CN109508743B - KNN improved algorithm-based method for repairing abnormal energy consumption data of lighting socket - Google Patents
KNN improved algorithm-based method for repairing abnormal energy consumption data of lighting socket Download PDFInfo
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Abstract
The invention relates to a method for repairing abnormal energy consumption data of an illumination socket based on a KNN improved algorithm, and belongs to the technical field of data processing of building energy consumption monitoring platforms. The basic idea of the algorithm is shown in an abstract chart, historical data can be roughly divided into different energy utilization modes, the energy utilization modes of data to be repaired are compared, and the data in the energy utilization modes are used for repairing missing data. The data repairing process by using the KNN improved algorithm comprises the following steps: s1, establishing an energy utilization mode according to the historical data; s2, aiming at any energy utilization mode, establishing an electricity utilization characteristic database; and S3, selecting data with similar energy modes from the historical data to repair the missing data based on the KNN improved algorithm. The method has the advantages that the slope of the electricity utilization characteristic line is combined, the KNN improved algorithm is used for repairing abnormal energy consumption data by using data with similar energy consumption modes in historical data, and the quality of the building energy consumption data is greatly improved.
Description
Technical Field
The invention relates to a data processing method, in particular to a method for repairing abnormal data of building energy consumption monitoring based on a KNN improved algorithm, and belongs to the technical field of data processing of building energy consumption monitoring platforms.
Background
In recent years, a public building energy consumption monitoring platform is established, and the realization of building energy consumption item measurement is an important content for the construction of a public building energy supervision system. At present, building energy consumption monitoring platforms of different scales are established in many provinces and cities of China, and rich data is accumulated. However, due to some technical obstacles, the overall quality of building monitoring data is low, and data abnormal problems such as data loss, data mutation and the like occur, through investigation and analysis, the abnormal data proportion of the existing building energy consumption monitoring platform is generally up to 20%, so that the energy consumption monitoring data is far from the real energy consumption of the building, and a large amount of energy consumption data cannot be fully utilized. With the continuous development of green buildings in China, people not only acquire more and more types and larger data quantity of building energy consumption data, but also have higher and higher requirements on data quality. In order to improve the data quality of the building energy consumption monitoring platform, in recent years, many scientific researchers at home and abroad focus on the problems existing in the energy consumption monitoring platform data, and the missing or abnormal data of the platform is solved and corrected by adopting methods such as a data mining technology, cluster analysis and the like. The invention discloses a construction energy consumption monitoring abnormal data repairing method based on a KNN improved algorithm, which aims at solving the problem of repairing abnormal data. The energy consumption of building electricity is generally divided into four items of energy consumption of lighting sockets, energy consumption of heating ventilation air conditioners, energy consumption of power equipment and energy consumption of special electricity. The patent provides a method for repairing abnormal energy consumption data of the lighting socket based on a KNN improved algorithm by adopting a big data analysis method aiming at the power consumption characteristics of the energy consumption of the lighting socket so as to improve the data quality of a building energy consumption monitoring platform.
Disclosure of Invention
The invention aims to provide a method for repairing abnormal energy consumption data of an illumination socket based on a KNN improved algorithm, which can effectively improve the quality of building energy consumption monitoring data.
The technical scheme of the invention is as follows:
a method for repairing abnormal energy consumption data of an illumination socket based on a KNN improved algorithm comprises the following steps:
s1, calculating the slope of the historical data electricity utilization characteristic line: aiming at a certain type of energy consumption per unit time, calculating the slope of the historical data electricity utilization characteristic line per unit time, assuming that m historical data electricity utilization characteristic lines per unit time are provided, and for any historical data electricity utilization characteristic line per unit time i, the electricity consumptions at the j moment and the j +1 moment are respectively Ei,jAnd Ei,j+1The slope of the electricity utilization characteristic line from the j time to the j +1 time is Li,jThe calculation formula of (2) is as follows:
wherein, tjIs the unit time length;
s2, calculating the slope of the electricity utilization characteristic line of the abnormal data in unit time: calculating the abnormal data position by the same method as that of step S1Slope l of electricity utilization characteristic line at 24 hoursj，j＝0,1,2，…,23；
S3, searching sample data: slope sequence l based on abnormal datajAnd historical data set slope sequence Li,jSearching the hour energy consumption data of the day with the minimum sequencing k days distance from the historical data set (m days); slope l of electrical characteristic line for abnormal datajThe slope of the electricity utilization characteristic line of the ith calendar history data is Li,jEuclidean distance p ofiThe calculation formula is as follows:
s4, determining a weighting coefficient: using the ratio of the inverse Euclidean distance to the sum of inverse Euclidean distances of k days as a weighting coefficient for weighting and summing abnormal data; the weighting coefficient formula is:
wherein q istThe weight p of the t day in k days nearest to the Euclidean distance calculated by the KNN algorithmtEuclidean distance of day t;
s5, repairing abnormal data: obtaining the repairing data of the abnormal data according to the k-day sample data and the weighting coefficient; assuming that the moment j is abnormal data, the data recovery formula is as follows:
wherein ejFor calculated repair energy consumption data, tjIs a unit time length, Ln,jIs the slope of the electricity utilization characteristic line of the nth day in the k-day calendar history data at the time j, qnThe weighting factor corresponding to the day.
The method is also suitable for repairing abnormal electricity utilization data of other power equipment with relatively regular electricity utilization loads.
Compared with the prior art, the method has the advantages that the method for repairing the abnormal data of the building energy consumption based on the KNN improved algorithm is provided, and the repairing quality of the data of the building energy consumption is greatly improved.
Drawings
FIG. 1 is a flow chart of an abnormal data repair operation.
FIG. 2 is a schematic diagram of the calculation of the slope of the electrical characteristic line.
Fig. 3 is a schematic diagram of sample data search.
Detailed Description
The following detailed description of the invention refers to the accompanying drawings that illustrate specific embodiments of the invention.
Referring to the attached figure 1, the method for repairing abnormal building energy consumption data based on the KNN improved algorithm comprises the following steps:
s1, referring to FIG. 2, calculating the slope of the historical data electricity utilization characteristic line in unit time, assuming a total of m historical data electricity utilization characteristic broken lines in unit time, and regarding any historical data electricity utilization characteristic line i in unit time, the electricity consumption at the j th moment and the j +1 th moment are respectively Ei,j、Ei,j+1The slope of the electricity utilization characteristic line from the j th moment to the j +1 th moment is Li,jThe calculation formula of (2) is as follows:
wherein t isjIs a unit time length.
S2, calculating the slope l of the electricity utilization characteristic line in the unit time of 24 hours in which the abnormal data are positioned by adopting the method of S1j(j＝0,1,2，…,23)；
S3, referring to the attached figure 3, calculating the slope sequence l of the electricity utilization characteristic line where the abnormal data are located by using an Euclidean distance calculation formulajAnd historical data set slope sequence Li,jAnd searching energy consumption data with the minimum day-hour distance of k days from the historical data set (m days). Slope l of electrical characteristic line for abnormal datajThe slope of the electricity utilization characteristic line of the ith calendar history data is Li,jEuclidean distance p ofiThe calculation formula is as follows:
and S4, using the ratio of the inverse Euclidean distance to the sum of inverse Euclidean distances of k days as a weighting coefficient for weighting and summing the abnormal data. The weighting coefficient formula is:
wherein q istThe weight p of the t day in k days nearest to the Euclidean distance calculated by the KNN algorithmtThe Euclidean distance of day t.
And S5, finally, obtaining the repairing data of the abnormal data according to the k-day sample data and the weighting coefficient. Assuming that the moment j is abnormal data, the data recovery formula is as follows:
wherein ejFor calculated repair energy consumption data, tjIs a unit time length, Ln,jIs the slope of the electricity utilization characteristic line of the nth day in the k-day calendar history data at the time j, qnThe weighting factor corresponding to the day.
Claims (1)
1. A method for repairing abnormal energy consumption data of an illumination socket based on a KNN improved algorithm is characterized by comprising the following steps:
s1, calculating the slope of the electricity utilization characteristic line of the historical data: aiming at a certain type of energy consumption per unit time, calculating the slope of the power utilization characteristic line of historical data per unit time, and assuming a common power consumptionmA historical data electricity utilization characteristic line in unit time, for any historical data electricity utilization characteristic line in unit timeiOf 1 atjTime and firstj+The electricity consumption at 1 moment is respectivelyE i, j AndE i, j+1 then it is firstjFrom time to timej The slope of the power utilization characteristic line of the historical data in the +1 moment is
The calculation formula of (2) is as follows:
Wherein,t jis the unit time length;
s2, calculating the slope of the electricity utilization characteristic line of the abnormal data in unit time: calculating the slope of the abnormal data electricity utilization characteristic line by the same method as the step S1l j，
；
S3, searching sample data: slope of electricity utilization characteristic line based on abnormal datal j Slope of power utilization characteristic line from historical dataL i,jEuclidean distance (similarity) of (m days), finding a ranking from the historical data setkHour energy consumption data for the day from the minimum day; slope of electrical characteristic line for abnormal datal j Slope of power utilization characteristic line from historical dataL i,jEuclidean distance ofp iThe calculation formula is as follows:
S4, determining a weighting coefficient: using the inverse Euclidean distance andkthe ratio of the sum of the inverse Tian-Euclidean distances is used as a weighting coefficient for weighting and summing abnormal data; the weighting coefficient formula is:
Wherein,q t calculated for KNN algorithm as nearest Euclidean distancekIn the middle of the daytThe weight of the day is calculated,p tis as followstThe Euclidean distance of day;
s5, repairing abnormal data: according tokObtaining the repairing data of the abnormal data by the sample data of the day and the weighting coefficient; suppose thatjThe time is abnormal data, and the data repair formula is as follows:
Whereine jIn order to calculate the repair energy consumption data,t jin terms of the length of the unit of time,L n,jis composed ofkThe first in the calendar history datanThe electricity utilization characteristic line of the sky isjThe slope of the time of day is,q nthe weighting factor corresponding to the day.
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| CN111723085B (en) * | 2020-06-28 | 2023-06-23 | 山东建筑大学 | Method and system for cleaning electric energy consumption data of public building air conditioner |
| CN112765549B (en) * | 2021-01-20 | 2022-10-21 | 大连理工大学 | KNN-Matrix method for repairing abnormal data of building energy consumption monitoring |
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