CN109359134B - Method for identifying energy consumption implicit abnormal data of lighting socket based on data mining - Google Patents
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Abstract
The invention belongs to the technical field of building energy consumption monitoring platform data processing, and provides a method for identifying illumination socket energy consumption recessive abnormal data based on data mining, which 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; s3, identifying the dominant abnormal data based on a clustering big data analysis method; and S4, identifying the recessive abnormal data based on the historical electricity utilization characteristic line. The method has the beneficial effects that the hidden abnormal energy consumption data of the lighting socket, which cannot be identified by the conventional big data analysis method, is identified based on the electricity utilization characteristic line, so that the building energy consumption data quality is greatly improved.
Description
Technical Field
The invention relates to a data processing method, in particular to a method for identifying illumination and socket energy consumption recessive abnormal data based on data mining, and belongs to the technical field of building energy consumption monitoring platform data processing.
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 public building energy consumption supervision platform data processing method based on data mining (patent application number is 201410482593.X), which is technically characterized by comprising four aspects of problem data primary identification, energy consumption mode characteristic parameter set establishment according to historical data, problem data fine identification and problem data intelligent supplement, and the energy consumption mode characteristic parameter set for a building is established by classifying the historical energy consumption data according to different energy consumption modes, so that the integrated processing of classification identification, cleaning and intelligent supplement of the problem data of an energy consumption supervision platform is realized. The method can realize the identification of missing data and mutation data, but can not identify monitoring data which have abnormal energy consumption and meet the requirement of threshold selection. 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 identifying hidden abnormal data of energy consumption of the lighting socket based on data mining by adopting a big data analysis method aiming at the electricity 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 solve the technical problem of providing a method for identifying the hidden abnormal energy consumption data of the lighting socket based on data mining, which can effectively improve the data quality of a public building energy consumption supervision platform.
The technical scheme of the invention is as follows:
a method for identifying energy consumption implicit abnormal data of an illumination socket based on data mining comprises the following steps:
s1, establishing an energy utilization mode according to the historical data: finely dividing the lighting and socket energy consumption data into energy utilization modes such as working days, holidays and the like according to whether the day of the energy consumption data is a holiday or not;
s2, establishing a power utilization characteristic database: aiming at any one energy utilization mode, converting the accumulated energy consumption into energy consumption per unit time, and sequentially storing the energy consumption per unit time according to unit time step length by taking days as a unit, wherein the energy consumption data per day is an electricity utilization characteristic line;
the energy consumption calculation formula per unit time is as follows:
Ej＝Sj-Sj-1formula (1)
Wherein E isjIs the energy consumption per unit time, SjThe cumulative reading of the meter at the j-th moment, Sj-1The accumulated reading number of the meter at the j-1 moment is obtained;
s3, explicit abnormal data identification: identifying dominant abnormal data such as data loss, data mutation and the like in the energy consumption data of any energy consumption mode due to the metering or transmission process based on a clustering big data analysis method;
s4, implicit abnormal data identification: judging whether the new power utilization characteristic line is consistent with the power utilization characteristic line of the historical data under the similar working conditions or not based on the historical power utilization characteristic line, if so, judging that the energy consumption data is normal, and if not, judging that the energy consumption data is implicitly abnormal;
the specific identification steps are as follows:
s41, calculating the slope of the historical data electricity utilization characteristic line
Assuming that m historical data electricity utilization characteristic lines are provided, for any historical data electricity utilization characteristic line i, the electricity consumption at the j th moment and the j +1 th moment are respectively Ei,jAnd Ei,j+1The slope of the electricity utilization characteristic line in the time from the j th to the j +1 th is ki,jThe calculation formula of (2) is as follows:
where Δ tiIs the unit time length;
s42, and the general change trend of the historical data electricity utilization characteristic line
Suppose that n (0) is total in the j-th to j + 1-th time points<n<m) slope k of electricity utilization characteristic line of historical datai,jIf the power utilization characteristic line occupation ratio is p-n/m (%), the corresponding power utilization characteristic line occupation ratio for reducing the power consumption is q-1-p, if p is greater than q, the overall power consumption in the time from j to j +1 is in an increasing trend, otherwise, the overall power consumption is reducedA few trends;
s43, calculating the slope K of the new data electricity utilization characteristic line at the moment by adopting the method of the step S41j
S44, identifying the recessive abnormal error data by using whether the new data electricity characteristic line is consistent with the historical data electricity characteristic line
If K isj>0 and p in this time period>q or Kj<0 and p in this time period<q, the new electricity utilization characteristic line in the time period is consistent with the trend of the historical data; counting the number a of time periods with consistent curve change trend within 24 hours, if a/24>And 90%, the new electricity utilization characteristics are normal, otherwise, hidden abnormal error data exist.
Compared with the prior art, the method has the advantages that the method for identifying the energy consumption recessive abnormal data of the lighting socket based on data mining is provided, and the quality of the building energy consumption data is greatly improved.
Drawings
FIG. 1 is a flow chart of explicit anomaly data identification according to the present invention.
Fig. 2 is a diagram illustrating conversion of accumulated energy consumption into energy consumption per unit time.
FIG. 3 is a flowchart of the identification of implicit abnormal data.
FIG. 4 is a schematic diagram of the calculation of the slope of the electrical characteristic line.
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 invention relates to a method for identifying energy consumption implicit abnormal data of a lighting socket based on data mining, which comprises the following steps:
s1, establishing an energy utilization mode according to historical data, and finely dividing the energy consumption data of the lighting and socket into energy utilization modes such as working days, holidays and the like according to whether the day of the energy consumption data is a holiday or not;
s2, referring to the attached figure 2, establishing an electricity utilization characteristic database, converting accumulated energy consumption data into energy consumption per unit time aiming at any energy utilization mode, sequentially storing the energy consumption per unit time according to unit time step length by taking days as a unit, wherein the energy consumption data per day is an electricity utilization characteristic line;
the energy consumption calculation formula per unit time is as follows:
Ej＝Sj-Sj-1formula (1)
Wherein E isjIs the energy consumption per unit time, SjThe cumulative reading of the meter at the j-th moment, Sj-1The accumulated reading number of the meter at the j-1 time is shown.
S3, identifying dominant abnormal data, namely identifying the dominant abnormal data such as data loss, data mutation and the like in the energy consumption data due to metering or transmission processes based on big data analysis methods such as K-keys, density clustering and the like;
and S4, recognizing the recessive abnormal data, judging whether the new power utilization characteristic line is consistent with the power utilization characteristic line of the history data under the similar working conditions or not based on the history power utilization characteristic line, if so, judging that the energy consumption data are normal, and if not, judging that the energy consumption data are recessive abnormal.
Referring to fig. 3, the implicit abnormal data identification specifically includes the following steps:
s41, calculating the slope of the historical data electricity utilization characteristic line;
referring to fig. 4, assuming that there are m historical data electricity utilization characteristic lines, for any one historical data electricity utilization characteristic line i, the electricity consumption amounts at the j th and j +1 th time points are respectively Ei,j、Ei,j+1The slope of the power utilization broken line from the j-th to the j + 1-th time is ki,jThe calculation formula of (2) is as follows:
where Δ tiIs a unit time length.
S42, describing the general change trend of the historical data electricity characteristic line;
suppose that n (0) is total in the j-th to j + 1-th time points<n<m) slope k of electricity utilization characteristic line of historical datai,j(i is more than or equal to 0 and less than or equal to n) is more than 0, the power utilization characteristic line occupation ratio of the power consumption increase at the moment is p-n/m (%), the corresponding power utilization characteristic line occupation ratio of the power consumption decrease is q-1-p,if p > q, the total electricity consumption in the time from j to j +1 is in an increasing trend, otherwise, the total electricity consumption is in a decreasing trend.
S43, calculating the slope K of the new data electricity utilization characteristic line at the moment by adopting the method S41j；
And S44, identifying the recessive abnormal error data by using whether the new data electricity utilization characteristic line is consistent with the historical data electricity utilization characteristic line or not.
If K isj>0, and p is within the time period>q or, Kj<0, and p is within the time period<And q, the new electricity utilization characteristic line in the time period is consistent with the historical data trend. Counting the number a of time periods with consistent curve change trend within 24 hours, if a/24>And 90%, the new electricity utilization characteristics are normal, otherwise, hidden abnormal error data exist.
Claims (1)
1. A method for identifying energy consumption implicit abnormal data of an illumination socket based on data mining is characterized by comprising the following steps:
s1, establishing an energy utilization mode according to the historical data: finely dividing the lighting and socket energy consumption data into working day and holiday energy utilization modes according to whether the day of the energy consumption data is a holiday or not;
s2, establishing a power utilization characteristic database: aiming at any one energy utilization mode, converting the accumulated energy consumption into energy consumption per unit time, and sequentially storing the energy consumption per unit time according to unit time step length by taking days as a unit, wherein the energy consumption data per day is an electricity utilization characteristic line;
the energy consumption calculation formula per unit time is as follows:
Ej＝Sj-Sj-1formula (1)
Wherein E isjIs the energy consumption per unit time, SjThe cumulative reading of the meter at the j-th moment, Sj-1The accumulated reading number of the meter at the j-1 moment is obtained;
s3, explicit abnormal data identification: identifying data loss and data mutation dominant abnormal data generated in the metering or transmission process in the energy consumption data of any energy consumption mode based on a clustering big data analysis method;
s4, implicit abnormal data identification: judging whether the new power utilization characteristic line is consistent with the power utilization characteristic line of the historical data under the similar working conditions or not based on the historical power utilization characteristic line, if so, judging that the energy consumption data is normal, and if not, judging that the energy consumption data is implicitly abnormal;
the specific identification steps are as follows:
s41, calculating the slope of the historical data electricity utilization characteristic line
Assuming that m historical data electricity utilization characteristic lines are provided, for any historical data electricity utilization characteristic line i, the electricity consumption at the j th moment and the j +1 th moment are respectively Ei,jAnd Ei,j+1The slope of the electricity utilization characteristic line in the time from the j th to the j +1 th is ki,jThe calculation formula of (2) is as follows:
where Δ tiIs the unit time length;
s42, and the general change trend of the historical data electricity utilization characteristic line
Suppose that the slope k of n historical data power utilization characteristic lines in the j to j +1 timei,jGreater than 0, wherein 0<n<If p is greater than q, the total electricity consumption in the time from the j to the j +1 is in an increasing trend, otherwise, the total electricity consumption is in a decreasing trend;
s43, calculating the slope K of the new data electricity utilization characteristic line at the moment by adopting the method of the step S41j；
S44, identifying the recessive abnormal error data by using whether the new data electricity characteristic line is consistent with the historical data electricity characteristic line
If K isj>0 and p within the j-th to j + 1-th time>q or Kj<0 and p within the j-th to j + 1-th time<q, the new electricity utilization characteristic line is consistent with the historical data trend from the j to the j + 1; counting the number a of time periods with consistent curve change trend within 24 hours, if a/24>90%, the new electricity utilizationThe characteristic is normal, otherwise, hidden abnormal error data exists.
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| CN104199961A (en) * | 2014-09-19 | 2014-12-10 | 北京建筑技术发展有限责任公司 | Data mining based public building energy consumption monitoring platform data processing method |
| CN104636999A (en) * | 2015-01-04 | 2015-05-20 | 江苏联宏自动化***工程有限公司 | Detection method for building abnormal energy consumption data |
| CN105588995A (en) * | 2015-12-11 | 2016-05-18 | 深圳供电局有限公司 | Line loss abnormity detection method for electric power metering automation system |
| CN106909664A (en) * | 2017-02-28 | 2017-06-30 | 国网福建省电力有限公司 | A kind of power equipment data stream failure recognition methods |
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| CN104199961A (en) * | 2014-09-19 | 2014-12-10 | 北京建筑技术发展有限责任公司 | Data mining based public building energy consumption monitoring platform data processing method |
| CN104636999A (en) * | 2015-01-04 | 2015-05-20 | 江苏联宏自动化***工程有限公司 | Detection method for building abnormal energy consumption data |
| CN105588995A (en) * | 2015-12-11 | 2016-05-18 | 深圳供电局有限公司 | Line loss abnormity detection method for electric power metering automation system |
| CN106909664A (en) * | 2017-02-28 | 2017-06-30 | 国网福建省电力有限公司 | A kind of power equipment data stream failure recognition methods |
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