequency distribution of non-manufacturing time. [Figure 7] It is a graph showing an example in which the effect of energy saving is obtained by obtaining a frequency distribution. [Figure 8] It is a graph showing another example of a frequency distribution. [Figure 9] It is a flowchart showing an example of the steps of a power consumption device identification method according to an embodiment. [Figure 10] It is a graph showing an example of a frequency distribution generated monthly. [Figure 11] It is a graph showing yet another example of a frequency distribution. [Figure 12] It is a graph showing yet another example of a frequency distribution.

Modes for Carrying Out the Invention

[0016] The embodiments of the power consumption equipment identification method and power consumption equipment identification system related to this disclosure will be described below with reference to the drawings. In the description of the drawings, the same or equivalent elements are denoted by the same reference numeral, and redundant explanations are omitted as appropriate. In addition, for the sake of ease of understanding, some parts of the drawings may be simplified or exaggerated, and the dimensional ratios, etc., are not limited to those shown in the drawings.

[0017] The power-consuming equipment identification system and power-consuming equipment identification method according to this embodiment are for identifying equipment that is consuming power in a factory where multiple pieces of equipment are in operation, and for promoting energy conservation. In this embodiment, energy conservation can be efficiently promoted by focusing on non-manufacturing time when products are not being manufactured rather than manufacturing time when products are being manufactured, and by analyzing the amount of power used by equipment during non-manufacturing time.

[0018] In this embodiment, a frequency distribution showing the relationship between the amount of electricity used and the duration of use is generated from the electricity usage data. A "frequency distribution" is a distribution of the frequencies in which the same value appears among the measured values. In this embodiment, the distribution of the duration of use (duration of use) within the measured electricity usage is generated as a frequency distribution. By generating this frequency distribution, it is possible to easily understand how much electricity is being used and for how long.

[0019] In this embodiment, a boundary value for electrical energy is set in the frequency distribution, and the frequency distribution of electrical energy greater than the boundary value is classified as the frequency distribution of manufacturing time, and the frequency distribution of electrical energy less than or equal to the boundary value is classified as the frequency distribution of non-manufacturing time. "Manufacturing time" refers to the time when products are manufactured in the factory. For example, "manufacturing time" is the time period when equipment is operating inside the factory to manufacture products. "Non-manufacturing time" refers to the time when products are not manufactured in the factory. For example, "non-manufacturing time" is the time period when equipment is operating inside the factory but no products are being manufactured.

[0020] In this embodiment, power-consuming equipment is identified from the frequency distribution of non-manufacturing time. In this embodiment, "power-consuming equipment" refers to equipment that uses more power compared to other equipment. By identifying "power-consuming equipment," energy-saving measures can be implemented for that equipment, thereby effectively reducing the overall energy consumption of the factory and contributing to more efficient energy conservation.

[0021] Figure 1 is a block diagram showing an example of the functional configuration of a power consumption device identification system 1 according to one embodiment. As shown in Figure 1, the power consumption device identification system 1 includes a power measuring instrument 2 that measures the amount of power used by a plurality of devices X installed in a factory K, and a power analysis system 10 that is able to communicate with the power measuring instrument 2 and analyzes the amount of power used measured by the power measuring instrument 2.

[0022] Factory K is a factory that manufactures products. "Products" include all kinds of products, for example, food and beverages, pharmaceuticals, semiconductors, electrical equipment, or transport equipment. "Products" may also include beverages, including beer, as an example. Equipment X of Factory K is equipment that operates using electricity. Equipment X is a tank for holding liquids, a cooling device, a heating device, an air conditioner, or a conveying device for transporting beverage containers (for example, bottles, cans, or kegs) for holding beverages. More specifically, Equipment X may be a bottle washing machine, a conveyor, an inspection machine, a pump, a fan, or a filler for filling beverage containers with beverages.

[0023] A power meter 2 is provided for each group of equipment G, which includes multiple pieces of equipment X. Factory K has multiple groups of equipment G, for example, which include a first group of equipment G1, a second group of equipment G2, a third group of equipment G3, and a fourth group of equipment G4. In this embodiment, unlike an office, Factory K has a very large number of pieces of equipment X. Therefore, providing a power meter 2 for each group of equipment G is effective in avoiding complexity in the configuration related to power measurement. For example, the power consumption equipment identification system 1 includes multiple power meters 2. As an example, the power meter 2 may be an existing electric meter that is pre-installed in Factory K.

[0024] For example, the power analysis system 10 is located outside of factory K. However, the power analysis system 10 may also be located inside factory K, and its location is not particularly limited. The power analysis system 10 is, for example, a computer system that analyzes the power consumption of multiple pieces of equipment X in factory K.

[0025] The power analysis system 10 may be composed of computers. The power analysis system 10 may include information terminals such as tablet devices, high-function mobile phones (smartphones), or laptop personal computers. The power analysis system 10 may be a distributed processing system composed of multiple computers, a client-server system, or a cloud system. The power analysis system 10 may include a power analysis program. The power analysis program may include, for example, a main module, a data acquisition module, a calculation module, and an output module.

[0026] The main module is a module that comprehensively manages the functions of the power analysis system 10. The functional components of the power analysis system 10 function when the data acquisition module, calculation module, and output module are executed. The power analysis program may be provided by recording it on a storage medium such as a CD-ROM, DVD-ROM, or semiconductor memory. Alternatively, the power analysis program may be provided via a communication network as a data signal superimposed on a carrier wave.

[0027] For example, the power analysis system 10 has an energy data analysis tool. The power analysis system 10 collects, analyzes, and visualizes energy data at regular intervals (for example, every hour) using spreadsheet software. The power analysis system 10 makes it easy to identify equipment X that is consuming a significant amount of power. Furthermore, since the power usage of each piece of equipment X is updated and visualized at regular intervals by the power analysis system 10, employees working at factory K can easily notice energy loss caused by specific pieces of equipment X. As a result, further energy conservation is promoted.

[0028] The power analysis system 10 comprises, as functional components, a data input unit 11, a frequency distribution generation unit 12, a power usage analysis unit 13, a time setting unit 14, a non-manufacturing time analysis unit 15, a manufacturing time analysis unit 16, and an equipment identification unit 17. The data input unit 11 inputs the amount of power usage measured by the power measuring instrument 2 into the power analysis system 10.

[0029] For example, power meter 2 measures the total power consumption of multiple devices X that make up the device group G. In this case, power meter 2 measures the sum of the power consumption of the multiple devices X. When the data input unit 11 inputs the power consumption measured by power meter 2 to the power analysis system 10, the frequency distribution generation unit 12 generates a frequency distribution from the power consumption that shows the relationship between the amount of power and the number of times it occurs.

[0030] Figure 2 shows exemplary frequency distributions B1 and B2 generated by the frequency distribution generation unit 12. The horizontal axis of the graph in Figure 2 represents the amount of electrical energy, and the vertical axis of the graph in Figure 2 represents the number of occurrence times. For example, frequency distribution B1 shows the frequency distribution of the first equipment group G1, and frequency distribution B2 shows the frequency distribution of the second equipment group G2.

[0031] Figure 3 is a schematic graph showing time-series data of power consumption in the first equipment group G1 and the second equipment group G2. As shown in Figures 1, 2, and 3, for example, the power consumption analysis unit 13 analyzes the power consumption of equipment X from the waveforms of frequency distributions B1 and B2. As an example, the waveform of frequency distribution B1 is steeper compared to the waveform of frequency distribution B2.

[0032] For example, the power usage analysis unit 13 analyzes that the waveform of frequency distribution B1 is steeper than the waveform of frequency distribution B2, indicating that equipment X in the first equipment group G1 has fewer starts and stops (the time-series data of power consumption is stable). Also, the power usage analysis unit 13 analyzes that the waveform of frequency distribution B2 is smoother than the waveform of frequency distribution B1, indicating that equipment X in the second equipment group G2 has more starts and stops (frequent ON / OFF cycles).

[0033] Figure 4 shows exemplary frequency distributions B3 and B4 generated by the frequency distribution generation unit 12. Figure 5 shows frequency distributions B5 and B6, which are different examples from those in Figure 4 and also generated by the frequency distribution generation unit 12. Figure 4 shows the graph when equipment X is a bottle washing machine, and Figure 5 shows the graph when equipment X is an air conditioner.

[0034] As shown in Figures 4 and 5, the frequency distribution generation unit 12 may generate frequency distributions B3, B4, B5, and B6 at regular intervals. Figures 4 and 5 show examples where the frequency distribution generation unit 12 generates frequency distributions B3, B4, B5, and B6 annually (last year and this year). More specifically, Figure 4 shows frequency distributions B3 and B4 for one year, and Figure 5 shows frequency distributions B5 and B6 for one month. In the example in Figure 5, the frequency distribution generation unit 12 generated frequency distribution B5 one year ago (last year), and after energy-saving measures were implemented for the air conditioner, the frequency distribution generation unit 12 generated frequency distribution B6.

[0035] As shown in Figure 5, energy-saving measures are implemented after frequency distribution B5 is generated, and then frequency distribution B6 is generated. This results in an increase in the number of times when the power consumption in frequency distribution B6 is low, indicating that the energy-saving measures are effective in the air conditioner.

[0036] Figure 6 shows the frequency distributions B7 and B8 generated by the frequency distribution generation unit 12. As shown in Figures 1 and 6, in this embodiment, a boundary value V for energy is set for the frequency distributions B7 and B8, and the time setting unit 14 divides the frequency distributions B7 and B8 according to the boundary value V. The boundary value V is the energy value used to distinguish between manufacturing time and non-manufacturing time.

[0037] For example, the time setting unit 14 sets the threshold value V to the amount of power at which the cumulative number of occurrence times decreases from the maximum value (40 kWh in the example in Figure 6) to the minimum value (0 kWh in the example in Figure 6) obtained as frequency distributions B7 and B8, and the threshold value V is less than or equal to the time threshold. Figure 6 shows an example where the time threshold is 400 hours in this year and the threshold value V is 20 kWh.

[0038] However, the time threshold and boundary value V are not limited to 400 hours and 20 kWh, but are changed as appropriate. For example, the time threshold is determined from the amount of energy used during manufacturing time and the amount of energy used during non-manufacturing time, which have been acquired in advance. The time setting unit 14 classifies the frequency distributions B7 and B8 at energy amounts greater than the boundary value V as the frequency distributions B71 and B81 for manufacturing time, and the frequency distributions B7 and B8 at energy amounts less than or equal to the boundary value V as the frequency distributions B72 and B82 for non-manufacturing time.

[0039] Figure 7 is an enlarged graph of the frequency distributions B72 and B82 during non-manufacturing time shown in Figure 6. The non-manufacturing time analysis unit 15 analyzes the frequency distributions B72 and B82 during non-manufacturing time. For example, the non-manufacturing time analysis unit 15 analyzes the power usage of equipment X from the waveforms of the frequency distributions B72 and B82 during non-manufacturing time.

[0040] For example, the non-manufacturing time analysis unit 15, like the power usage analysis unit 13, analyzes not only the amount of power used and the number of hours it occurs, but also the frequency of starting and stopping for each group of equipment G. For example, if equipment X is a bottle conveying device, the level of operation may decrease, and in such cases, the proportion of energy used during non-manufacturing time will be high.

[0041] Therefore, when the non-manufacturing time analysis unit 15 analyzes the power usage of equipment X from the frequency distributions B72 and B82 during non-manufacturing time, it is possible to grasp the amount of electricity used during non-manufacturing time. If the proportion of electricity used during non-manufacturing time is large, it is effective in implementing energy-saving measures that focus on non-manufacturing time. By analyzing the power usage, the non-manufacturing time analysis unit 15 can identify a frequency distribution B8 with a smaller amount of electricity than frequency distribution B7, indicating that the energy-saving measures were effective in the following year (this year). Furthermore, from the sum of electricity usage and frequency (Σ(electricity amount × frequency)), it is possible to determine whether energy saving is more effective during non-manufacturing time or manufacturing time.

[0042] The manufacturing time analysis unit 16 analyzes the frequency distributions B71 and B81 during the manufacturing time. The manufacturing time analysis unit 16 analyzes the power usage of equipment X from the waveforms of the frequency distributions B71 and B81 during the manufacturing time. The content analyzed by the manufacturing time analysis unit 16 is the same as, for example, the content analyzed by the non-manufacturing time analysis unit 15.

[0043] Figure 8 shows Figure 6 as a monthly frequency distribution B9. The equipment identification unit 17 identifies equipment X that is consuming power from the frequency distribution B9 during non-manufacturing hours. In the example in Figure 8, it is shown that during non-manufacturing hours, the amount of power consumed in the frequency distribution B9 during the summer (e.g., June to September) is greater than the amount of power consumed in the frequency distribution B9 during the winter (e.g., December to March). The equipment identification unit 17 identifies equipment X from this characteristic. In the example in Figure 8, the equipment identification unit 17 identifies equipment X as an air conditioner that consumes more power during the summer.

[0044] Next, the method for identifying power-consuming equipment according to this embodiment will be described with reference to Figure 9. Figure 9 is a flowchart showing an example of the steps of the method for identifying power-consuming equipment according to this embodiment. First, as shown in Figure 1, each of the multiple power measuring instruments 2 measures the power consumption of each group of equipment G, and the power analysis system 10 acquires the power consumption (step S1). At this time, the power consumption measured by the power measuring instruments 2 is input to the power analysis system 10 by the data input unit 11.

[0045] Next, the frequency distribution generation unit 12 generates a frequency distribution from the power consumption measured by the power meter 2 (step S2, the process of setting the frequency distribution). As an example, the frequency distribution generation unit 12 generates frequency distributions B3 and B7 shown in Figures 4 and 6. Then, the power usage analysis unit 13 analyzes the power usage of the specified equipment X from the waveforms of frequency distributions B3 and B7. As a specific example, it analyzes that equipment X in frequency distribution B3 has more starts and stops than equipment X in frequency distribution B7.

[0046] Next, the time setting unit 14 sets the manufacturing time and non-manufacturing time (step S3). Specifically, the time setting unit 14 sets the amount of energy (20 kWh in the example in Figure 6) at which the cumulative value of the occurrence time when moving from the maximum to the minimum value of the energy obtained as frequency distributions B3 and B7 is less than or equal to the time threshold as the boundary value (step of setting the amount of energy that is less than or equal to the time threshold as the boundary value).

[0047] The time setting unit 14 classifies frequency distributions B3 and B7 where the energy amount is greater than the boundary value V into manufacturing time frequency distributions B31 and B71, and frequency distributions B3 and B7 where the energy amount is less than or equal to the boundary value V into non-manufacturing time frequency distributions B32 and B72 (classification step). For example, the non-manufacturing time analysis unit 15 may analyze frequency distributions B32 and B72, and the manufacturing time analysis unit 16 may analyze frequency distributions B31 and B71 (step S4).

[0048] After the time setting unit 14 distinguishes between manufacturing time and non-manufacturing time, the equipment identification unit 17 identifies the characteristics of equipment X that consumes the respective amounts of power in frequency distributions B32 and B72 from the frequency distributions B32 and B72 of the non-manufacturing time (equipment identification process, step S5).

[0049] Furthermore, the non-manufacturing time analysis unit 15 analyzes the frequency distribution B72 to determine if the start-stops are below a certain value. Based on the results of this analysis, the equipment identification unit 17 identifies equipment X that is consuming the amount of power shown in the frequency distribution B72 as equipment that operates at a constant output during non-manufacturing hours in the summer. Note that the above-mentioned equipment X is not limited to bottle washing machines or air conditioners, but includes various pieces of equipment in factory K.

[0050] After identifying the equipment X that is consuming electricity, energy-saving measures are implemented for that equipment X. For example, if the identified equipment X is a fan installed inside factory K, the amount of electricity consumed by equipment X can be effectively reduced by taking measures to narrow the air conditioning range and reduce the airflow for the fan.

[0051] Next, the effects and advantages obtained from the power-consuming equipment identification method and power-consuming equipment identification system 1 according to this embodiment will be described. In the power-consuming equipment identification method and power-consuming equipment identification system 1 according to this embodiment, as shown in Figure 1, a power measuring instrument 2 is provided in the factory K where the product is manufactured to measure the amount of power used for each group of equipment G, which includes multiple pieces of equipment X, and the amount of power used measured by the power measuring instrument 2 is analyzed by the power analysis system 10. Since a power measuring instrument 2 is provided for each group of equipment G, which includes multiple pieces of equipment X, the number of power measuring instruments 2 can be reduced relative to the number of pieces of equipment X, thus reducing the effort and cost of installing the power measuring instruments 2. Accordingly, the configuration for the process of identifying the equipment X that is consuming power can be simplified.

[0052] As shown in Figure 2, the power analysis system 10 generates frequency distributions B1 and B2 from the power consumption measured by the power meter 2, showing the relationship between power consumption and the number of times it occurs. By generating frequency distributions B1 and B2, it is easy to understand how much power is being used and how often. Furthermore, by calculating Σ(power consumption × frequency) at the peaks in frequency distributions B1 and B2, power consumption can be easily calculated, making it easier to anticipate the effects of energy-saving measures. When energy-saving measures are actually implemented, the amount of power consumption reduced by the energy-saving measures can be easily determined by calculating and comparing frequency distributions B1 and B2 before and after the measures are implemented. In addition, conventionally, the amount of energy saved was calculated by calculating the amount of power consumed from the capacity of the equipment or power measurement values ​​and the assumed operating time of the equipment in question, but in this embodiment, the amount of energy saved can be easily calculated from the frequency distribution graph.

[0053] Furthermore, at factory K, the power consumption of equipment X is greater during manufacturing hours when products are being manufactured than during non-manufacturing hours. As shown in Figure 6, in this embodiment, the frequency distribution B7 is divided into manufacturing time frequency distribution B71 where the power consumption is greater than the boundary value V, and non-manufacturing time frequency distribution B72 where the power consumption is less than or equal to the boundary value V. Moreover, for multiple pieces of equipment X in factory K, the peaks are more pronounced in the non-manufacturing time frequency distribution B72 than in the manufacturing time frequency distribution B71, making it easier to identify the equipment X that is consuming power.

[0054] Furthermore, from an energy-saving perspective, it is more effective to reduce the amount of power consumed by equipment X during non-manufacturing times than by equipment X during manufacturing times. Therefore, in this embodiment, equipment X that consumes power is identified from the frequency distribution B72 of non-manufacturing times. As a result, equipment X that consumes power during non-manufacturing times can be reliably identified, and energy conservation can be more effectively promoted by implementing energy-saving measures on the identified equipment X.

[0055] As mentioned above, the process of identifying equipment may include a step of analyzing the power usage of the equipment X to be identified from the waveform of the frequency distribution B72 of non-manufacturing time. If the waveform of the frequency distribution B7, which shows the relationship between the amount of power and the number of times it occurs, is steep, it can be seen that equipment X with few starts and stops (e.g., an air conditioner) is consuming power, and if the waveform is gentle, it can be seen that equipment X with many starts and stops is consuming power. As mentioned above, by analyzing the power usage of equipment X from this waveform, equipment X can be identified more reliably.

[0056] As described above, the power consumption device identification method according to this embodiment may include a step of setting the energy amount at which the cumulative number of occurrence times when moving from the maximum to the minimum value of the energy amount obtained as frequency distribution B7 is less than or equal to a time threshold as the boundary value V. In the classification step described above, the frequency distribution B7 at energy amounts greater than the boundary value V may be set as the frequency distribution B71 of manufacturing time, and the frequency distribution B7 at energy amounts less than or equal to the boundary value V may be set as the frequency distribution B72 of non-manufacturing time. In this case, the boundary value V can be set using the cumulative number of occurrence times when moving from the maximum to the minimum value of the energy amount. Then, since the frequency distribution B71 of manufacturing time and the frequency distribution B72 of non-manufacturing time can be classified using the boundary value V, the classification of manufacturing time and non-manufacturing time can be performed with higher accuracy.

[0057] Embodiments of the power consumption device identification method and power consumption device identification system relating to this disclosure have been described above. However, this disclosure is not limited to the embodiments described above and may be modified or applied to other things without changing the gist of each claim. That is, this disclosure can be modified in various ways without changing the gist of the claims, and the functions of each part of the power consumption device identification system, as well as the content and order of each step of the power consumption device identification method, can be changed as appropriate without departing from the gist of the above.

[0058] For example, the method for identifying power-consuming equipment according to the present disclosure may include a step of calculating the manufacturing time from the frequency distribution generated in the step of generating the frequency distribution. For example, the manufacturing time analysis unit 16 may obtain the manufacturing time from the frequency distribution. FIG. 12 is a graph in which the time during which wastewater moves between wastewater tanks is used as the manufacturing time (operation time). When the frequency distribution generation unit 12 creates the graph shown in FIG. 12, the manufacturing time analysis unit 16, for this year, there are peaks at three points of 100 m 3 / h, 80 m 3 / h, and 50 m 3 / h, so the accumulated hours of frequencies of 40 m 3 / h or more may be calculated as the manufacturing time, which is 2973 hours. Also, for last year, the manufacturing time analysis unit 16, there are peaks at four points of 100 m 3 / h, 90 m 3 / h, 80 m 3 / h, and 70 m 3 / h, so the accumulated hours of frequencies of 60 m 3 / h or more may be calculated as the manufacturing time, which is 3468 hours.

[0059] As described above, the method for identifying power-consuming equipment may include a step of calculating the manufacturing time from the frequency distribution generated in the step of generating the frequency distribution. In this case, the manufacturing time of the product can be obtained from the frequency distribution. Thus, when the method for identifying power-consuming equipment includes the step of calculating the manufacturing time, the manufacturing time of the product can be obtained from the frequency distribution.

[0060] In the foregoing embodiment, as shown in FIG. 8, an example of identifying equipment X as an air conditioner from the frequency distribution B7 has been described. As described above, all types of equipment X are included. FIG. 10 shows an example in which the frequency distribution generation unit 12 generates the frequency distribution B11, and the equipment identification unit 17 identifies equipment X as a freeze prevention heater from the frequency distribution B11. As shown in FIG. 10, in summer (for example, from June to August), the power consumption amount becomes constant by only using the control power supply, and the peak of the frequency distribution B11 becomes sharp. On the other hand, in winter (for example, from December to February), it can be seen that the peak of the frequency distribution B11 becomes gentle due to the start and stop of the freeze prevention heaters.

[0061] Figure 10 shows an example where the frequency distribution generation unit 12 generates frequency distribution B11 every month. However, the frequency at which the frequency distribution generation unit 12 generates frequency distributions is not particularly limited. As shown in Figure 11, the frequency distribution generation unit 12 may generate frequency distributions B12 and B13 every year. As mentioned above, if the frequency distribution generation unit 12 generates frequency distribution B12 one year in advance, the equipment identification unit 17 identifies equipment X, and after energy-saving measures are taken for equipment X, the frequency distribution generation unit 12 generates frequency distribution B13 again, thereby clearly understanding the energy-saving effect on equipment X. As a result, it is possible to improve the quality of future energy-saving measures for equipment X.

[0062] In the embodiments described above, a power analysis system 10 comprising a data input unit 11, a frequency distribution generation unit 12, a power usage status analysis unit 13, a time setting unit 14, a non-manufacturing time analysis unit 15, a manufacturing time analysis unit 16, and an equipment identification unit 17 was described. However, the configuration of the power analysis system is not limited to the above example. For example, a power analysis system may not have any of the data input unit 11, frequency distribution generation unit 12, power usage status analysis unit 13, time setting unit 14, non-manufacturing time analysis unit 15, manufacturing time analysis unit 16, and equipment identification unit 17. [Explanation of Symbols]

[0063] 1...Power consumption equipment identification system, 2...Power measuring instrument, 10...Power analysis system, 11...Data input section, 12...Frequency distribution generation section, 13...Power usage situation analysis section, 14...Time setting section, 15...Non-manufacturing time analysis section, 16...Manufacturing time analysis section, 17...Equipment identification section, B1, B2, B3 ,B4,B5,B6,B7,B8,B9,B11,B12,B13,B31,B32,B71,B72,B81,B82...frequency distribution, G...device group, G1...first device group, G2...second device group, G3...third device group, G4...fourth device group, K...factory, V...boundary value, X...device.

Claims

1. A power meter is provided for each group of equipment that uses electricity in a factory where products are manufactured, and measures the amount of electricity used by the group of equipment. A power consumption analysis method performed using a power analysis system that analyzes the amount of power consumption measured by the power measuring instrument, The process of generating a frequency distribution showing the relationship between the amount of electricity and the number of hours of occurrence from the amount of electricity used by the power analysis system, A step of classifying the frequency distribution where the amount of energy is greater than the boundary value as the frequency distribution of the manufacturing time of the product, and the frequency distribution where the amount of energy is less than or equal to the boundary value as the frequency distribution of the non-manufacturing time of the product, The process includes setting the energy amount obtained as the frequency distribution to be the threshold value at which the cumulative value of the number of occurrence times decreases from the maximum value to the minimum value of the energy amount, and the energy amount at which this decreases is less than or equal to a time threshold. In the above-mentioned classification process, the frequency distribution for the amount of energy greater than the boundary value is defined as the frequency distribution for the manufacturing time, and the frequency distribution for the amount of energy less than or equal to the boundary value is defined as the frequency distribution for the non-manufacturing time. Power consumption analysis method.

2. A power meter is provided for each group of equipment that uses electricity in a factory where products are manufactured, and measures the amount of electricity used by the group of equipment. A power consumption analysis method performed using a power analysis system that analyzes the amount of power consumption measured by the power measuring instrument, The process of generating a frequency distribution showing the relationship between the amount of electricity and the number of hours of occurrence from the amount of electricity used by the power analysis system, A step of classifying the frequency distribution where the amount of energy is greater than the boundary value as the frequency distribution of the manufacturing time of the product, and the frequency distribution where the amount of energy is less than or equal to the boundary value as the frequency distribution of the non-manufacturing time of the product, The process of generating the frequency distribution includes a step of calculating the manufacturing time from the generated frequency distribution, Equipped with, Power consumption analysis method.

3. A power meter is provided for each group of equipment that uses electricity in a factory where products are manufactured, and measures the amount of electricity used by the group of equipment. A power analysis system that analyzes the amount of power consumption measured by the power measuring instrument, Equipped with, The aforementioned power analysis system, A frequency distribution generation unit generates a frequency distribution showing the relationship between the amount of electricity and the number of occurrence times from the amount of electricity used, A time setting unit that classifies the frequency distribution where the power consumption is greater than the boundary value as the frequency distribution of the product's manufacturing time, and the frequency distribution where the power consumption is less than or equal to the boundary value as the frequency distribution of the product's non-manufacturing time, Equipped with, The time setting unit sets the energy amount at which the cumulative value of the number of occurrence times when moving from the maximum value to the minimum value of the energy amount obtained as the frequency distribution is less than or equal to the time threshold as the boundary value. The time setting unit sets the frequency distribution for the amount of energy greater than the boundary value as the frequency distribution for the manufacturing time, and the frequency distribution for the amount of energy less than or equal to the boundary value as the frequency distribution for the non-manufacturing time. Power consumption analysis system.

4. A power meter is provided for each group of equipment that uses electricity in a factory where products are manufactured, and measures the amount of electricity used by the group of equipment. A power analysis system that analyzes the amount of power consumption measured by the power measuring instrument, Equipped with, The aforementioned power analysis system, A frequency distribution generation unit generates a frequency distribution showing the relationship between the amount of electricity and the number of occurrence times from the amount of electricity used, A time setting unit that classifies the frequency distribution where the power consumption is greater than the boundary value as the frequency distribution of the product's manufacturing time, and the frequency distribution where the power consumption is less than or equal to the boundary value as the frequency distribution of the product's non-manufacturing time, A manufacturing time analysis unit calculates the manufacturing time from the frequency distribution generated by the frequency distribution generation unit, Equipped with, Power consumption analysis system.

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