CN116308884B - Renewable energy intelligent management method and system based on big data application - Google Patents
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Abstract
The invention relates to the field of intelligent management of big data and renewable energy sources, in particular to an intelligent management method and system of renewable energy sources based on big data application. Which comprises the following steps: classifying and integrating the data information; calculating average values of consumption peaks and valleys of a plurality of new sources of users in each time period, and carrying out classification integration; determining peak and valley ranges for a single time period; distinguishing energy consumption entry ports; the peak end comparison data is abnormal, renewable power is added for use, the valley end comparison data is abnormal, and the power grid is adopted for purchasing power; the average value extraction mode of the conventional three time periods is adopted, the energy consumption ranges of all the custom regions are integrated, the electricity consumption peaks and the electricity consumption valleys are embodied, the electricity consumption modes are determined according to different time periods when electricity is consumed in the day, the electricity consumption modes are converted at any time after the electricity consumption is abnormal, and the electricity consumption quality of users is ensured.
Description
Technical Field
The invention relates to the field of intelligent management of big data and renewable energy sources, in particular to an intelligent management method and system of renewable energy sources based on big data application.
Background
Along with the continuous improvement of environmental awareness, renewable power also starts to enter an environmental-friendly row, a large number of power generation devices such as wind power generation, solar power generation and tidal power generation are established to replace the original power generation mode in order to improve the proportion of renewable power in the power, meanwhile, in order to benefit people, the solar power generation device is installed in common families, and the inexhaustible electric quantity of the family can be imported into a power grid to increase part of income, so that the proportion of the renewable electric quantity in the whole electric quantity is increased.
Big data is taken as the vocabulary of the IT industry which is the most hot at present, and the data warehouse, the data security, the data analysis, the data mining and the like are caused accordingly, and the big data analysis is caused simultaneously with the coming of the big data age; with the continuous development of big data, the application field of the data is more and more extensive.
In the practical application process, more large factories want power supply offices to purchase electric quantity to calculate electric charge, but specific electricity consumption conditions of the factories cannot be accurately mastered, the purchase is more and leads to insufficient utilization, the purchase is less and leads to the fact that electricity consumption cannot be normal, and a renewable energy source mode is adopted to determine electricity demand.
Disclosure of Invention
The invention aims to provide a renewable energy intelligent management method and system based on big data application, so as to solve the problems in the background technology.
In order to achieve the above object, in one aspect, the present invention provides a renewable energy intelligent management method based on big data application, comprising the following steps:
s1, dividing a power supply natural day into n time periods, dividing a power supply range into m power supply areas, collecting current day power utilization data of all users in the power supply areas in each time period in a regional mode, and inputting the current day power utilization data into a data transmission module 10;
s2, recording peak values and valley values of daily electricity data of all users in the power supply area of each time period in a regional manner through the data analysis module 20, and inputting the peak values and the valley values into the data management module 30 for classification and integration; the data management module 30 includes a data comparison unit 31 and a data storage unit 32;
the data analysis module 20 calculates peak-to-valley ratio P of each power supply region in each time period i And P j And the peak-to-valley variance product Q i And Q j The following formula is satisfied:
wherein i is a time period subscript, which indicates that one power supply natural day is divided into an ith time period in n time periods; j is a power supply area subscript, and represents that a power supply range is divided into a j-th power supply area in m power supply areas; k is a user subscript, l j The number of users representing the j-th power supply area; aijk represents the peak value of the kth user in the jth power supply area in the ith time period; bijk represents the valley of the kth user in the jth power supply area in the ith time period; p (P) i A peak-to-valley ratio representing the i-th period; p (P) j A peak-to-valley ratio indicating a j-th power supply region;representing the peak-to-average value; />Representation Gu Junzhi; q (Q) i Peak Gu Fangcha product representing the i-th time period; q (Q) j Peak Gu Fangcha product representing the jth power supply region;
s3, leading peak-to-valley ratios and peak-to-valley variance products of different areas into a data comparison unit 31, and determining an average value range of each area after data comparison;
the average value range of each area determined by the data comparison unit 31 and the average value data of each time period of the data analysis module 20 are input into the data storage unit 32 to be integrated and stored again, and the peak value range and the valley value range of each area in a single time period are determined;
determining the priority of renewable power supply according to peak-to-valley ratios and/or peak-to-valley variance products of different time periods and/or power supply areas, wherein the power supply proportion of renewable energy sources is lower when the peak-to-valley ratios and/or the peak-to-valley variance products are larger in the time periods or the power supply areas;
s4, the data storage unit 32 inputs the peak value and valley value range data to the data input module 40, and distinguishes energy consumption entry ports, namely a peak end 41 and a valley end 42, wherein the peak end 41 adopts a power grid to purchase electricity, normal electricity utilization is ensured, and the valley end 42 adopts renewable electricity;
s5, inputting current electricity consumption data into the data input module 40, enabling electricity consumption peak and electricity consumption valley data to enter peak ends 41) and valley ends 42 respectively, inputting the current electricity consumption data into the data management module 30 for comparison, wherein the comparison data of the peak ends 41 are abnormal, renewable electricity is added according to priority for use, and the comparison data of the valley ends 42 are abnormal, so that electricity purchasing of a power grid is adopted.
As a further improvement of the technical scheme, in S1, the power supply area is a user-defined area, and the planning initial acquisition time period is as follows: 0 time-8 time; 8-16, 16-24.
On the other hand, the invention also provides a renewable energy intelligent management system based on big data application, which is used for realizing the renewable energy intelligent management method based on big data application, and comprises a data conveying module 10, a data analysis module 20, a data management module 30, a data input module 40 and an instruction end 50;
the data transmission module 10 is used for collecting current daily electricity utilization data of a user; the data analysis module 20 and the data management module 30 cooperate to determine specific electricity data ranges of a single time period in all areas, and store the specific electricity data ranges through the data management module 30;
the data input module 40 can input user power consumption data, classify the input data, compare the classified data with the data management module 30, and send the data to the command terminal 50 to convert the power consumption mode after an abnormality occurs.
As a further improvement of the present solution, the data transmission module 10 includes a data acquisition unit 11 and a data classification unit 12 for data acquisition and classification integration.
As a further improvement of the technical scheme, the data management module at least comprises a data comparison unit and a data storage unit.
As a further improvement of the technical scheme, the data input module at least comprises a peak end and a valley end which are respectively used for entering electricity consumption peak data and electricity consumption valley data, wherein the data input module adopts a Jaro-Winkler algorithm to match the daily electricity consumption data with the data of the peak end and the valley end, and the algorithm is as follows:
the Jaro distance of the set electricity data is d j The length of the prefix shared by the electricity consumption data and the peak end or the valley end is L, the range factor of the prefix is p, and the distance d of Jaro-Winkler w The calculation formula of (2) is:
d w ＝d j +L×p(1-d j )；
l is 4 characters, and p is less than or equal to 0.25;
p is assigned a value of 0.1, d w The larger the electricity consumption data is, the larger the similarity between the electricity consumption data and the data of the peak end and the data of the valley end is.
As a further improvement of the technical scheme, the data management module adopts the Jaro-Winkler algorithm to match the electricity consumption data in the data storage unit with the electricity consumption data in the data analysis module, and the algorithm is used for comparing the 7-day current electricity consumption data.
Compared with the prior art, the invention has the beneficial effects that:
1. in the method and the system for intelligently managing the renewable energy based on big data application, the average value extraction mode of the conventional three time periods is adopted, the energy consumption range of each custom region is integrated, the electricity consumption peak and the electricity consumption valley are embodied, the electricity consumption mode is determined according to different time periods when electricity is consumed in the day, the electricity consumption mode is converted at any time after the electricity consumption is abnormal, the electricity consumption of the high peak period adopts the power grid to purchase electricity, the renewable electric power is added after the peak value is exceeded, and the renewable electric power is added to completely cope with the voltage due to the fact that the peak value exceeds the smaller range, meanwhile, the expenditure of electricity consumption cost is reduced, the electricity consumption valley section adopts the renewable electric power, and the electricity consumption quality can be changed into the power grid to purchase electricity for the normal electricity consumption after the peak value exceeds the electricity consumption;
2. according to the renewable energy intelligent management method and system based on big data application, electricity consumption data can be recalculated and stored periodically (generally 7 days), a new specific time period of peak value and valley value is determined, and the new specific time period is used for next comparison of current day electricity consumption data.
Drawings
Fig. 1 is an overall flow diagram of the present invention.
The meaning of each reference sign in the figure is:
10. a data transmission module; 20. a data analysis module; 30. a data management module; 31. a data comparison unit; 32. a data storage unit; 40. a data input module; 41. a peak end; 42. a valley end; 50. an instruction end.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples
The aim of the embodiment is to provide a renewable energy intelligent management system based on big data application, which comprises a data transmission module 10, a data analysis module 20, a data management module 30, a data input module 40 and an instruction end 50;
the data transmission module 10, the data transmission module 10 at least comprises a data acquisition unit and a data classification unit, and is used for acquiring the user energy consumption data and classifying and integrating the energy consumption data in different areas and different time periods.
The data management module 30 at least comprises a data comparison unit 31, and the data analysis module 20 and the data comparison unit 31 work cooperatively to determine specific energy consumption ranges of single time periods of all areas and store the specific energy consumption ranges through the data comparison unit 31;
the data management module 30 matches the electricity usage data in the data storage unit 32 with the electricity usage data in the data analysis module 20 using the Jaro-Winkler algorithm, which is used in a comparison of 7 day current electricity usage data.
The data input module 40 at least includes a peak end 41 and a valley end 42, which are respectively used for entering electricity consumption peak data and electricity consumption valley data, wherein the data input module 40 adopts a Jaro-Winkler algorithm to match the daily electricity consumption data with the data of the peak end 41 and the valley end 42, and the algorithm is as follows:
the Jaro distance of the set electricity data is d j The length of the prefix of the electricity consumption data and the peak end 41 or the valley end 42 is L, the range factor of the prefix is p, and the calculation formula of the Jaro-Winkler distance is as follows:
d w ＝d j +L×p(1-d j )；
l is 4 characters, and p is less than or equal to 0.25;
p is assigned a value of 0.1, d w The larger the description electricity consumption data and the data similarity of the peak end 41 and the valley end 42 are larger
The data input module 40 can enter the user energy consumption data, classify the entered data, compare the classified data through the data management module 30, and send the data to the instruction terminal 50 to convert the power consumption mode after abnormality occurs.
Referring to fig. 1, the embodiment of the invention also provides a renewable energy intelligent management method based on big data application, which comprises the following steps:
s1, dividing a power supply natural day into n time periods, dividing a power supply range into m power supply areas, collecting current day power utilization data of all users in the power supply areas in each time period in a regional mode, and inputting the current day power utilization data into a data transmission module 10;
s2, recording peak values and valley values of daily electricity data of all users in the power supply area of each time period in a regional manner through the data analysis module 20, and inputting the peak values and the valley values into the data management module 30 for classification and integration; the data management module 30 includes a data comparison unit 31 and a data storage unit 32;
the data analysis module 20 calculates peak-to-valley ratio P of each power supply region in each time period i And P j And the peak-to-valley variance product Q i And Q j The following formula is satisfied:
wherein i is a time period subscript, which indicates that one power supply natural day is divided into an ith time period in n time periods; j is a power supply area subscript, and represents that a power supply range is divided into a j-th power supply area in m power supply areas; k is a user subscript, l j The number of users representing the j-th power supply area; aijk represents the peak value of the kth user in the jth power supply area in the ith time period; bijk represents the valley of the kth user in the jth power supply area in the ith time period; p (P) i A peak-to-valley ratio representing the i-th period; p (P) j A peak-to-valley ratio indicating a j-th power supply region;representing the peak-to-average value; />Representation Gu Junzhi; q (Q) i Peak Gu Fangcha product representing the i-th time period; q (Q) j Peak Gu Fangcha product representing the jth power supply region;
s3, leading peak-to-valley ratios and peak-to-valley variance products of different areas into a data comparison unit 31, and determining an average value range of each area after data comparison;
the average value range of each area determined by the data comparison unit 31 and the average value data of each time period of the data analysis module 20 are input into the data storage unit 32 to be integrated and stored again, and the peak value range and the valley value range of each area in a single time period are determined;
determining the priority of renewable power supply according to peak-to-valley ratios and/or peak-to-valley variance products of different time periods and/or power supply areas, wherein the power supply proportion of renewable energy sources is lower when the peak-to-valley ratios and/or the peak-to-valley variance products are larger in the time periods or the power supply areas;
s4, the data storage unit 32 inputs the peak value and valley value range data to the data input module 40, and distinguishes energy consumption entry ports, namely a peak end 41 and a valley end 42, wherein the peak end 41 adopts a power grid to purchase electricity, normal electricity utilization is ensured, and the valley end 42 adopts renewable electricity;
s5, inputting current electricity consumption data into the data input module 40, enabling electricity consumption peak and electricity consumption valley data to enter peak ends 41) and valley ends 42 respectively, inputting the current electricity consumption data into the data management module 30 for comparison, wherein the comparison data of the peak ends 41 are abnormal, renewable electricity is added according to priority for use, and the comparison data of the valley ends 42 are abnormal, so that electricity purchasing of a power grid is adopted.
In the step S1, the power supply area is a user-defined area, and the planning initial acquisition time period is as follows: 0 time-8 time; 8-16, 16-24.
The method adopts an average value extraction mode of three conventional time periods, integrates the energy consumption range of each custom region, materializes the electricity consumption peak and the electricity consumption valley, determines the electricity consumption mode according to different time periods when electricity is consumed in the day, converts the electricity consumption mode at any time after the electricity consumption is abnormal, adopts the power grid to purchase electricity in the peak period, adds renewable power after exceeding the peak value, and can completely cope with voltage by adding the renewable power due to smaller exceeding range of the peak value, simultaneously reduces the expenditure of electricity consumption cost, adopts renewable electricity consumption in the electricity consumption valley period, changes the electricity consumption mode into the power grid to purchase electricity for normal electricity consumption after exceeding, and ensures the electricity consumption quality of users;
meanwhile, the method can also periodically (generally 7 days) recalculate and store the electricity consumption data, determine a new specific time period of peak value and valley value, and be used for next current-day electricity consumption data comparison.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (4)
1. The renewable energy intelligent management method based on big data application is characterized by comprising the following steps of:
s1, dividing a power supply natural day into n time periods, dividing a power supply range into m power supply areas, collecting current day power utilization data of all users in the power supply areas in each time period in a regional mode, and inputting the current day power utilization data into a data transmission module (10);
s2, recording peak values and valley values of daily electricity data of all users in the power supply area of each time period in a zoned manner through a data analysis module (20), and inputting the peak values and the valley values into a data management module (30) for classification and integration; the data management module (30) comprises a data comparison unit (31) and a data storage unit (32);
the data analysis module (20) calculates the peak-to-valley ratio P of each power supply area in each time period i And P j And the peak-to-valley variance product Q i And Q j The following formula is satisfied:
wherein i is a time period subscript, which indicates that one power supply natural day is divided into an ith time period in n time periods; j is a power supply area subscript, and represents that a power supply range is divided into a j-th power supply area in m power supply areas; k is a user subscript, l j The number of users representing the j-th power supply area; aijk represents the peak value of the kth user in the jth power supply area in the ith time period; bijk represents the valley of the kth user in the jth power supply area in the ith time period; p (P) i A peak-to-valley ratio representing the i-th period; p (P) j A peak-to-valley ratio indicating a j-th power supply region;representing the peak-to-average value; />Representation Gu Junzhi; q (Q) i Peak Gu Fangcha product representing the i-th time period; q (Q) j Peak Gu Fangcha product representing the jth power supply region;
s3, leading peak-to-valley ratios and peak-to-valley variance products of different areas into a data comparison unit (31), and determining an average value range of each area after data comparison;
inputting the average value range of each area determined by the data comparison unit (31) and the average value data of each time period of the data analysis module (20) into the data storage unit (32) for re-integration and storage, and determining the peak value range and the valley value range of each area in a single time period;
determining the priority of renewable power supply according to peak-to-valley ratios and/or peak-to-valley variance products of different time periods and/or power supply areas, wherein the power supply proportion of renewable energy sources is lower when the peak-to-valley ratios and/or the peak-to-valley variance products are larger in the time periods or the power supply areas;
s4, the data storage unit (32) inputs the peak value and valley value range data to the data input module (40), the energy consumption input ports are distinguished, namely a peak end (41) and a valley end (42), the peak end (41) adopts a power grid to purchase electricity, normal electricity utilization is ensured, and the valley end (42) adopts renewable electricity;
s5, inputting current electricity consumption data into the data input module (40), enabling electricity consumption peak and electricity consumption valley data to enter a peak end (41) and a valley end (42) respectively, inputting the current electricity consumption data into the data management module (30) for comparing the electricity consumption data, enabling the peak end (41) to compare abnormal data, adding renewable electric power according to priority for use, enabling the valley end (42) to compare abnormal data, and purchasing electricity by a power grid.
2. The intelligent renewable energy management method based on big data application according to claim 1, wherein the method comprises the following steps: in the step S1, the power supply area is a user-defined area, and the planning initial acquisition time period is three: 0 time-8 time, 8 time-16 time, 16 time-24 time.
3. A renewable energy intelligent management system based on big data application, for implementing the renewable energy intelligent management method based on big data application according to any one of claims 1-2, characterized in that: the system comprises a data transmission module (10), a data analysis module (20), a data management module (30), a data input module (40) and an instruction end (50);
the data transmission module (10) is used for collecting current electricity consumption data of a user; the data analysis module (20) and the data management module (30) work cooperatively and are used for determining specific electricity utilization data ranges of single time periods of all areas and storing the specific electricity utilization data ranges through the data management module (30);
the data input module (40) can input user electricity data, classify the input data, compare the classified data through the data management module (30), and send the data to the instruction end (50) to convert the electricity consumption mode after abnormality occurs.
4. The big data application based renewable energy intelligent management system of claim 3, wherein: the data transmission module (10) comprises a data acquisition unit (11) and a data classification unit (12) and is used for data acquisition and classification integration.
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