CN104199961A - Data mining based public building energy consumption monitoring platform data processing method - Google Patents
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Abstract
The invention discloses a data mining based public building energy consumption monitoring platform data processing method which is characterized by including the steps of S1, primarily recognizing problem data; S2, creating an energy consumption mode characteristic parameter set according to historical data; S3, precisely recognizing the problem data; and S4, performing intelligent supplement to the problem data. The method has the advantages of intellectuality, real-time performance and universality; by classifying the historical energy consumption data according to different energy consumption modes and creating the energy consumption mode characteristic parameter set, the method realizes integral process of classified recognition, cleaning and intelligent supplement to the problem data of the energy consumption monitoring platform and solves the problems of poor intellectuality of data processing methods and reasonability of problem data supplement methods of existing public building energy consumption monitoring platform, and further, can effectively realize quality evaluation, energy consumption estimation and problem data alarm of the energy consumption monitoring platform while providing reliable troubleshooting proposal.
Description
Technical field
The present invention relates to a kind of data processing method, be specifically related to a kind of Energy Consumption of Public Buildings supervising platform data processing method based on data mining, belong to technical field of data processing.
Background technology
According to statistics, global building energy consumption accounts for greatly 1/3rd of energy total flow.In the energy-consuming main body of China, the shared ratio of building energy consumption has reached 35%, along with social development, and China's building energy consumption total amount and also have and continue the trend that rises in the ratio of energy overall consumption.Wherein, Energy Consumption of Public Buildings is high all the time, although public building quantity only accounts for 5% left and right of total asd number, energy consumption accounts for 22% of building total energy consumption.Meanwhile, China is being faced with the dual-pressure of energy shortage and ecological deterioration, and building energy conservation reduces discharging extremely urgent.Therefore monitoring, control Energy Consumption of Public Buildings become an important process of building energy conservation.
Set up Energy Consumption of Public Buildings supervising platform, carry out the important content that metering separate energy consumption is public building energy management system.It combines infotech with energy efficiency supervision, realize the Real-Time Monitoring of Energy Consumption of Public Buildings and management.At present, many public buildings have been set up energy consumption supervising platform, and have accumulated mass data in operation process.Yet mass data has also been brought " data disaster ", reason due to aspects such as " technology " and " management ", in energy consumption supervising platform operational process, can produce a large amount of in-problem data, managerial personnel are difficult to effectively find and process these problem datas, finally cause energy consumption monitoring data and the true energy consumption of building to differ greatly.Not only the poor monitoring emery consumption of public buildings platform of the quality of data can not promote carrying out of Building Energy-saving Work, also can disturb, mislead normally carrying out of Building Energy-saving Work.Therefore, how to find in time and process these problem datas, improving the energy consumption supervising platform quality of data is a problem demanding prompt solution.
Current problem data disposal route roughly can be divided into two classes: a class is simple setting threshold values facture, and another kind is relatively intelligent data processing method.Set threshold method threshold values and be difficult to determine, threshold values is chosen improper very big on data processed result impact, and lack of wisdom.Conventional intelligent processing method, considers deficiency to factors such as season, regional environments at present, and dynamic adaptable is not ideal enough, and lacks intelligent problem data compensation process.
Summary of the invention
For the problem such as the lack of wisdom of current energy consumption data disposal route, data filling means be reasonable not, the object of the present invention is to provide a kind of Energy Consumption of Public Buildings supervising platform data processing method based on data mining that can effectively improve the Energy Consumption of Public Buildings supervising platform quality of data and energy consumption supervision level.
In order to realize above-mentioned target, the present invention adopts following technical scheme:
An Energy Consumption of Public Buildings supervising platform data processing method based on data mining, is characterized in that, comprises the following steps:
S1, problem data are tentatively identified: identify the shortage of data producing due to metering, transmission and recording unit fault in energy consumption data, the problem data of data sudden change;
S2, according to historical data, set up with can pattern feature parameter set: according to influence factor by building and/or equipment energy consumption data are meticulous is divided into some use energy patterns, calculate mathematical expectation and the variance of energy pattern energy consumption data collection for every class, using mathematical expectation and variance as the characteristic parameter by energy pattern, set up the data set that comprises institute's available energy pattern feature parameter, wherein, aforementioned affect factor comprises: energy consumption data occurs constantly, occurs whether sky, place is constantly off-day, meteorologic parameter;
S3: the meticulous identification of problem data: according to described in S2 with can pattern feature parameter data set by energy consumption data with affiliated with can pattern match, according to can pattern feature parameter set judge whether energy consumption data peels off, if peeled off, this energy consumption point is used energy exceptional data point for what extremely cause with energy behavior due to user;
S4: problem data intelligence is supplemented: by problem data with can pattern match, according to can pattern feature parameter set, adopt Lagrangian Arithmetic to carry out intelligence to problem data supplementary.
The aforesaid Energy Consumption of Public Buildings supervising platform data processing method based on data mining, is characterized in that, aforementioned S1, problem data are tentatively identified, and comprise the following steps:
S11: problem data is classified, comprise missing data and accidental data;
S12: obtain missing data, accidental data energy consumption maximal value, i.e. threshold values F in the unit interval according to clustering algorithm;
S13: gauging table accumulative total registration data are carried out to figure identification, set up the identification formula for missing data and accidental data:
Missing data identification formula:
A
i=0 or A
ifor empty formula (1)
Accidental data identification formula:
A
i-A
i-1<0 or A
i-A
i-1>F formula (2)
A wherein
ibe i gauging table accumulative total registration constantly, A
i-1be i-1 gauging table accumulative total registration constantly, F is threshold values;
S14: identify missing data, accidental data according to formula (1) and formula (2), record the number of missing data number, accidental data in energy consumption data;
S15: it is 0 that the gauging table accumulated value of the accidental data identifying is composed.
The aforesaid Energy Consumption of Public Buildings supervising platform data processing method based on data mining, is characterized in that, aforementioned S2, according to historical data, sets up with can pattern feature parameter set, comprises the following steps:
S21: be whether working day label, meteorologic parameter label for energy consumption data adds;
S22: according to energy consumption data whether sky, place constantly occurs constantly, occurs be off-day, meteorologic parameter by building and/or equipment with energy pattern meticulous be divided into some classes;
S23: calculate mathematical expectation and the variance of the corresponding energy consumption data collection of energy pattern for every class, this mathematical expectation and variance are as the characteristic parameter by energy pattern;
S24: set up the data set that comprises institute's available energy pattern feature parameter.
The aforesaid Energy Consumption of Public Buildings supervising platform data processing method based on data mining, is characterized in that aforementioned S4: problem data intelligence is supplemented, and comprises the following steps:
S41: the problem data that statistics needs to comprise in supplementary data section is counted out, problem data is corresponding with can pattern, there is forward and backward moment gauging table and accumulate registration in problem data;
S42: according to energy pattern feature parameter set, adopt Lagrangian Arithmetic to supplement problem data, supplement value and calculate by following formula:
Wherein, E
ibe the supplementary value of i problem data, μ
ibe the mathematical expectation with energy mode data of i problem data coupling, △
ibe the variance of using energy mode data of i problem data coupling, n is the number of problem data section inner question data, and A is the gauging table accumulation registration of problem data generation previous moment, and B is that after problem data occurs, the gauging table in a moment is accumulated registration.
Usefulness of the present invention is: method of the present invention has intelligent, real-time and versatility; By to historical energy consumption data according to difference with can pattern classification, set up energy consumption pattern feature parameter set for building, realized the Classification and Identification of energy consumption supervising platform problem data, cleaning, the supplementary integrated treatment of intelligence, overcome the intelligent deficiency of current Energy Consumption of Public Buildings supervising platform data processing method, problem data compensation process lacks rational problem; Meanwhile, use method of the present invention to can be good at realizing that the evaluation of the energy consumption supervising platform quality of data, energy consumption are estimated, problem data is reported to the police, and reliable malfunction elimination suggestion is provided.
Accompanying drawing explanation
Fig. 1 is the overview flow chart of the Energy Consumption of Public Buildings supervising platform data processing method based on data mining of the present invention;
Fig. 2 is the process flow diagram of the preliminary identification of problem data;
Fig. 3 sets up the process flow diagram with energy pattern feature parameter set according to historical data;
Fig. 4 is the supplementary process flow diagram of problem data intelligence.
Embodiment
Below in conjunction with the drawings and specific embodiments, the present invention is done to concrete introduction.
With reference to Fig. 1, a kind of Energy Consumption of Public Buildings supervising platform data processing method based on data mining of the present invention, it comprises the following steps:
S1, problem data are tentatively identified: this step is mainly to identify the shortage of data producing due to metering, transmission and recording unit fault in energy consumption data, the problem data of data sudden change.
With reference to Fig. 2, the preliminary identification of problem data specifically comprises the following steps:
S11: problem data is classified, comprise missing data and accidental data.
S12: calculate missing data, accidental data energy consumption maximal value, i.e. threshold values F in the unit interval according to clustering algorithm.
S13: gauging table accumulative total registration data are carried out to figure identification, set up the identification formula for missing data and accidental data:
Missing data identification formula:
A
i=0 or A
ifor empty formula (1)
Accidental data identification formula:
A
i-A
i-1<0 or A
i-A
i-1>F formula (2)
A wherein
ibe i gauging table accumulative total registration constantly, A
i-1be i-1 gauging table accumulative total registration constantly, F is threshold values.
S14: identify missing data, accidental data according to formula (1) and formula (2), record the number of missing data number, accidental data in energy consumption data.
S15: it is 0 that the gauging table accumulated value of the accidental data identifying is composed.
S2, according to historical data, set up with can pattern feature parameter set: this step is mainly that whether sky, place occurs constantly, occurs be constantly the influence factors such as off-day, meteorologic parameter by building and/or equipment energy consumption data are meticulous is divided into some energy patterns of using according to energy consumption data, calculate mathematical expectation and the variance of energy pattern energy consumption data collection for every class, using mathematical expectation and variance as the characteristic parameter by energy pattern, set up the data set that comprises institute's available energy pattern feature parameter.
With reference to Fig. 3, according to historical data, set up with specifically comprising the following steps by pattern feature parameter set:
S21: be whether working day label, meteorologic parameter label for energy consumption data adds.
S22: according to energy consumption data whether sky, place constantly occurs constantly, occurs be off-day, meteorologic parameter by building and/or equipment with energy pattern meticulous be divided into some classes.
S23: calculate mathematical expectation and the variance of the corresponding energy consumption data collection of energy pattern for every class, this mathematical expectation and variance are as the characteristic parameter by energy pattern.
S24: set up the data set that comprises institute's available energy pattern feature parameter.
S3: the meticulous identification of problem data: this step is mainly by energy consumption data and affiliated with can pattern match, according to described in S2 with can pattern feature parameter data set judge whether energy consumption data peels off, if peeled off, this energy consumption point is used energy exceptional data point for what extremely cause with energy behavior due to user.
S4: problem data intelligence is supplemented: this step be mainly by problem data with can pattern match, according to can pattern feature parameter set, adopt Lagrangian Arithmetic to carry out intelligence to problem data supplementary.
With reference to Fig. 4, problem data intelligence is supplemented and is specifically comprised the following steps:
S41: the problem data that statistics needs to comprise in supplementary data section is counted out, problem data is corresponding with can pattern, there is forward and backward moment gauging table and accumulate registration in problem data.
S42: according to energy pattern feature parameter set, adopt Lagrangian Arithmetic to supplement problem data, supplement value and calculate by following formula:
Wherein, E
ibe the supplementary value of i problem data, μ
ibe the mathematical expectation with energy mode data of i problem data coupling, △
ibe the variance of using energy mode data of i problem data coupling, n is the number of problem data section inner question data, and A is the gauging table accumulation registration of problem data generation previous moment, and B is that after problem data occurs, the gauging table in a moment is accumulated registration.
With Beijing's office building, with energy data instance, data processing method of the present invention is elaborated the whole year in 2013 below.
Read annual use energy data, adopt clustering algorithm to calculate energy consumption threshold values F:
F＝205kWh/h。
Problem data is tentatively identified:
If A
i=0 or A
ifor sky, think A
ifor the missing data producing due to equipment failure;
If A
i-A
i-1<0 or A
i-A
i-1>F, thinks A
ifor the accidental data causing due to equipment failure.
Through preliminary identification, find 485 of missing datas, 20 of accidental datas.
For ease of analyzing the criterion of power consumption mode, need to construct new attribute: in the data post for the preliminary identification of process, whether be working day and meteorological parameter tags.According to being whether working day, whole day 24 hours, temperature height, be divided into 144 kinds with can pattern by annual data are meticulous, calculate every kind with mathematical expectation μ and variance Δ that can pattern.Data after preliminary identification are used can pattern match.If energy consumption data value, outside the region (μ-n Δ, μ+n Δ) limiting by mathematical expectation corresponding to energy pattern and variance of its coupling, thinks that this energy consumption data can abnormal data for using.N should get the real number between 2-3, and occurrence is selected the requirement of the quality of data according to data actual conditions and user, and the larger qualifications to problem data identification of n value is looser.In present case, n value gets 2.
Through the meticulous identification of problem data, find, with 115 of energy abnormal datas, with only carrying out mark by abnormal data, not reject.
According to problem data intelligence compensation process, read each problem data section front and back gauging table accumulation registration and problem section and comprise data point number, and problem data and its are used to energy pattern match, according to formula (2), 485 missing datas and 20 accidental datas are supplemented, 115 abnormal datas are calculated to its normal data estimated value.
According to the problem data number of statistics, calculate missing data rate and be 5.5%, accidental data rate is 0.2%, with energy abnormal data rate be 1.3%, normal data rate is 93%.
By These parameters, not only can assess the specific building energy consumption supervising platform quality of data, can also carry out lateral comparison to the different energy consumption supervising platform qualities of data.
For actual motion building, can according to historical data with estimating out the energy consumption scope in a period of time in future by pattern feature parameter set.
For the real time data collecting, using of correspondence can pattern constantly need to first to judge its generation.If this registration of gauging table accumulation is constantly A
i, previous moment gauging table accumulation registration is A
i-1if, A
ibe 0 or null value, A
ifor missing data, to related personnel, report to the police, now should preferentially investigate data transmission system and whether break down; If A
i-A
i-1<0 or A
i-A
i-1>F, A
ifor accidental data, to related personnel, report to the police, now should preferentially investigate metering outfit and whether break down; If A
i-A
i-1< μ-2 Δ or A
i-A
i-1> μ+2 Δ, A
ifor with can abnormal data, to related personnel, report to the police, now should preferentially investigate in building, whether exist abnormal with can behavior.
As can be seen here, method of the present invention is passed through problem data Classification and Identification, on the basis of excavating at historical energy consumption data, set up with energy pattern feature parameter set, by building energy consumption Real-time Monitoring Data and affiliated with can pattern match and carry out Outlier Analysis with model identical historical data, thereby judgement is historical or whether current energy consumption data is normal, not only realized the Classification and Identification to energy consumption supervising platform problem data, clean, the integrated treatment that intelligence is supplementary, and be the evaluation of the energy consumption supervising platform quality of data, energy consumption is estimated, problem data is reported to the police and malfunction elimination suggestion provides powerful support.
In sum, method of the present invention has the following advantages:
1, intelligent.Can carry out Classification and Identification and intelligent supplementing to building energy consumption data, in building actual moving process, current data be analyzed, judge whether data have problems, to problem data, can instruct supervisor to investigate fault.
2, real-time.Can in building actual moving process, to current data point, analyze, pinpoint the problems in time and report to the police.
3, versatility.Can identify any public building or equipment operating by energy pattern, then accordingly energy consumption data be carried out to analyzing and processing.
It should be noted that, above-described embodiment does not limit the present invention in any form, and all employings are equal to replaces or technical scheme that the mode of equivalent transformation obtains, all drops in protection scope of the present invention.
Claims (4)
1. the Energy Consumption of Public Buildings supervising platform data processing method based on data mining, is characterized in that, comprises the following steps:
S1, problem data are tentatively identified: identify the shortage of data producing due to metering, transmission and recording unit fault in energy consumption data, the problem data of data sudden change;
S2, according to historical data, set up with can pattern feature parameter set: according to influence factor by building and/or equipment energy consumption data are meticulous is divided into some use energy patterns, calculate mathematical expectation and the variance of energy pattern energy consumption data collection for every class, using mathematical expectation and variance as the characteristic parameter by energy pattern, set up the data set that comprises institute's available energy pattern feature parameter, wherein, described influence factor comprises: energy consumption data occurs constantly, occurs whether sky, place is constantly off-day, meteorologic parameter;
S3: the meticulous identification of problem data: according to described in S2 with can pattern feature parameter data set by energy consumption data with affiliated with can pattern match, according to can pattern feature parameter set judge whether energy consumption data peels off, if peeled off, this energy consumption point is used energy exceptional data point for what extremely cause with energy behavior due to user;
S4: problem data intelligence is supplemented: by problem data with can pattern match, according to can pattern feature parameter set, adopt Lagrangian Arithmetic to carry out intelligence to problem data supplementary.
2. the Energy Consumption of Public Buildings supervising platform data processing method based on data mining according to claim 1, is characterized in that, described S1, problem data are tentatively identified, and comprise the following steps:
S11: problem data is classified, comprise missing data and accidental data;
S12: obtain missing data, accidental data energy consumption maximal value, i.e. threshold values F in the unit interval according to clustering algorithm;
S13: gauging table accumulative total registration data are carried out to figure identification, set up the identification formula for missing data and accidental data:
Missing data identification formula:
A
i=0 or A
ifor empty formula (1)
Accidental data identification formula:
A
i-A
i-1<0 or A
i-A
i-1>F formula (2)
A wherein
ibe i gauging table accumulative total registration constantly, A
i-1be i-1 gauging table accumulative total registration constantly, F is threshold values;
S14: identify missing data, accidental data according to formula (1) and formula (2), record the number of missing data number, accidental data in energy consumption data;
S15: it is 0 that the gauging table accumulated value of the accidental data identifying is composed.
3. the Energy Consumption of Public Buildings supervising platform data processing method based on data mining according to claim 1, is characterized in that, described S2, according to historical data, sets up with can pattern feature parameter set, comprises the following steps:
S21: be whether working day label, meteorologic parameter label for energy consumption data adds;
S22: according to energy consumption data whether sky, place constantly occurs constantly, occurs be off-day, meteorologic parameter by building and/or equipment with energy pattern meticulous be divided into some classes;
S23: calculate mathematical expectation and the variance of the corresponding energy consumption data collection of energy pattern for every class, this mathematical expectation and variance are as the characteristic parameter by energy pattern;
S24: set up the data set that comprises institute's available energy pattern feature parameter.
4. the Energy Consumption of Public Buildings supervising platform data processing method based on data mining according to claim 1, is characterized in that described S4: problem data intelligence is supplemented, and comprises the following steps:
S41: the problem data that statistics needs to comprise in supplementary data section is counted out, problem data is corresponding with can pattern, there is forward and backward moment gauging table and accumulate registration in problem data;
S42: according to energy pattern feature parameter set, adopt Lagrangian Arithmetic to supplement problem data, supplement value and calculate by following formula:
Wherein, E
ibe the supplementary value of i problem data, μ
ibe the mathematical expectation with energy mode data of i problem data coupling, △
ibe the variance of using energy mode data of i problem data coupling, n is the number of problem data section inner question data, and A is the gauging table accumulation registration of problem data generation previous moment, and B is that after problem data occurs, the gauging table in a moment is accumulated registration.
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Cited By (14)
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| CN104636999A (en) * | 2015-01-04 | 2015-05-20 | 江苏联宏自动化系统工程有限公司 | Detection method for building abnormal energy consumption data |
| CN105759608A (en) * | 2016-02-26 | 2016-07-13 | 北京邮电大学 | Energy-saving method and system |
| CN106250905A (en) * | 2016-07-08 | 2016-12-21 | 复旦大学 | A kind of real time energy consumption method for detecting abnormality of combination colleges and universities building structure feature |
| CN106384300A (en) * | 2016-09-27 | 2017-02-08 | 山东建筑大学 | Big data and fuzzy model-based building abnormal energy consumption detection method and system |
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| CN110135649A (en) * | 2019-05-21 | 2019-08-16 | 山东建筑大学 | Short-term building energy consumption interval prediction method, system, medium and equipment |
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