CN109359134A - A kind of recognition methods of the light socket energy consumption recessiveness abnormal data based on data mining - Google Patents
A kind of recognition methods of the light socket energy consumption recessiveness abnormal data based on data mining Download PDFInfo
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Abstract
The invention belongs to building energy consumption monitoring platform technical field of data processing, provide a kind of recognition methods of light socket energy consumption recessiveness abnormal data based on data mining, comprising the following steps: S1, are established according to historical data with energy mode；S2, energy mode is used for any one, establish electricity consumption property data base；S3, it is based on cluster big data analysis method, identifies dominant abnormal data；S4, it is based on history electricity consumption characteristic curve, identifies recessive abnormal data.The invention has the beneficial effects that being based on electricity consumption characteristic curve, the recessive abnormal energy consumption data of light socket that conventional big data analysis method can not recognize, greatly the promotion building energy consumption quality of data are identified.
Description
Technical field
The present invention relates to a kind of data processing methods, and in particular to a kind of illumination and socket energy consumption based on data mining are hidden
The recognition methods of sexual abnormality data belongs to building energy consumption monitoring platform technical field of data processing.
Background technique
In recent years, monitoring emery consumption of public buildings platform is established, realizes that building energy consumption metering separate is public building energy prison
One important content of pipe System Construction.Currently, many provinces and cities of China all establish the building energy consumption monitoring platform of different scales,
And have accumulated data abundant.However due to the obstacle of some technical aspects, cause building monitoring data total quality not high,
There is the data exceptions problems such as shortage of data, data mutation, by investigation and analysis, the abnormal number of existing building energy consumption monitoring platform
It is generally up to 20% according to ratio, therefore energy consumption monitoring data differ greatly with true energy consumption is built, and lead to these a large amount of energy consumptions
Data, which are unable to get, to be made full use of.With the continuous development of China's green building, people not only plant the acquisition of building energy consumption data
Class is more and more, data volume is increasing, and higher and higher to data quality requirement.In order to improve building energy consumption monitoring platform
The quality of data, in recent years, domestic and international many scientific research personnel focus on energy consumption monitoring platform data there are the problem of, using data
The methods of digging technology, clustering, solve and amendment platform lacks or abnormal data.Patent of invention discloses a kind of based on number
According to the Energy Consumption of Public Buildings supervising platform data processing method (number of patent application 201410482593.X) of excavation, technology is special
Sign include problem data tentatively identify, established according to historical data finely identified with energy pattern feature parameter set, problem data and
Problem data intelligently in terms of supplement four, by using energy pattern classification according to different to historical energy consumption data, establishes energy for building
Consume pattern feature parameter set, realize to the Classification and Identification of energy consumption supervising platform problem data, cleaning, intelligently supplement integration at
Reason.The identification to missing data and accidental data may be implemented in this method, but for some abnormal with energy, and energy consumption data meets
Threshold value is chosen desired monitoring data and can not be identified.Building electricity consumption energy consumption be generally divided into light socket energy consumption, Heating,Ventilating and Air Conditioning energy consumption,
Power-equipment energy consumption and special electricity consumption four.This patent uses electrical feature for light socket energy consumption, using big data
Analysis method proposes a kind of recognition methods of light socket energy consumption recessiveness abnormal data based on data mining, is built with improving
Build the quality of data of energy consumption monitoring platform.
Summary of the invention
The technical problem to be solved in the present invention is to provide one kind can effectively improve Energy Consumption of Public Buildings supervising platform data
The recognition methods of the light socket energy consumption recessiveness abnormal data based on data mining of quality.
Technical scheme is as follows:
A kind of recognition methods of the light socket energy consumption recessiveness abnormal data based on data mining, steps are as follows:
S1, established according to historical data with energy mode: whether day where the moment occurs according to energy consumption data is that festivals or holidays will
Illumination and socket energy consumption data are finely divided into working day, festivals or holidays etc. with energy mode；
S2, it establishes electricity consumption property data base: using energy mode for any one, accumulative energy consumption is converted into unit time energy
Consumption, and be daily unit, unit time energy consumption is successively stored by unit time step-length, the energy consumption data of every day be one with
Electrical feature line；
Unit time energy consumption formulas are as follows:
Ej=Sj-Sj-1Formula (1)
Wherein, EjFor unit time energy consumption, SjAdd up registration, S for jth moment gauge tablej-1It is tired for jth -1 moment gauge table
Count registration；
S3, dominant disorder data recognition: any energy consumption number with energy mode is identified based on cluster big data analysis method
Since the shortage of data of metering or transmission process generation, data are mutated codominance abnormal data in；
S4, recessive disorder data recognition: it is based on history electricity consumption characteristic curve, judges new electricity consumption characteristic curve and close regime history
Whether data electricity consumption characteristic curve is consistent, if unanimously, energy consumption data is normal, if inconsistent, energy consumption data is recessive abnormal；
Specific identification step is as follows:
S41, historical data electricity consumption characteristic curve slope calculate
Assuming that a shared m historical data electricity consumption characteristic curve, for any bar historical data electricity consumption characteristic curve i, jth and the
The electricity consumption at j+1 moment is respectively Ei,jAnd Ei,j+1, then the slope of electricity consumption characteristic curve is k in jth to+1 moment of jthi,jCalculating
Formula are as follows:
Wherein Δ tiFor unit time span；
The general morphologictrend of S42, historical data electricity consumption characteristic curve
Assuming that total n (0 < n < m) historical data electricity consumption characteristic curve slope k in jth to+1 moment of jthi,j(0≤i≤n) is big
In 0, then the increased electricity consumption characteristic curve accounting of electricity consumption is p=n/m (%) in the moment, and the electricity consumption of corresponding electricity consumption reduction is special
Sign line accounting is q=1-p, illustrates that electricity consumption totally shows a increasing trend in jth to+1 moment of jth if p > q, otherwise is electricity
Overall is in reduction trend；
S43, the moment new data electricity consumption characteristic curve slope K is calculated using the method for step S41j
S44, the recessive exception error of new data electricity consumption characteristic curve identification whether consistent with historical data electricity consumption characteristic curve is utilized
Data
If Kj> 0 and the period in p > q or Kj< 0 and the period in p < q, then it is new in the period to use electrical feature
Line is consistent with historical data trend；In 24 hours, the number a of statistic curve variation tendency consistent period, if a/24 >
90%, then it is new normal with electrical feature, otherwise there are recessive exception error data.
Compared with prior art, the beneficial effects of the invention are as follows provide a kind of light socket energy consumption based on data mining
The recognition methods of recessive abnormal data, greatly the promotion building energy consumption quality of data.
Detailed description of the invention
Fig. 1 is the dominant disorder data recognition flow chart of the present invention.
Fig. 2 is that accumulative energy consumption is converted into unit time energy consumption schematic diagram.
Fig. 3 is recessive disorder data recognition work flow diagram.
Fig. 4 is that electricity consumption characteristic curve slope calculates schematic diagram.
Specific embodiment
Below in conjunction with summary of the invention and the Figure of description specific embodiment that the present invention will be described in detail.
Referring to attached drawing 1, a kind of identification side of light socket energy consumption recessiveness abnormal data based on data mining of the invention
Method comprising following steps:
S1, it is established according to historical data with energy mode, whether day where the moment occurs according to energy consumption data is that festivals or holidays will
Illumination and socket energy consumption data are finely divided into working day, festivals or holidays etc. with energy mode；
S2, reference attached drawing 2, establish electricity consumption property data base, are turned accumulative energy consumption data with energy mode for any one
It is changed to unit time energy consumption, and is daily unit, unit time energy consumption is successively stored by unit time step-length, the energy of every day
Consumption data are an electricity consumption characteristic curve；
Unit time energy consumption formulas are as follows:
Ej=Sj-Sj-1Formula (1)
Wherein, EjFor unit time energy consumption, SjAdd up registration, S for jth moment gauge tablej-1It is tired for jth -1 moment gauge table
Count registration.
S3, dominant disorder data recognition identify energy consumption number based on the big data analysis method such as K-keans, Density Clustering
Shortage of data, data mutation codominance abnormal data in due to being generated in metering or transmission process；
S4, recessive disorder data recognition, are based on history electricity consumption characteristic curve, judge new electricity consumption characteristic curve and close regime history
Whether data electricity consumption characteristic curve is consistent, if unanimously, energy consumption data is normal, if inconsistent, energy consumption data is recessive abnormal.
Referring to attached drawing 3, recessive disorder data recognition specifically includes the following steps:
S41, historical data electricity consumption characteristic curve slope calculate；
Referring to attached drawing 4, it is assumed that a shared m historical data electricity consumption characteristic curve, for any bar historical data electrical feature
The electricity consumption at line i, jth and+1 moment of jth is respectively Ei,j、Ei,j+1, then the slope of electricity consumption broken line is in jth to+1 moment of jth
ki,jCalculation formula are as follows:
Wherein Δ tiFor unit time span.
The general morphologictrend description of S42, historical data electricity consumption characteristic curve；
Assuming that total n (0 < n < m) historical data electricity consumption characteristic curve slope k in jth to+1 moment of jthi,j(0≤i≤n) is big
In 0, then the increased electricity consumption characteristic curve accounting of electricity consumption is p=n/m (%) in the moment, and the electricity consumption of corresponding electricity consumption reduction is special
Sign line accounting is q=1-p, illustrates that electricity consumption totally shows a increasing trend in jth to+1 moment of jth if p > q, otherwise is electricity
Overall is in reduction trend.
S43, the moment new data electricity consumption characteristic curve slope K is calculated using the method for S41j；
S44, the recessive exception error of new data electricity consumption characteristic curve identification whether consistent with historical data electricity consumption characteristic curve is utilized
Data.
If Kj> 0, and p > q in the period, alternatively, Kj< 0, and p < q in the period, then use new in the period
Electrical feature line is consistent with historical data trend.In 24 hours, the number a of statistic curve variation tendency consistent period, if
A/24 > 90%, then it is new normal with electrical feature, otherwise there are recessive exception error data.
Claims (1)
1. a kind of recognition methods of the light socket energy consumption recessiveness abnormal data based on data mining, which is characterized in that step is such as
Under:
S1, established according to historical data with energy mode: whether day where the moment occurs according to energy consumption data is that will illuminate festivals or holidays
Finely it is divided into working day, festivals or holidays energy mode with socket energy consumption data；
S2, it establishes electricity consumption property data base: using energy mode for any one, accumulative energy consumption is converted into unit time energy consumption,
And be daily unit, unit time energy consumption is successively stored by unit time step-length, the energy consumption data of every day is an electricity consumption
Characteristic curve；
Unit time energy consumption formulas are as follows:
Ej=Sj-Sj-1Formula (1)
Wherein, EjFor unit time energy consumption, SjAdd up registration, S for jth moment gauge tablej-1Show for jth -1 moment gauge table is accumulative
Number；
S3, dominant disorder data recognition: it is identified in any energy consumption data with energy mode based on cluster big data analysis method
Shortage of data, the dominant abnormal data of data mutation generated due to metering or transmission process；
S4, recessive disorder data recognition: it is based on history electricity consumption characteristic curve, judges new electricity consumption characteristic curve and close regime history data
Whether electricity consumption characteristic curve is consistent, if unanimously, energy consumption data is normal, if inconsistent, energy consumption data is recessive abnormal；
Specific identification step is as follows:
S41, historical data electricity consumption characteristic curve slope calculate
Assuming that a shared m historical data electricity consumption characteristic curve, for any bar historical data electricity consumption characteristic curve i, jth and jth+1
The electricity consumption at moment is respectively Ei,jAnd Ei,j+1, then the slope of electricity consumption characteristic curve is k in jth to+1 moment of jthi,jCalculation formula
Are as follows:
Wherein Δ tiFor unit time span；
The general morphologictrend of S42, historical data electricity consumption characteristic curve
Assuming that total n historical data electricity consumption characteristic curve slope k in jth to+1 moment of jthi,jGreater than 0, wherein 0 < n < m, 0≤i≤
N, then the increased electricity consumption characteristic curve accounting of electricity consumption is p=n/m (%) in the moment, and electrical feature is used in corresponding electricity consumption reduction
Line accounting is q=1-p, illustrates that electricity consumption totally shows a increasing trend in jth to+1 moment of jth if p > q, otherwise total for electricity
Body is in reduction trend；
S43, the moment new data electricity consumption characteristic curve slope K is calculated using the method for step S41j；
S44, the recessive exception error data of new data electricity consumption characteristic curve identification whether consistent with historical data electricity consumption characteristic curve are utilized
If Kj> 0 and the period in p > q or Kj< 0 and the period in p < q, then electricity consumption characteristic curve new in the period with
Historical data trend is consistent；In 24 hours, the number a of statistic curve variation tendency consistent period, if a/24 > 90%,
It is then new normal with electrical feature, otherwise there are recessive exception error data.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112686286A (en) * | 2020-12-18 | 2021-04-20 | 博锐尚格科技股份有限公司 | Building operation energy consumption abnormity identification method, system and computer readable storage medium |
| WO2021120829A1 (en) * | 2019-12-20 | 2021-06-24 | 上海市建筑科学研究院有限公司 | Method for diagnosing energy consumption of lighting socket branches of buildings during non-operational periods |
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| US20160356859A1 (en) * | 2015-06-02 | 2016-12-08 | Passivsystems Limited | Fault detection in energy generation arrangements |
| 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 |
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| US20160356859A1 (en) * | 2015-06-02 | 2016-12-08 | Passivsystems Limited | Fault detection in energy generation arrangements |
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| WO2021120829A1 (en) * | 2019-12-20 | 2021-06-24 | 上海市建筑科学研究院有限公司 | Method for diagnosing energy consumption of lighting socket branches of buildings during non-operational periods |
| CN112686286A (en) * | 2020-12-18 | 2021-04-20 | 博锐尚格科技股份有限公司 | Building operation energy consumption abnormity identification method, system and computer readable storage medium |
| CN112686286B (en) * | 2020-12-18 | 2024-05-28 | 博锐尚格科技股份有限公司 | Building operation energy consumption abnormality identification method, system and computer readable storage medium |
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