Power load prediction system and method based on dual-mode communication

Abstract

The invention relates to the technical field of power prediction, and discloses a power load prediction system and method based on dual-mode communication, and the method comprises the steps: obtaining the power utilization data of a user in a certain period, judging the use attribute of each device of the user, obtaining the data of other users in the same region as a data reference, and carrying out the integration to form the power prediction data of the user, according to the power load prediction system and method based on dual-mode communication, the power utilization habit of the user is judged, the use attribute of each device of the user is judged, and when the user does not use a certain device temporarily in a short time and the device does not have the use habit of the user, the user can use the device. According to the obtained use habits of other users using the same equipment in the same region on the equipment, the use habits of the user on the equipment are estimated, so that the power load prediction data of the users are more accurate, the deviation of power distribution is reduced as much as possible, certain shortage or redundancy in power distribution and use is avoided, and the power distribution efficiency is improved. And the waste of resources is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power forecasting, and in particular to a system and method for forecasting electric power load based on dual-mode communication. Background Art

[0002] Electricity load forecasting is a vital part of modern power system management, involving all aspects of electricity production, dispatching, distribution and consumption. In order to effectively respond to the growing demand for electricity and improve energy efficiency, the electricity load forecasting system came into being. Accurate load forecasting helps power companies to reasonably dispatch power generation resources, avoid power waste or shortages, and reduce unnecessary energy costs by reducing spare capacity, thereby improving economic benefits. With the development of renewable energy (such as wind and solar energy), load forecasting is crucial to optimizing its application. The electricity load forecasting system based on dual-mode communication can effectively improve the flexibility and reliability of data transmission by combining two different communication methods, and provide strong support for real-time monitoring and prediction of power load. The application of this technology can improve the level of intelligence in power management and provide important guarantees for the sustainable development of future power systems. The existing power load forecasting system and method based on dual-mode communication cannot judge the power usage habits of users based on their power usage information within a certain period, and cannot judge the usage attributes of devices based on the user's usage habits of each device. When a user has not used a certain device in a short period of time and the device does not have the user's usage habits, it is impossible to estimate the user's usage habits of the device based on the usage habits of other users in the same area who use the same device, which makes the user's power load forecasting data have certain deviations, thereby causing deviations in power distribution, which may cause certain shortages or redundancies in the distribution and use of power, causing certain waste of resources, and its practicality has certain limitations. Summary of the invention

[0003] The present invention provides a power load forecasting system and method based on dual-mode communication, which are used to promote the solution of the problems mentioned in the background technology.

[0004] The present invention provides the following technical solution: a method for predicting power load based on dual-mode communication, comprising: Get target users; Get the collection cycle; Through data collection strategies, the target user's electricity consumption data within the collection period is obtained to form historical electricity consumption data; Based on historical electricity consumption data, preliminary estimated data is formed through preliminary analysis strategies; Obtain all electrical devices of the target user to form a device set; Identify each element of the device set in turn as an analysis device; Execute device analysis strategy on the analysis device to form device analysis data; The equipment analysis data includes seasonal equipment and non-seasonal equipment; If the device analysis data is seasonal equipment, the first estimation strategy is executed to form first estimation data; If the device analysis data is a non-seasonal device, a second estimation strategy is executed to form second estimation data; If the preliminary estimated data = the first estimated data or the second estimated data, then calculate the integrated estimated data, the integrated estimated data = (preliminary estimated data × 5) × (1 + 30%); If the preliminary estimated data ≠ the first estimated data or the second estimated data, then calculate the integrated estimated data, integrated estimated data = (first estimated data or second estimated data × 5) × (1 + 30%) Obtain the power consumption corresponding to the integrated estimated data and generate the power estimation results for the target users.

[0005] As a method for predicting power load based on dual-mode communication described in the present invention, wherein: the acquisition collection cycle is specifically: Get the current date; Get the month of the current date and set it as the current month; Get all dates of the current month to form a date collection; Extract the first element in the date set and set it as the sampling date; Extract all interval elements between the current date and the sub-date in the date set to form an interval set; Get the number of elements in the interval set and set it as the number of intervals; Get the number of elements in the date collection and set it as the number of dates; If the number of intervals + 2 ≥ the number of dates × 50%, the median date of the current month is extracted and identified as the judgment date; If the number of intervals + 2 < the number of dates × 50%, then calculate the judgment date, judgment date = current date - (number of intervals + 2) Set the collection duration; Calculate the collection date, collection date = determination date - collection duration; The collection period is formed by taking the collection date as the starting date and the determination date as the ending date.

[0006] As a method for predicting power load based on dual-mode communication described in the present invention, the data collection strategy is specifically as follows: Get the collection cycle; Set the analysis duration; Extract collection date; Taking the collection date as the starting date, each interval analysis duration forms a data collection point within the collection period; Obtain the collection date, determination date and all data collection points to form a time set; Sequentially extract two adjacent elements in the time set and define them as the previous time element and the next time element respectively; The period formed between the previous time element and the subsequent time element is defined as the acquisition period; Obtain all collection time periods within the collection cycle to form a time period set; Obtain the electricity consumption data corresponding to each element in the time period set and define it as historical data; Each historical data is matched one by one with each element in the time period set to form a historical data set.

[0007] As a method for predicting power load based on dual-mode communication described in the present invention, the preliminary analysis strategy is specifically: Get the device collection; Get historical data collection; Get the collection cycle and time period set; Get the period number of each element in the acquisition period, which is defined as the element period; Each element period is matched one by one with each element in the acquisition period to form a period set; Set the analysis period set; Extract each element in the analysis period set in turn and set it as the analysis period number; Extract and analyze all elements corresponding to the periodic number in the periodic set to form a periodic element set; Identify each element in the periodic element set as a determination periodic element in turn; Extract all time periods containing determination period elements in the time period set to form a determination time period set; Extract the element whose determination period element is the end time of the period from the determination period set, and define it as the primary selection period; Extract all the preliminary selection periods within the collection period to form a preliminary selection set; Extract the historical data corresponding to each element in the preliminary selection set in the historical data set, and define it as the preliminary selection historical data; Calculate the preliminary estimated value, which is the sum of the preliminary historical data of all elements in the preliminary set ÷ the number of elements in the preliminary set; Extracting a preliminary estimate of each element in the analysis period set; Each preliminary estimated value is matched one-to-one with each element in the analysis period set to form a preliminary estimated set; Calculate the preliminary estimated data, the preliminary estimated data = the sum of the preliminary estimated values ​​of all elements in the preliminary estimated set.

[0008] As a method for predicting power load based on dual-mode communication described in the present invention, wherein: the device analysis strategy is specifically: Obtain target user's usage data of the analysis device and set it as target usage data; If there is no usage data for the analyzed device, the reference analysis strategy is executed; If the analysis device has usage data, all historical usage data of the analysis device are obtained to form a historical usage set; Get the data collection time of each element in the historical usage set and set it as the historical time point; Each historical time point is matched one by one with each element in the historical usage set to form a historical collection set; Get the data collection date of each element in the historical collection set and set it as the historical date; Match each historical date with each element in the historical collection set one by one to form a historical date set; Get the data collection month of each element in the historical date set and set it as the historical month; Match each historical month with each element in the historical date set one by one to form a historical month set; Set month collection; Identify each element in the historical month set as a month element in turn; If the month set contains a month element, the month element is determined to be the first month element; If the month set does not contain a month element, the month element is determined to be the second month element; Get the number of elements in the historical month collection, set as the number of months; Get the number of elements in the historical month set that determine the month element as the first month element, and define it as the number of determined months; If the number of judged months = the number of months, the equipment analysis data is judged to be seasonal equipment; If the number of judged months is less than the number of months, the equipment analysis data is judged to be non-seasonal equipment.

[0009] As a method for power load forecasting based on dual-mode communication described in the present invention, wherein: the reference analysis strategy is specifically: Get the area where the target user is located and set it as the target area; Obtain all users who use analysis devices in the target area to form a user set; Identify each element in the user set as an analysis user in turn; Obtain all usage data of the analysis user on the analysis device, define it as analysis usage data, and form an analysis usage collection; Get the data collection time of each element in the analysis collection and set it as the analysis time point; Each analysis time point is matched one by one with each element in the analysis use set to form an analysis collection set; Get the data collection date of each element in the analysis collection set and set it as the analysis date; Match each analysis date with each element in the analysis collection set one by one to form an analysis date set; Get the data collection month of each element in the analysis date set and set it as the analysis month; Match each analysis month with each element in the analysis date set one by one to form an analysis month set; Get the month collection; Identify each element in the analysis month set as a determination month element in turn; If the month set contains a determination month element, the month element is determined to be the first reference month; If the month set does not contain the determined month element, the month element is determined to be the second reference month; Get the number of elements in the analysis month collection and set it as the number of analysis months; Get the number of elements in the analyzed month set that determine the month element as the first reference month, and set it as the number of reference months; If the number of reference months = the number of analysis months, the user is considered a seasonal user; If the number of reference months is less than the number of analysis months, the user is considered a non-seasonal user; Get the number of elements in the user set and set it as the number of users; Obtain the number of elements in the user set that determine that the user is a seasonal user, and define it as the number of analyzed users; If the number of users = the number of analyzed users, the device analysis data is determined to be a seasonal device; If the number of users is greater than the number of analyzed users, the device analysis data is determined to be a non-seasonal device.

[0010] As a method for predicting power load based on dual-mode communication described in the present invention, wherein: the first prediction strategy is specifically: Get the device collection; Get a collection of periodic elements; Identify each element in the set of periodic elements as an analysis periodic element in turn; Extract all time periods that contain the analysis period elements in the time period set to form a period time period set; Extract the element whose analysis period element is the end time of the period from the period time set and define it as the selected period; Extract all selected time periods within the collection cycle to form a selection set; Identify each element in the device set as a selection device in turn; Obtaining usage data of the selected device in the time period corresponding to each element in the selected set, and defining it as selected usage data; Each selected usage data is matched one-to-one with each element in the selection set to form a device usage set; Calculate the selection estimate of the selected device in the number of analysis cycles, where the selection estimate = the sum of all the selection usage data in the device usage set ÷ the number of elements in the device usage set; Calculate the estimated analysis value, where the estimated analysis value = the sum of the selected estimated values ​​of all elements in the device set; Extract the analysis estimate for each element in the analysis period set; Match each analysis estimate value with each element in the analysis period set one by one to form an element estimate set; Calculate the first estimated data, where the first estimated data=the sum of the analyzed estimated values ​​of all elements in the element estimation set.

[0011] As a method for predicting power load based on dual-mode communication described in the present invention, wherein: the second prediction strategy is specifically: Get the device collection; Get a collection of periodic elements; Identify each element in the set of periodic elements as an analysis periodic element in turn; Extract all time periods that contain the analysis period elements in the time period set to form a period time period set; Extract the element whose analysis period element is the end time of the period from the period time set and define it as the selected period; Extract all selected time periods within the collection cycle to form a selection set; Identify each element in the device set as a selection device in turn; Obtain the user set corresponding to the selected device and define it as the selected user set; Identify each element in the selected user set as a selected user in turn; Obtaining usage data of the selected user of the selected device in the time period corresponding to each element in the selected set, and defining it as the selected user data; Each selected user data is matched one-to-one with each element in the selected set to form a device user set; Calculate the judgment estimate value of the selected device in the number of analysis cycles, the judgment estimate value = the sum of all selected user data in the device user set ÷ the number of elements in the device user set; Extracting the judgment estimate of each element in the analysis period set of the selected device; Each judgment estimate is matched one-to-one with each element in the analysis period set to form a selection estimate set; Calculate the device estimate value, where the device estimate value = the sum of the judgment estimate values ​​of all elements in the selected estimate set; Extract the device estimate for each element in the selected user set; Match each device estimate value with each element in the selected user set one by one to form a user estimate set; Calculate the reference estimated value of the selected device, where the reference estimated value = the sum of the device estimated values ​​of all elements in the user estimated set / the number of elements in the user estimated set; Calculate the second estimated data, where the second estimated data=the sum of the reference estimated values ​​of all elements in the device set.

[0012] The present invention also discloses a system for executing a power load forecasting method based on dual-mode communication, which includes: Data collection module: used to obtain the target user's electricity consumption data within the collection period; Data analysis module: used to form preliminary estimated data based on historical electricity consumption data through preliminary analysis strategies; Device analysis module: used to analyze each device of the target user and determine whether the device is a seasonal device; Data integration module: used to calculate and integrate the estimated data based on the preliminary estimated data and the first estimated data or the second estimated data, and generate the power estimation result of the target user.

[0013] The present invention has the following beneficial effects: 1. The power load forecasting system and method based on dual-mode communication obtains the user's power consumption information within a certain period, determines the user's power consumption habits, and preliminarily estimates the user's power load data for a week, so as to compare the power load data estimated according to the equipment usage habits, integrate and form more accurate power load data, and make the user's power load forecast data more accurate, so as to reduce the deviation of power distribution as much as possible, avoid causing certain shortages or redundancies in the distribution and use of power, and reduce the waste of resources.

[0014] 2. The power load forecasting system and method based on dual-mode communication obtains the user's usage of each device, determines the user's usage habits of each device, and thus determines the usage attributes of the device, that is, whether the device is a seasonal device such as an air conditioner, and re-estimates the user's power load data for a week based on the usage of the device, and compares the power load data with the preliminary estimated power load data to form more accurate power load data, so that the user's power load forecast data is more accurate, thereby reducing the deviation of power distribution as much as possible, avoiding certain shortages or redundancies in the distribution and use of power, and reducing waste of resources.

[0015] 3. The power load forecasting system and method based on dual-mode communication, when a user does not use a certain device in a short period of time and the device does not have the user's usage habits, obtains the usage of the device by other users in the same area who use the same device, comprehensively judges the usage attributes of the device, that is, whether the device is a seasonal device, and comprehensively estimates the usage of the device by the user based on the usage of the device by other users, and estimates the user's power load data for a week based on the usage of the device again, and compares the power load data with the initially estimated power load data to integrate more accurate power load data, so that the user's power load forecast data is more accurate, thereby reducing the deviation of power distribution as much as possible, avoiding certain shortages or redundancies in the distribution and use of power, and reducing waste of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of the power load forecasting method based on dual-mode communication of the present invention; Figure 2 The system block diagram of the present invention is a method for predicting power load based on dual-mode communication. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Embodiment 1, a method for predicting power load based on dual-mode communication, see Figure 1 ,include: Get target users; Get the collection cycle; Through the data collection strategy, the target user's electricity consumption data within the collection period is obtained to form historical electricity consumption data, where the electricity consumption data is the electricity consumption; Based on historical electricity consumption data, preliminary estimated data is formed through preliminary analysis strategies; Obtain all electrical devices of the target user to form a device set; Identify each element of the device set in turn as an analysis device; Execute device analysis strategy on the analysis device to form device analysis data; The equipment analysis data includes seasonal equipment and non-seasonal equipment; If the device analysis data is seasonal equipment, the first estimation strategy is executed to form first estimation data; If the device analysis data is a non-seasonal device, a second estimation strategy is executed to form second estimation data; If the preliminary estimated data = the first estimated data or the second estimated data, then calculate the integrated estimated data, the integrated estimated data = (preliminary estimated data × 5) × (1 + 30%); If the preliminary estimated data ≠ the first estimated data or the second estimated data, then calculate the integrated estimated data, integrated estimated data = (first estimated data or second estimated data × 5) × (1 + 30%) Obtain the power consumption corresponding to the integrated estimated data and generate the power estimation results for the target users.

[0019] Through the above method, the user's electricity usage habits are judged according to the electricity usage information of a certain period, and the usage attributes of the equipment are judged according to the user's usage habits of each equipment. When the user has not used a certain equipment in a short period of time and the equipment does not have the user's usage habits, the user's usage habits of the equipment are estimated by obtaining the usage habits of other users who use the same equipment in the same area, so that the user's power load forecast data is more accurate, thereby reducing the deviation of power distribution as much as possible, avoiding certain shortages or redundancies in the distribution and use of electricity, and reducing waste of resources.

[0020] Embodiment 2: This embodiment is an improvement made on the basis of embodiment 1. In the power load forecasting method based on dual-mode communication, the acquisition collection cycle is specifically: Get the current date; Get the month of the current date and set it as the current month; Get all dates of the current month to form a date collection; Extract the first element in the date set and set it as the sampling date; Extract all interval elements between the current date and the sub-sample date in the date set to form an interval set, wherein the interval set does not contain the sub-sample date and the current date; Get the number of elements in the interval set and set it as the number of intervals; Get the number of elements in the date collection and set it as the number of dates; If the number of intervals + 2 ≥ the number of dates × 50%, the median date of the current month is extracted and identified as the determination date, and the median date is the 15th; If the number of intervals + 2 < the number of dates × 50%, then calculate the judgment date, judgment date = current date - (number of intervals + 2) Set the collection period, which is 6 months; Calculate the collection date, collection date = determination date - collection duration; The collection period is formed by taking the collection date as the starting date and the determination date as the ending date.

[0021] This embodiment also provides that the data collection strategy is specifically: Get the collection cycle; Set the analysis time, which is 24 hours; Extract collection date; Taking the collection date as the starting date, each interval analysis duration forms a data collection point within the collection period; Obtain the collection date, determination date and all data collection points to form a time set; Sequentially extract two adjacent elements in the time set and define them as the previous time element and the next time element respectively; The period formed between the previous time element and the subsequent time element is defined as the acquisition period; Obtain all collection time periods within the collection cycle to form a time period set; Obtain the electricity consumption data corresponding to each element in the time period set and define it as historical data; Each historical data is matched one by one with each element in the time period set to form a historical data set.

[0022] Embodiment 3: This embodiment is an improvement made on the basis of Embodiment 2. In this embodiment, the device analysis strategy is specifically as follows: Obtaining target user usage data of the analysis device as target usage data, wherein the usage data is energy consumption data of the device; If there is no usage data for the analyzed device, the reference analysis strategy is executed; If the analysis device has usage data, all historical usage data of the analysis device are obtained to form a historical usage set; Get the data collection time of each element in the historical usage set and set it as the historical time point; Each historical time point is matched one by one with each element in the historical usage set to form a historical collection set; Get the data collection date of each element in the historical collection set and set it as the historical date; Match each historical date with each element in the historical collection set one by one to form a historical date set; Get the data collection month of each element in the historical date set and set it as the historical month; Match each historical month with each element in the historical date set one by one to form a historical month set; Set a month set, which is June to September and December to March, with a total of 8 elements; Identify each element in the historical month set as a month element in turn; If the month set contains a month element, the month element is determined to be the first month element; If the month set does not contain a month element, the month element is determined to be the second month element; Get the number of elements in the historical month set and define it as the number of months, where the number of elements is the number of months in the historical month set. For example, if the elements in the historical date set are January 3, January 4, February 3, and June 3, the elements in the historical month set are January, January, February, and June, then the number of elements in the historical month set is 3; Get the number of elements in the historical month set that determine the month element as the first month element, and define it as the number of determined months; If the number of months is determined to be equal to the number of months, the device analysis data is determined to be a seasonal device, that is, the device is used in specific months, indicating that the device may be a seasonal device such as an air conditioner; If the number of months is determined to be less than the number of months, the device analysis data is determined to be a non-seasonal device, that is, the device is not used in a specific month, indicating that the device may be a non-seasonal device such as a television.

[0023] The reference analysis strategy is specifically: Get the area where the target user is located and set it as the target area; Obtain all users who use analysis devices in the target area to form a user set; Identify each element in the user set as an analysis user in turn; Obtain all usage data of the analysis user on the analysis device, define it as analysis usage data, and form an analysis usage collection; Get the data collection time of each element in the analysis collection and set it as the analysis time point; Each analysis time point is matched one by one with each element in the analysis use set to form an analysis collection set; Get the data collection date of each element in the analysis collection set and set it as the analysis date; Match each analysis date with each element in the analysis collection set one by one to form an analysis date set; Get the data collection month of each element in the analysis date set and set it as the analysis month; Match each analysis month with each element in the analysis date set one by one to form an analysis month set; Get a month set, which is June to September and December to March, with a total of 8 elements; Identify each element in the analysis month set as a determination month element in turn; If the month set contains a determination month element, the month element is determined to be the first reference month; If the month set does not contain the determined month element, the month element is determined to be the second reference month; Get the number of elements in the analysis month set and define it as the analysis month number, where the number of elements is the number of months in the historical month set. For example, if the elements in the historical date set are January 3, January 4, February 3, and June 3, the elements in the historical month set are January, January, February, and June, then the number of elements in the historical month set is 3; Get the number of elements in the analyzed month set that determine the month element as the first reference month, and set it as the number of reference months; If the number of reference months = the number of analysis months, the user is determined to be a seasonal user, that is, the user uses the device only in specific months; If the number of reference months is less than the number of analysis months, the user is determined to be a non-seasonal user, that is, the user does not use the device in a specific month; Get the number of elements in the user set and set it as the number of users; Obtain the number of elements in the user set that determine that the user is a seasonal user, and define it as the number of analyzed users; If the number of users = the number of analyzed users, the device analysis data is determined to be a seasonal device, that is, the device is used in specific months, indicating that the device may be a seasonal device such as an air conditioner; If the number of users is greater than the number of analyzed users, the device analysis data is determined to be a non-seasonal device, that is, the device is not used in a specific month, indicating that the device may be a non-seasonal device such as a television.

[0024] Embodiment 4: This embodiment is an improvement made on the basis of embodiment 3. In this embodiment, the preliminary analysis strategy is specifically as follows: Get the device collection; Get historical data collection; Get the collection cycle and time period set; Obtain the period number of each element in the acquisition period, which is defined as the element period. The period number is the number corresponding to each element in a week. For example, if the element is Monday, the period number of the element is 1. Each element period is matched one by one with each element in the acquisition period to form a period set; Setting an analysis period set, wherein the analysis period set is a set formed by seven elements from Monday to Sunday; Extract each element in the analysis period set in turn and set it as the analysis period number; Extract and analyze all elements corresponding to the periodic number in the periodic set to form a periodic element set; Identify each element in the periodic element set as a determination periodic element in turn; Extract all time periods containing determination period elements in the time period set to form a determination time period set; Extract the element whose determination period element is the end time of the period from the determination period set, and define it as the primary selection period; Extract all the preliminary selection periods within the collection period to form a preliminary selection set; Extract the historical data corresponding to each element in the preliminary selection set in the historical data set, and define it as the preliminary selection historical data; Calculate the preliminary estimated value, which is the sum of the preliminary historical data of all elements in the preliminary set ÷ the number of elements in the preliminary set; Extracting a preliminary estimate of each element in the analysis period set; Each preliminary estimated value is matched one-to-one with each element in the analysis period set to form a preliminary estimated set; Calculate the preliminary estimated data, the preliminary estimated data = the sum of the preliminary estimated values ​​of all elements in the preliminary estimated set.

[0025] This embodiment also provides that the first estimation strategy is specifically: Get the device collection; Get a collection of periodic elements; Identify each element in the set of periodic elements as an analysis periodic element in turn; Extract all time periods that contain the analysis period elements in the time period set to form a period time period set; Extract the element whose analysis period element is the end time of the period from the period time set and define it as the selected period; Extract all selected time periods within the collection cycle to form a selection set; Identify each element in the device set as a selection device in turn; Obtaining usage data of the selected device in the time period corresponding to each element in the selected set, and defining it as selected usage data; Each selected usage data is matched one-to-one with each element in the selection set to form a device usage set; Calculate the selection estimate of the selected device in the number of analysis cycles, where the selection estimate = the sum of all the selection usage data in the device usage set ÷ the number of elements in the device usage set; If the current month is in the month set, the analysis estimate is calculated, and the analysis estimate = the sum of the selected estimates of all elements in the device set; If the current month is not in the month set, the analysis estimate is calculated, and the analysis estimate = the sum of the selected estimates of the elements corresponding to all non-seasonal devices in the device set; Extract the analysis estimate for each element in the analysis period set; Match each analysis estimate value with each element in the analysis period set one by one to form an element estimate set; Calculate the first estimated data, where the first estimated data=the sum of the analyzed estimated values ​​of all elements in the element estimation set.

[0026] This embodiment also provides that the second estimation strategy is specifically: Get the device collection; Get a collection of periodic elements; Identify each element in the set of periodic elements as an analysis periodic element in turn; Extract all time periods that contain the analysis period elements in the time period set to form a period time period set; Extract the element whose analysis period element is the end time of the period from the period time set and define it as the selected period; Extract all selected time periods within the collection cycle to form a selection set; Identify each element in the device set as a selection device in turn; Obtain the user set corresponding to the selected device and define it as the selected user set; Identify each element in the selected user set as a selected user in turn; Obtaining usage data of the selected user of the selected device in the time period corresponding to each element in the selected set, and defining it as the selected user data; Each selected user data is matched one-to-one with each element in the selected set to form a device user set; Calculate the judgment estimate value of the selected device in the number of analysis cycles, the judgment estimate value = the sum of all selected user data in the device user set ÷ the number of elements in the device user set; Extracting the judgment estimate of each element in the analysis period set of the selected device; Each judgment estimate is matched one-to-one with each element in the analysis period set to form a selection estimate set; Calculate the device estimate value, where the device estimate value = the sum of the judgment estimate values ​​of all elements in the selected estimate set; Extract the device estimate for each element in the selected user set; Match each device estimate value with each element in the selected user set one by one to form a user estimate set; Calculate the reference estimated value of the selected device, where the reference estimated value = the sum of the device estimated values ​​of all elements in the user estimated set / the number of elements in the user estimated set; If the current month is in the month set, calculate the second estimated data, the second estimated data = the sum of the reference estimated values ​​of all elements in the device set; If the current month is not in the month set, the second estimated data is calculated, where the second estimated data = the sum of the reference estimated values ​​of the elements corresponding to all non-seasonal devices in the device set.

[0027] Embodiment 5: This embodiment also discloses a system for executing a power load prediction method based on dual-mode communication, see Figure 2 ,include: Data collection module: used to obtain the target user's electricity consumption data within the collection period; Data analysis module: used to form preliminary estimated data based on historical electricity consumption data through preliminary analysis strategies; Device analysis module: used to analyze each device of the target user and determine whether the device is a seasonal device; Data integration module: used to calculate and integrate the estimated data based on the preliminary estimated data and the first estimated data or the second estimated data, and generate the power estimation result of the target user.

[0028] In this embodiment, the power usage habits of users are determined based on their power usage information within a certain period, and the usage attributes of devices are determined based on the user's usage habits of each device. When a device is not used by the user for a short period of time, the user's usage habits for the device are estimated based on the usage habits of other users in the same area who use the same device. This makes the user's power load forecast data more accurate, thereby reducing the deviation in power distribution as much as possible, avoiding certain shortages or redundancies in power distribution and use, and reducing waste of resources.

Claims

1. A method for power load forecasting based on dual-mode communication, characterized in that: include: Get target users; Get the collection cycle; Through data collection strategies, the target user's electricity consumption data within the collection period is obtained to form historical electricity consumption data; Based on historical electricity consumption data, preliminary estimated data is formed through preliminary analysis strategies; Obtain all electrical devices of the target user to form a device set; Identify each element of the device set in turn as an analysis device; Execute device analysis strategy on the analysis device to form device analysis data; The equipment analysis data includes seasonal equipment and non-seasonal equipment; If the device analysis data is seasonal equipment, the first estimation strategy is executed to form first estimation data; If the device analysis data is a non-seasonal device, a second estimation strategy is executed to form second estimation data; If the preliminary estimated data = the first estimated data or the second estimated data, then calculate the integrated estimated data, the integrated estimated data = (preliminary estimated data × 5) × (1 + 30%); If the preliminary estimated data ≠ the first estimated data or the second estimated data, then calculate the integrated estimated data, integrated estimated data = (first estimated data or second estimated data × 5) × (1 + 30%) Obtain the power consumption corresponding to the integrated estimated data and generate the power estimation results for the target users.

2. The method for predicting power load based on dual-mode communication according to claim 1, characterized in that: The acquisition collection cycle is specifically: Get the current date; Get the month of the current date and set it as the current month; Get all dates of the current month to form a date collection; Extract the first element in the date set and set it as the sampling date; Extract all interval elements between the current date and the sub-date in the date set to form an interval set; Get the number of elements in the interval set and set it as the number of intervals; Get the number of elements in the date collection and set it as the number of dates; If the number of intervals + 2 ≥ the number of dates × 50%, the median date of the current month is extracted and identified as the judgment date; If the number of intervals + 2 < the number of dates × 50%, then calculate the judgment date, judgment date = current date - (number of intervals + 2) Set the collection duration; Calculate the collection date, collection date = determination date - collection duration; The collection period is formed by taking the collection date as the starting date and the determination date as the ending date.

3. The method for power load forecasting based on dual-mode communication according to claim 1, characterized in that: The data collection strategy is specifically as follows: Get the collection cycle; Set the analysis duration; Extract collection date; Taking the collection date as the starting date, each interval analysis duration forms a data collection point within the collection period; Obtain the collection date, determination date and all data collection points to form a time set; Sequentially extract two adjacent elements in the time set and define them as the previous time element and the next time element respectively; The period formed between the previous time element and the subsequent time element is defined as the acquisition period; Obtain all collection time periods within the collection cycle to form a time period set; Obtain the electricity consumption data corresponding to each element in the time period set and define it as historical data; Each historical data is matched one by one with each element in the time period set to form a historical data set.

4. The method for power load forecasting based on dual-mode communication according to claim 1, characterized in that: The preliminary analysis strategy is specifically as follows: Get the device collection; Get historical data collection; Get the collection cycle and time period set; Get the period number of each element in the acquisition period, which is defined as the element period; Each element period is matched one by one with each element in the acquisition period to form a period set; Set the analysis period set; Extract each element in the analysis period set in turn and set it as the analysis period number; Extract and analyze all elements corresponding to the periodic number in the periodic set to form a periodic element set; Identify each element in the periodic element set as a determination periodic element in turn; Extract all time periods containing determination period elements in the time period set to form a determination time period set; Extract the element whose determination period element is the end time of the period from the determination period set, and define it as the primary selection period; Extract all the preliminary selection periods within the collection period to form a preliminary selection set; Extract the historical data corresponding to each element in the preliminary selection set in the historical data set, and define it as the preliminary selection historical data; Calculate the preliminary estimated value, which is the sum of the preliminary historical data of all elements in the preliminary set ÷ the number of elements in the preliminary set; Extracting a preliminary estimate of each element in the analysis period set; Match each preliminary estimate value with each element in the analysis period set one by one to form a preliminary estimate set; Calculate preliminary estimated data, preliminary estimated data = the sum of preliminary estimated values ​​of all elements in the preliminary estimated set.

5. The method for power load forecasting based on dual-mode communication according to claim 1, characterized in that: The device analysis strategy is specifically: Obtain target user's usage data of the analysis device and define it as target usage data; If there is no usage data for the analyzed device, the reference analysis strategy is executed; If the analysis device has usage data, all historical usage data of the analysis device are obtained to form a historical usage set; Get the data collection time of each element in the historical usage set and set it as the historical time point; Each historical time point is matched one by one with each element in the historical usage set to form a historical collection set; Get the data collection date of each element in the historical collection set and set it as the historical date; Match each historical date with each element in the historical collection set one by one to form a historical date set; Get the data collection month of each element in the historical date set and set it as the historical month; Match each historical month with each element in the historical date set one by one to form a historical month set; Set month collection; Identify each element in the historical month set as a month element in turn; If the month set contains a month element, the month element is determined to be the first month element; If the month set does not contain a month element, the month element is determined to be the second month element; Get the number of elements in the historical month collection, set as the number of months; Get the number of elements in the historical month set that determine the month element as the first month element, and define it as the number of determined months; If the number of judged months = the number of months, the equipment analysis data is judged to be seasonal equipment; If the number of judged months is less than the number of months, the equipment analysis data is judged to be non-seasonal equipment.

6. The method for power load forecasting based on dual-mode communication according to claim 5, characterized in that: The reference analysis strategy is specifically: Get the area where the target user is located and set it as the target area; Obtain all users who use analysis devices in the target area to form a user set; Identify each element in the user set as an analysis user in turn; Obtain all usage data of the analysis user on the analysis device, define it as analysis usage data, and form an analysis usage collection; Get the data collection time of each element in the analysis collection and set it as the analysis time point; Each analysis time point is matched one by one with each element in the analysis use set to form an analysis collection set; Get the data collection date of each element in the analysis collection set and set it as the analysis date; Match each analysis date with each element in the analysis collection set one by one to form an analysis date set; Get the data collection month of each element in the analysis date set and set it as the analysis month; Match each analysis month with each element in the analysis date set one by one to form an analysis month set; Get the month collection; Identify each element in the analysis month set as a determination month element in turn; If the month set contains a determination month element, the month element is determined to be the first reference month; If the month set does not contain the determined month element, the month element is determined to be the second reference month; Get the number of elements in the analysis month collection and set it as the number of analysis months; Get the number of elements in the analyzed month set that determine the month element as the first reference month, and set it as the number of reference months; If the number of reference months = the number of analysis months, the user is considered a seasonal user; If the number of reference months is less than the number of analysis months, the user is considered a non-seasonal user; Get the number of elements in the user set and set it as the number of users; Obtain the number of elements in the user set that determine that the user is a seasonal user, and define it as the number of analyzed users; If the number of users = the number of analyzed users, the device analysis data is determined to be a seasonal device; If the number of users is greater than the number of analyzed users, the device analysis data is determined to be a non-seasonal device.

7. The method for power load forecasting based on dual-mode communication according to claim 1, characterized in that: The first estimation strategy is specifically: Get the device collection; Get a collection of periodic elements; Identify each element in the set of periodic elements as an analysis periodic element in turn; Extract all time periods that contain the analysis period elements in the time period set to form a period time period set; Extract the element whose analysis period element is the end time of the period from the period time set and define it as the selected period; Extract all selected time periods within the collection cycle to form a selection set; Identify each element in the device set as a selection device in turn; Obtaining usage data of the selected device in the time period corresponding to each element in the selected set, and defining it as selected usage data; Each selected usage data is matched one-to-one with each element in the selection set to form a device usage set; Calculate the selection estimate of the selected device in the number of analysis cycles, where the selection estimate = the sum of all the selection usage data in the device usage set ÷ the number of elements in the device usage set; Calculate the estimated analysis value, where the estimated analysis value = the sum of the selected estimated values ​​of all elements in the device set; Extract the analysis estimate for each element in the analysis period set; Match each analysis estimate value with each element in the analysis period set one by one to form an element estimate set; Calculate the first estimated data, where the first estimated data=the sum of the analyzed estimated values ​​of all elements in the element estimation set.

8. The method for power load forecasting based on dual-mode communication according to claim 1, characterized in that: The second estimation strategy is specifically: Get the device collection; Get a collection of periodic elements; Identify each element in the set of periodic elements as an analysis periodic element in turn; Extract all time periods that contain the analysis period elements in the time period set to form a period time period set; Extract the element whose analysis period element is the end time of the period from the period time set and define it as the selected period; Extract all selected time periods within the collection cycle to form a selection set; Identify each element in the device set as a selection device in turn; Obtain the user set corresponding to the selected device and define it as the selected user set; Identify each element in the selected user set as a selected user in turn; Obtaining usage data of the selected user of the selected device in the time period corresponding to each element in the selected set, and defining it as the selected user data; Each selected user data is matched one-to-one with each element in the selected set to form a device user set; Calculate the judgment estimate value of the selected device in the number of analysis cycles, the judgment estimate value = the sum of all selected user data in the device user set ÷ the number of elements in the device user set; Extracting the judgment estimate of each element in the analysis period set of the selected device; Each judgment estimate is matched one-to-one with each element in the analysis period set to form a selection estimate set; Calculate the device estimate value, where the device estimate value = the sum of the judgment estimate values ​​of all elements in the selected estimate set; Extract the device estimate for each element in the selected user set; Match each device estimate value with each element in the selected user set one by one to form a user estimate set; Calculate the reference estimated value of the selected device, where the reference estimated value = the sum of the device estimated values ​​of all elements in the user estimated set / the number of elements in the user estimated set; Calculate the second estimated data, where the second estimated data=the sum of the reference estimated values ​​of all elements in the device set.

9. A system for executing the power load forecasting method based on dual-mode communication according to claim 1, characterized in that: include: Data collection module: used to obtain the target user's electricity consumption data within the collection period; Data analysis module: used to form preliminary estimated data based on historical electricity consumption data through preliminary analysis strategies; Device analysis module: used to analyze each device of the target user and determine whether the device is a seasonal device; Data integration module: used to calculate and integrate the estimated data based on the preliminary estimated data and the first estimated data or the second estimated data, and generate the power estimation result of the target user.

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