JP5279662B2 - Power display device, power display method and program - Google Patents

Description

  The present invention relates to a power display device, a power display method, and a program for generating and displaying power-related information for raising awareness of energy saving.

  Conventionally, a system for monitoring power consumption has been disclosed in order to achieve energy saving (see, for example, Patent Document 1). In order to raise awareness of energy saving to the user who uses the electrical equipment as in this system, it is effective to clearly indicate the amount of power used to the user.

JP 2006-309325 A

  In order to raise the awareness of energy saving to users of electrical equipment, it is more effective to compare and display the current power consumption and the target power usage and clearly indicate the difference to the user. . This is because if the difference from the target of the power usage is displayed in comparison, the wasteful power usage becomes obvious at a glance.

  For example, the average power consumption in the top runners of neighboring houses or the average power consumption in the same month of the previous year in the same household is adopted as the comparison target that is the target of power consumption. However, even if a comparison display with the average power consumption of neighboring houses is performed, the situation of the household, such as the number of members, differs depending on the house, so the comparison display appropriately represents wasteful power consumption. Often not.

  In addition, it is difficult to urge to immediately adjust the electrical equipment simply by comparing and displaying the average power consumption on the same day of the previous year in the same household. In order to have such a tension, it is desirable to perform a comparative display of power consumption in real time.

  In the comparison display of the power consumption in real time, the transition of the current power consumption is displayed in a graph superimposed on the transition of the target power consumption to be compared. In general, the past power consumption is used as the target power consumption.

  In order to make the user aware of energy saving by the comparison display, it is necessary to select an appropriate target as a target from past power consumption, but this selection is difficult. For example, even on the same day of the previous year, the usage status of the electrical equipment may be significantly different due to the difference in weather or the difference between weekdays and holidays.

  The present invention has been made in view of the above circumstances, and provides a power display device, a power display method, and a program capable of generating power-related information that can provoke energy saving awareness more reliably. The purpose is to do.

In order to achieve the above object, in the power display device of the present invention, the data acquisition unit acquires data relating to the usage status of the electrical equipment. The recording unit records the data acquired by the data acquisition unit in time series and records it as time series data. Matching degree calculation unit receives the first time-series data of recent predetermined period from the recording unit, when the recording portion of the second predetermined period of the first time than series data based on the reference point before A series of data is input, and the first time series data and the second time series data are converted into vectors having values based on the data of each period when divided into a plurality of periods, respectively, and compared. Thus, the process of calculating the degree of coincidence when the first time series data and the second time series data are overlapped is repeatedly performed while shifting the reference time point of the second time series data . The specifying unit specifies a reference time point corresponding to the highest matching degree from among the reference time points corresponding to each of the plurality of matching degrees calculated by the matching degree calculation unit. The display control unit reads from the recording unit time-series data for a certain period based on the identified reference time point and the latest time-series data for a certain period, and displays an image for comparing and displaying the two time-series data read. Generate. The display device displays the image output from the display control unit. Further, the match degree calculation unit decomposes the first and second time series data into vectors while changing the length of each period, sequentially calculates the match degrees, and integrates the sequentially calculated match degrees. By doing this, the final matching degree when the first time-series data and the second time-series data are superimposed is calculated.

  According to the present invention, it is possible to identify a past time point that best matches the current usage status of the electrical equipment based on the data related to the usage status of the electrical equipment acquired by the data acquisition unit. The transition of the past power usage based on the identified time point can be regarded as the expected transition of the power usage in the current usage status of the electrical equipment. Therefore, the transition of the past power consumption based on the identified time point is useful information for making the user more surely conscious of energy saving.

It is a block diagram which shows the structure of the electric power display apparatus which concerns on form 1 of this invention. 2A is an example of time-series data recorded in the power log data recording unit in FIG. 1, and FIG. 2B shows a time-series data comparison method in the power match degree calculation unit in FIG. It is a figure for demonstrating. It is a flowchart which shows the analysis process of the analyzer which concerns on Embodiment 1 of this invention. It is an example of present data. It is an example (the 1) of the representative value vector of present data. It is an example (the 1) of the fluctuation value vector of present data. It is an example (the 2) of the representative value vector of present data. It is an example (the 2) of the fluctuation value vector of present data. It is an example (the 3) of the representative value vector of present data. It is an example (the 3) of the fluctuation value vector of present data. It is an example of log data. It is an example (the 1) of the representative value vector of log data. It is an example (the 1) of the fluctuation value vector of log data. It is an example (the 2) of the representative value vector of log data. It is an example (the 2) of the fluctuation value vector of log data. It is an example (the 3) of the representative value vector of log data. It is an example (the 3) of the fluctuation value vector of log data. It is a table for demonstrating the calculation formula of an integrated value. It is an example of the comparison display of electric power consumption. It is a block diagram which shows the structure of the electric power display apparatus which concerns on Embodiment 2 of this invention. FIGS. 21A and 21B are examples of registered normal patterns. It is an example of the special pattern registered. It is a figure which shows the calculation method of a representative value and a fluctuation value.

Embodiment 1 FIG.
First, Embodiment 1 of the present invention will be described in detail with reference to the drawings. The power display device according to this embodiment is a system for generating power-related information for ventilating a user of an electrical facility to be aware of energy saving.

  As shown in FIG. 1, the power display device 100 according to this embodiment includes a data acquisition unit 1 and an analysis device 2.

  The data acquisition unit 1 includes a plurality of sensors. In FIG. 1, a power sensor 11, an air temperature sensor 12, and a date / time sensor 13 are shown as sensors included in the data acquisition unit 1.

  The power sensor 11 detects power data such as a power value supplied to an electrical facility (not shown) that is a target of energy saving. The power data is detected by the power sensor 11 at a predetermined sampling interval and output to the analysis device 2.

  The temperature sensor 12 detects temperature data around the electrical equipment. The temperature data is detected by the temperature sensor 12 at a predetermined sampling interval and output to the analysis device 2.

  The date / time sensor 13 includes a timer (not shown). The date / time sensor 13 detects the year / month / date / time at a predetermined sampling interval based on the count value of the timer, and outputs it to the analysis device 2.

  The analysis device 2 includes a log data recording unit 3, a matching degree calculation unit 4, a parameter management unit 5, a specifying unit 6, and a display control unit 7. The analysis device 2 is a computer. The analysis device 2 includes a CPU, a memory, and the like. The function of each component of the analysis device 2 is realized by the CPU executing the control program stored in the memory.

  The log data recording unit 3 includes a power log data recording unit 31, an air temperature log data recording unit 32, and a date / time log data recording unit 33.

  The power log data recording unit 31 records the power data output from the power sensor 11 as log data in time series.

  The temperature log data recording unit 32 records the temperature data output from the temperature sensor 12 as log data in time series.

  The date / time log data recording unit 33 records the date / time data output from the date / time sensor 13 as log data in a time series. For the date / time data, for example, a periodic function with one year as one cycle can be adopted.

  The coincidence degree calculation unit 4 includes a power coincidence degree calculation unit 41, an air temperature coincidence degree calculation unit 42, and a date / time coincidence degree calculation unit 43.

  As shown in FIG. 2A, the power matching degree calculation unit 41 inputs time-series data (power data A) for a recent fixed period ΔT from the power log data recording unit 31. Further, the power match degree calculation unit 41 inputs time series data that is past the power data A, that is, time series data (power data B) for a certain period ΔT with respect to the time T. The power match degree calculation unit 41 compares the power data A and the power data B. More specifically, as shown in FIG. 2B, the power coincidence degree calculation unit 41 superimposes the power data A on the power data B and calculates the degree of coincidence between them.

  Further, the power coincidence degree calculation unit 41 inputs the power data B of a certain period ΔT with respect to the time point T while shifting the time point T of superposition in the time series direction, and calculates the degree of coincidence each time.

  Similar to the power match degree calculation unit 41, the temperature match degree calculation unit 42 and the date match degree calculation unit 43 refer to the log data recording unit 3 (the temperature log data recording unit 32 and the date / time log data recording unit 33). The degree of coincidence when the time series data of the recent fixed period ΔT is superimposed on the time series data of the fixed period ΔT with the past time T as a reference is sequentially calculated while shifting the time T of the overlapping.

  The parameter management unit 5 manages parameter setting values necessary for calculation of the degree of coincidence in the degree of coincidence calculation unit 4. Such parameters include, for example, the coefficient of the coincidence calculation formula. The parameter setting values managed by the parameter management unit 5 can be set by the user via the parameter input unit 51.

  The specifying unit 6 inputs the degree of coincidence calculated by the power coincidence degree calculating unit 41, the temperature coincidence degree calculating unit 42, and the date / time degree of coincidence calculating unit 43. The identifying unit 6 identifies a time point T at which the degree of matching output from the degree of matching calculating unit 4 is the highest overall.

  More specifically, the specifying unit 6 integrates the degree of coincidence input from the power coincidence degree calculating unit 41, the temperature coincidence degree calculating unit 42, and the date / time degree of coincidence calculating unit 43. Subsequently, the specifying unit 6 specifies a time point T at which the integrated value is maximum.

  The display control unit 7 inputs the time T specified by the specifying unit 6. The display control unit 7 can read the log data recorded in the log data recording unit 3. The display control unit 7 controls the display device 9 described later based on these inputs. The display control unit 7 reads time-series data of a certain period ΔT with the identified time T as a reference from the log data recording unit 3. Further, the display control unit 7 reads time series data of the latest fixed period ΔT at the present time from the log data recording unit 3. The display control unit 7 generates a graph image (comparison display image) in which the graphs showing the transitions of the two read time-series data are superimposed, and outputs them to the display device 9.

  The display device 9 displays the image data output from the display control unit 7.

  Note that the display control unit 7 can switch data to be displayed on the display device 9 such as power data, temperature data, and date / time data according to designation by a user or the like.

  Next, the operation of the power display device 100 according to the present embodiment will be described. FIG. 3 shows an analysis process performed by the analysis apparatus 2. This analysis processing is executed as necessary, for example, when an execution instruction is input from a host device (not shown).

  In the following, data of a certain period ΔT including the current time output from the power log data recording unit 31, the temperature log data recording unit 32, and the date / time log data recording unit 33 are collectively expressed as current data R (m, t). . Here, t is time. Moreover, m is any one of 1-3. R (1, t) is power data, R (2, t) is temperature data, and R (3, t) is date / time data.

  Similarly, the data of the past fixed period ΔT output from the power log data recording unit 31, the temperature log data recording unit 32, and the date / time log data recording unit 33 is hereinafter referred to as log data L (m, T). T is a reference time point (see FIG. 2B) of the power data B to be superimposed on the power data A. The log data recorded in the power log data recording unit 31 is L (1, T), the log data recorded in the temperature log data recording unit 32 is L (2, T), and the date / time log data recording The log data recorded in the unit 33 is L (3, T).

  First, the coincidence calculation unit 4 inputs the current data R (m, t) (step S1). That is, the power match degree calculation unit 41 inputs the current data R (1, t), the temperature match degree calculation unit 42 inputs the current data R (2, t), and the date / time match degree calculation unit 43 The current data R (3, t) is input.

  Subsequently, the coincidence degree calculation unit 4 initializes the count value n to 1 (step S2). This count value n is used for vector conversion of the current data R (1, t) to R (3, t) and the like.

  Subsequently, the degree-of-match calculation unit 4 decomposes the current data R (m, t) into a representative value vector Rc (m, Δt (n)) and a variation value vector Rv (m, Δt (n)). (Step S3). Here, for example, the current data R (m, t) shown in FIG. 4 is divided by the period Δt (1) as shown in FIGS. 5 and 6, respectively, and the element Rc (m, Δt ( 1), i) and the element Rv (m, Δt (1), i) of the variation vector are generated.

  More specifically, as shown in FIG. 5, the average value of the current data R (m, t) is obtained for each period Δt (1). This average value becomes each element Rc (m, Δt (1), i) of the representative value vector Rc (m, Δt (1)). Further, as shown in FIG. 6, the amount of increase / decrease in the current data R (m, t) is obtained for each period Δt (1). This increase / decrease amount becomes each element Rv (m, Δt (1), i) of the fluctuation value vector Rv (m, Δt (1)). In this way, the degree-of-match calculation unit 4 divides the current data R (m, t) into periods Δt (1), obtains an average value and an increase / decrease amount for each period, and uses them as elements. Element Rc (m, Δt (1), i) of representative value vector Rc (m, Δt (1)), element Rv (m, Δt (1)) of variation value vector Rv (m, Δt (1)) , I).

  Here, finally, representative value vector Rc (1, Δt (1)) of power data, fluctuation value vector Rv (1, Δt (1)), and representative value vector Rc (2, Δt (1) of temperature data. )), Fluctuation value vector Rv (2, Δt (1)), representative value vector Rc (3, Δt (1)) of date and time data, fluctuation value vector Rv (3, Δt (1)). .

Subsequently, the coincidence degree calculation unit 4 determines whether or not the count value n exceeds N _max (step S4). N _max is the number of periods Δt (n) subject to vector decomposition. Usually, N _max >> 1, and at this time, the count value n is 1, so the determination is negative (step S4; No), and the coincidence degree calculation unit 4 increments the count value n by 1. (Step S5), the process returns to Step S3.

  Subsequently, the degree-of-match calculation unit 4 decomposes the current data R (m, t) into a representative value vector Rc (m, Δt (n)) and a variation value vector Rv (m, Δt (n)). (Step S3). Here, for example, the current data R (m, t) shown in FIG. 4 is divided by the period Δt (2) as shown in FIGS. 7 and 8, respectively, and the representative value vector Rc (m, Δt (2) ) Elements Rc (m, Δt (2), i) and each element Rv (m, Δt (2), i) of the fluctuation value vector Rv (m, Δt (2)) are generated. Δt (2) is twice as large as Δt (1).

Subsequently, the coincidence degree calculation unit 4 determines whether or not the count value n exceeds N _max (step S4). If the determination here is negative (step S4; No), the coincidence degree calculation unit 4 increments the count value n by 1 (step S5) and returns to step S3.

  Subsequently, the degree-of-match calculation unit 4 decomposes the current data R (m, t) into a representative value vector Rc (m, Δt (n)) and a variation value vector Rv (m, Δt (n)). (Step S3). Here, for example, the current data R (m, t) shown in FIG. 4 is divided by the period Δt (3) as shown in FIGS. 9 and 10, respectively, and the representative value vector Rc (m, Δt (3) ) Element Rc (m, Δt (3), i) and each element Rv (m, Δt (2), i) of the variation value vector Rv (m, Δt (3)) are generated. Δt (3) is twice as large as Δt (2).

Subsequently, the coincidence degree calculation unit 4 determines whether or not the count value n exceeds N _max (step S4). If the determination here is negative (step S4; No), the coincidence degree calculation unit 4 increments the count value n by 1 (step S5) and returns to step S3.

  In this way, steps S3 → S4 → S5 are repeated, and the representative value vector Rc (m, Δt (n)) and the variation value vector Rv (m, Δt (n)) created in different periods are calculated. The

When the count value n exceeds N _max (step S4; Yes), the coincidence calculation unit 4 initializes the variable T to 0 (step S6). Subsequently, the matching degree calculation unit 4 inputs log data L (m, T) from the log data recording unit 3 (step S7). That is, the power matching degree calculation unit 41 inputs log data L (1, T), the temperature matching degree calculation unit 42 inputs log data L (2, T), and the date / time matching degree calculation unit 43 Log data L (3, T) is input.

  Subsequently, the coincidence degree calculation unit 4 initializes the count value n to 1 again (step S8).

  Subsequently, the degree-of-match calculation unit 4 decomposes the log data L (m, T) into a representative value vector Lc (m, Δt (n)) and a variation value vector Lv (m, Δt (n)). (Step S9). Here, for example, the current data L (m, T) shown in FIG. 11 is divided by the period Δt (1) as shown in FIGS. 12 and 13, and Lc (m, Δt (1) of the representative value vector. ) Element Lc (m, Δt (1), i) and an element Lv (m, Δt (1), i) of the variation vector Lv (m, Δt (1)) are generated.

  Subsequently, the degree-of-match calculation unit 4 uses the following equation (1) to calculate the representative value vectors Rc (m, Δt (n)) and Lc (m, Δt (n)) with the time T as a reference. The inner product Cc (m, Δt (n), T) is calculated (step S10).

ここでは、ｎ＝１であり、Ｔ＝０であるので、代表値ベクトルＲｃ（ｍ，Δｔ（１））、Ｌｃ（ｍ，Δｔ（１））の内積Ｃｃ（ｍ，Δｔ（１），０）が算出される。

Here, since n = 1 and T = 0, the inner product Cc (m, Δt (1), 0) of the representative value vectors Rc (m, Δt (1)) and Lc (m, Δt (1)). ) Is calculated.

  Subsequently, the degree-of-match calculation unit 4 uses the following equation (2) to calculate the variation value vectors Rv (m, Δt (n)) and Lv (m, Δt (n)) with respect to the time T. The inner product Cv (m, Δt (n), T) is calculated (step S11).

ここでは、ｎ＝１であり、Ｔ＝０であるので、変動値ベクトルＲｖ（ｍ，Δｔ（１））、Ｌｖ（ｍ，Δｔ（１））の内積Ｃｖ（ｍ，Δｔ（１），０）が算出される。

Here, since n = 1 and T = 0, the inner product Cv (m, Δt (1), 0) of the variation vector Rv (m, Δt (1)) and Lv (m, Δt (1)). ) Is calculated.

Subsequently, the coincidence degree calculation unit 4 determines whether or not the count value n exceeds N _max (step S12). Here, since the count value n is 1, the determination is negative (step S12; No), the coincidence calculation unit 4 increments the count value n by 1 (step S13), and the process returns to step S9.

  Subsequently, the degree-of-match calculation unit 4 decomposes the log data L (m, T) into a representative value vector Lc (m, Δt (n)) and a variation value vector Lv (m, Δt (n)). (Step S9). Here, for example, the current data L (m, T) shown in FIG. 11 is divided by the period Δt (2) as shown in FIGS. 14 and 15, and the representative value vector Lc (m, Δt (2)). Element Lc (m, Δt (2), i) and element Lv (m, Δt (2), i) of the variation value vector (m, Δt (2)) are generated.

  Subsequently, the degree-of-match calculation unit 4 uses the above formula (1) to calculate the inner product Cc () of the representative value vectors Rc (m, Δt (2)) and Lc (m, Δt (2)) at T = 0. m, Δt (2), 0) is calculated (step S10).

  Subsequently, the degree-of-match calculation unit 4 uses the above equation (2) to calculate the inner product Cv () of the variation value vectors Rv (m, Δt (2)) and Lv (m, Δt (2)) at T = 0. m, Δt (2), 0) is calculated (step S11).

Subsequently, the coincidence degree calculation unit 4 determines whether or not the count value n exceeds N _max (step S12). Here, since the count value n is 2, the determination is negative (step S12; No), and the coincidence calculation unit 4 increments the count value n by 1 (step S13), and returns to step S9.

  Subsequently, the degree-of-match calculation unit 4 decomposes the log data L (m, T) into a representative value vector Lc (m, Δt (n)) and a variation value vector Lv (m, Δt (n)). (Step S9). Here, for example, the current data L (m, T) shown in FIG. 11 is divided by the period Δt (3) as shown in FIGS. 16 and 17, and the representative value vector Lc (m, Δt (3)). Element Lc (m, Δt (3), i) and element Lv (m, Δt (3), i) of the variation vector Lv (m, Δt (3)) are generated.

  Subsequently, the degree-of-match calculation unit 4 uses the above formula (1) to calculate the inner product Cc () of the representative value vectors Rc (m, Δt (3)) and Lc (m, Δt (3)) at T = 0. m, Δt (3), 0) is calculated (step S10).

  Subsequently, the degree-of-match calculation unit 4 uses the above equation (2) to calculate the inner product Cv () of the variation value vectors Rv (m, Δt (3)) and Lv (m, Δt (3)) at T = 0. m, Δt (3), 0) is calculated (step S11).

In this manner, steps S9 → S10 → S11 → S12 → S13 are repeated, and the representative value vector Lc (m, Δt (n)) and the fluctuation value vector Lv (m, Δt () of the log data L (m, T) are repeated. n)) is obtained (step S9), and the representative value vectors Rc (m, Δt (n)) and Lc (m, Δt (n)) created for the different periods Δt (1) to Δt (N _max ), respectively. Inner product Cc (m, Δt (n), T), variation value vector Rv (m, Δt (n)), and inner product Cv (m, Δt (n), T) of Lv (m, Δt (n)). Are respectively calculated (steps S10 and S11). Here, inner products Cc (m, Δt (n), 0) and Cv (m, Δt (n), T) at T = 0 are calculated.

FIG. 18 shows the inner product Cc (m, Δt (n), T) of the representative value vector and the inner product Cv of the variation value vector in the period Δt (1) to Δt (N _max ) obtained so far. (M, Δt (n), T) is shown.

When the count value n exceeds N _max (step S12; Yes), the coincidence degree calculation unit 4 uses the following equation (3) to calculate both inner products Cc (m, Δt (n), T), Cv (m , Δt (n), T) is calculated (S14). Here, since T = 0, the integrated value S (m, 0) is obtained.

ここで、αｃ、αｖ、β（ｍ，Δｔ（ｎ））、γ（ｍ）は、係数である。αｃは、代表値ベクトルの内積Ｃｃ（ｍ，Δｔ（ｎ），Ｔ）の重みであり、αｖは、変動値ベクトルの内積Ｃｖ（ｍ，Δｔ（ｎ），Ｔ）の重みである。代表値に重点を置いて合致度を算出するのか、変動値に重点を置いて合致度を算出するのかを、αｃ、αｖの値で調整することができる。

Here, αc, αv, β (m, Δt (n)), and γ (m) are coefficients. αc is the weight of the inner product Cc (m, Δt (n), T) of the representative value vector, and αv is the weight of the inner product Cv (m, Δt (n), T) of the variation value vector. Whether the degree of coincidence is calculated with emphasis on the representative value or the degree of coincidence is calculated with emphasis on the variation value can be adjusted by the values of αc and αv.

Β (m, Δt (n)) is a weight of the period Δt (n). Of the periods Δt (1) to Δt (N _max ), which period Δt (n) is to be emphasized can be adjusted by the value of β (m, Δt (n)).

  γ (m) is the weight of power data, temperature data, and date / time data. It can be adjusted by the value of γ (m) which data is to be emphasized and the degree of coincidence is calculated.

  These coefficients are managed by the parameter management unit 5, and the user can freely set their values via the parameter input unit 51.

  Subsequently, the specifying unit 6 adds the integrated values S (1, T), S (2, T), and S (3, T) to calculate the added value S (T) (step S15).

Subsequently, the degree-of-match calculation unit 4 determines whether or not the overlapping time T exceeds _Tmax (step S16). Here, since T = 0, the determination is negative (step S16; No), and the coincidence degree calculation unit 4 increments T by dt (step S17), and returns to step S7. Here, dt = ΔT.

Thereafter, the log data L (m, T) is input (step S7) and the count value n is initialized (step S8) while shifting the time point T by dt, and the vector decomposition of the log data L (m, T) is performed. (Step S9), calculation of inner product Cc (m, Δt (n), T) (Step S10), calculation of inner product Cv (m, Δt (n), T) (Step S11), N _max of count value n The comparison (step S12) and the increment of the count value n (step S13) are repeated. When the count value n exceeds N _max (step S12; Yes), the integrated value S (m, T) at the time T is calculated (step S14), and the added value S (T) is calculated (step S14). Step S15).

Subsequently, the degree-of-match calculation unit 4 determines whether or not the overlapping time T exceeds _Tmax (step S16). If the determination is negative (step S16; No), the coincidence degree calculation unit 4 increments T by dt (step S17) and returns to step S7.

  In this way, the accumulated value S (m, T) corresponding to each time T is calculated while shifting the time T (step S14), and the added value S (T) is calculated (step S15).

When the overlapping time T exceeds _Tmax (step S16; Yes), the specifying unit 6 specifies the time T at which the added value S (T) is maximum (step S18).

  Subsequently, the display control unit 7 sends the time series data of a certain period ΔT including the current time and the time series data based on the time T specified in step S18 from the log data recording unit 3 to the display device 9. The comparison is displayed (step S19).

  For example, it is assumed that the current time is 6 o'clock in the morning, and the fixed period ΔT is, for example, 6 hours from 0 o'clock in the evening to 6 o'clock in the morning. As shown in FIG. 19, the display control unit 7 compares the current data R (1, t) and its log data L (1, T) with the display device 9 as data from midnight to 6:00 am. Display.

  Thereafter, power data is detected from the power sensor 11, and the power data is recorded in the power log data recording unit 31. The display control unit 7 inputs the latest data from the power log data recording unit 31 and displays the latest data on the display device 9 following R (1, t), and logs after the log data L (1, T). Data is also input from the power log data recording unit 31 and displayed on the display device 9.

  In the example shown in FIG. 19, the power data of today and the log data are almost the same from midnight to 6:00 am, but the power data of the day greatly increases the log data after 6:00 am. It has exceeded. By looking at this comparison display and knowing that the current power data is higher than the comparison target, the user (tenant) using the electrical equipment, for example, during cooling, sets the set temperature higher or turns on the lighting. It becomes possible to take measures such as reducing the power supplied by darkening or the like.

  After execution of step S19, the analysis device 2 ends the process.

  As described above in detail, according to this embodiment, based on various time-series data regarding the usage status of the electrical equipment detected by the various sensors 11 to 13, the current usage status of the electrical equipment most closely matches. Identify past time points. The transition of the past time series data based on the identified time point can be regarded as the expected transition of the time series data in the current usage status of the electrical equipment. By comparing and displaying the transition of the current time-series data and the transition of past time-series data based on the specified time point, it is possible to urge the user of the electrical equipment to be more surely aware of energy saving. .

  Further, according to this embodiment, the time series data is converted into a representative value or a vector of fluctuation amount for each predetermined sampling period, and the correlation is detected. As a result, the correlation can be calculated with the data size reduced without losing the characteristics of the time-series data, so it is the best match with the current usage status of the electrical equipment in high accuracy and in a short time. A past time point can be specified.

  Further, according to this embodiment, the weight of the representative value and the weight of the fluctuation amount can be adjusted when calculating the degree of coincidence. For this reason, it is possible to calculate the degree of coincidence with an emphasis on the representative value (that is, the value of the time series data), or to calculate the degree of coincidence with an emphasis on the fluctuation value (that is, the fluctuation of the time series data). You can also.

  Further, according to this embodiment, the degree of coincidence of time series data is calculated in a plurality of different sampling periods. As a result, it is possible to extract from small fluctuations in the time series data to rough fluctuations and obtain their correlation. As a result, it is possible to accurately identify the past time point that best matches the current usage status of the electrical equipment.

  Further, according to this embodiment, since the sampling period is changed to an integral multiple, it is possible to cover from a very small change to a very large change. For this reason, the variation range covered can be enlarged.

  Further, according to this embodiment, when calculating the degree of coincidence, weights can be set for a plurality of different sampling periods. As a result, it is possible to adjust whether to focus on fine fluctuations or to emphasize rough fluctuations.

  Further, according to this embodiment, when calculating the degree of coincidence, weights can be set for power data, temperature data, and date / time data, respectively. This makes it possible to adjust which data is important.

Embodiment 2. FIG.
Next, a second embodiment of the present invention will be described.

  As shown in FIG. 20, the analysis device 2 according to this embodiment is different from the analysis device 2 according to the first embodiment in that it further includes a pattern recording unit 20 and a pattern processing unit 8.

  The pattern recording unit 20 stores a typical appearance pattern of data output from the data acquisition unit 1. The appearance patterns stored in the pattern recording unit 20 include a normal pattern 21 and a special pattern 22.

  FIGS. 21A and 21B show an example of the normal pattern 21. FIG. 21A shows a typical waveform pattern showing the transition of temperature data on a clear day. On a clear day, the morning becomes considerably cold due to radiative cooling, but the temperature rises greatly during the day. Therefore, a typical pattern on a sunny day is a waveform pattern with large fluctuations in temperature, as shown in FIG.

On the other hand, FIG. 21 (B) shows a typical waveform pattern showing the transition of the temperature data on a rainy day. As shown in FIG. 21B, the temperature does not change much on a rainy day.
Therefore, a typical pattern on a rainy day is a waveform pattern with small fluctuations in temperature, as shown in FIG.

  As these waveform patterns, those statistically obtained based on the temperature fluctuations on a plurality of sunny days and rainy days can be used.

  FIG. 22 shows an example of the special pattern 22. The special pattern 22 shown in FIG. 22 is a typical waveform pattern showing the transition of the power usage amount on the day when a festival in the evening is held, for example. In general, power consumption is low at night and is high during the day, but if there is a festival in the evening, the power consumption will not decrease even in the evening, and will remain high. become. For example, a waveform pattern shown in FIG. 22 is recorded in the pattern recording unit 20 as the special pattern 22. These special patterns 22 can also be obtained statistically based on the power data of festival days held every year.

  The pattern processing unit 8 includes a power pattern processing unit 81, an air temperature pattern processing unit 82, and a date / time pattern processing unit 83.

  The power pattern processing unit 81 receives the power data output from the power log data recording unit 31. The degree of coincidence between the input power data and the normal pattern 21 and the special pattern 22 recorded in the pattern recording unit 20 is calculated in the same manner as the power coincidence degree calculating unit 41, and the pattern with the highest degree of coincidence is identified. And output to the specifying unit 6.

  Similarly, the temperature data and date data output from the temperature log data recording unit 32 and the date log data recording unit 33 are also input to the temperature pattern processing unit 82 and the date and time pattern processing unit 83. The degree of coincidence between the input air temperature data and date / time data and the normal pattern 21 and the special pattern 22 recorded in the pattern recording unit 20 is calculated, the pattern having the highest degree of coincidence is specified, and each pattern is output to the specifying unit 6 To do.

  Here, the matching degree of the pattern output from the power pattern processing unit 81 is G (1, k), the matching degree of the pattern output from the temperature pattern processing unit 82 is G (2, k), and the date / time pattern processing unit 83. Let G (3, k) be the degree of match of the pattern output from k is a unique identification number of the normal pattern 21 and the special pattern 22 recorded in the pattern recording unit 20.

  The specifying unit 6 is output from the pattern processing unit 8 in addition to the integrated value S (m, T) as the degree of coincidence with the log data output from the coincidence degree calculating unit 4 as in the above embodiments. The matching degree G (m, k) of the selected pattern is also input. Further, the specifying unit 6 calculates not only the added value S (T) of the integrated value S (m, T) but also the added value G (k) of the pattern matching degree G (m, k).

  In principle, the specifying unit 6 specifies the log data at the time T at which S (T) is maximum as a comparison target of the current data. However, when S (T) is less than a predetermined threshold, a pattern with the highest G (k) can be selected as comparison target data.

  As described above in detail, according to this embodiment, typical waveform patterns (normal pattern 21 and special pattern 22) that are likely to appear in the time-series data acquired by the data acquisition unit 1 are stored in advance. . Then, the degree of coincidence G (m, k) between the current time series data and the time series data having a typical waveform pattern is calculated, and the waveform pattern having the highest degree of coincidence G (k) is specified.

  In this case, the identified waveform pattern and the current time-series data are compared and displayed. Thus, even if there is no log data that matches the current time series data and the time point T cannot be specified, if there is a typical waveform pattern that matches, the current time series data, Comparison display with time-series data having the waveform pattern can be performed.

  Various other waveform patterns can be recorded as the waveform pattern recorded in the pattern recording unit 20. For example, a variety of waveform patterns, such as a fluctuation pattern of power data in one week (pattern that is smaller on holidays and larger on weekdays), a fluctuation pattern of power data in one year (large in summer and winter, and small in spring and summer) Can be recorded and compared with current time-series data.

  Further, among the waveform patterns recorded in the pattern recording unit 20, the waveform patterns to be compared with the current time series data may be narrowed down. For example, only the normal pattern 21 or only the special pattern 22 may be compared.

  In each of the above embodiments, as shown in FIG. 23, the average value of the data within the period Δt (n) is used as the representative value. However, the present invention is not limited to this, and an intermediate value between the initial value and the final value is used as the representative value. It is good. Further, in each of the above embodiments, the difference between the opening price and the closing price of the period Δt (n) is set as the fluctuation value. However, the present invention is not limited to this, and the difference between the opening price and the representative value, or the representative value and the closing price. The difference may be a variation value.

  In each of the above embodiments, the degree of coincidence of time-series data is calculated based on the inner product of the representative value vector and the variation vector, but the present invention is not limited to this. For example, the correlation between both time series data is obtained based on the difference between the representative value vector and the difference vector, and the degree of coincidence between the time series data is calculated assuming that the smaller the difference is, the higher the correlation is. It may be.

  Alternatively, the representative value vector Rc (m, Δt (n)) and the fluctuation value vector Rv (m, Δt (n)) may be calculated as follows.

  First, the representative value vector Rc (m, Δt (1)) in a predetermined sampling period Δt (1) is calculated in the same manner as in the above embodiments. Subsequently, a representative value and a difference are calculated for the adjacent representative value Rc (m, Δt (1), i). This set of representative values is a representative value vector Rc (m, Δt (2)) in the sampling period Δt (2), and this set of differences is a variation value vector Rv (m, Δt (2)).

  Similarly, from the representative value vector Rc (m, Δt (2)), the representative value and the difference of the adjacent elements are obtained, and the set of them is represented by the representative value vector Rc (m, Δt (3)) and the variation value. Let it be a vector Rv (m, Δt (3)). Similarly, the representative value vector Rc (m, Δt (n)) and the variation value vector Rv (m, Δt (n)) are obtained.

  This calculation method corresponds to a cascade calculation of discrete wavelets, and dilutes high frequency components included in waveform data as the number of calculations is increased. Of course, the representative value vector Lc (m, Δt (n)) and the fluctuation value vector Lv (m, Δt (n)) can be calculated in the same manner.

  In each of the above embodiments, the power sensor 11, the temperature sensor 12, and the date / time sensor 13 are employed as the sensors included in the data acquisition unit 1, but other types of sensors may be employed. For example, a humidity sensor may be employed.

  In each of the above embodiments, the program executed by the power display apparatus 100 is a flexible disk, a CD-ROM (Compact Disk Read-Only Memory), a DVD (Digital Versatile Disk), an MO (Magneto-Optical Disk), or the like. It is also possible to configure a system that executes the above-described processing by storing the program in a computer-readable recording medium, distributing the program, and installing the program.

  Further, the program may be stored in a disk device or the like of a predetermined server device on a communication network such as the Internet, and may be downloaded, for example, superimposed on a carrier wave.

  In addition, when the above functions are realized by sharing an OS (Operating System), or when the functions are realized by cooperation between the OS and an application, only the part other than the OS may be stored in a medium and distributed. You may also download it.

  The present invention is suitably used to realize energy saving in electrical equipment and the like.

DESCRIPTION OF SYMBOLS 1 Data acquisition part 2 Analysis apparatus 3 Log data recording part 4 Matching degree calculation part 5 Parameter management part 6 Specification part 7 Display control part 8 Pattern processing part 9 Display apparatus 11 Power sensor 12 Temperature sensor 13 Date sensor 20 Pattern recording part 21 Normal Pattern 22 Special pattern 23 Pattern input unit 31 Power log data recording unit 32 Temperature log data recording unit 33 Date and time log data recording unit 41 Power match degree calculation unit 42 Temperature match degree calculation unit 43 Date and time match degree calculation unit 51 Parameter input unit 81 Power Pattern processing unit 82 Temperature pattern processing unit 83 Date and time pattern processing unit 100 Power display device

Claims (13)

A data acquisition unit for acquiring data on the usage status of electrical equipment;
A recording unit that records the data acquired by the data acquisition unit in time series and records it as time series data,
Type the first time-series data of recent predetermined period from said recording portion, the second time series data of said predetermined period to the first time than series data based on the reference point before from the recording unit And converting the first time-series data and the second time-series data into vectors having values based on the data of each period when each of the first time-series data and the second time-series data is divided into a plurality of periods. By doing so, the process of calculating the degree of coincidence when the first time series data and the second time series data are overlapped is repeatedly performed while shifting the reference time point of the second time series data. A degree calculator,
A specifying unit for specifying the reference time point corresponding to the highest match degree from the reference time points corresponding to each of the plurality of match degrees calculated by the match degree calculation unit;
Read the time-series data of the fixed period with the specified reference time point as a reference and the latest time-series data of the fixed period from the recording unit, and generate an image for comparing and displaying the two read time-series data A display control unit,
A display device for displaying an image output from the display control unit;
Equipped with a,
The degree of match calculation unit
While changing the length of each period, the first time series data and the second time series data are decomposed into vectors, and the degree of match is sequentially calculated,
The final matching degree when the first time series data and the second time series data are overlapped is calculated by accumulating the degree of matching calculated sequentially.
Power display device. The degree of match calculation unit
When the first and second time-series data are divided into a plurality of periods , a representative value vector whose element is a representative value of the data of each period, and a fluctuation whose element is a fluctuation value of the data of each period Convert to a value vector,
An addition value of the first correlation value between the representative value vectors and the second correlation value between the variation value vectors is obtained, and the first time-series data and the second time are calculated based on the addition value. Calculate the degree of match when the series data is overlaid ,
The power display device according to claim 1. The degree of match calculation unit
The weight of the first correlation value and the weight of the second correlation value at the time of obtaining the addition value can be adjusted.
The power display device according to claim 2. Before Symbol The degree-of-match calculating unit,
While changing the length of each period, the first time series data and the second time series data are decomposed into the representative value vector and the variation value vector, and the addition value is sequentially calculated,
An integrated value of each added value calculated sequentially is calculated, and based on the calculated integrated value, a degree of coincidence when the first time series data and the second time series data are overlaid is calculated.
The power display device according to claim 3. The degree of match calculation unit
Changing the length of each period to an integer multiple;
The power display device according to claim 4. The degree of match calculation unit
It is possible to adjust the weight of each addition value when integrating the addition value ,
The power display device according to claim 4 or 5. The degree of match calculation unit
Calculating the inner product of the representative value vectors as the first correlation value;
An inner product between the variation value vectors is calculated as the second correlation value.
The power display device according to claim 2, wherein the power display device is a power display device. The degree of match calculation unit
Calculating a difference between the representative value vectors as the first correlation value;
Calculating a difference between the variation value vectors as the second correlation value;
The power display device according to claim 2, wherein the power display device is a power display device. The data acquired by the data acquisition unit includes a plurality of types of data,
The degree of match calculation unit
For each piece of data, calculate the degree of match,
A weighted sum of the calculated degrees of match is calculated as a final match.
The power display device according to claim 1, wherein the power display device is a power display device. A pattern recording unit for recording the third time series data with a typical waveform patterns in data acquired by the data acquisition unit,
Enter the time series data of the first from the recording unit, the type of time-series data of the third from the pattern recording unit, the first time series data, the third time series data and the respective When the first time-series data and the third time-series data are overlapped by converting the values based on the data of each period when divided into a plurality of periods into vectors having elements and comparing them. A pattern processing unit for calculating the degree of match of
Further comprising
The specific part is:
The third time series data having the highest degree of match with the first time series data calculated by the pattern processing unit is specified as a comparison target.
The power display device according to claim 1, wherein the power display device is a power display device. The display device
A display device for displaying the first time-series data and the third time-series data in a superimposed manner at a specified reference time point;
Power display device according to claim 1 0, characterized in that. A data acquisition process in which the computer acquires data on the usage status of the electrical equipment;
Computer, a recording step of recording the time-series data as a series of data acquired in the data acquisition process in time series,
A computer inputs first time series data of a recent fixed period recorded in the recording step, and a reference time point before the first time series data recorded in the recording step is used as a reference. the enter the second time series data over a period of time, said first time series data, and said second time-series data, the based on the data of each period when each divided into a plurality of periods A process of calculating a degree of coincidence when the first time-series data and the second time-series data are overlapped by converting to a vector having values as elements, and comparing the first time-series data and the second time-series data. A degree-of-match calculation step that is repeatedly performed while shifting the reference time point of the data ;
A specifying step in which the computer specifies the highest reference time from among the reference time points corresponding to each of the plurality of match degrees calculated in the match degree calculation step;
The computer reads the time-series data of the fixed period based on the reference time point specified in the specifying step and the latest time-series data of the fixed period from the recording unit, and the read two time-series data A display control process for generating an image for comparison display;
A display step in which the computer displays the image output in the display control step;
Only including,
In the matching degree calculation step,
While changing the length of each period, the first time series data and the second time series data are decomposed into vectors, and the degree of match is sequentially calculated,
The final matching degree when the first time series data and the second time series data are overlapped is calculated by accumulating the degree of matching calculated sequentially.
Power display method. A data acquisition procedure for acquiring data on the usage status of electrical equipment,
Recording procedure for recording the data acquired by the data acquisition procedure in time series and recording as time series data;
The first time-series data recorded in the recording procedure for the most recent fixed period is input, and the second of the fixed period based on the reference time point before the first time-series data by the recording procedure . To the vector having the values based on the data of each period when the first time series data and the second time series data are each divided into a plurality of periods as elements. A process of calculating the degree of coincidence when the first time-series data and the second time-series data are overlaid by converting and comparing is performed by shifting the reference time point of the second time-series data. While repeating the matching degree calculation procedure ,
A specifying procedure for specifying the highest reference time from among the reference time points corresponding to each of the plurality of match degrees calculated in the match degree calculation procedure;
Read the time-series data of the fixed period based on the reference time point specified by the specifying procedure and the latest time-series data of the fixed period from the recording unit, and compare and display the read two time-series data Display control procedure for generating an image to be
A display procedure for displaying an image output from the display control unit;
To the computer,
In the match degree calculation procedure,
While changing the length of each period, the first time series data and the second time series data are decomposed into vectors, and the degree of match is sequentially calculated,
A procedure for calculating a final degree of coincidence when the first time-series data and the second time-series data are superimposed by accumulating the degree of coincidence calculated sequentially,
A program that causes a computer to execute.

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