JP2018116665A - Energy consumption data processing system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an energy consumption data processing system capable of discriminating the age structure and wake-up time of a household based on the energy consumption data at each time of the day in the past predetermined period. SOLUTION: The power consumption data of a household for at least two weeks of summer and winter at each time of the day is taken in, and the maximum value and the minimum power consumption at each time are taken for the taken-in power consumption data. Difference from the value The fluctuation width, which is an absolute value, is obtained, and it is determined whether or not the fluctuation width in the time zone considered to be bedtime is smaller than that in other time zones. If the fluctuation range is small and organized, the household is judged to be an elderly household. In addition, the time zone in which the swing width changes significantly from small to large is determined to be the wake-up time of the household. [Selection diagram] Fig. 2

Description

  The present invention relates to an energy consumption data processing system, and more specifically, based on energy consumption data at each time of the day in a past predetermined period of a household, the age composition of the household and the time and situation of waking up are determined. The present invention relates to an energy consumption data processing system capable of accurately detecting changes in household life.

In recent years, due to the increase in gas and electricity prices associated with soaring crude oil prices, awareness of energy conservation is increasing in households and companies. For this reason, providing various services related to energy saving and power saving according to the demands of homes and companies is conducted as a business. The contents of the service include, for example, how to use equipment and facilities (including buildings) to reduce wasteful energy consumption by analyzing power consumption data for each period of a certain period of time for each customer's season The content of services varies from advice related to maintenance / inspection of equipment / facilities, repair / replacement or new introduction of equipment / facilities, and brokerage work for equipment / facilities.
By the way, energy consumption data such as power consumption has a mirror effect that reflects the actual life of consumers and consumes changes in various lifestyles, such as when they are at home, when they are away, when they visit, when they sleep, and when they get up. The amount of power is reflected. For example, in home life, if there is a life change that is significantly different from the normal life (standard life) of the month, such as the year-end and New Year holidays, it will be affected, and energy consumption will change, and some signs will appear in the data . Therefore, in addition to the various consulting services for reducing the energy consumption described above, so-called monitoring services that detect any abnormalities / emergencies of the customer from the data of the customer's energy consumption are provided as business. An invention relating to a dangerous state determination system related to a monitoring service is known (see, for example, Patent Document 1).
In the above system, a histogram relating to power consumption for each day is generated from power time-series data of N days (N: positive integer) determined arbitrarily, and each generated histogram of power consumption for each day is generated. , Separation Metrics σ _b ² / σ _w that separates into two classes, a low-consumption class with low power consumption and a high-consumption class with large power consumption, using Discriminant Analysis Method ² (σ _b ² : class-variance (Between-Class Variance), σ _w ² : class-in-class variance (Within-Class Variance)) is calculated every day, and the calculated degree of separation σ _b ² / σ _w ² By deriving the threshold value on the day when the maximum value is obtained, the derived threshold value is regarded as the maximum value of power consumption during automatic operation of the electrical equipment installed in the target house. , Staying home It is set as a first threshold value t1 to determine whether the human one hazardous condition, is set to be (Patent Document 1).

On the other hand, for each generated daily power consumption histogram, the minimum power consumption is extracted as the daily minimum power consumption, and the minimum power consumption is extracted from the extracted daily minimum power consumption. The amount is extracted as the minimum power consumption in N days, and further, based on the extracted minimum power consumption, the minimum value of the power consumption at the time of automatic device operation related to the electric device installed in the target house is calculated. By calculating, the minimum value of the calculated power consumption during automatic operation of the device is set as a second threshold t2 for determining the presence or absence of the other dangerous state of the person living in the house ( Patent Document 1).

  And, when the state that does not exceed the first threshold continues until the power consumption amount in the house exceeds a predetermined limit time, or when the state that does not decrease below the second threshold continues, It is determined that the person in the house is in a dangerous state where the device cannot be operated (Patent Document 1).

JP 2013-1331096 A

  According to the dangerous state determination system described in Patent Document 1, the state where the power consumption in the home does not exceed the first threshold t1 continues and exceeds a predetermined limit time, for example, one day (24 hours), A situation in which a person in the house does not perform any operation to start driving the device is a situation that does not occur in a normal state unless a situation that rarely occurs such as when the person in the house leaves the house. The effect is described (Cited document 1).

Furthermore, the state in which the power consumption in the home does not decrease below the second threshold t2 continues, and an operation in which a person in the home stops driving the device after a predetermined limit time, for example, one day (24 hours). It is stated that a situation in which the event is not performed at all is a situation that does not occur in a normal state, except in the case of a special event such as a special event such as a home party held all night. (Cited document 1).

However, in the above dangerous state determination system, it is necessary to observe the transition of power consumption in the house for a long time (for example, one day) in order to make a final determination. It is thought that measures cannot be taken promptly. In addition, the power data captured for histogram creation includes, in addition to normal power consumption data (normal values) during daily life, power consumption data (life abnormal values) related to hosting a party, etc. Power consumption data (measurement abnormal value) or missing power consumption data (missing value) is considered to be included, and these normal values, abnormal living values, abnormal measurement values, and missing values A histogram relating to the power consumption per day created based on the power consumption data included in all, and the first threshold value t1 and the second threshold value t2 used when determining the dangerous state are data other than normal values. Since so-called noise is included, the judgment results obtained from the above dangerous state judgment system are considered to have room for further improvement in terms of accuracy, accuracy, and reliability. .
Therefore, the present invention has been made in view of the above-described problems of the prior art, and its purpose is to determine the age structure of a household based on the energy consumption data at each time of the day in the past predetermined period of the household. Based on the degree of deviation from the above standard life model while capturing real-time energy consumption data of the household based on the standard life model that accurately reflects the normal life of the household as well as determining the wake-up time It is an object of the present invention to provide an energy consumption data processing system that can accurately detect changes in the energy consumption.

  In order to achieve the above object, in the energy consumption data processing system according to the present invention, a data acquisition unit for acquiring household energy consumption data, and the age composition, wake-up time and life of the household based on the energy consumption data An energy consumption data processing system comprising: a data operation unit that analyzes changes; and a data output unit that outputs analysis results relating to household life changes analyzed by the data operation unit, the data operation unit comprising: The energy consumption amount data for each time period for each day in the household's past predetermined period is captured, and the maximum value and the minimum value of the power consumption amount at each time corresponding to the bedtime period for the captured energy consumption data. Obtain the fluctuations that are the absolute difference and the fluctuations in the fluctuations over the entire time period. And characterized by determining the age structure and wake-up time of the household based on the change in increase and decrease of the amplitude.

The inventor of the present application stores energy consumption data for each time period for each day in a predetermined period (for example, every month of summer and winter) for each household (for example, elderly households and young households (non-elderly households)). For example, as a result of earnest analysis of the fluctuation range related to (power consumption data), it was found that there is a unique tendency. That is, regarding the fluctuation range related to the power consumption data of elderly households, in the summer and winter, for example, during the time zone considered to be bedtime from 1 am to 6 am, the fluctuation amount of power consumption is other time. Smaller than the obi. On the other hand, for young households, there is no peculiar tendency that the amplitude of fluctuation is small as it appears in elderly households, and the amplitude is almost constant and relatively large throughout the time. Yes.
In addition, we found that there is a unique trend in the fluctuation of the swing width throughout the entire time zone of each household. In other words, as described above, the swing width of elderly households is smaller than that of other time slots, but the swing width is limited to a certain time (time slot). It changes remarkably from small to large, and after that time, the amplitude is almost constant. On the other hand, the fluctuation of the swing range of young households will be described in detail later, but the swing range changes significantly from small to large at a certain time (time zone) as seen in elderly households. There is no clear tendency.
In other words, by analyzing the energy consumption of each household for a certain period in the past for a certain household and checking whether the fluctuation of energy consumption is smaller than that of other periods The above unique tendency shows that it is possible to discriminate whether the household is an elderly household or a young household. At the same time, the peculiar tendency shows that the wake-up time of the household can be discriminated from the time (time zone) when the swing width changes significantly from small to large.
Therefore, in the energy consumption data processing system of the present invention, the energy consumption data of a certain predetermined period for a certain household is acquired, and the fluctuation width (maximum value and minimum value) of the energy consumption in the time zone corresponding to the sleeping time zone. The absolute age value of the household and the change in fluctuation of the swing throughout the entire time zone are focused on to determine the age structure of the household and to determine the wake-up time of the household.

  A second feature of the energy consumption data processing system according to the present invention is that the period is at least two weeks in summer and winter.

  As will be described in detail later, there is a slight difference between the elderly and young households in summer and winter energy consumption data. In the above configuration, in order to determine the age configuration of the household in consideration of the above difference, the age configuration of the household is determined based on the energy consumption amount of at least two weeks in summer and winter.

  A third feature of the energy consumption data processing system according to the present invention is that the bedtime is a time zone from 1 am to 6 am.

  In the above configuration, in order to be able to easily discriminate the small unit of the fluctuation range, the small unit of the fluctuation amount is discriminated by paying attention to the time zone in which the fluctuation amount of the energy consumption is particularly small. To do.

  A fourth feature of the energy consumption data processing system according to the present invention is that the data calculation unit removes abnormal life data from the captured energy consumption data, so that each time period of a day in the daily life of a household Create a “standard life model” equivalent to the energy consumption load curve of the household, obtain the degree of deviation from the standard life model while capturing the energy consumption data of the household in real time, and alarm level for the household based on the degree of deviation Is to set.

  In the above configuration, in order to prevent large changes in life that may occur in the future in the future, a model road curve (standard life model) related to energy consumption in each time zone of normal life is created, Using the standard life model as a reference, the degree of deviation from the standard life model is obtained for the energy consumption data of the household captured in real time, and the change in the household life is detected based on the degree of deviation, and the degree of deviation is calculated. Set an appropriate alarm level according to your request.

  A fifth feature of the energy consumption data processing system according to the present invention is that the energy consumption is a main power amount of a household.

  The above configuration has an advantage that it is easy to detect changes in household age structure, wake-up time, and household life because the main power consumption is always consumed and can be constantly measured among the energy consumed by humans.

According to the energy consumption data processing system of the present invention, it becomes possible to suitably determine the age configuration and wake-up time of a household based on the energy consumption data at each time in the past predetermined period of the household.
In addition, when creating the above standard life model that accurately reflects the normal consumption life of a household based on the energy consumption data from which abnormal life values have been removed, the household energy consumption data is captured in real time. It is possible to accurately detect changes in household life based on the degree of deviation from the standard life model, and to set an appropriate alarm level according to the degree of deviation from the standard life model. As a result, it is possible to prevent major lifestyle changes that may occur in the future through energy consumption data.

It is block explanatory drawing which shows the structure of the energy consumption data processing system which concerns on this invention. It is a flowchart which shows the power consumption data processing which concerns on this invention. It is a graph which shows the power consumption of each time of the day for every season of an elderly household. It is a graph which shows the power consumption of each time of the day for every season of a young household. It is explanatory drawing which respectively shows the power consumption data concerning a standard life, and the power consumption data concerning an extraordinary life. It is a graph which shows the standard life model and life abnormal model which concern on the power consumption in July of a user's house, and each power consumption in July 6 and July 14, respectively.

 Hereinafter, the present invention will be described in more detail with reference to embodiments shown in the drawings.

  FIG. 1 is an explanatory block diagram showing the configuration of an energy consumption data processing system 100 according to the present invention. Although details will be described later with reference to FIGS. 2 to 6, this energy consumption data processing system 100 is used in a predetermined period (two or more weeks in summer and winter, preferably one year) of the user's home 1. Each time of day (here, a time having a time width, for example, a time width of one hour such as 0:00 to 1:00 in the case of 0:00,..., 23:00 to 24:00 in the case of 23:00. ) Energy consumption (power consumption in this embodiment) based on past measurement data (vibration width), the age structure and wake-up time of the user's home 1 can be determined, and the household of the user's home 1 The power consumption amount load curve (hereinafter referred to as “standard life model”) at each time of day in normal life of the user is created in advance, and the user's home 1 is captured in real time with the power consumption data at each time of the user. Home 1 The degree of deviation from the standard life model of the power consumption data is evaluated step by step, and any sign (sign) related to the life change of the user's home 1 is accurately detected, and the alarm level corresponding to the evaluation result is monitored The monitoring unit 30 is a system that can prevent a significant change in life that may occur in the user's home 1 in the future by taking appropriate measures in accordance with the received alarm level. In particular, the age composition of the user's home 1 is checked by checking the fluctuation width related to the power consumption data in the time zone from 1 am to 6:00 am, which is the sleeping time period, and the fluctuation change of the fluctuation width throughout the whole time period. And wake-up time. Hereinafter, each configuration of the present system will be described.

  The configuration of the system includes a data processing server (power consumption data processing for acquiring power consumption data from a power meter such as a smart meter in the user's home 1 and executing power consumption data processing according to the present invention described later. Device) 10, communication network 20 that enables two-way communication between data processing server 10 and user home 1, and user home 1 according to an analysis evaluation result relating to power consumption data by data processing server 10. The monitoring unit 30 is configured to include a monitoring unit 30 that performs predetermined measures, and the user home 1 that is a monitoring target of the monitoring unit 30. Hereinafter, each configuration will be described in more detail.

  A user home 1 shown in FIG. 1 is provided with a transmission / distribution line 9 to which power (commercial power) is supplied from the outside. The transmission and distribution line 9 is a line for supplying commercial power from an electric power company. The in-home wiring 7 is a power distribution facility that is separated by a power receiving facility such as the power transmission / distribution line 9 and the distribution board 2, and is a facility for a resident of the house to use a power source. The distribution board 2 is a device having a built-in branch circuit or breaker for in-house power distribution.

  The watt-hour meter 5 is a power measuring device that measures (measures) the power usage of the entire user home 1, that is, the total power usage used by the user. As such a watt-hour meter 5, a smart meter which is a device for automatically reading an integrated wattmeter of each building through a communication network 20 described later by an electric power company or the like may be used.

  The watt-hour meter 5 is connected to the communication device 6 via the communication line 8 and transmits the measured power consumption (usage amount) data (measurement data) to the communication device 6. In the present embodiment, the watt-hour meter 5 measures the power consumption amount, for example, for only 15 minutes in one hour, and converts the measured power consumption data into one hour (60 minutes) at one hour intervals. You may make it transmit to the communication apparatus 6. FIG.

  The communication device 6 receives the power usage data transmitted from the watt hour meter 5 and transmits the received data to the data processing server 10 through the communication network 20. In the example shown in FIG. 1, the watt hour meter 5 and the communication device 6 are configured separately, but they may be configured integrally. When the watt-hour meter 5 is a smart meter, both are integrated.

  The data processing server 10 is a computer or the like for performing power consumption data processing described later on power consumption data transmitted from the communication device 6 of the user home 1. The data processing server 10 is installed and operated by, for example, a company that conducts research and consulting related to energy saving, a company that provides various types of information such as information for energy saving support, and the like. As shown in FIG. 1, the data processing server 10 receives power consumption data transmitted from the communication device 6 via the communication network 20. The data processing server 10 includes a communication unit 11, a data processing unit 12, and a data storage unit 13.

  The communication unit 11 is connected to the communication network 20 and modulates (encodes) transmission data into a data format corresponding to the network standard, or demodulates (decodes) received data into a data format corresponding to its own system. It is a data transmitting / receiving device that transmits and receives data. Specifically, the communication unit 11 receives power consumption data transmitted from the communication device 6 via the communication network 20 and sends an alarm level corresponding to the analysis result related to the power consumption data to the monitoring unit 30. Send.

  The data processing unit 12 is a calculation processing unit that executes a calculation related to the power consumption data processing described later on the power consumption data of the user home 1. As shown in FIG. 1, the data processing unit 12 includes a data acquisition unit 12a that acquires power consumption data measured by the watt-hour meter 5, and original data of the power consumption acquired by the data acquisition unit 12a. A data calculation unit 12b that performs calculations related to power consumption data processing, which will be described later, and a data output unit that outputs analysis results related to power consumption data calculated by the data calculation unit 12b to the communication unit 11 and the data storage unit 13. 12c.

  The data storage unit 13 includes an operating system that controls and controls both hardware and software, a program related to power consumption data processing according to the present invention, which will be described later, and the amount of power consumption acquired by the data acquisition unit 12a. The original data and the analysis result of the power consumption calculated by the data calculation unit 12b are stored (stored). For example, various data such as a hard disk, a memory, a DVD disk, a Blu-ray disk capable of reading and writing data can be used. Storage media, including storage media that will be developed in the future.

  The data processing unit 12 shown in FIG. 1 shows a part of functional blocks of the data processing server 10. Here, only functions necessary for data processing of the present invention are extracted and illustrated, and illustration and description of other functions are omitted. In addition to the data processing server 10 shown in the figure, a similar function can be given using a general personal computer or the like. In this case, the computer program executed by the data processing unit 12 is a program for causing the computer to function as each means illustrated. Further, this computer program may be incorporated in an existing application program. The computer program recorded on a computer-readable recording medium such as a CD-ROM may be installed in the computer, or may be downloaded through a communication network such as the Internet.

  The communication network 20 is, for example, the Internet, but may be any communication network 20 as long as two-way communication is possible between the data processing server 10 and the communication device 6 in the user's home 1, and may be, for example, a wireless communication network.

  The monitoring unit 30 is, for example, a so-called security company that specializes in observation and monitoring as a business, but may be a relative of the user's home 1 or a care support provider.

FIG. 2 is a flowchart showing power consumption data processing according to the present invention.
First, in step S1, power consumption data is captured from the watt-hour meter 5 of the user home 1 (target household). The power consumption data is the power consumption [Wh] at each time (each time zone) of the past for at least two weeks in the past at least summer and winter, preferably one year for one year.

Next, in step S2, the fluctuation width of the power consumption data at each time is obtained. The fluctuation width is obtained by calculating the difference between the maximum value and the minimum value of the power consumption at each time.
Incidentally, FIG. 3 is a graph showing power consumption data at each time of the summer and winter of the elderly household group. FIG. 3A shows power consumption data in summer, and FIG. 3B shows power consumption data in winter. Each plot in each graph indicates a maximum value, an average value, and a minimum value of power consumption for each time. Further, the vertical line connecting the maximum value and the minimum value indicates the fluctuation range of the power consumption at that time. As is clear from these graphs, it can be seen that the fluctuation width at each time in the time zone from 1 am to 6 am, which is considered to be a bedtime time, is smaller than other time zones regardless of summer or winter. . In addition, when looking at the change in fluctuation of the amplitude over time, the amplitude changes markedly from small to large at a certain time (for example, 7 am) regardless of summer or winter, and after that time zone For example, in the time zone from 8 o'clock to 23 o'clock, it can be seen that the swing width shows a substantially constant value. In other words, the wake-up time of an elderly household can be understood from the characteristic that the amplitude changes remarkably from small to large.

  On the other hand, FIG. 4 is a graph showing power consumption data at each time of the summer and winter of a young household. 4A shows power consumption data in summer, and FIG. 4B shows power consumption data in winter. As is clear from these graphs, the amplitude at each time in the time zone from 1 am to 6 am is not small compared to the amplitude in the same time zone of the elderly household in FIG. I understand. Also, looking at the change in fluctuation of the amplitude over time, except for the time zone from 9:00 to 17:00 in winter (FIG. 4 (b)), the increase / decrease change in the amplitude is almost zero, and the amplitude is no matter the time of day. It shows a large and almost constant value. In other words, the fluctuation of power consumption in young households does not exist as a small unit, as seen in the 1 to 6 o'clock time zone, which is considered the bedtime of elderly households. Even in the belt, the fluctuation width is large and shows a substantially constant value. Therefore, when the fluctuation amount in the time zone considered to be the bedtime is smaller than the other time zones in the power consumption data of a certain household, the household is considered an elderly household. In addition, the swing range at each time in the time zone from 9:00 to 17:00 in the winter of young households (Fig. 4 (b)) is smaller than other time zones, but the extent of the small unity is Not significant compared to elderly households. This small group of young households is thought to be due to the low percentage of young households staying at home during this time period.

  Moreover, looking at each power consumption (absolute value) in each time zone of elderly households and young households, each household power consumption at each time of the time zone from 8:00 to 17:00 is younger households. While it is higher than that of households, the power consumption of each time in the time zone from 18:00 to 23:00 is higher in young households than in elderly households. This is thought to be because the staying rate of elderly households is higher in the time zone from 8:00 to 17:00 compared to younger households, while in the time zone from 18:00 to 23:00, air conditioners for younger households ( This is probably because the usage rate of coolers or heaters is higher than that of elderly households.

The following is a summary of the characteristics of the amount of fluctuation, the increase / decrease change of the fluctuation amount, and the amount of power consumption at each time of the power consumption read from the power consumption data at each time shown in FIG. 3 and FIG. It becomes as follows.
(1) For example, the swing range in the time zone from 1 o'clock to 6 o'clock, which is considered to be a bedtime for elderly households, is smaller than in other time zones regardless of summer or winter.
(1 ′) The swing of the elderly household in the winter from 22:00 to 23:00 is the largest throughout the winter.
(2) The swing width of young households is large and almost constant compared to elderly households throughout the entire time period, except during the winter hours from 8:00 to 17:00.
(2 ′) The amount of power consumption at each time of the elderly household changes markedly from small to large at a certain time, for example, 7 am.
(3) Regarding the fluctuation of the swing range of the elderly household, the swing range changes markedly from small to large at a certain time, for example, 7 am, regardless of summer or winter.
(4) With regard to the change in fluctuation of the winter swing of young households, the swing width changes from large to small at a certain time, for example, 8:00 am, and the swing width decreases from large to small at a certain time, for example, 18:00. To change.
(5) The power consumption of the elderly household from 8:00 to 17:00 is larger than that of the young household.
(6) The amount of power consumed by young households during the summer from 18:00 to 23:00 is larger than that of elderly households.

  Therefore, in this embodiment, whether or not the user home 1 is an elderly household by analyzing the power consumption data of the user home 1 captured in real time based on the characteristics of (1) and (3) above, for example. , And the wake-up time of the household.

  Next, in step S3, it is determined whether or not the fluctuation amount of the power consumption that is considered to be a bedtime is smaller than other time zones, and the magnitude of the absolute value is also determined. If the fluctuation width obtained in step S2 is equal to or smaller than a predetermined value (for example, the magnitude and variance of the statistically processed power consumption at each time), it is determined that it is smaller than other time zones (YES). And it progresses to step S4 and determines with the user house 1 being an elderly household in step S4. On the other hand, if the fluctuation width is not less than or equal to the predetermined value, it is determined that it is not smaller than other time zones (No), and the process proceeds to step S11. In step S11, it is determined that the user's home 1 is a non-elderly household, and the processing executed in steps S7 to S9 (processing of detecting a change in life while capturing current power consumption data) is particularly necessary. It is determined that there is none, and the process ends (END).

  Next, in step S5, power consumption data relating to a standard life is extracted, and a power consumption model for each predetermined time zone of a standard life is created. As shown in FIG. 5 (a), the power consumption curve of the day on which an event that is rarely performed in normal life (for example, a home party for the year-end and New Year holidays) is performed is a standard consumption at each time. It appears as a daily power consumption curve A that deviates from the power consumption curve group B. On the other hand, the power consumption curve in a normal life where no events such as a home party are performed is a power consumption curve group B in which the power consumption at each time of day falls within the standard fluctuation range. appear. Here, the power consumption curve group B is extracted as the power consumption data related to the standard life of the user home 1. Then, for the extracted power consumption curve group B, as shown in FIG. 5 (b), representative values subjected to statistical processing for each time are obtained, and curves obtained by plotting the obtained representative values for each time are steps. It becomes the power consumption model load curve (standard life model) for every predetermined time of the standard life in the user's home 1 as a reference of the degree of deviation used in the processing according to S7 to S9. The power consumption curve A is used as a life abnormality model described later.

  Next, in step S6, power consumption data related to extraordinary life is extracted, and a power consumption model load curve (life abnormality model) for each predetermined time period of the extraordinary life of the user's home 1 is obtained. ). Here, the power consumption amount curve A shown in FIG. 5A is a power consumption amount model for each predetermined time period of one day in an extraordinary life.

  Next, in step S7, the watt hour meter 5 of the user's home 1 is accessed and current power consumption data is captured.

  Next, in step S8, it is determined whether or not the power consumption captured in step S7 is within the standard life range. The determination is made by comparing with the standard life model created in step S5. That is, when the captured power consumption amount at the analysis target time is within the range of the standard life model (Yes), it is determined that the analysis of the divergence degree is unnecessary, and the capturing of the power consumption amount is continued. On the other hand, if the captured power consumption exceeds (exceeds) the standard life model (No), it is determined that analysis of the degree of deviation is necessary, and the process proceeds to step S9.

  Next, in step S9, the degree of deviation from the standard life model is analyzed. The analysis of the degree of deviation is performed based on the absolute value of the excess amount from the standard life model. The absolute value of the excess amount is calculated by, for example, calculating | (power consumption at the taken analysis target time) − (power consumption at the corresponding time in the standard life model)} |. Details will be described later with reference to FIG.

FIG. 6 is a graph showing the standard life model and the life abnormality model related to the power consumption in July of the user's home 1 and the respective power consumptions on July 6 and July 14.
First, it can be seen that the power consumption (×) on July 6 is almost within the range of the standard life model (◇) and the life abnormality model (□). The power consumption during the 9 o'clock time range exceeds the range of both the standard life model (◇) and the life abnormality model (□), but the level of deviation from the standard life model (◇) is negligible. That is, it is in the normal range. Quantitatively, the excess amount V with respect to the standard life model power consumption (◇) is the standard life model (◇) and Life abnormal model (□) and maximum excess amount V _max for example, refer to the set value less than A in between The divergence level that is within the range of the life abnormality model (□) is defined as the “normal range”.

On the other hand, for the power consumption (△) on July 14, the power consumption during the time period from 0:00 to 10:00 is almost within the normal range of the standard life model (◇) or the life abnormal model (□). subsided and whereas the 11 o'clock for the power consumption of the time zone of the standard living model (◇) life excess amount V is greater than the reference set value a of the maximum excess amount V _max for abnormal model (□) The boundary is reached. However, the degree of divergence of the power consumption in this time zone from the standard life model (◇) is not serious. Quantitatively, for example excess amount V with respect to the standard life model power consumption (◇) is the standard life model (◇) and Life abnormal model (□) maximum excess amount V _max of the reference set value A or more between the The divergence level within the range of the reference set value B and within the range of the life abnormality model (□) is defined as “divergence level 1” here. (A <<B; A and B are referenced from the database held by the observation target.)

On the other hand, for the power consumption time zone 19 o'clock 13 July 14 (△), degree of deviation relative to the standard life model (◇) is greater than the reference set value B of the maximum overrun V _max life The boundary of the anomaly model (□) has been reached. Therefore, the degree of divergence of the power consumption in this time zone with respect to the standard life model (◇) is set to a level of caution. Quantitatively, for example excess amount V of consumed energy for a standard living model (◇) is the standard life model (◇) and Life abnormal model (□) maximum reference set value of the excess quantity V _max between the above B The divergence level within the range of the maximum excess amount V _max or less and within the range of the life abnormality model (□) is defined as “divergence level 2” here.

Also, the power consumption of the time zone of 21 o'clock of July 14 (△), degree of deviation to the standard life model (◇) exceeds the above-mentioned maximum excess amount of V _max, Note and living abnormal model (□) The boundary is exceeded. Therefore, the degree of divergence of the power consumption in this time zone from the standard life model (モ デ ル) is at a serious level. Quantitatively, for example excess amount V with respect to the standard life model power consumption (◇) is in the range exceeding the maximum excess amount V _max between the standard life model (◇) and Life abnormal model (□) The deviation level exceeding the life abnormality model (□) is defined as “divergence level 3”.

  Returning again to FIG. 2, finally, in step S <b> 10, the divergence level is replaced with an appropriate alarm level, and the analysis result is transmitted to the monitoring unit 30. For example, the divergence level 1 is “alarm level 1”, and it is considered appropriate to implement follow-up observation as a measure, for example. The deviation level 2 is set to “alarm level 2”, and it is considered appropriate to carry out, for example, telephone contact as a measure. Further, the divergence level 3 is “alarm level 3”, and as a measure, for example, a home visit can be considered.

As described above, according to the energy consumption data processing system 100 of the present invention, it is possible to suitably determine the age structure and wake-up time of a household based on the fluctuation width of the energy consumption data at each time of the household. become.
In addition, when creating the above standard life model that accurately reflects the normal consumption life of a household based on the energy consumption data from which abnormal life values have been removed, the household energy consumption data is captured in real time. It is possible to accurately detect changes in household life based on the degree of deviation from the standard life model, and to set an appropriate alarm level according to the degree of deviation from the standard life model. As a result, it is possible to prevent major lifestyle changes that may occur in the future in the household using the energy consumption data.

  The embodiment of the present invention is not limited to the above, and various modifications are included without departing from the gist of the features of the present invention. For example, in step S3, the age structure and wake-up time of the household are determined based on the amplitude of the power consumption, but the age structure and wake-up time of the household may be determined based on the absolute value of the power consumption. It is possible to determine the age structure and wake-up time of the household based on the fluctuation amount and the absolute value of the power consumption. Moreover, in the said embodiment, although electric power is selected as energy consumption data, it is not limited only to electric power, It is also possible to select either water supply, gas, or kerosene. Further, the degree of divergence (divergence level) from the standard life model is not limited to the embodiment described above, and can be determined individually and specifically according to household attributes and specific living behavior. .

DESCRIPTION OF SYMBOLS 1 User's house 2 Distribution board 5 Electricity meter 6 Communication apparatus 7 In-home wiring 8 Communication line 9 Transmission and distribution line 10 Data processing server (energy consumption data processing apparatus)
DESCRIPTION OF SYMBOLS 11 Communication part 12 Data processing part 12a Data acquisition part 12b Data calculating part 12c Data output part 13 Data storage part 20 Communication network 30 Monitoring part 100 Energy consumption data processing system

Claims (5)

A data acquisition unit for acquiring household energy consumption data, a data calculation unit for analyzing household age composition, wake-up time, and life changes based on the energy consumption data, and household life analyzed by the data calculation unit An energy consumption data processing system comprising: a data output unit that outputs an analysis result relating to a change;
The data calculation unit captures energy consumption data for each time period for each day in a household's past predetermined period, and the maximum value of power consumption at each time corresponding to a sleeping time period for the captured energy consumption data The difference between the absolute value and the minimum value is obtained as an amplitude and an increase / decrease change in the amplitude over the entire time period. Based on the amplitude of the power consumption and the increase / decrease change in the amplitude, the age structure of the household and An energy consumption data processing system characterized by determining a wake-up time.   The energy consumption data processing system according to claim 1, wherein the period is a period of at least two weeks of at least summer and winter.   The energy consumption data processing system according to claim 1, wherein the bedtime is a time zone from 1 am to 6 am.   The data calculator removes abnormal life data from the captured energy consumption data and creates a “standard life model” corresponding to the energy consumption load curve for each time of day in the daily life of the household. 4. The degree of deviation from the standard life model is obtained while capturing household energy consumption data in real time, and an alarm level for the household is set based on the degree of deviation. The energy consumption data processing system described.   5. The energy consumption data processing system according to claim 1, wherein the energy consumption is a main power amount of a household.

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