CN104700328A - Heating and ventilating pipeline loss analysis method based on self-learning model - Google Patents
Heating and ventilating pipeline loss analysis method based on self-learning model Download PDFInfo
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Abstract
The invention provides a heating and ventilating pipeline loss analysis method based on a self-learning model. The method mainly comprises the steps that the total loss rate p of a heating and ventilating pipeline in a building is subjected to Fourier transform, and the value of a static state loss coefficient k of the heating and ventilating pipeline in the building can be obtained; the static state loss coefficient k of the heating and ventilating pipeline in the building is set to satisfy a quadratic polynomial; the total loss rate of a certain period is obtained, and a static state harmonic loss coefficient k is computed; through the static state harmonic loss coefficient, static state harmonic loss changing speed L is computed; and by judging the value of the L in a working cycle, the loss situation of the heating and ventilating pipeline is analyzed, and corresponding processing is carried out. The dredging maintaining working of the pipeline can be reminded in time, and periodic cleaning and monitoring maintaining are replaced or reduced.
Description
Technical field
The present invention relates to and whether block, leak or disturb for heating and ventilating pipeline the method carrying out judging, heating installation (cold air) loss analytical approach in concrete a kind of heating and ventilating pipeline.
Background technology
Along with the generally application with central air conditioner that continues to bring out of heavy construction, building energy consumption increases greatly, and worldwide energy shortage increases the weight of increasingly, therefore, improve the efficiency of energy utilization in building, utilize the energy rationally and effectively, advance that heavy construction is energy-conservation just seems very important.At present, many enterprises and scientific research institutions adopt various measures and means one after another, implement intelligent management from different levels, different angles to heavy construction, try hard to realize energy-conservation.
One of the energy saving means the most widely taked in large public building is exactly install automatic building control system (BAs), by the collection to overall electricity consumption data, rely on the loss of comparative analysis cold energy in transmitting procedure of leaving water temperature and return water temperature, remove the time of user's using air-condition and the accumulation of temperature, calculate user and use energy, by total with electric flux under divide decomposition in proportion, and need regularly to carry out dress watch to pipeline, dredging pipeline and service conduit complete.This pattern achieves certain achievement in realization is energy-saving and cost-reducing, but carry out energy-saving monitoring respectively owing to being only aimed at each energy consumption equipment, lack a kind of energy consumption analysis based on the overall situation and management and dispatching measure, no matter be from the effect of actual motion or from system perspective, energy-saving effect is all not ideal enough.And cannot locate in time, exactly and find problematic part, causing problem cannot to be processed timely and effectively and recover.Further, each cleaning, maintenance all need regular execution, and manpower consumption and fault consumption all can be larger.
In addition, by installing a large amount of collection in worksite equipment additional, cold collecting device is installed additional to the turnover loop of each cold quantity transmission pipeline, by the difference of in-out end (not being the backwater end) cold in timely comparative analysis loop, also can solve the above problems.But, bring great cost will to the construction of system.The water inlet of each loop respectively fills a cold meter with the turnover port of backwater, will bring the workload of great cost and plant maintenance.
Summary of the invention
The present invention, for overcoming above-mentioned defect, proposes a kind of heating and ventilating pipeline loss analysis method based on self learning model, mainly comprises the following steps:
The first step, carries out Fourier transform to the total losses rate p of heating and ventilating pipeline in building construction, can obtain
, wherein a
0be the value of the quiescent dissipation coefficient k of heating and ventilating pipeline in building construction;
Second step, in setting building construction, the quiescent dissipation coefficient k of heating and ventilating pipeline meets following polynomial expression, namely
Wherein, t is time variable, and M represents polynomial expression item number, a '
mfor multinomial coefficient; Owing to only paying close attention to the change in the lifting interval of once item loss in quiescent dissipation in building construction, therefore be 2 by M value, obtain quadratic term function k=f (t)=a '
0+ a '
1t+a '
2t
2;
3rd step, obtains the total losses rate in N number of sampling time section of building construction regular period, wherein, comprises n+1 total losses rate { p in each sampling time section
0, p
1, p
2, p
3... p
n, a of described N number of sampling time section is calculated respectively by formula (1)
0value, then can form ordered series of numbers set { (1, k
1), (2, k
2), (3, k
3) ... (N, k
n), wherein 1,2 ..., N represents each sampling time section, k
1, k
2... k
nrepresent the k value of corresponding sampling time section;
4th step, is carried out the binomial fitting of least square method, determines a ' by the k value of the N number of sampling time section the 3rd step obtained
0, a '
1, a '
2numerical value;
5th step, to binomial k=f (t)=a '
0+ a '
1t+a '
2t
2differential obtains: L=
it represents the rate of change of quiescent dissipation coefficient, and sets M ' for the maximum sampling time section in the described regular period, calculates L=a '
1+ 2a '
2the value of M ';
6th step, by judging the value of L in the described regular period, analyzing heating and ventilating pipeline loss situation in building construction, and carrying out respective handling.
Accompanying drawing explanation
Fig. 1 is according to typical Heating,Ventilating and Air Conditioning (HVAC) schematic piping diagram of the present invention;
Fig. 2 is the schematic diagram of total losses rate according to an embodiment of the invention;
Fig. 3 is the schematic diagram of quiescent dissipation rate according to still another embodiment of the invention;
Fig. 4 is the schematic diagram of the quiescent dissipation rate according to one more embodiment of the present invention;
Fig. 5 is the schematic diagram of the quiescent dissipation rate according to one more embodiment of the present invention; .
Embodiment
Fig. 1 is typical Heating,Ventilating and Air Conditioning (HVAC) schematic piping diagram, supposes there be m refrigeration machine, the cold end of n bar.Under not considering any loss situation in theory, then have:
∑ Ecin
m=∑ Ecout
n, wherein, Ecin
mrepresent that m refrigeration machine is at pipeline head end input cold, Ecout
nrepresent and go out cold sum at n with cold end.
In actual moving process, be necessarily there is loss, loss cold Δ Ec is:
ΔEc＝∑Ecin
m-∑Ecout
n
All analyses in this model are all carry out based on this waste.
Δ Ec as a stochastic distribution of the energy loss in heating and ventilating pipeline transmitting procedure, be one with the function of time correlation.Δ Ec has the once item loss (t is time variable) of time correlation, i.e. f
static(t)=k* ∑ Ecinm and have other environmental coefficients impact component f
environment(t, ∑ Ecin
m), wherein, k represents a coefficient irrelevant with the time.
Wherein, the once item loss f of time correlation
statict () represents static energy consume consumption, namely due to the inherent loss that the problem such as material, device of physical equipment produces, be also called basic loss.Basic loss is inevitable, but can reduce.F
environment(t, ∑ Ecin
m) component represents due to human factor or the energy loss relevant to the high power of flow velocity.This component is mainly subject to the impact of a large amount of uncertain factor, has larger undulatory property and real-time, is not analyzed in this article, only as the parameter of analytic process.
Based on the two-phase energy consumption component of Δ Ec, can reach a conclusion: f
statict () is applicable to periodically rectification, to improve static energy loss.And f
environment(t, ∑ Ecin
m) be applicable to adjustment and the maintenance of real-time management measure, with improve with can rationality.
In actual moving process, can consider owing to all belonging to periodically regulating measures project with the adjustment that can be accustomed to and overhaul of the equipments, therefore most of energy consumption loss concentrate on f
staticon (t), f
environment(t, ∑ Ecin
m) can as in environment location free procedure to the adjustment of the high-order term of energy consumption loss, therefore, in mathematical operation concentrate to f
statict () is analyzed.
The total losses rate of our define system is p, formula is brought into waste computing formula and obtains:
(t is the time)
Namely have
For different time samplings, we think that p is variable, and k is constant.Then in time domain, sample analysis can draw k value by the sampled value analysis of p.K value then has time independence.A static constant in time.
We suppose that p has the discrete sampled point { p in one group of time domain
0, p
1, p
2, p
3... p
n, engrave when being in the equally distributed n+1 of moment 0 to moment n respectively, in this section of time domain, p value, as discrete variable, has basic value k value constant.Then Fourier transform is carried out to this section of time domain p value, can obtain
Wherein a
0be 0 ordered coefficients, a
nfor higher-order coefficients
By { p
0, p
1, p
2, p
3... p
nformula (2) is brought into n with the moment 0, the coefficient a drawing first n+1 time can be analyzed
0to a
n, b
1to b
n
Definition band g (t) is as high order adjustment, and a
0exist as the constant of time independence in the analytic function of p.
Make comparisons with formula (1), because we need a function had nothing to do with environmental parameter analysis, therefore need the basic parameter of an elimination high order oscillation waveform, therefore, we are by a
0as the definition of energy consumption base value component ratio k.Have simultaneously:
High order on frequency domain launches, and basic frequency is defined as cycle sample time.
Basic value a can be obtained to the analysis result of above discrete certificate
0.Fluctuation frequency band is
Wherein
value be cycle sample time n+1, i.e. the time width in n+1 moment of sampling spot distribution in early stage.
Analyze according to this, work as a
0namely, when k value is high within the time period, be the performance that static energy consumption increases.Now namely describe for cold loop exist a large amount of cold consumption consumption transmission on leakage and use-pattern unreasonable on (as temperature is allocated unreasonable, usage time interval is unreasonable, and frequency converter does not have frequency conversion etc.).
In the application, we set up following physical model carry out periodicity adjustment:
1: building macroscopic view quiescent dissipation rate k
avg
2: each region quiescent dissipation rate k
n, wherein n Representative Region Field Number.
Because each region exists energy consumption weight, therefore have in theory:
But the quiescent dissipation due to each region is understood some share and belong to dynamic conditioning scope (the causing the diversity defined herein by the difference of energy period of each region) in the scope of the overall building of macroscopic view.
So the adjustment respectively in building macro readjustment of direction and each region should independently be carried out.Be described for building macro readjustment of direction below.
We set { the k of overall building in N
1, k
2, k
3... k
nthere is characteristic conforms polynomial expression:
Wherein, t is time variable.M represents polynomial expression item number.
Namely development trend then for the k value in N has a series of set { (1, k
1), (2, k
2), (3, k
3) ... (n, k
n)
The macroscopic view only paying close attention to this variable due to us rises and downtrending, only can pay close attention to the change be once elevated in interval.Still 2 are got to M, a quadratic term function can be obtained
k＝f(t)＝a′
0+a′
1t+a′
2t
2
This function is carried out in a set to the binomial fitting of least square method, can a ' be determined
0, a '
1, a '
2value.This binomial differential is obtained:
Get the maximal value that M ' is sampling instant, then can obtain current time quiescent dissipation rate ascending velocity
L＝a′
1+2a′
2M′
In system operation, can month by month, year by year instant by quiescent dissipation rate to be prompted to user by modes such as information prompting, graph transformations.The data run by long time integration substitute into formula, and its prompting principle is followed listed in Table:
| Sequence number | Formula | Explanation |
| 1 | L<0.01 | Quiescent dissipation rate is steady, and fluctuation within a narrow range, does not point out |
| 2 | 0.01<L<0.1 | The change of quiescent dissipation rate is obvious, alerting |
| 3 | L>0.1 | Quiescent dissipation rate sharply changes, prompt alarm |
Check processing is carried out for change unconspicuous needs.
Should rectify and improve in time after a management cycle completes for significant change, contains that it increases.
Should rectify and improve for jumpy immediately, to avoid the further deterioration of equipment or management, cause serious consequence
For the static energy consumption proportion of goods damageds in a measurement period, we can get its mathematical expectation
as the reference substance in the next management cycle.To observe the fluctuation situation of the static energy consumption proportion of goods damageds in different cycles, and can be used as the data basis of the static specific energy loss of a design characteristic area, for manual analysis.
Below provide concrete case analysis:
We are in actual items, from 1 day May in 2009, market, Guangzhou building have been carried out to the collecting work of the energy consumption data of 6 months by a definite date.In gatherer process, we as a collection period, obtained data to every 20 days.Be below the typical data in July:
Such as, Fig. 2 carries out FFT computing to above data, substitutes into formula 2, t and gets 1-12, can obtain k value and be about 0.028.
5-8 month data analysis
We get 5-8 month sampled data and calculate k value and refer to following table:
| Time (month) | 5 | 6 | 7 | 8 |
| K value | 0.024 | 0.023 | 0.028 | 0.026 |
So, the quafric curve that obtains of institute's matching is as shown in Figure 3:
k＝-0.00025t
2+0.00435t-0.00785
Its time diffusion is:
L＝k′＝-0.0005t+0.00435
For the data slope L in August, its value is
L
t＝8＝-0.0005×8+0.00435＝0.00035
Gained L value is less than 0.01, and quiescent dissipation rate is steady, and fluctuation within a narrow range, does not need alarm notification
Observe its tendency data and curve, also can find, to August, the change of k value is mild.Can think that native system is in the healthy state run at present.
5-10 month data analysis
We get the data analysis k value in 5-10 month, refer to following table:
So, secondary is carried out to it and obtains function to matching, as shown in Figure 4:
k＝0.0667-0.0128t+0.001*t
2
Its time diffusion is:
L＝k′＝0.002t+0.0128
For the data slope L in October, can obtain its value is
L
t＝10＝0.002×10+0.0128＝0.0328
Gained L value is greater than 0.01, is less than 0.1, and the change of quiescent dissipation rate obviously, needs alarm notification, carries out periodicity rectification.Observe its tendency data and curve, also can find, to 9-10 month, k value increases faster, can think that native system is in the state of inferior health operation at present.Need to rectify and improve when the next one rectification cycle arrives.
By this data statistics situation, next cycle arrive before (November) rectify and improve, then predict the level that at least can return to August.Otherwise by estimating, when using month to next air-conditioning, its static energy consumption proportion of goods damageds are estimated to reach 0.0469, then whether improve the difference of the static energy consumption proportion of goods damageds bringing 0.0209.As Fig. 5.
By the use habit in this building, calculate with the behaviour in service in October, then will save the power consumption of general 2.8 ten thousand degree, estimate the annual power consumption more than 250,000 degree.
The present invention is by total water inlet metering and each water outlet metering statistics, estimated the possibility of loss generation by mathematics model analysis with less measuring equipment, and measured by the analysis implemented and calculate, the dredging maintenance work of pipeline can be reminded in time, replace or reduce periodic cleaning and supervision maintenance work.
Claims (2)
1. based on a heating and ventilating pipeline loss analysis method for self learning model, mainly comprise the following steps: the first step, Fourier transform is carried out to the total losses rate p of heating and ventilating pipeline in building construction, can obtain
Second step, in setting building construction, the quiescent dissipation coefficient k of heating and ventilating pipeline meets following polynomial expression, namely
Wherein, t is time variable, and M represents polynomial expression item number, a '
mfor multinomial coefficient; Owing to only paying close attention to the change in the lifting interval of once item loss in quiescent dissipation in building construction, therefore be 2 by M value, obtain quadratic term function k=f (t)=a '
0+ a '
1t+a '
2t
2;
3rd step, obtains the total losses rate in N number of sampling time section of building construction regular period, wherein, comprises n+1 total losses rate { p in each sampling time section
0, p
1, p
2, p
3... p
n, a of described N number of sampling time section is calculated respectively by formula (1)
0value, then can form ordered series of numbers set { (1, k
1), (2, k
2), (3, k
3) ... (N, k
n), wherein 1,2 ..., N represents each sampling time section, k
1, k
2... k
nrepresent the k value of corresponding sampling time section;
4th step, is carried out the binomial fitting of least square method, determines a ' by the k value of the N number of sampling time section the 3rd step obtained
0, a '
1, a '
2numerical value;
5th step, to binomial k=f (t)=a '
0+ a '
1t+a '
2t
2differential obtains:
it represents the rate of change of quiescent dissipation coefficient, and sets M ' for the maximum sampling time section in the described regular period, calculates L=a '
1+ 2a '
2the value of M ';
6th step, by judging the value of L in the described regular period, analyzing heating and ventilating pipeline loss situation in building construction, and carrying out respective handling.
2., as claimed in claim 1 based on the heating and ventilating pipeline loss analysis method of self learning model, wherein in the 5th step, for the described regular period, as L<0.01, represent that quiescent dissipation rate is steady, fluctuation within a narrow range, does not point out; As 0.01<L<0.1, represent the change of quiescent dissipation rate obviously, alerting; As L>0.1, quiescent dissipation rate sharply changes, prompt alarm.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101872437A (en) * | 2009-04-27 | 2010-10-27 | 深圳市中电电力技术有限公司 | Energy-saving processing method and system thereof |
| US20120209552A1 (en) * | 2005-01-27 | 2012-08-16 | Electro Industries/Gauge Tech | Intelligent electronic device with enhanced power quality monitoring and communication capabilities |
| CN103105529A (en) * | 2013-01-22 | 2013-05-15 | 广西电网公司电力科学研究院 | Harmonic wave electric energy measuring system based on parameter analysis and control method thereof |
| CN103472298A (en) * | 2013-09-15 | 2013-12-25 | 珠海派诺科技股份有限公司 | Method for analyzing harmonic energy loss of electromechanical device |
-
2015
- 2015-04-08 CN CN201510166929.6A patent/CN104700328A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120209552A1 (en) * | 2005-01-27 | 2012-08-16 | Electro Industries/Gauge Tech | Intelligent electronic device with enhanced power quality monitoring and communication capabilities |
| CN101872437A (en) * | 2009-04-27 | 2010-10-27 | 深圳市中电电力技术有限公司 | Energy-saving processing method and system thereof |
| CN103105529A (en) * | 2013-01-22 | 2013-05-15 | 广西电网公司电力科学研究院 | Harmonic wave electric energy measuring system based on parameter analysis and control method thereof |
| CN103472298A (en) * | 2013-09-15 | 2013-12-25 | 珠海派诺科技股份有限公司 | Method for analyzing harmonic energy loss of electromechanical device |
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