CN115660445A - Power consumption early warning method and device and computer equipment - Google Patents
Power consumption early warning method and device and computer equipment Download PDFInfo
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Abstract
The application discloses a power consumption early warning method and device and computer equipment, and relates to the technical field of electric power. The method comprises the following steps: determining a target area and a target date of the power consumption risk to be detected; when the target date is in the peak electricity utilization month of the target area, target data are obtained; the target data includes: the predicted monthly power consumption and the highest-temperature and lowest-temperature difference value corresponding to the target date, and the daily power consumption maximum value and the daily power consumption minimum value corresponding to the month; calculating the power consumption fluctuation upper bound value and the power consumption fluctuation lower bound value of the target area on the target date based on the target data; and when the electricity consumption is greater than or equal to the upper bound value or less than or equal to the lower bound value, prompting that the electricity consumption is abnormal in the target area. Therefore, the power consumption of the target area can be warned, and the measures such as peak shifting production, peak avoiding production and the like can be flexibly adjusted based on the warning. Further, the demand and safety of power consumption in the target area can be ensured.
Description
Technical Field
The embodiment of the application relates to the technical field of electric power, in particular to a power consumption early warning method, a power consumption early warning device and computer equipment.
Background
At present, under the influence of factors such as high temperature, the power supply is in short supply, and even the risk of large-area power failure exists. In order to relieve the shortage of power supply, various places take a series of measures such as peak shifting, peak avoiding, alternate break, power giving, negative control and power limiting.
However, many industrial parks still suffer from irregular power usage. For example, some industrial parks still use a large amount of electricity during peak electricity utilization periods, resulting in extremely high electricity utilization safety hazards. And other industrial parks use too little electricity during peak electricity periods and are allocated more electricity, resulting in no electricity being available to other industrial parks.
Therefore, how to establish a power consumption early warning scheme becomes a technical problem which needs to be solved urgently. The power consumption of the industrial park is warned based on the power consumption early warning scheme, so that the measures of peak shifting production, peak avoiding production and the like can be flexibly adjusted based on the warning. Furthermore, the power demand and the power utilization safety of the industrial park can be ensured.
Disclosure of Invention
The embodiment of the application provides a power consumption early warning method and device and computer equipment. The technical scheme is as follows:
according to an aspect of the embodiments of the present application, a power consumption early warning method is provided, which may include the steps of:
determining a target area and a target date of the power consumption risk to be detected;
when the target date is in the peak electricity month of the target area, target data are obtained; the target data includes: the predicted monthly power consumption and the highest-temperature and lowest-temperature difference value corresponding to the target date, and the daily power consumption maximum value and the daily power consumption minimum value corresponding to the month;
calculating an upper bound value and a lower bound value of the fluctuation of the electricity consumption of the target area on the target date based on the target data;
and when the electricity consumption is greater than or equal to the upper bound value or less than or equal to the lower bound value, prompting that the electricity consumption abnormality exists in the target area.
Alternatively, the step of calculating the upper and lower bounds of fluctuation of the electricity usage of the target area on the target date based on the target data may include:
judging whether the monthly electricity consumption trend score corresponding to the target date is greater than a monthly electricity consumption trend threshold value or not;
when the current power consumption trend is greater than the monthly power consumption trend threshold, calculating a power consumption fluctuation upper bound value and a power consumption fluctuation lower bound value of the target date by using a first formula group;
and when the current value is less than the negative value of the monthly electricity utilization trend threshold value, calculating the electricity utilization fluctuation upper bound value and the electricity utilization fluctuation lower bound value of the target date by using a second formula group.
Optionally, the first formula set may include:
the second set of equations may include:
the device is
Is the upper bound value of
Is the lower bound value of
Is a second correction constant, the
The monthly power consumption of the jth month, the
Is at a maximum value of
Is at a minimum value of
The number of days of the j month
Is the difference between the highest temperature and the lowest temperature, the
Is the maximum daily power in the j-th month, the
The minimum value of the daily electricity consumption in the j month.
Optionally, in an embodiment of the present application, the method may further include:
when the monthly power utilization trend score is judged to be larger than the monthly power utilization trend threshold value, judging that the monthly power utilization requirement rises;
and when the monthly power utilization trend score is judged to be smaller than the negative numerical value of the monthly power utilization trend threshold, judging that the monthly power utilization requirement is reduced.
Optionally, in this embodiment of the present application, the monthly electricity usage trend score is calculated by the following formula:
the device is
A power consumption trend score of j months, which
For the years, the
Is a first
In the course of the year, the user can select the required time,
is the power consumption vector of the ith year and the jth month.
Optionally, in this embodiment of the present application, the method may further include:
when the target date is not in the electricity consumption peak month of the target area, acquiring the daily electricity consumption of the target area on the target date;
judging whether the daily electric quantity is larger than a first calculated value or not through an abnormal electricity consumption decision inequality; the first calculation value is calculated based on the highest temperature and lowest temperature difference value and the maximum value of the daily electricity quantity;
if the calculated value is larger than the first calculated value, the target area is prompted to have abnormal electricity consumption.
Optionally, in this embodiment of the present application, the power consumption anomaly decision inequality may include:
the
The daily power of the target area on the target date, the
Is a sixth threshold value of
Is the maximum daily power in the j-th month, the
Is the highest temperature and lowest temperature difference.
Optionally, in an embodiment of the present application, the method may further include:
calculating the similarity of annual power consumption of two continuous years based on the historical monthly power consumption vector of the target area;
calculating an average electricity consumption similarity score according to the electricity consumption similarity of the year;
when the average electricity utilization similarity score is larger than a first threshold value, determining that periodicity exists in electricity utilization of the target area;
when the electricity consumption of the target area is periodic, calculating the electricity consumption fluctuation score of the target area;
when the power consumption fluctuation score is larger than a second threshold value, determining that the target area power consumption exists in a power consumption peak month;
when the electricity consumption peak month exists, judging whether the target date is in the electricity consumption peak month of the target area or not through an electricity consumption peak month judgment inequality;
wherein, the calculation formula of the annual power consumption similarity of two consecutive years comprises:
the calculation formula of the average electricity utilization similarity score comprises the following steps:
the calculation formula of the electricity fluctuation score comprises the following steps:
the inequality for judging the peak month of electricity consumption comprises the following steps:
wherein ,
wherein, the
The similarity of annual power consumption between the i-th year and the i + 1-th year
To average electricity utilization similarity score, the
Scoring the electricity consumption fluctuation of the ith year, the
Is the maximum value of the average monthly power consumption corresponding to the target area
The average value of the monthly power consumption corresponding to the target area is obtained;
is the electricity consumption vector of 1 month in the ith year,
a vector of electricity usage for 12 months of the ith year,
represents the power consumption vector of j months of the ith year
For the number of years, the
Is a first correction constant.
According to another aspect of the embodiments of the present application, there is provided a power usage amount warning apparatus, which may include:
the first determining module is used for determining a target area and a target date of the power consumption risk to be detected;
the first acquisition module is used for acquiring target data when the target date is in the peak electricity month of the target area; the target data includes: the predicted monthly power consumption and the highest-temperature and lowest-temperature difference value corresponding to the target date, and the daily power consumption maximum value and the daily power consumption minimum value corresponding to the month;
the calculation module is used for calculating the fluctuation upper bound value and the fluctuation lower bound value of the electricity consumption of the target area on the target date based on the target data;
and the first prompting module is used for prompting that the target area has abnormal electricity consumption when the electricity consumption is greater than or equal to the upper bound value or less than or equal to the lower bound value.
Optionally, in this embodiment of the present application, the calculation module may specifically be configured to:
judging whether the monthly electricity consumption trend score corresponding to the target date is greater than a monthly electricity consumption trend threshold value or not;
when the current date is greater than the monthly electricity trend threshold value, calculating an electricity consumption fluctuation upper bound value and an electricity consumption fluctuation lower bound value of the target date by using a first formula group;
and when the current value is less than the negative value of the monthly electricity utilization trend threshold value, calculating the electricity utilization fluctuation upper bound value and the electricity utilization fluctuation lower bound value of the target date by using a second formula group.
Optionally, the first formula set may include:
the second set of equations may include:
the device is
Is the upper bound value of
Is the lower bound value of
Is a second correction constant, the
Is the monthly power consumption of month j, the
Is at a maximum value of
Is a minimum value of
The number of days of the j month
Is the difference between the highest temperature and the lowest temperature, the
Is the maximum daily power in the j-th month, the
The minimum value of the daily electricity consumption in the j month.
Optionally, in this embodiment of the present application, the apparatus may further include a determination module; the determination module is configured to:
when the monthly power utilization trend score is judged to be larger than the monthly power utilization trend threshold value, judging that the monthly power utilization requirement rises;
and when the monthly power utilization trend score is judged to be smaller than the negative numerical value of the monthly power utilization trend threshold, judging that the monthly power utilization requirement is reduced.
Optionally, in this embodiment of the present application, the monthly electricity usage trend score is calculated by the following formula:
the device is
A power usage trend score of j months, which
For the years, the
Is a first
The time of the year is as follows,
is a vector of power usage for month j of year i.
Optionally, in this embodiment of the present application, the method may further include:
the second acquisition module is used for acquiring the daily electric quantity of the target area on the target date when the target date is not in the peak electricity utilization month of the target area;
the judging module is used for judging whether the daily electric quantity is larger than a first calculated value or not through an abnormal electricity consumption decision inequality; the first calculation value is calculated based on the highest temperature and lowest temperature difference value and the maximum value of the daily electricity quantity;
and the second prompting module is used for prompting that the power consumption in the target area is abnormal if the power consumption is larger than the first calculated value.
Optionally, in this embodiment of the present application, the power consumption anomaly decision inequality may include:
the device is
The daily power of the target area on the target date, the
Is a sixth threshold value of
Is the maximum daily power in the j-th month, the
Is the highest temperature and lowest temperature difference.
Optionally, in this embodiment of the present application, the apparatus may further include: an electricity peak month determination module to:
calculating the similarity of annual power consumption of two continuous years based on the historical monthly power consumption vector of the target area;
calculating an average electricity consumption similarity score according to the electricity consumption similarity of the year;
when the average electricity utilization similarity score is larger than a first threshold value, determining that periodicity exists in electricity utilization of the target area;
when the electricity consumption of the target area is periodic, calculating the electricity consumption fluctuation score of the target area;
when the electricity consumption fluctuation score is larger than a second threshold value, determining that the electricity consumption peak month exists in the target area;
when the electricity consumption peak month exists, judging whether the target date is in the electricity consumption peak month of the target area or not through an electricity consumption peak month judgment inequality;
wherein, the calculation formula of the annual power consumption similarity of two consecutive years comprises:
the calculation formula of the average electricity utilization similarity score comprises the following steps:
the calculation formula of the electricity fluctuation score comprises the following steps:
the inequality is judged in the peak month of power consumption and includes:
wherein ,
wherein, the
The similarity of annual power consumption between the i-th year and the i + 1-th year
To average electricity utilization similarity score, the
Scoring the electricity consumption fluctuation of the ith year, the
Is the maximum value of the average monthly power consumption corresponding to the target area
The average value of the monthly power consumption corresponding to the target area is obtained;
as a vector of electricity usage for 1 month of the ith year,
a vector of electricity usage for 12 months of the ith year,
represents the power consumption vector of j months of the ith year
For the number of years, the
Is a first correction constant.
According to still another aspect of embodiments of the present application, there is provided a computer device, which includes a processor and a memory, where the memory stores therein a computer program, and the computer program is loaded and executed by the processor to implement any one of the above electricity consumption warning methods.
According to a further aspect of embodiments of the present application, there is provided a computer-readable storage medium having a computer program stored therein, the computer program being loaded and executed by a processor to implement any one of the above power usage early warning methods.
According to yet another aspect of embodiments herein, there is provided a computer program product comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer readable storage medium, and executes the computer instructions, so that the computer device executes any one of the above electricity consumption early warning methods.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
by applying the power consumption early warning method provided by the embodiment of the application, the target area and the target date of the power consumption risk to be detected can be determined. Wherein the target data may be obtained when the target date is in the peak electricity month of the target zone. Wherein the target data may include: and the predicted monthly power consumption and the highest-temperature and lowest-temperature difference value corresponding to the target date, and the daily power consumption maximum value and the daily power consumption minimum value corresponding to the month. Then, based on the target data, the electricity usage fluctuation upper and lower bound values of the target area at the target date may be calculated. When the electricity consumption is greater than or equal to the upper bound value or less than or equal to the lower bound value, the abnormality of the electricity consumption in the target area can be prompted. Therefore, the power consumption of the target area can be warned, and measures such as peak shifting production, peak avoiding production and the like can be flexibly adjusted based on the warning. Further, the demand and safety of power consumption in the target area can be ensured.
Drawings
Fig. 1 is a flowchart of a power consumption early warning method according to an embodiment of the present disclosure;
fig. 2 is a block diagram of a power consumption early warning device according to an embodiment of the present disclosure;
fig. 3 is a block diagram of a computer device according to an embodiment of the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
At present, the phenomenon of irregular power utilization still exists in many industrial parks. For example, some industrial parks still use a large amount of electricity during peak electricity utilization periods, resulting in extremely high electricity utilization safety hazards. While other industrial parks may use too little electricity during peak electricity periods and may be allocated more electricity, resulting in other industrial parks being unavailable.
In order to solve the above technical problem, in an embodiment of the present application, a power consumption early warning method is provided, where the method includes:
determining a target area and a target date of the power consumption risk to be detected;
when the target date is in the peak electricity month of the target area, target data are obtained; the target data includes: the predicted monthly power consumption and the highest-temperature and lowest-temperature difference value corresponding to the target date, and the daily power consumption maximum value and the daily power consumption minimum value corresponding to the month;
calculating the fluctuation upper bound value and the fluctuation lower bound value of the electricity consumption of the target area on the target date based on the target data;
and when the electricity consumption is more than or equal to the upper limit value or less than or equal to the lower limit value, prompting that the electricity consumption abnormality exists in the target area.
By applying the power consumption early warning method provided by the embodiment of the application, the target area and the target date of the power consumption risk to be detected can be determined. Wherein the target data may be obtained when the target date is in the peak electricity month of the target zone. Wherein the target data may include: and the predicted monthly power consumption and the highest and lowest temperature difference corresponding to the target date, and the daily power consumption maximum value and the daily power consumption minimum value corresponding to the month. Then, based on the target data, the electricity usage fluctuation upper and lower bound values of the target area at the target date may be calculated. When the electricity consumption is greater than or equal to the upper bound value or less than or equal to the lower bound value, the fact that the electricity consumption is abnormal in the target area can be prompted. Therefore, the power consumption of the target area can be warned, and measures such as peak shifting production, peak avoiding production and the like can be flexibly adjusted based on the warning. Further, the demand and safety of power consumption in the target area can be ensured.
A power consumption early warning method provided in the embodiment of the present application is described below with reference to fig. 1. Fig. 1 is a flowchart of a power consumption early warning method according to an embodiment of the present application. Referring to fig. 1, the method may include the steps of:
s101: determining a target area and a target date of the power consumption risk to be detected;
for example, the target area to be detected with the electricity utilization risk is determined to be an industrial park a, and the target date is 10 months and 11 days in 2022.
S102: when the target date is in the peak electricity month of the target area, target data are obtained; the target data includes: the predicted monthly power consumption and the highest-temperature and lowest-temperature difference value corresponding to the target date, and the daily power consumption maximum value and the daily power consumption minimum value corresponding to the month;
wherein, when the month 10 is the peak month of electricity consumption of the industrial park A, the month 10 and the day 11 in the year 2022 are the peak month of electricity consumption of the industrial park A. At this time, target data: and predicting the obtained monthly power consumption of 10 months, the highest temperature and lowest temperature difference value corresponding to 10 months and 11 days, the maximum value of the daily power consumption corresponding to 10 months and the minimum value of the daily power consumption.
For the industrial park A, the number of days of 10 months can be acquired for 30 days, the daily electricity consumption of 10 months and 11 months in 2022 years of the park and the temperature at 12 noon can be acquired
。
If the electricity consumption of the park is influenced by the temperature, the electricity consumption can be based on the historical 10 months and 11 days of the industrial park A
If it satisfies
Then the difference between the maximum value and the minimum value of the daily electricity quantity of the historical day can be obtained
，
Is the set fifth threshold.
Then, for the difference between the maximum value and the minimum value of the daily electric quantity of the day every year, a difference value prediction model obtained based on LSTM (Long Short-Term Memory) training is used, and the difference value obtained through prediction is used as the highest temperature and lowest temperature difference value
. If not satisfied with
Let us order
. Thus, the maximum temperature and minimum temperature difference corresponding to 10 months and 11 days can be obtained.
Wherein, the peak month of electricity consumption of the industrial park A can be determined by:
collecting the data of the electric meter record of the industrial park A for n years continuously, wherein n is a set collection constant (years), the data of the electric meter record can be accurate to the daily electric quantity of the park, and correspond to the weather temperature of 12 noon every day, and the data of the electric quantity used for 12 months every year obtained by counting the park form aAnnual power consumption vector
Wherein i represents the number of years,
representing the power consumption vector of 1 month in the ith year, and then calculating the power consumption vector of two consecutive years according to the cosine similarity
And
the similarity of the power consumption is calculated according to the similarity of the annual power consumption in two consecutive years:
then, an average electricity consumption similarity score can be calculated according to the obtained annual electricity consumption similarity:
when average power utilization similarity score
Greater than a set first threshold
And judging that the electricity utilization condition of the park has periodicity. Wherein the content of the first and second substances,
and calculating the average power utilization similarity score of the park for collected park data with periodic power utilization conditions, and determining the infimum of the score according to the distribution of the score to obtain the park data.
When the electricity utilization condition of the park is periodic, calculating the electricity utilization fluctuation score of the park:
when the park electricity utilization fluctuation score is larger than a set second threshold value
And judging that the park has peak electricity months. Wherein
Calculating the power consumption fluctuation score for the park data of the month with the peak power consumption through manual marking, and determining the infimum limit of the score according to the distribution of the score to obtain the threshold value. When the electricity peak month exists in the park, extracting the maximum value of the average electricity consumption of the park month:
and, average monthly electricity usage in the campus:
average power consumption per month
When the following inequality is satisfied, the month is judged as the peak electricity month. Wherein
And training the obtained first correction constant for the historical data. This captures the peak months of electricity usage for the detected campus.
wherein ,
the similarity of the annual power consumption of the ith year and the (i + 1) th year,
in order to average the electricity usage similarity score,
the electricity consumption fluctuation score of the ith year,
the maximum value of the average monthly power consumption corresponding to the target area,
the average value of the monthly power consumption corresponding to the target area is taken; the above-mentioned
As a vector of power consumption for 1 month of the ith year, the
A vector of electricity usage for 12 months of the ith year,
a vector representing the amount of power used in j months of the ith year,
in the form of a number of years,
is a first correction constant.
Wherein, whether the power consumption of the industrial park is influenced by the temperature can be determined by the following modes:
the method comprises the following steps of extracting daily electricity consumption data and temperature data of weather above 35 ℃ every month and daily electricity consumption data and temperature data of weather below zero in the industrial park A, and respectively forming two types of data (namely the daily electricity consumption data and the temperature data) into two pairs of vectors:
wherein ,
as a first daily power usage vector,
is the second daily power vector. The lengths of the two pairs of vectors are respectively
J denotes month and i denotes year. Wherein the temperature dependence can be calculated:
wherein ,
in the form of a first temperature vector, the temperature vector,
is a second temperature vector. When the vector length is 0, the temperature dependency is 0.
Then, the temperature average influence score can be recalculated:
average effect score when temperature
Is greater than a set third threshold
And in time, judging that the power consumption of the park is influenced by high temperature. Average influence score when temperature
Is greater than a set third threshold
And in time, judging that the power consumption of the park is influenced by low temperature. And when the park power consumption is influenced by high temperature or low temperature, judging that the park power consumption is influenced by temperature.
S103: calculating the fluctuation upper bound value and the fluctuation lower bound value of the electricity consumption of the target area on the target date based on the target data;
in an implementation manner, step S103 may specifically be:
judging whether the monthly electricity utilization trend score corresponding to the target date is larger than a monthly electricity utilization trend threshold value or not;
when the current date is greater than the monthly electricity trend threshold value, calculating an electricity consumption fluctuation upper bound value and an electricity consumption fluctuation lower bound value of the target date by using a first formula group;
and when the current value is less than the negative value of the monthly electricity utilization trend threshold value, calculating the electricity utilization fluctuation upper bound value and the electricity utilization fluctuation lower bound value of the target date by using a second formula group.
Wherein the first formula set may include:
the second set of equations may include:
the device is
Is the upper bound value of
Is the lower bound value of
Is a second correction constant, the
The monthly power consumption of the jth month, the
Is at a maximum value of
Is at a minimum value of
The number of days of the j month
Is the difference between the highest temperature and the lowest temperature, the
Is the maximum daily power in the j-th month, the
The minimum value of the daily electricity consumption in the j month.
Therefore, the calculation formulas for calculating the fluctuation upper bound value and the fluctuation lower bound value of the power consumption can be determined based on the monthly power consumption trend score, so that different formulas are used for calculating aiming at different trend scores, and the calculation results of the fluctuation upper bound value and the fluctuation lower bound value of the power consumption are more fit for the actual situation and are more accurate.
And when the monthly power utilization trend score is judged to be larger than the monthly power utilization trend threshold value, judging that the monthly power utilization demand rises. At this time, the trend of power usage of the campus can be determined to be rising. And when the monthly power utilization trend score is judged to be smaller than the negative numerical value of the monthly power utilization trend threshold, judging that the monthly power utilization requirement is reduced. At this time, the power usage trend of the campus, which is a decline, can be determined.
And when the monthly electricity utilization trend score corresponding to the target date does not belong to the two conditions, judging that the electricity utilization requirement of the month is not changed.
The calculation formula of the monthly power consumption trend score is as follows:
the device is
A power consumption trend score of j months, which
For the years, the
Is as follows
In the course of the year, the user can select the required time,
is a vector of power usage for month j of year i.
S104: and when the electricity consumption is more than or equal to the upper limit value or less than or equal to the lower limit value, prompting that the electricity consumption abnormality exists in the target area.
When the electricity consumption is larger than or equal to the upper limit value, the electricity consumption of the target area is shown to exceed or be equal to the upper line value of the normal electricity consumption. Under the condition, measures such as peak shifting production or power limitation can be taken, so that the power utilization safety of the park is ensured. And when the electricity consumption is less than or equal to the lower bound, the electricity consumption is less than or equal to the offline value of the normal electricity consumption. Under this kind of circumstances, can carry out the power scheduling, with the power scheduling to the district that the power consumption is in short supply, realize the rational distribution of electric power.
It is understood that in these cases, which indicate that the power usage is risky (a safety risk of power usage or a risk of unreasonable allocation of power resources), a risk alert may be issued. The prompt may be a prompt by sending a short message, or a prompt by sending an alarm sound, which is not limited to this, and is not limited to this.
By applying the power consumption early warning method provided by the embodiment of the application, the target area and the target date of the power consumption risk to be detected can be determined. Wherein the target data may be acquired when the target date is in the peak electricity month of the target zone. Wherein the target data may include: and the predicted monthly power consumption and the highest and lowest temperature difference corresponding to the target date, and the daily power consumption maximum value and the daily power consumption minimum value corresponding to the month. Then, based on the target data, the electricity usage fluctuation upper and lower bound values of the target area at the target date may be calculated. When the electricity consumption is greater than or equal to the upper bound value or less than or equal to the lower bound value, the fact that the electricity consumption is abnormal in the target area can be prompted. Therefore, the power consumption of the target area can be warned, and measures such as peak shifting production, peak avoiding production and the like can be flexibly adjusted based on the warning. Further, the demand and safety of power consumption in the target area can be ensured.
Optionally, when the target date is not in the peak electricity month of the target area, obtaining the daily electricity consumption of the target area on the target date;
judging whether the daily electric quantity is larger than a first calculated value or not through a power consumption abnormity decision inequality; the first calculation value is calculated based on the highest temperature and lowest temperature difference value and the maximum value of the daily electricity quantity;
if the calculated value is larger than the first calculated value, the target area is prompted to have abnormal electricity consumption.
Wherein, the power consumption abnormal decision inequality may include:
the
The daily power of the target area on the target date, the
Is a sixth threshold value of
Is the maximum daily power in the j-th month, the
Is the highest temperature and lowest temperature difference.
For example, when 10 months is not the peak electricity month of industrial park a, 10 months and 11 days in 2022 are in the peak non-electricity month of industrial park a. At this time, it can be determined whether the power consumption abnormal decision inequality is true, that is, whether the daily power is greater than a first calculation value, where the first calculation value is: the sixth threshold value is multiplied by the maximum daily electricity consumption value in the j-th month + the predicted maximum daily electricity consumption value corresponding to the month. If the daily electricity consumption is larger than the first calculated value, the daily electricity consumption is greatly increased compared with the maximum daily electricity consumption, and the fact that the electricity consumption abnormality exists in the industrial park A is prompted. Therefore, the measures such as peak shifting production or electricity limitation can be taken, and the electricity safety of the park is ensured.
According to another aspect of the embodiment of the application, a power consumption early warning device is provided. Fig. 2 is a block diagram of a power consumption early warning device according to an embodiment of the present application. Referring to fig. 2, the apparatus may include:
the first determining module 201 is configured to determine a target area and a target date of the power consumption risk to be detected;
a first obtaining module 202, configured to obtain target data when the target date is in the peak electricity month of the target area; the target data includes: the predicted monthly power consumption and the highest-temperature and lowest-temperature difference value corresponding to the target date, and the daily power consumption maximum value and the daily power consumption minimum value corresponding to the month;
a calculating module 203, configured to calculate, based on the target data, an upper bound and a lower bound of fluctuation in electricity consumption of the target area on the target date;
the first prompting module 204 is configured to prompt that the power consumption is abnormal in the target area when the power consumption is greater than or equal to the upper bound value or less than or equal to the lower bound value.
Optionally, in this embodiment of the present application, the calculating module 203 may specifically be configured to:
judging whether the monthly electricity utilization trend score corresponding to the target date is larger than a monthly electricity utilization trend threshold value or not;
when the current power consumption trend is greater than the monthly power consumption trend threshold, calculating a power consumption fluctuation upper bound value and a power consumption fluctuation lower bound value of the target date by using a first formula group;
and when the current value is less than the negative value of the monthly electricity utilization trend threshold value, calculating the electricity utilization fluctuation upper bound value and the electricity utilization fluctuation lower bound value of the target date by using a second formula group.
Optionally, the first formula set may include:
the second set of equations may include:
the device is
Is the upper bound value of
Is the lower bound value of
Is a second correction constant, the
The monthly power consumption of the jth month, the
Is at a maximum value of
Is at a minimum value of
The number of days of the j month
Is the difference between the highest temperature and the lowest temperature, the
Is the maximum daily power in the j-th month, the
Is the minimum value of daily electricity in the j-th month.
Optionally, in this embodiment of the present application, the apparatus may further include a determination module; the determination module is configured to:
when the monthly power utilization trend score is judged to be larger than the monthly power utilization trend threshold value, judging that the monthly power utilization requirement rises;
and when the monthly power utilization trend score is judged to be smaller than the negative numerical value of the monthly power utilization trend threshold, judging that the monthly power utilization requirement is reduced.
Optionally, in this embodiment of the present application, the monthly electricity usage trend score is calculated by the following formula:
the
A power consumption trend score of j months, which
For the number of years, the
Is as follows
In the course of the year, the user can select the required time,
is the power consumption vector of the ith year and the jth month.
Optionally, in this embodiment of the present application, the method may further include:
the second acquisition module is used for acquiring the daily electric quantity of the target area on the target date when the target date is not in the peak electricity utilization month of the target area;
the judging module is used for judging whether the daily electric quantity is larger than a first calculated value or not through an abnormal electricity consumption decision inequality; the first calculation value is calculated based on the highest temperature and lowest temperature difference value and the maximum value of the daily electricity quantity;
and the second prompting module is used for prompting that the target area has abnormal electricity consumption if the current value is larger than the first calculated value.
Optionally, in this embodiment of the present application, the power consumption anomaly decision inequality may include:
the
Daily power consumption of the target area on the target date, the
Is a sixth threshold value of
Is the maximum daily power in the j-th month, the
Is the highest temperature and lowest temperature difference.
Optionally, in this embodiment of the present application, the apparatus may further include: an electricity peak month determination module to:
calculating the similarity of annual power consumption of two continuous years based on the historical monthly power consumption vector of the target area;
calculating an average electricity consumption similarity score according to the electricity consumption similarity of the year;
when the average electricity utilization similarity score is larger than a first threshold value, determining that periodicity exists in electricity utilization of the target area;
when the target area electricity utilization is periodic, calculating the electricity utilization fluctuation score of the target area;
when the electricity consumption fluctuation score is larger than a second threshold value, determining that the electricity consumption peak month exists in the target area;
when the electricity consumption peak month exists, judging whether the target date is in the electricity consumption peak month of the target area or not through an electricity consumption peak month judgment inequality;
wherein, the calculation formula of the annual power consumption similarity of two consecutive years comprises:
the calculation formula of the average electricity utilization similarity score comprises the following steps:
the calculation formula of the electricity fluctuation score comprises the following steps:
the inequality is judged in the peak month of power consumption and includes:
wherein ,
wherein, the
The similarity of annual power consumption between the i-th year and the i + 1-th year
To average electricity utilization similarity score, the
Scoring the electricity consumption fluctuation of the ith year, the
Is the maximum value of the average monthly power consumption corresponding to the target area
The average value of the monthly power consumption corresponding to the target area is obtained;
is the electricity consumption vector of 1 month in the ith year,
a vector of electricity usage for 12 months of the ith year,
represents the power consumption vector of j months of the ith year
For the number of years, the
Is a first correction constant.
It should be noted that, when the apparatus provided in the foregoing embodiment implements the functions thereof, only the division of the functional modules is illustrated, and in practical applications, the functions may be distributed by different functional modules according to needs, that is, the internal structure of the apparatus may be divided into different functional modules to implement all or part of the functions described above. In addition, the apparatus and method embodiments provided by the above embodiments belong to the same concept, and specific implementation processes thereof are described in the method embodiments for details, which are not described herein again.
According to still another aspect of embodiments of the present application, there is provided a computer-readable storage medium, in which a computer program is stored, and the computer program is loaded and executed by a processor to implement any one of the above electricity consumption early warning methods.
Optionally, the computer-readable storage medium may include: ROM (Read-Only Memory), RAM (Random Access Memory), SSD (Solid State drive), or optical disc. The Random Access Memory may include a ReRAM (resistive Random Access Memory) and a DRAM (Dynamic Random Access Memory), among others
According to yet another aspect of embodiments herein, there is provided a computer program product comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer readable storage medium, and executes the computer instructions, so that the computer device executes any one of the above electricity consumption early warning methods.
An embodiment of the present application further provides a computer device, and referring to fig. 3, fig. 3 is a block diagram of a structure of the computer device provided in an embodiment of the present application. The computer device comprises a processor 301 and a memory 302, wherein the memory 302 stores a computer program, and the computer program is loaded and executed by the processor 301 to realize any one of the above power consumption early warning methods.
In addition, the computer apparatus typically includes: a processor 301 and a memory 302.
The processor 301 may include one or more processing cores, such as a 4-core processor, a 17-core processor, and so on. The processor 301 may be implemented in at least one hardware form of a DSP (Digital Signal Processing), an FPGA (Field Programmable Gate Array), and a PLA (Programmable Logic Array). The processor 301 may also include a main processor and a coprocessor, where the main processor is a processor for Processing data in an awake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 301 may be integrated with a GPU, which is responsible for rendering and drawing the content that the display screen needs to display. In some embodiments, the processor 301 may further include an AI (Artificial Intelligence) processor for processing computing operations related to machine learning.
Those skilled in the art will appreciate that the configuration shown in FIG. 3 is not intended to be limiting of computer devices and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components may be used.
It should be understood that reference herein to "a plurality" means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. In addition, the step numbers described herein only show an exemplary possible execution sequence among the steps, and in some other embodiments, the steps may also be executed out of the numbering sequence, for example, two steps with different numbers are executed simultaneously, or two steps with different numbers are executed in a reverse order to the illustrated sequence, which is not limited in this application. The above embodiments may also be combined arbitrarily, and the combination scheme is not described herein again.
The above description is only exemplary of the present application and should not be taken as limiting the present application, and any modifications, equivalents, improvements and the like that are made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (10)
1. A power consumption early warning method is characterized by comprising the following steps:
determining a target area and a target date of the power consumption risk to be detected;
when the target date is in the peak electricity month of the target area, target data are obtained; the target data includes: the predicted monthly power consumption and the highest-temperature and lowest-temperature difference value corresponding to the target date as well as the daily power consumption maximum value and the daily power consumption minimum value corresponding to the month;
calculating a power consumption fluctuation upper bound value and a power consumption fluctuation lower bound value of the target area on the target date based on the target data;
and when the electricity consumption is more than or equal to the upper bound value or less than or equal to the lower bound value, prompting that the electricity consumption abnormality exists in the target area.
2. The method of claim 1, wherein calculating an upper bound and a lower bound for power usage fluctuations for the target area on the target date based on the target data comprises:
judging whether the monthly electricity consumption trend score corresponding to the target date is larger than a monthly electricity consumption trend threshold value or not;
when the current power consumption trend is larger than the monthly power consumption trend threshold value, calculating a power consumption fluctuation upper bound value and a power consumption fluctuation lower bound value of the target date by using a first formula group;
and when the current value is less than the negative value of the monthly electricity utilization trend threshold value, calculating an upper bound value and a lower bound value of electricity utilization fluctuation of the target date by using a second formula group.
3. The method of claim 2, wherein the first set of equations comprises:
the second set of equations includes:
wherein ,
in order to be the upper bound value, the value of the upper bound value,
in order to be the lower-bound value,
is a second correction constant, ep j The monthly power usage for the jth month,
is the maximum value of the number of the optical fibers,
is the minimum value of the number of the optical fibers,
is the number of days of the j-th month,
is the maximum temperature and minimum temperature difference, emax j Is the maximum value of daily electricity consumption in the jth month, emin j Is the minimum value of the daily electric quantity in the j month.
4. The method of claim 2, further comprising:
when the monthly power utilization trend score is judged to be larger than the monthly power utilization trend threshold value, judging that the power utilization requirement of the month rises;
and when the monthly power utilization trend score is judged to be smaller than the negative value of the monthly power utilization trend threshold, judging that the power utilization requirement of the month is reduced.
5. The method according to any one of claims 2-4, wherein the monthly electricity usage trend score is calculated by the formula:
wherein ,eqj The power usage trend score for the month of j,
in the form of a number of years,
is a first
In the course of the year, the user can select the required time,
is a vector of power usage for month j of year i.
6. The method of claim 1, further comprising:
when the target date is not in the peak electricity month of the target area, acquiring the daily electricity consumption of the target area on the target date;
judging whether the daily electric quantity is larger than a first calculated value or not through a power consumption abnormity decision inequality; the first calculation value is calculated based on the highest temperature and lowest temperature difference value and the maximum value of the daily electricity consumption;
and if the calculated value is larger than the first calculated value, prompting that the target area has abnormal electricity consumption.
7. The method of claim 6, wherein the power usage exception decision inequality comprises:
wherein ,
for the daily charge of the target area on the target date,
is the sixth threshold, emax j Is the maximum value of the daily electricity consumption in the j-th month,
and the highest temperature and lowest temperature difference is obtained.
8. The method of claim 1, further comprising:
calculating annual power consumption similarity of two continuous years based on historical monthly power consumption vectors of the target area;
calculating an average electricity consumption similarity score according to the annual electricity consumption similarity;
when the average electricity utilization similarity score is larger than a first threshold value, determining that periodicity exists in electricity utilization of the target area;
when the electricity utilization of the target area is periodic, calculating the electricity utilization fluctuation score of the target area;
when the electricity utilization fluctuation score is larger than a second threshold value, determining that the electricity utilization peak month exists in the target area electricity utilization;
when the electricity consumption peak month exists, judging whether the target date is in the electricity consumption peak month of the target area or not through an electricity consumption peak month judgment inequality;
wherein, the calculation formula of the annual power consumption similarity of two consecutive years comprises:
the calculation formula of the average electricity utilization similarity score comprises the following steps:
the calculation formula of the electricity fluctuation score comprises the following steps:
the inequality is judged in the peak month of power consumption and includes:
wherein ,
wherein ,
for the similarity of the annual power consumption of the ith year and the (i + 1) th year,
in order to average the electricity usage similarity score,
the electricity consumption fluctuation score of the ith year,
the average monthly power utilization maximum value corresponding to the target area,
the average value of the monthly power consumption corresponding to the target area is obtained;
is the electricity consumption vector of 1 month in the ith year,
a vector of electricity usage for 12 months of the ith year,
represents the power usage vector for j months of the ith year,
the number of the years is the number of years,
is a first correction constant.
9. A power consumption amount early warning device, characterized in that the device includes:
the first determination module is used for determining a target area and a target date of the power consumption risk to be detected;
the first acquisition module is used for acquiring target data when the target date is in the peak electricity month of the target area; the target data includes: the predicted monthly power consumption and the highest-temperature and lowest-temperature difference value corresponding to the target date, and the daily power consumption maximum value and the daily power consumption minimum value corresponding to the month;
the calculation module is used for calculating the fluctuation upper bound value and the fluctuation lower bound value of the electricity consumption of the target area on the target date based on the target data;
and the first prompting module is used for prompting that the target area has abnormal electricity consumption when the electricity consumption is more than or equal to the upper bound value or less than or equal to the lower bound value.
10. A computer device, characterized in that the computer device comprises a processor and a memory, in which a computer program is stored, which computer program is loaded and executed by the processor to implement the method according to any of claims 1 to 8.
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Address after: Building 7, No. 124 Dongbao Road, Dongcheng Street, Dongguan City, Guangdong Province, 523015 Patentee after: Guangdong Prophet Big Data Co.,Ltd. Country or region after: China Address before: Room 102, Building 7, No. 124, Dongbao Road, Dongcheng Street, Dongguan City, Guangdong Province, 523015 Patentee before: Dongguan prophet big data Co.,Ltd. Country or region before: China |