CN104636999B - A kind of abnormal energy data detection method of building - Google Patents

A kind of abnormal energy data detection method of building Download PDF

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CN104636999B
CN104636999B CN201510001544.4A CN201510001544A CN104636999B CN 104636999 B CN104636999 B CN 104636999B CN 201510001544 A CN201510001544 A CN 201510001544A CN 104636999 B CN104636999 B CN 104636999B
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data
building
subsequence
abnormal
energy
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CN104636999A (en
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张璐
屈晨
曹道柱
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Jiangsu Alliance Wisdom Energy Ltd By Share Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/08—Construction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2462—Approximate or statistical queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2465—Query processing support for facilitating data mining operations in structured databases
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

Abstract

The invention discloses a kind of abnormal energy data detection methods of building, and initial data is sorted out first, in accordance with time series；It refers again to industry index to be judged, finds out the abnormal data of special time period；Then it using the separate-blas estimation technology in data mining, filters out with the exceptional value in energy data；The abnormal point in data is finally found out using the Time Series Similarity based on Discrete Fourier Transform.The method of abnormal data precise positioning in the data provided by the invention to energy for building, it may replace the empirical value determining method generally used in current energy industry, by the application of the analysis method and Discrete Fourier Transform principle of artificial intelligence in data mining, the erroneous judgement to exception energy data is prevented, reports by mistake and fails to report, effective exception energy monitoring information is provided for energy unit and personnel.

Description

A kind of abnormal energy data detection method of building
Technical field
The present invention relates to building energy consumption monitoring field, the abnormal energy data detection method of specifically a kind of building.
Background technology
In building energy consumption monitoring system, exception can detect be the safe efficient operation of guarantee system an important ring, by The function, operation maintenance personnel can know in real time with can extremely, be accurately positioned abort situation, in time exclude or repair system failure.When Before, judge exception can data substantially dependent on expert or user experience, with reference to industry index, set fixed limit or Threshold value, once monitoring data, which exceeds limit or reaches threshold value, triggers alarm.And in actual motion, with energy situation climate, people The conditions such as member, season influence, complex with energy data fluctuations situation, abnormal obvious by single static data is set to judge It loses precisely.It is found by being tracked to the operation of more systems, abnormal system acquisition when occurring is less than and judging wrong report phenomenon by accident It happens occasionally, not only can do nothing to help user accurately with energy, can also generate interference to user job in some cases.
Invention content
Separate-blas estimation technology in data mining is combined by the present invention with practical experience judgement, the accurate abnormal number of screening According to realization is to the abnormal effective judgement with energy data of building in maximum magnitude, to overcome defect in the prior art.
The purpose of the present invention is achieved through the following technical solutions：
A kind of abnormal energy data detection method of building：
Step (1):Building energy consumption data are acquired by frequency acquisition of fixed time period, establish the time series with energy data D:
D={ dt| t=t0,t1,t2,...,tn-1}；
In formula, dtFor element, the gathered data in t moment is represented；
Step (2):It according to building actual conditions simultaneously based on industry target setting threshold value, compares with the threshold value, during extraction Between abnormal elements in sequence, i.e., abnormal gathered data, using abnormal elements as the subsequence D of D1；It is removed in time series D Subsequence D of the abnormal elements as D2：D2={ d1,d2,...,dp}；
Step (3):Using the Outlier mining method based on deviation, with regular length m by D2It is split as s sub- sequences Row, D2Subsequence D2sIt is expressed as：
D2s={ ds,ds+1,...,ds+m-1, 2≤m≤p
In formula, m is the element number of subsequence, and p represents D2Element number；
The smoothing factor of each subsequence is calculated with dissimilarity function, smoothing factor with defining threshold value is compared, is judged whether Exceed:The subsequence exceeded is denoted as D21；Without departing from subsequence be denoted as normal data sequence；
Step (4):Time Series Similarity based on discrete Fourier transform searches D respectively1And D21In abnormal elements, By D1And D21Multiple subsequences are split as with regular length respectively, with discrete Fourier transform by subsequence time series data from time domain Spatial alternation obtains Fourier coefficient sequence, Fourier coefficient D to domain spacef：
In formula, element numbers of the n for subsequence, dtFor the element in subsequence, f=0 ..., n-1；
Given standard energy consumption sequence, given threshold, by D1And D21In the subsequence that splits out respectively with standard energy consumption sequence Between calculate Euclidean distance, more than given threshold value sequence for abnormal data sequence, what it is less than or equal to threshold value is then normal data sequence Row.
Building actual conditions and industry index include：Spring, period in autumn, air conditioning electricity should be less than standby power consumption amount；Night Between the rest period, the electricity consumption of public building room lighting and should maintain extremely low energy consumption with water；Office building festivals or holidays electricity consumption should be small In work daily power consumption；Commercial building non operation time section energy consumption should be less than business hours section energy consumption；Office building, teaching building etc. are built Building the power consumption values fluctuation range during normal use should be relatively steady.
Preferably, dissimilarity function is defined as in step (3)：
In step (4), the Euclidean distance of two sequences is expressed as：X in formulafAnd yfRespectively sequence X With coefficients of the sequence Y after discrete Fourier transform.
Beneficial effects of the present invention：
The present invention provides a kind of methods of the abnormal data precise positioning in data to energy for building, may replace current energy The empirical value determining method generally used in the industry of source, analysis method and discrete Fu by artificial intelligence in data mining The application of vertical leaf transformation principle prevents the erroneous judgement to exception energy data, reports by mistake and fail to report, is provided with for energy unit and personnel Abnormal use of effect can monitoring information.
Description of the drawings
Fig. 1 is the step flow chart of the method for the present invention.
Specific embodiment
Below in conjunction with specific embodiment, the invention will be further described.
Embodiment 1
A kind of abnormal energy data detection method of building：
Step (1):Building energy consumption data are acquired by frequency acquisition of fixed time period, establish the time series with energy data D:
D={ dt| t=t0,t1,t2,...,tn-1}；
In formula, dtFor element, the gathered data in t moment is represented；
Step (2):It according to building actual conditions simultaneously based on industry target setting threshold value, compares with the threshold value, during extraction Between abnormal elements in sequence, i.e., abnormal gathered data, using abnormal elements as the subsequence D of D1；It is removed in time series D Subsequence D of the abnormal elements as D2：D2={ d1,d2,...,dp}；
Step (3):Using the Outlier mining method based on deviation, with regular length m by D2It is split as s sub- sequences Row, D2Subsequence D2sIt is expressed as：
D2s={ ds,ds+1,...,ds+m-1, 2≤m≤p
In formula, m is the element number of subsequence, and p represents D2Element number；
The smoothing factor of each subsequence is calculated with dissimilarity function, smoothing factor with defining threshold value is compared, is judged whether Exceed:The subsequence exceeded is denoted as D21；Without departing from subsequence be denoted as normal data sequence；
Step (4):Time Series Similarity based on discrete Fourier transform searches D respectively1And D21In abnormal elements, By D1And D21Multiple subsequences are split as with regular length respectively, with discrete Fourier transform by subsequence time series data from time domain Spatial alternation obtains Fourier coefficient sequence, Fourier coefficient D to domain spacef：
In formula, element numbers of the n for subsequence, dtFor the element in subsequence, f=0 ..., n-1；
Given standard energy consumption sequence, given threshold, by D1And D21In the subsequence that splits out respectively with standard energy consumption sequence Between calculate Euclidean distance, more than given threshold value sequence for abnormal data sequence, what it is less than or equal to threshold value is then normal data sequence Row.
Embodiment 2：
Such as the abnormal energy data detection method of building a kind of in embodiment 1, spring, period in autumn, air conditioning electricity should be less than Standby power consumption amount；Night rest periods, the electricity consumption of public building room lighting and should maintain extremely low energy consumption with water；Office building section is false Daily power consumption should be less than work daily power consumption；Commercial building non operation time section energy consumption should be less than business hours section energy consumption；Office Power consumption values fluctuation range should be relatively steady during normal use for the building such as building, teaching building.
Embodiment 3：
Such as the abnormal energy data detection method of building a kind of in embodiment 1 or 2, dissimilarity function is defined as：
In the embodiment above, the Time Series Similarity that step (4) is converted based on discrete Fourier finding step again (2), the subsequence D filtered out in (3)1And D21In abnormal elements.
The energy consumption data of building, time series there are certain law variation, such as seasonal variations generate air-conditioning Electricity consumption data variation, Campus buildingss the energy consumption regularity between winter and summer vacations reduce, commercial building energy consumption rule during festivals or holidays Property raising etc..It can be based on the variation of above-mentioned energy consumption data rule and make each class standard energy consumption sequence.
Principle is changed using discrete Fourier, when the method based on time series similarity analysis passes through multiple data points Countershaft connects into curve, and line is expanded to by point, and the similarity or diversity factor between line and line are analyzed, and thus can will isolate Energy consumption data be connected into it is regular with can behavior curve, therefrom find out outlier, position abnormal data.
By D1And D21Multiple subsequences are split as with regular length respectively, with Discrete Fourier Transform by ordinal number during subsequence Domain space is transformed to according to from time domain space, obtains following fourier coefficient：
Wherein, element numbers of the n for subsequence, dtFor the element in subsequence, f=0 ..., n-1；
It is whether similar using two sequences of Euclidean distance measurement, if the Euclidean distance of two sequences is less than given threshold value ε, Then think that the two sequences are similar, that is, meet equation below：
According to Parserval theories, following equation is also set up：
By D1And D21In the subsequence that splits out and calculate Euclidean distance between standard energy consumption sequence, more than the sequence of given threshold value Abnormal data sequence is classified as, what it is less than or equal to threshold value is then normal data sequence.
Heretofore described threshold value can be provided by limited trials or experience.
Above example is merely to illustrate technical scheme of the present invention rather than limiting the scope of the invention, although The present invention is explained in detail with reference to preferred embodiment, it will be understood by those of ordinary skill in the art that, it can be to this hair Bright technical solution is modified or equivalent replacement, without departing from the spirit and scope of technical solution of the present invention.

Claims (3)

1. a kind of abnormal energy data detection method of building, it is characterised in that:Method is
Step (1):Building energy consumption data are acquired by frequency acquisition of fixed time period, establish the time series D with energy data:
D={ dt| t=t0,t1,t2,...,tn-1}；
In formula, dtFor element, the gathered data in t moment is represented；
Step (2):According to building actual conditions and industry target setting threshold value is based on, is compared with the threshold value, extraction time sequence Abnormal elements in row, using abnormal elements as the subsequence D of D1；The abnormal elements are removed in time series D as D's Subsequence D2：D2={ d1,d2,...,dp}；
Step (3):Using the Outlier mining method based on deviation, with regular length m by D2It is split as s subsequence, D2's Subsequence D2sIt is expressed as：
D2s={ ds,ds+1,...,ds+m-1, 2≤m≤p
In formula, m is the element number of subsequence, and p represents D2Element number；
The smoothing factor of each subsequence is calculated with dissimilarity function, smoothing factor with defining threshold value is compared, is seen if fall out: The subsequence exceeded is denoted as D21；Without departing from subsequence be denoted as normal data sequence；Dissimilarity function is defined as：
In formula, A is adjacent two numerical value dq-1, dqBetween difference, B is follow-up adjacent two numbers dq, dq+1Between difference, m is collection object Length,It is xor operator, is defined as
Step (4):Time Series Similarity based on discrete Fourier transform searches D respectively1And D21In abnormal elements, by D1 And D21Multiple subsequences are split as with regular length respectively, with discrete Fourier transform by subsequence time series data from time domain space Domain space is transformed to, obtains Fourier coefficient sequence, Fourier coefficient Df：
In formula, element numbers of the n for subsequence, dtFor the element in subsequence, f=0 ..., n-1；
Given standard energy consumption sequence, given threshold, by D1And D21In the subsequence that splits out calculate respectively and standard energy consumption sequence Between Euclidean distance, Euclidean distance is more than the sequence of given threshold value for abnormal data sequence, less than or equal to threshold value then for just Regular data sequence.
2. a kind of abnormal energy data detection method of building according to claim 1, it is characterised in that:Build actual conditions And industry index includes：Spring, period in autumn, air conditioning electricity should be less than standby power consumption amount；Night rest periods, office building, religion It learns building room lighting electricity consumption and should maintain extremely low energy consumption with water；Office building festivals or holidays electricity consumption should be less than work daily power consumption； Commercial building non operation time section energy consumption should be less than business hours section energy consumption；Office building, the teaching building energy during normal use Consumption value fluctuation range should be relatively steady.
3. a kind of abnormal energy data detection method of building according to claim 1, it is characterised in that:The Europe of two sequences Formula distance is expressed as：X in formulafAnd yfRespectively sequence X and sequence Y be after discrete Fourier transform Number.
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