CN104915562B - Build efficiency diagnostic method and system - Google Patents
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Abstract
The application provides a kind of building efficiency diagnostic method and system.Its method includes: to determine prediction model and its precision based on model training according to history electricity subitem data, the history ad hoc rules information of corresponding historical external parameter and collection；To prediction model and its accuracy selection diagnostic threshold；Corresponding prediction electricity subitem data are obtained by the prediction model by current external parameter and the ad hoc rules information of collection；Based on the diagnostic threshold, comparison prediction electricity subitem data and current electricity subitem data are to determine efficiency situation.To solve to promote the extremely determining accuracy problems of energy consumption in building energy efficiency monitoring management, it can determine that energy consumption is abnormal, improves efficiency supervisory efficiency in time.Due to timely and accurately positioning abnormal energy consumption, architectural control personnel can be helped to act in time conscientiously, reduce energy waste.
Description
Technical field
This application involves data monitoring diagnostic fields, more particularly to building efficiency diagnostic method and system.
Background technique
With the trend of energy-saving and emission-reduction, building energy consumption metering is increasingly standardized, and with the development of internet technology, accordingly
All kinds of building energy efficiency monitoring control management platforms are also gradually established.These platforms can be by carrying out scene in real time to energy for building
The processing such as monitoring, analysis, find the energy waste point in energy for building in time, guidance and operation maintenance personnel assisted to implement section
Can management work, and find and determine energy waste point, need in real time/diagnose accurately and in time the building efficiency (such as: it is each
Kind of building energy consumption etc.) technology.In actual operation, there are building energy consumption metering type it is single, using manual analysis when it is common
The problems such as manager's analysis ability is limited and professional diagnosis is undermanned, cause a large amount of power consumption data to pile up and can not be from data
In accurately and timely excavate implicit information, and energy-efficient constructive path can not be provided, and then waste the data prison of initial cost building
(acquisition) system of survey, and still can not accurately and timely find the energy waste point of energy for building, it is unable to satisfy energy efficiency monitoring association
Help energy-efficient effect.
It is then desired to perfect existing energy efficiency monitoring management platform be improved, by measuring acquisition power consumption to energy for building
Data are simultaneously diagnosed automatically (without people's Analysis of Division of Labor) and position the position for obtaining using energy abnormal and time.Construct this nothing
The automatic efficiency diagnostic techniques of manual analysis is needed, timely determines that energy consumption is abnormal, improves energy efficiency monitoring management so as to more acurrate
Efficiency reduces unnecessary energy waste in building, to save efficiency, avoid wasting.
Summary of the invention
The main purpose of the application is to provide a kind of building efficiency diagnostic method and system, to solve building energy efficiency monitoring
The extremely determining accuracy problems of energy consumption are promoted in management, and then improve asking for the efficiency supervisory efficiency for determining energy consumption exception in time
Topic.So that can timely and accurately position abnormal energy consumption without manual analysis, architectural control personnel are helped to act in time conscientiously, from
And reduce energy waste.
On the one hand the application provides a kind of building efficiency diagnostic method, comprising: according to history electricity subitem data, acquisition
The history ad hoc rules information of the corresponding historical external parameter and collection that obtain when history electricity subitem data, selects to set phase
That answers can be carried out trained power consumption prediction model, then the model training based on the power consumption prediction model to setting, with determination
Power consumption prediction model and its precision；Threshold value selection is carried out to power consumption prediction model and its precision, to obtain diagnostic threshold；According to adopting
The ad hoc rules information of the corresponding external parameter obtained and collection when collecting current electricity subitem data, passes through the power consumption and predicts mould
Type obtains predicting electricity subitem data accordingly；Based on the diagnostic threshold, compares the prediction electricity subitem data and adopt accordingly
The current electricity subitem data of collection, to determine efficiency situation；Wherein, electricity subitem data are acquired in real time by metering separate mode.
Wherein, according to history electricity itemize data, the acquisition history electricity subitem data when the corresponding historical external that obtains
The history ad hoc rules information of parameter and collection, selection can be carried out trained power consumption prediction model to set accordingly, then be based on
To the model training of the power consumption prediction model of setting, to determine power consumption prediction model and its precision, further includes: according to each
The analysis of item history electricity subitem data obtains power consumption feature, and corresponding historical external parameter and history ad hoc rules is combined to believe
Breath selects power consumption prediction model using the standard deviation coefficient of variation and/or related coefficient, predicts mould to set power consumption to be trained
Type；History electricity is itemized into data, corresponding historical external parameter and the history ad hoc rules information of collection as model training
Sample is trained the prediction model of setting, determines the model parameter and its model accuracy of prediction model.
Wherein, power consumption feature is obtained according to the analysis to every history electricity subitem data, and combines corresponding historical external
Parameter and history ad hoc rules information select power consumption prediction model using the standard deviation coefficient of variation and/or related coefficient, with setting
Power consumption prediction model to be trained, comprising: its power consumption feature is analyzed to the history electricity subitem data of input, in conjunction with corresponding history
External parameter and history ad hoc rules information are sentenced according to the dispersion of standard deviation coefficient of variation evaluation and/or according to related coefficient
The disconnected degree of correlation uses regression model and/or feature averaging model as power consumption prediction model to be trained using setting.
Wherein, the external parameter includes the temperature in meteorologic parameter；The ad hoc rules information includes in social information
Job specification information；Electricity subitem data, by data acquisition device according to the frequency of 15 minutes/time to surveyed electricity consumption branch
Electrisity consumption, electric current and/or voltage are acquired, and each electric branch is divided in specific electricity consumption subitem and obtains the power consumption number of each branch
According to, and daily each branch power consumption data are obtained using data aggregate, using each branch power consumption data as electricity subitem data；
Meteorologic parameter directly obtains outdoor dry bulb by the frequency collection of 15 minutes/time by the temperature sampler at meteorological data collection end
The temperature data of temperature, and the multiple temperature datas acquired in special time period are averaging by data average treatment
Value, is calculated the temperature in the meteorologic parameter that the external parameter includes；Alternatively, passing through the building place for carrying out efficiency diagnosis
Weather station database in ground searches for the temperature obtained in the meteorologic parameter that the external parameter includes indirectly；Social information passes through society
Meeting information data collection terminal arranged to assign job specification to the same day where corresponding electric data of itemizing according to the national legal festivals and holidays
Information.
Wherein, it is based on the diagnostic threshold, the current electricity subitem for comparing the prediction electricity subitem data and acquiring accordingly
Data, to determine efficiency situation, comprising: the error for calculating prediction electricity subitem data and corresponding current electricity subitem data, according to
The diagnostic threshold obtains efficiency diagnostic result, and exports the efficiency situation.
Wherein, the error for calculating prediction electricity subitem data and corresponding current electricity subitem data, according to the diagnostic threshold,
It obtains efficiency diagnostic result, and exports the efficiency situation, comprising: calculate prediction electricity subitem data and corresponding current electricity subitem
The relative error and absolute error of data, when relative error be more than the relative threshold upper limit or be lower than relative threshold lower limit when again
Compare absolute error, and grade, the alarm of showed different are set up separately according to the size of absolute error.
On the other hand the application provides a kind of building efficiency diagnostic system, comprising: model determining device, for according to history
The specific rule of history of the corresponding historical external parameter and collection that are obtained when electricity subitem data, acquisition history electricity subitem data
Then information, selection can be carried out trained power consumption prediction model to determine accordingly, then predict mould based on to the determining power consumption
The model training of type, to determine power consumption prediction model and its precision；Diagnostic threshold device, for power consumption prediction model and its essence
Degree carries out threshold value selection, to obtain diagnostic threshold；Prediction meanss are corresponding for obtaining when electricity subitem data current according to acquisition
External parameter and collection ad hoc rules information, by the power consumption prediction model obtain accordingly predict electricity subitem data；
Diagnostic device is compared, for being based on the diagnostic threshold, compares the current electricity that the prediction electricity subitem data and acquired accordingly
Subitem data, to determine efficiency situation；Wherein, model determining device, further includes: setting is used for basis to training pattern device
Power consumption feature is obtained to the analysis of every history electricity subitem data, and combines corresponding historical external parameter and history ad hoc rules
Information selects power consumption prediction model using the standard deviation coefficient of variation and/or related coefficient, to set prediction model to be trained；
And model training apparatus, for by history electricity itemize data, corresponding historical external parameter and collection history ad hoc rules
Sample of the information as model training is trained the power consumption prediction model of setting, determines the model ginseng of power consumption prediction model
Several and its model accuracy；Wherein, electricity subitem data are acquired in real time by metering separate mode.
Wherein, the model determining device, comprising: its power consumption feature is analyzed to the history electricity subitem data of input, in conjunction with
Corresponding historical external parameter and history ad hoc rules information, according to the evaluation of the standard deviation coefficient of variation dispersion and/or according to
The degree of correlation of related coefficient judgement uses regression model and/or feature averaging model to predict as power consumption to be trained to set
Model.
Wherein, the external parameter includes the temperature in meteorologic parameter；The ad hoc rules information includes in social information
Job specification information；Electricity subitem data, by data acquisition device according to the frequency of 15 minutes/time to surveyed electricity consumption branch
Electrisity consumption, electric current and/or voltage are acquired, and each electric branch is divided in specific electricity consumption subitem and obtains the power consumption number of each branch
According to, and daily each branch power consumption data are obtained using data aggregate, using each branch power consumption data as electricity subitem data；
Meteorologic parameter directly obtains outdoor dry bulb by the frequency collection of 15 minutes/time by the temperature sampler at meteorological data collection end
The temperature data of temperature, and the multiple temperature datas acquired in special time period are averaging by data average treatment
Value, is calculated the temperature in the meteorologic parameter that the external parameter includes, as meteorologic parameter；Alternatively, by carrying out efficiency
The building location weather station database of diagnosis searches for the temperature obtained in the meteorologic parameter that the external parameter includes indirectly；Society
Meeting information arranges to work as to where corresponding electric data of itemizing by social information's data collection terminal according to the national legal festivals and holidays
It assigns job specification information.
Wherein, diagnostic device is compared, further includes: calculate the phase of prediction electricity subitem data and corresponding current electricity subitem data
Efficiency diagnostic result is shown according to the diagnostic threshold to error and absolute error, and exports the efficiency situation, according to exhausted
Grade, the alarm of showed different are set up separately to the size of error.
It compared with prior art, can be real in other words by monitoring in time according to the efficiency diagnostic techniques scheme of the application
When monitoring collection energy for building environment in energy consumption data, and find and determine the abnormal energy consumption that occurs in energy for building, will
It is carried on existing building energy efficiency management platform, and foundation diagnosis algorithm carries out mass data to automatically analyze diagnosis, can be with
It improves discovery in time and determines the energy efficiency monitoring efficiency of management of energy consumption exception, promotes the accuracy of diagnosis, to help to build conscientiously
It builds administrative staff to act in time, the waste for reducing energy waste, saving efficiency, avoiding manpower financial capacity.
Detailed description of the invention
The drawings described herein are used to provide a further understanding of the present application, constitutes part of this application, this Shen
Illustrative embodiments and their description please are not constituted an undue limitation on the present application for explaining the application.In the accompanying drawings:
Fig. 1 is the flow chart of an embodiment of the building efficiency diagnostic method of the application；
Fig. 2 is the flow chart that an embodiment of prediction model is set in the present processes；
Fig. 3 is the schematic diagram that an embodiment of prediction model is set in the application method；
Fig. 4 is the schematic diagram of an embodiment of efficiency prediction and diagnosis in the application method；
Fig. 5 is the structural block diagram of an embodiment of the building efficiency diagnostic system of the application.
Specific embodiment
The main idea of the present application lies in that obtaining power consumption data based on metering separate, being aided with the outside for influencing to build efficiency
Parameter simultaneously establishes corresponding energy consumption prediction model, real-time monitoring and the actual state for obtaining building subitem electricity consumption data, i.e., effectively
Diagnosis building efficiency in ground to adjust and exclude exception in time, saves the energy, reduces resource to accurately determine abnormal energy consumption
Waste.
To keep the purposes, technical schemes and advantages of the application clearer, below in conjunction with the application specific embodiment and
Technical scheme is clearly and completely described in corresponding attached drawing.Obviously, described embodiment is only the application one
Section Example, instead of all the embodiments.Based on the embodiment in the application, those of ordinary skill in the art are not doing
Every other embodiment obtained under the premise of creative work out, shall fall in the protection scope of this application.
According to an embodiment of the present application, a kind of building efficiency diagnostic method is provided.
It is the flow chart 100 of an embodiment of the building efficiency diagnostic method of the application with reference to Fig. 1, Fig. 1.
In step S110, according to history electricity itemize data, acquisition the electricity subitem data when the corresponding history that obtains outside
The history ad hoc rules information of portion's parameter and collection is based on model training, to determine prediction model and its precision.
Wherein, electricity subitem data, external parameter, ad hoc rules information are basic datas.
In one embodiment, electricity subitem data are included at least to the energy in various buildings (such as: public building)
(mainly including that electric energy uses i.e. electricity consumption) is used, the obtained power consumption data of metering separate are carried out, such as: illumination and socket are used
Electricity, air conditioning electricity, power electricity consumption, the special power consumption data with other electricity consumptions etc..Wherein, public building may include but unlimited
In office building, market building, comprehensive office building etc..Wherein, illumination and socket power such as: public domain illumination, corridor and
The electricity consumption of several subitems such as emergency lighting, outdoor landscape illumination, vault light, light socket；Power electricity consumption is such as: elevator, water
The electricity consumption of pump, ventilation blower and the several subitems of Dynamic Synthesis；It is special with other electricity consumptions such as: information centre, laundry, kitchen and dining room,
The electricity consumption in swimming pool bathroom, gymnasium and other several subitems.
Since the diagnosis of building efficiency is relatively microcosmic, it is concerned about in single building with (normal, exception, exception the case where energy
Position etc.), and the energy consumption system multiplicity in building, it is complicated with energy feature, for example, the air-conditioning system of large-scale public construction, device category
It is more, composition it is different, cause it higher to data dependency degree, type, quality and the quantity of data will affect even determine efficiency shape
The diagnosis depth and precision of condition.The electricity subitem data acquired in real time in the way of metering separate in the present embodiment, compared to
Existing energy depot investigation method (generaI investigation mode obtains, a kind of method of update cycle length) and data resolution method are (by dividing
Active power, reactive power and harmonic wave variation of analysis measurement summary table etc. are aided with common device with electrical feature and obtain equipment component electricity
Consumption, calculating complexity is without a kind of blanket method), it can more clearly know all kinds of real-time shapes with energy equipment in building
State facilitates the raising of the efficiency and accuracy of efficiency diagnosis.
In one embodiment, the acquisition of electricity subitem data can use power consumption data collection terminal, that is, include conventional
Data acquisition device (data collector etc.) is carried out in conjunction with the mode of building electricity consumption subitem model and data aggregate.For example, data
Acquisition device can according to the frequency of 15 minutes/time to the electrisity consumption of surveyed electricity consumption branch (or even including corresponding electric current, voltage
Deng) be acquired, each electric branch is divided in specific electricity consumption subitem according to subitem model for building and obtains the power consumption of each branch
Data, further, it is possible to obtain each branch power consumption data by day/daily using data aggregate.The power consumption data of acquisition are point
Item metering, thus these power consumption data are as electricity subitem data, i.e., the electricity point of the electric subitem data of each branch, daily each branch
Item data.Acquired in the past electricity subitem data preserve, as electricity subitem data historical data, that is, history electricity itemize data,
And the needs acquired in real time are then current electricity subitem data according to its electricity subitem data for carrying out efficiency diagnosis.
In one embodiment, the corresponding external parameter that obtains and collection is specific when acquiring the electricity subitem data
Rule Information, while successively can be acquisition electricity subitem data, the social information of collected out door climatic parameter and collection.
Out door climatic parameter at least may include the outer dry-bulb temperature of building respective compartments, and social information at least may include legal according to country
Festivals or holidays arrange to the job specification information, etc. assigned daily.
Further, the acquisition of out door climatic parameter can use meteorological data collection end, that is, include that temperature sampler combines
The average processing mode of data carries out.Such as: temperature sampler directly obtains outdoor dry bulb temperature by the frequency collection of 15 minutes/time
The temperature data of degree is averaged to the multiple temperature datas acquired in special time period by data average treatment, is calculated
To the representative temperature by day, as out door climatic parameter (external parameter)；It can also be by building location weather station database
The temperature is obtained indirectly.It is corresponding with the electricity subitem acquisition time section of data (power consumption data), equally, the also conduct acquired in the past
History out door climatic parameter (historical external parameter) saves, and what is acquired in real time is then used as current outdoor meteorologic parameter (current external
Parameter) to be used for current efficiency diagnostic process.
Further, the collection of social information can use social information's data collection terminal/search (collection) end, according to country
Legal festivals and holidays arrange to assign work to the same day where the same day where corresponding history electricity subitem data or current electricity subitem data
Property information can guarantee social information's (concretely job specification information etc.) and electricity subitem data in the period at the same time
It is inside corresponding.Such as: the job specification information of corresponding history electricity subitem data says social information as history social information
Saving is history ad hoc rules information preservation, and corresponds to the job specification information of current electricity subitem data or say social information's conduct
Current social information preservation, that is, current ad hoc rules information preservation.
It in one embodiment, can be first according to preservation to determine prediction model and its precision based on model training
The specific rule of history of corresponding historical external parameter and collection obtained when history electricity subitem data, acquisition the electricity subitem data
Then information carries out model selection processing, then completes model training and determine corresponding prediction model.Below in conjunction with this Shen shown in Fig. 2
Prediction model is set in the application method shown in the flow chart 200 and Fig. 3 of the embodiment that please set prediction model in method
An embodiment schematic diagram, descriptive model selection and model training are to determine an example of prediction model.
Step S210 according to the analysis to every history electricity subitem data, and combines corresponding historical external parameter and goes through
History ad hoc rules information, sets prediction model to be trained.
Wherein, to every history electricity subitem data analysis of input, corresponding power consumption feature can be obtained.For example power consumption is high
Or it is low.In conjunction with corresponding historical external parameter (such as meteorologic parameter at that time: temperature), history ad hoc rules information (such as society
Information: job specification information), selection is to determine the power consumption prediction model that can be trained accordingly.
Such as: the history electricity subitem data of electrisity consumption height, nonworkdays that history electricity subitem data on weekdays indicate
The electrisity consumption of expression is low, and electrisity consumption is low, etc. when electrisity consumption is high when external temperature is high or low, external temperature is suitable for,
It can choose and use regression model or feature averaging model as prediction model to be trained.The number for example, analysis of history electricity is itemized
It is found that working day and nonworkdays difference are little, can choose feature averaging model as prediction model；Analysis of history electricity point
It is found that temperature influence is very big for item number, and data discrete is serious, can choose regression model；It even can be according to the history electricity of analysis
The power consumption feature for data of itemizing, the whole data using regression model, certain parts are not discrete and feature is used to be averaged mould to it
Two class models of type are combined and are predicted respectively to obtain model parameter and its precision；Etc..
In one embodiment, it is preferable to determine prediction model using two model selection algorithms.
(1) with the related coefficient of WW
WW is one of the social parameter on the same day, such as: whether the same day is working day, and if it is working day, then value is 1, non-
Working day, then value was 0 (division of working day and nonworkdays is subject to the legal festivals and holidays).Such as: it can be according to the phase with WW
Relationship number, judges whether related with job specification, and the electricity subitem data in relation to working day and nonworkdays certainly will distinguish, nothing
Pass then can directly select feature averaging model, etc..A specific example, can set the related coefficient of data group X Yu data group Y
Calculation formula is as follows:
Algorithm shown in the example, can mainly be used to judge whether the item data (such as electricity subitem data) has with job specification
It closes, the result of judgement will affect the selection of prediction model.Such as: data group X is daily electricity subitem data x, andIt is i.e. average
Value；Data group Y is the job specification y on the same day, on weekdays when take 1, take 0 in nonworkdays, andFor average value；And ρXYTable
Show the related coefficient of electricity subitem data and job specification.Thus, if calculated result ρXYIt is sufficiently large, illustrate the group subitem power consumption with
Job specification correlation is larger, then prediction model is separately selected to working day and nonworkdays, if calculated result ρXYLess,
Illustrate that group subitem power consumption and job specification correlation are smaller, then need not separate selection prediction model.
(2) the standard deviation coefficient of variation
The standard deviation coefficient of variation is the dispersion degree for evaluating data, and eliminates the height and meter of variate-value level
The different influence of unit is measured, unitized dispersion degree evaluation number is equivalent to.Such as: by standard variation difference coefficient CV see whether
It is discrete, each discrete portions (usually each season is a part) are distinguished if discrete, are split into after different piece again
Continued to judge dispersion with CV, uses regression model, such as influence of the temperature to air conditioning energy consumption if still very discrete, in addition,
It can also be to single part feature averaging model, etc. if single part is not discrete.A specific example, can set standard deviation
The calculation formula of the coefficient of variation is as follows:
Algorithm shown in the example, wherein σ indicates the standard deviation of this group of data,Indicate the average value of this group of data, xiIt indicates
Each value of this group of data, n indicate the number of this group of data.CVσIndicate the standard deviation coefficient of variation of this group of data.The example institute
Show algorithm, xiIndicate electricity subitem data, CVσIt can be used to judge the dispersion degree of electricity subitem energy consumption, judging result will affect pre-
Survey the selection of model.
Above-mentioned algorithm is preferably, according to the information of auxiliary to show that the degree of correlation, dispersion etc. combine corresponding electricity subitem number
According to power consumption feature select the example of prediction model appropriate as needed, not as the limitation to application scheme.
Step S220, by history electricity subitem data, the history ad hoc rules information of corresponding historical external parameter and collection
As the sample of model training, the prediction model of setting is trained, determines the model parameter and its model essence of prediction model
Degree.
In one embodiment, by history electricity subitem data represent electrisity consumption, corresponding meteorologic parameter (temperature) and
Social information's (job specification information) is input to the prediction model of setting as sample characteristics, and (such as: regression model or feature are average
Model etc.) in, after being trained, the model parameter and corresponding precision of the prediction model are obtained, so as to utilize the prediction
Model is predicted.Wherein, the precision of prediction model can choose the coefficient of corresponding model, come whether judgment models meet precision
It is required that the coefficient of determination R2 and predicted root mean square error coefficient of variation CVRMSE of regression model can be taken by taking regression model as an example
To judge.
In step S120, threshold value selection is carried out to prediction model and its precision, to obtain diagnostic threshold.
In one embodiment, can threshold value select by way of, according to after training determine prediction model and its
Precise manner show that efficiency diagnoses required threshold value, may include relative threshold and/or absolute threshold.
By taking prediction model is regression model as an example, selected to determine diagnosis in other words according to the parameter R2 and CVRMSE of model
Relative threshold: R2 >=0.8 and CVRMSE≤10%, relative threshold are ± 20%；R2>=0.8 and 10%<CVRMSE≤
20%, relative threshold is ± 25%；R2 >=0.75 and CVRMSE≤5%, relative threshold be ± 30%, i.e., precision of forecasting model compared with
High then corresponding threshold range is just smaller.That is, if relative threshold is ± 20%, the estimated value that we obtain later
Relative error with measured value cannot be more than ± 20%, otherwise be considered as exception.
The specific rule of the corresponding external parameter obtained and collection in step S130, electricity subitem data current according to acquisition
Then information obtains predicting electricity subitem data accordingly by the prediction model.
Wherein, external parameter current or real-time acquisition or collection are obtained, real-time and ad hoc rules information input
Efficiency prediction is carried out into the prediction model of trained mistake, obtained predicted value predicts that electricity subitem data (indicate prediction
Power consumption).Wherein, acquisition (current) external parameter and real-time (current) the specific rule of collection in real time have been noted above in step s 110
The then content of information, details are not described herein.
In one embodiment, an embodiment of efficiency prediction and diagnosis is carried out in the application method as shown in Figure 4
Schematic diagram, by the external parameter for newly acquiring, collecting (temperature in meteorologic parameter), ad hoc rules information (in social information
Job specification information) it is input in the prediction model of trained mistake as sample characteristics, corresponding predicted value is obtained, that is, is predicted
Electricity subitem data.Further, it can also determine whether to meet precision, such as referring to the precision of prediction model, error then acquires greatly very much
Data may be wrong.
In step S140, it is based on the diagnostic threshold, compare the prediction electricity subitem data and is acquired accordingly current
Electricity subitem data, to determine efficiency situation.
In one embodiment, by prediction electricity subitem data (i.e. subitem power consumption prediction data/prediction power consumption of acquisition
Value) and corresponding collected current electricity subitem data (i.e. the electricity consumption data of the subitem measured in real time/actual measurement power consumption value) into
Row comparison, according to the diagnostic threshold obtained before, obtains diagnostic result.Such as: whether the two difference percentage has been more than setting
The diagnostic threshold, the case where given threshold can be will exceed, be considered as the efficiency situation (principle shown in Figure 4 of power consumption exception
Figure).
Further, it is possible to export efficiency situation, such as: the diagnostic result numerical value display output of certain branch subitem efficiency situation is simultaneously
It is identified in threshold range with green, indicates normal；The diagnostic result numerical value display output of certain branch subitem efficiency situation is simultaneously
It is identified outside threshold range with red, indicates abnormal；Etc..Furthermore it is also possible to according to the difference surveyed between prediction power consumption
Off course degree and corresponding diagnostic threshold, diagnostic result can be divided into that slight and apparent efficiency is abnormal, and showed different
Alarm.
Wherein, prediction power consumption value and actual measurement power consumption value relative error and absolute error can also be calculated, when in relative error
Absolute error can further be compared when more than the relative threshold upper limit or lower than relative threshold lower limit, and according to absolute error
Size sets up grade separately, it is higher and it is relatively low be all divided into three, to export more intuitive as a result, making staff more can be intuitively
Understand fault degree.
Fig. 5 schematically shows the structural block diagram of an embodiment of the building efficiency diagnostic system according to the application.Root
According to one embodiment of the application, which may include:
Model determining device 510, for according to history electricity itemize data, acquisition it is described electricity subitem data when obtain it is corresponding
Historical external parameter and collection history ad hoc rules information, be based on model training, to determine prediction model and its precision.It should
Device concrete function and processing are referring to step S110.
Wherein, in model determining device 510 further include: setting is to training pattern device 511, for according to every history
The analysis of electricity subitem data, and corresponding historical external parameter and history ad hoc rules information are combined, set prediction to be trained
Model, the device concrete function and processing are referring to step S210；And model training apparatus 512, it is used for number that history electricity is itemized
Sample according to, corresponding historical external parameter and the history ad hoc rules information of collection as model training, the prediction to setting
Model is trained, and determines the model parameter and its model accuracy of prediction model, and the device concrete function and processing are referring to step
S220。
Diagnostic threshold device 520, for carrying out threshold value selection to prediction model and its precision, to obtain diagnostic threshold.It should
The concrete function of device and processing are referring to step S120.
Prediction meanss 530, for according to the corresponding external parameter and collection obtained when acquiring current electricity subitem data
Ad hoc rules information obtains predicting electricity subitem data accordingly by the prediction model.The concrete function of the device and processing
Referring to step S130.
Diagnostic device 540 is compared, for being based on the diagnostic threshold, comparing the prediction electricity subitem data and adopting accordingly
The current electricity subitem data of collection, to determine efficiency situation.The concrete function of the device and processing are referring to step S140.
The processing and function realized by the system of the present embodiment essentially correspond to earlier figures 1~method shown in Fig. 4
Embodiment, therefore not detailed place in the description of the present embodiment, may refer to the related description in previous embodiment, do not do herein superfluous
It states.
The advantages of scheme of the application and good effect include: that data class needed for 1) diagnosing is less, and are typical number
According to being easily obtained, without being updated transformation to existing building energy consumption metering system, may be directly applied to practical building efficiency pipe
Platform.2) it is measured during diagnosis without choosing specific operation, would not also influence the use of energy for building system, it can
With summarized from history subitem power consumption data each subitem with can feature, to be carried out to real-time actual measurement subitem electricity consumption data
Efficiency diagnosis.
Further, the scheme of the application believes the subitem power consumption historical data of acquisition and corresponding meteorologic parameter and society
Breath is used as training data, automatically selects power consumption prediction model by improved efficiency diagnostic method and system, and computation model waits for
Determine parameter, model accuracy and diagnostic threshold, brings the actual measurement power consumption that need to be diagnosed and corresponding meteorologic parameter and social information into mould
Type calculates prediction power consumption, the power consumption data surveyed and predicted by comparison, provides each subitem efficiency height in specific time
Diagnostic result, with realize to public building subitem electricity consumption efficiency diagnose, and for low-energy-efficiency point it is timely exclude provide accordingly
Information support.For the application by the analysis to public building subitem power consumption historical data, the electricity consumption for summarizing different subitems is special
Point is simultaneously characterized with corresponding prediction model, and for diagnosing the efficiency height of subitem electricity consumption, while the electricity consumption of quantitative is different
Constant value checks failure for building maintenance administrative staff and provides tutorial message, to effectively reduce energy waste.In addition, utilizing
Diagnosis algorithm can diagnose mass data automatically, substantially save manpower, have significant practice value.
In a typical configuration, calculating equipment includes one or more processors (CPU), input/output interface, net
Network interface and memory.
Memory may include the non-volatile memory in computer-readable medium, random access memory (RAM) and/or
The forms such as Nonvolatile memory, such as read-only memory (ROM) or flash memory (flashRAM).Memory is showing for computer-readable medium
Example.
Computer-readable medium includes permanent and non-permanent, removable and non-removable media can be by any method
Or technology come realize information store.Information can be computer readable instructions, data structure, the module of program or other data.
The example of the storage medium of computer includes, but are not limited to phase change memory (PRAM), static random access memory (SRAM), moves
State random access memory (DRAM), other kinds of random access memory (RAM), read-only memory (ROM), electric erasable
Programmable read only memory (EEPROM), flash memory or other memory techniques, read-only disc read only memory (CD-ROM) (CD-ROM),
Digital versatile disc (DVD) or other optical storage, magnetic cassettes, tape magnetic disk storage or other magnetic storage devices
Or any other non-transmission medium, can be used for storage can be accessed by a computing device information.As defined in this article, it calculates
Machine readable medium does not include non-temporary computer readable media (transitory media), such as the data-signal and carrier wave of modulation.
It should also be noted that, the terms "include", "comprise" or its any other variant are intended to nonexcludability
It include so that the process, method, commodity or the equipment that include a series of elements not only include those elements, but also to wrap
Include other elements that are not explicitly listed, or further include for this process, method, commodity or equipment intrinsic want
Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including described want
There is also other identical elements in the process, method of element, commodity or equipment.
It should be understood by those skilled in the art that, embodiments herein can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the application, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The above description is only an example of the present application, is not intended to limit this application, for those skilled in the art
For member, various changes and changes are possible in this application.Within the spirit and principles of this application, it is made it is any modification,
Equivalent replacement, improvement etc., should be included within the scope of the claims of this application.
Claims (10)
1. a kind of building efficiency diagnostic method characterized by comprising
According to history electricity itemize data, the acquisition history electricity subitem data when the corresponding historical external parameter and collection that obtain
History ad hoc rules information, selection to set can be carried out trained power consumption prediction model accordingly, then based on the institute to setting
The model training of power consumption prediction model is stated, to determine power consumption prediction model and its precision；
Threshold value selection is carried out to power consumption prediction model and its precision, to obtain diagnostic threshold；
The ad hoc rules information of the corresponding external parameter obtained and collection when electricity subitem data current according to acquisition, by described
Power consumption prediction model obtains predicting electricity subitem data accordingly；
Based on the diagnostic threshold, the current electricity subitem data for comparing the prediction electricity subitem data and acquiring accordingly, with true
Surely imitate situation；
Wherein, electricity subitem data are acquired in real time by metering separate mode.
2. the method as described in claim 1, which is characterized in that itemized according to history electricity subitem data, the acquisition history electricity
The history ad hoc rules information of the corresponding historical external parameter and collection that obtain when data, selection can be carried out accordingly with setting
Trained power consumption prediction model, then the model training based on the power consumption prediction model to setting, to determine that power consumption predicts mould
Type and its precision, further includes:
Power consumption feature is obtained according to the analysis to every history electricity subitem data, and combines corresponding historical external parameter and history
Ad hoc rules information selects power consumption prediction model using the standard deviation coefficient of variation and/or related coefficient, to set electricity to be trained
Consume prediction model；
History electricity is itemized into data, corresponding historical external parameter and the history ad hoc rules information of collection as model training
Sample is trained the prediction model of setting, determines the model parameter and its model accuracy of prediction model.
3. method according to claim 2, which is characterized in that obtain power consumption according to the analysis to every history electricity subitem data
Feature, and corresponding historical external parameter and history ad hoc rules information are combined, using the standard deviation coefficient of variation and/or phase relation
Number selection power consumption prediction model, to set power consumption prediction model to be trained, comprising:
Its power consumption feature is analyzed to the history electricity subitem data of input, in conjunction with corresponding historical external parameter and history ad hoc rules
Information, the dispersion according to the evaluation of the standard deviation coefficient of variation and/or the degree of correlation according to related coefficient judgement, are used back with setting
Return model and/or feature averaging model as power consumption prediction model to be trained.
4. the method as described in one of claims 1 to 3 characterized by comprising
The external parameter includes the temperature in meteorologic parameter；
The ad hoc rules information includes the job specification information in social information；
Electricity subitem data, by data acquisition device according to the frequency of 15 minutes/time to electrisity consumption, the electric current of surveyed electricity consumption branch
And/or voltage is acquired, and each electric branch is divided in specific electricity consumption subitem and obtains the power consumption data of each branch, and utilizes number
Daily each branch power consumption data are obtained according to polymerization, using each branch power consumption data as electricity subitem data；
Meteorologic parameter directly obtains outdoor by the frequency collection of 15 minutes/time by the temperature sampler at meteorological data collection end
The temperature data of dry-bulb temperature, and flat are asked to the multiple temperature datas acquired in special time period by data average treatment
The temperature in the meteorologic parameter that the external parameter includes is calculated in mean value；Alternatively, passing through the building institute for carrying out efficiency diagnosis
Search for the temperature obtained in the meteorologic parameter that the external parameter includes indirectly in ground weather station database；
Social information is arranged according to the national legal festivals and holidays to corresponding electricity subitem data by social information's data collection terminal
Job specification information is assigned on the day of place.
5. the method as described in one of claim 1-3, which is characterized in that be based on the diagnostic threshold, compare the prediction electricity
Subitem data and the current electricity subitem data acquired accordingly, to determine efficiency situation, comprising:
The error for calculating prediction electricity subitem data and corresponding current electricity subitem data obtains efficiency according to the diagnostic threshold
Diagnostic result, and export the efficiency situation.
6. method as claimed in claim 5, which is characterized in that calculate prediction electricity subitem data and corresponding current electricity subitem number
According to error obtain efficiency diagnostic result according to the diagnostic threshold, and export the efficiency situation, comprising:
The relative error and absolute error for calculating prediction electricity subitem data and corresponding current electricity subitem data, when in relative error
Compare absolute error again when more than the relative threshold upper limit or lower than relative threshold lower limit, and is set up separately according to the size of absolute error
Grade, the alarm of showed different.
7. a kind of building efficiency diagnostic system characterized by comprising
Model determining device, for according to history electricity itemize data, the acquisition history electricity subitem data when obtain it is corresponding
The history ad hoc rules information of historical external parameter and collection, selection is to determine that can be carried out trained power consumption accordingly predicts mould
Type, then based on the model training to the determining power consumption prediction model, to determine power consumption prediction model and its precision；
Diagnostic threshold device, for carrying out threshold value selection to power consumption prediction model and its precision, to obtain diagnostic threshold；
Prediction meanss, the ad hoc rules of the corresponding external parameter obtained and collection when for electricity subitem data current according to acquisition
Information obtains predicting electricity subitem data accordingly by the power consumption prediction model；
Diagnostic device is compared, for being based on the diagnostic threshold, prediction electricity is compared and itemizes data and what is acquired accordingly work as
Preceding electricity subitem data, to determine efficiency situation；Wherein,
Model determining device, further includes:
Setting for obtaining power consumption feature according to the analysis to every history electricity subitem data, and is combined to training pattern device
Corresponding historical external parameter and history ad hoc rules information select power consumption using the standard deviation coefficient of variation and/or related coefficient
Prediction model, to set prediction model to be trained；And model training apparatus, it is used for by history electricity subitem data, accordingly
The sample of historical external parameter and the history ad hoc rules information of collection as model training, to the power consumption prediction model of setting into
Row training, determines the model parameter and its model accuracy of power consumption prediction model
Wherein, electricity subitem data are acquired in real time by metering separate mode.
8. system as claimed in claim 7, which is characterized in that the model determining device, comprising:
Its power consumption feature is analyzed to the history electricity subitem data of input, in conjunction with corresponding historical external parameter and history ad hoc rules
Information, the dispersion according to the evaluation of the standard deviation coefficient of variation and/or the degree of correlation according to related coefficient judgement, are used back with setting
Return model and/or feature averaging model as power consumption prediction model to be trained.
9. system as claimed in claim 8 characterized by comprising
The external parameter includes the temperature in meteorologic parameter；
The ad hoc rules information includes the job specification information in social information；
Electricity subitem data, by data acquisition device according to the frequency of 15 minutes/time to electrisity consumption, the electric current of surveyed electricity consumption branch
And/or voltage is acquired, and each electric branch is divided in specific electricity consumption subitem and obtains the power consumption data of each branch, and utilizes number
Daily each branch power consumption data are obtained according to polymerization, using each branch power consumption data as electricity subitem data；
Meteorologic parameter directly obtains outdoor by the frequency collection of 15 minutes/time by the temperature sampler at meteorological data collection end
The temperature data of dry-bulb temperature, and flat are asked to the multiple temperature datas acquired in special time period by data average treatment
The temperature in the meteorologic parameter that the external parameter includes is calculated, as meteorologic parameter in mean value；Alternatively, by carrying out energy
The building location weather station database of effect diagnosis searches for the temperature obtained in the meteorologic parameter that the external parameter includes indirectly；
Social information is arranged according to the national legal festivals and holidays to corresponding electricity subitem data by social information's data collection terminal
Job specification information is assigned on the day of place.
10. the system as described in one of claim 7 to 9, which is characterized in that comparison diagnostic device, further includes:
The relative error and absolute error for calculating prediction electricity subitem data and corresponding current electricity subitem data, according to the diagnosis
Threshold value obtains efficiency diagnostic result, and exports the efficiency situation, sets up grade separately according to the size of absolute error, display is different
The alarm of degree.
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| CN105759608B (en) * | 2016-02-26 | 2018-12-28 | 北京邮电大学 | A kind of power-economizing method and system |
| CN107590022B (en) * | 2016-07-08 | 2021-06-25 | 上海东方延华节能技术服务股份有限公司 | Instrument collected data restoration method for building energy consumption subentry measurement |
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| CN108764626B (en) * | 2018-04-19 | 2022-03-15 | 武汉钢铁有限公司 | Energy-saving diagnosis method and device |
| CN109298270A (en) * | 2018-11-21 | 2019-02-01 | 珠海格力电器股份有限公司 | One kind determining equipment fault detection method and its detection system based on electricity consumption |
| CN110456690A (en) * | 2019-07-23 | 2019-11-15 | 安徽集弘物联科技有限公司 | A kind of intelligent type comprehensive managing power consumption control system |
| CN110516847A (en) * | 2019-07-27 | 2019-11-29 | 中建科技有限公司 | A kind of building energy consumption exception feedback method and device |
| CN111968356A (en) * | 2020-08-11 | 2020-11-20 | 重庆电子工程职业学院 | Intelligent building energy consumption monitoring system and method |
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