CN110033172A - A kind of efficiency various dimensions evaluation method, apparatus and system - Google Patents
A kind of efficiency various dimensions evaluation method, apparatus and system Download PDFInfo
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Abstract
The invention discloses a kind of efficiency various dimensions evaluation methods, apparatus and system, initially set up the sample database of the efficiency evaluation characteristic quantity based on energy consumption index Yu thermal comfort index, pass through clustering, typical characterization collection is obtained, efficiency feature vector to be assessed and typical characterization collection are carried out by intelligent Matching using Fuzzy Pattern Recognition Method on this basis.The method proposed through the invention, Real-Time Evaluation can be carried out to the thermal comfort of terminal passenger, the influence of the factors to air terminal building energy consumption such as reaction weather, airport scale comprehensively simultaneously, various dimensions objectively evaluate comprehensively to be realized to terminal efficiency, lowers terminal system energy consumption while guaranteeing degree of passenger comfort.
Description
Technical field
The invention belongs to energy efficiency monitorings and assessment technique field, and in particular to a kind of efficiency various dimensions evaluation method, device
And system, it is suitable for airport building.
Background technique
With the rapid development of civil aviaton's industry, terminal scale is increasing, and structure becomes increasingly complex, and energy consumption is also increasingly
Increase, accounts for about 50% or more of airport total energy consumption, reduction air terminal building energy consumption is of increasing concern, and part airport establishes
Terminal energy efficiency monitoring system carries out real-time monitoring to energy consumption index.
Terminal is as large-scale public place, and building energy consumption is mainly due to suitable Indoor Thermal, wet environment is built, to mention
High degree of passenger comfort, therefore the reduction that terminal indoor environmental quality pursues merely energy consumption index can not be detached from.Terminal at present
Indoor environment state modulator is primarily upon the underlying parameters such as temperature, humidity, and related thermal environment parameter is not yet reflected adult body heat
Comfort index.Foreign scholar proposes the indexs such as predictive mean vote index PMV, but the index meter by numerous studies
Complexity is calculated, is not yet used in terminal actual motion.
Therefore need to establish the monitoring model online of human thermal comfort index, research Different climate area, different scales boat
The various dimensions evaluation method for building efficiency of standing is evaluated from energy consumption index and the multiple dimensions of thermal comfort index, to embody four
To the requirement of " green, wisdom, humanity " terminal in type airport construction.
Summary of the invention
In view of the above-mentioned problems, the present invention proposes a kind of efficiency various dimensions evaluation method, apparatus and system, complete based on building
The efficiency evaluation typical case of energy consumption and passenger's thermal comfort various dimensions characterizes the foundation of collection, and will be to using Fuzzy Pattern Recognition Method
It assesses efficiency feature vector and typical characterization collection carries out intelligent Matching, to objectively respond terminal energy efficiency indexes, comprehensively for boat
Stand building energy resource system economical operation provide guidance.
In order to achieve the above technical purposes, reach above-mentioned technical effect, the invention is realized by the following technical scheme:
In a first aspect, the present invention provides a kind of efficiency various dimensions evaluation methods, comprising the following steps:
Energy consumption index and thermal comfort index are calculated based on relevant historical data, obtains the sample of efficiency evaluation characteristic quantity
Library；
Based on the energy consumption index, the binding occurrence of thermal comfort index and guidance value to the sample database of efficiency evaluation characteristic quantity
It is normalized；
By clustering algorithm, cluster point is carried out to the sample database of the efficiency evaluation characteristic quantity by normalized
Analysis obtains typical characterization collection；
The real-time running data of acquisition system, and energy consumption index and thermal comfort are calculated based on the real-time running data
Index forms energy efficiency evaluation characteristic quantity to be assessed；
Using Fuzzy Pattern Recognition Method, by energy efficiency evaluation characteristic quantity to be assessed and the typical characterization collection carry out mode
Matching, completes the efficiency various dimensions overall merit of system.
Preferably, the energy consumption index is the hot and cold load index of terminal unit area；The thermal comfort index is pre-
Count average hotness index.
Preferably, the calculating process of the thermal comfort index are as follows:
According to the thermal environment test data of setting, thermal comfort index value is calculated；
Indoor thermal environment parameters of temperature t is established by machine learning algorithma, humidityMean radiant temperature θmr, wind speed Va
With the statistical model of thermal comfort index；
The indoor thermal environment parameter that will acquire is input in the statistical model, obtains thermal comfort index value.
Preferably, the machine learning algorithm is neural network algorithm or algorithm of support vector machine.
Preferably, the thermal comfort index is divided into 7 grades, respectively hot, warm, micro- warm, moderate, micro- cool, cool and cold.
Preferably, described special to efficiency evaluation based on the energy consumption index, the binding occurrence of thermal comfort index and guidance value
The normalization formula that the sample database of sign amount is normalized are as follows:
Wherein, ω0For index guidance value, ωmaxFor Index Constraints value；
It include several groups index feature vector ω in the sample database of the efficiency evaluation characteristic quantity by normalized
=[ωx ωy], wherein ωxTo normalize energy consumption index, ωyTo normalize thermal comfort index.
Preferably, described by clustering algorithm, to the sample database of the efficiency evaluation characteristic quantity by normalized
Clustering is carried out, typical characterization collection is obtained, specifically includes the following steps:
(1) Fuzzy Weighting Exponent m, cluster numbers k (2≤k≤n), iteration stopping threshold values ε and the number of iterations b, initialization are set
Cluster centre v(0)；
(2) subordinated-degree matrix is calculatedAnd update ith cluster center
(3) it transfinites judgement: ifOr the number of iterations is more than stipulated number, then stops clustering, otherwise
It is transferred to step (2)；
(4) cluster result is exported.
Second aspect, the present invention provides a kind of efficiency various dimensions evaluating apparatus, comprising:
Sample database obtains module, for calculating energy consumption index and thermal comfort index based on relevant historical data, obtains
The sample database of efficiency evaluation characteristic quantity；
Preprocessing module, for being commented based on the energy consumption index, the binding occurrence of thermal comfort index and guidance value efficiency
The sample database of valence characteristic quantity is normalized；
Cluster Analysis module, for passing through clustering algorithm, to the efficiency evaluation characteristic quantity by normalized
Sample database carries out clustering, obtains typical characterization collection；
Energy efficiency evaluation characteristic quantity obtains module, for obtaining the real-time running data of system, and is based on the real time execution
Data calculate energy consumption index and thermal comfort index, form energy efficiency evaluation characteristic quantity to be assessed；
Pattern Matching Module, for use Fuzzy Pattern Recognition Method, by energy efficiency evaluation characteristic quantity to be assessed with it is described
Typical case's characterization collection carries out pattern match, completes the efficiency various dimensions overall merit of system.
The third aspect, the present invention provides a kind of efficiency various dimensions evaluation systems, comprising:
Processor is adapted for carrying out each instruction；And
Equipment is stored, is suitable for storing a plurality of instruction, described instruction is suitable for being loaded by processor and being executed in first aspect and appoint
Step described in one.
Compared with prior art, beneficial effects of the present invention:
A kind of efficiency various dimensions evaluation method proposed by the present invention, apparatus and system, can thermal comfort to passenger into
Row Real-Time Evaluation, while influence of the reaction climatic factor, airport scale to air terminal building energy consumption comprehensively, realize that various dimensions are comprehensive
It objectively evaluates, lowers terminal system energy consumption while guaranteeing degree of passenger comfort.
Detailed description of the invention
Fig. 1 is the flow chart of the efficiency various dimensions evaluation method in an embodiment of the present invention；
Fig. 2 is the architecture diagram using hardware system of the invention.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to embodiments, to the present invention
It is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to
It limits the scope of protection of the present invention.
Application principle of the invention is explained in detail with reference to the accompanying drawing.
Embodiment 1
The present invention provides a kind of efficiency various dimensions evaluation methods, comprising the following steps:
Step (1) obtain system relevant historical data, and based on the relevant historical data calculate energy consumption index and
Thermal comfort index obtains the sample database of efficiency evaluation characteristic quantity；
The energy consumption index is the hot and cold load index of terminal unit area；The thermal comfort index is estimated average
Hotness index, it is preferable that the thermal comfort index is divided into 7 grades, and respectively hot (- 3) are warmed up (- 2), micro- warm (- 1), fits
In (0), it is micro- cool (1), it is cool (2) and cold (3)；
The Index Formula of thermal comfort index in the prior art are as follows:
PMV=[0.303exp (- 0.036M)+0.0275] L
L={ M-W-3.05*10-3[5733-6.99(M-W)-pa]-0.42[(M-W)-58.15]-1.7*10-5M(5867-
pa)-0.0014M(34-ta)-3.96*10-8fcl[(tcl+273)4-(θmr+273)4]-fclhc(tcl-ta)}
tcl=35.7-0.028* (M-W)-Icl{3.96*10-8fcl[(tcl+273)4-(θmr+273)4]-fclhc(tcl-ta)}
Wherein, PMV is thermal comfort index, and L is intermediate variable, and M is human body metabolic rate, and W is physical activity generation
Mechanical work, taFor ambient air temperature, paFor ambient water vapor pressure,For humidity, θmrFor mean radiant temperature, VaFor wind
Speed, hcFor cross-ventilation heat transfer coefficient, tclFor dressing human surface temperature, IclTo wear thermal resistance, f clothesclTo wear coefficient clothes；As it can be seen that
The calculating of the prior art is excessively complicated, and since partial parameters are difficult to monitor on-line, and the present invention proposes in primary study room
Influence of the thermal environment to thermal comfort index, establishes the prediction model of thermal comfort index and indoor thermal environment, specifically use with
Lower step obtains:
(1.1) according to the thermal environment test data of setting, thermal comfort index is calculated using method in the prior art
Value；
(1.2) indoor thermal environment parameters of temperature t is established by machine learning algorithma, humidityMean radiant temperature θmr、
Wind speed VaWith the statistical model of thermal comfort index；In a specific embodiment of the invention, the machine learning algorithm is
The machine learning algorithm is neural network algorithm；In another specific embodiment of the present invention, the machine learning algorithm
For algorithm of support vector machine；Step (1.2) is actually exactly to utilize indoor thermal environment parameters of temperature ta, humidityMean radiant temperature
θmr, wind speed VaAnd the thermal comfort index value calculated carries out model training, until obtaining satisfied training pattern, and makees
For statistical model；
(1.3) the real-time indoor thermal environment parameter that will acquire is input in the statistical model, is obtained thermal comfort and is referred to
Scale value.
For the calculation method of the energy consumption index using the prior art, specific calculating process is not hair of the invention
Where bright point, therefore does not do and excessively repeat；
Step (2) obtain energy consumption index, thermal comfort index binding occurrence and guidance value, and be based on the energy consumption index,
The binding occurrence of thermal comfort index is normalized with sample database of the guidance value to efficiency evaluation characteristic quantity；
The binding occurrence of the energy consumption index, thermal comfort index and the specific value of guidance value are needed according to actual ring
Border determines, in a specific embodiment of the invention, the energy consumption index, thermal comfort Index Constraints value and guidance value
It chooses by regional location locating for airport (severe cold area, cold district, hot-summer and cold-winter area, hot summer and warm winter region, mild area)
And year passenger throughput (being higher than 10,000,000 person-times, be lower than 10,000,000 person-times) is divided into 10 classes；Due to energy consumption index and thermal comfort
The binding occurrence of index and the specific setting of guidance value are not therefore protection content of the invention is not done and excessively repeated；
It is described based on the energy consumption index, the binding occurrence of thermal comfort index and guidance value to the sample of efficiency evaluation characteristic quantity
The normalization formula that this library is normalized are as follows:
Wherein, ω0For index guidance value, ωmaxFor Index Constraints value；
It include several groups index feature vector ω in the sample database of the efficiency evaluation characteristic quantity by normalized
=[ωx ωy], wherein ωxTo normalize energy consumption index, ωyTo normalize thermal comfort index.
Step (3) carries out the sample database of the efficiency evaluation characteristic quantity by normalized by clustering algorithm
Clustering obtains typical characterization collection；In a specific embodiment of the invention, the clustering algorithm of the step (3) can be adopted
With fuzzy C-means clustering method, the principle is as follows:
If data setWherein n is number of samples, and s is the number of attribute, xj=[x1j,
x2j,…,xsj] (1≤j≤n) be j-th of sample, sample set is divided into k class, algorithm may be expressed as:
In formula: Jm(u, v) indicates cluster objective function (i.e. the inter- object distance sum of squares function containing fuzzy set theory)；Indicate sample xjTo cluster centre viDistance,uijIndicate that j-th of sample belongs to
The degree of membership of ith cluster；M indicates FUZZY WEIGHTED index (m > 1).
The step (3) specifically includes following sub-step:
(3.1) Fuzzy Weighting Exponent m, cluster numbers k (2≤k≤n), iteration stopping threshold values ε and the number of iterations b are set, initially
Change cluster centre v(0)；
(3.2) subordinated-degree matrix is calculatedAnd update ith cluster center
(3.3) it transfinites judgement: ifOr the number of iterations is more than stipulated number, then stops clustering, it is no
Then it is transferred to step (3.2)
(3.4) cluster result is exported；In a specific embodiment of the invention, the cluster result may include inefficient
High-comfort, efficient high-comfort, inefficient low comfort three classes typical condition；
Step (4) obtain system real-time running data, and based on the real-time running data calculate energy consumption index and
Thermal comfort index forms energy efficiency evaluation characteristic quantity to be assessed；
Step (5) uses Fuzzy Pattern Recognition Method, and energy efficiency evaluation characteristic quantity to be assessed and the typical characterization are collected
Pattern match is carried out, the efficiency various dimensions overall merit of system is completed；It is in the step (5) that energy efficiency evaluation to be assessed is special
Sign amount and the typical characterization collection carry out pattern match using the prior art, therefore do not do excessive repeat.
Fig. 2 is a kind of airport wisdom energy managing and control system in the prior art, which includes that control layer, coordination on the spot are controlled
Preparative layer and Optimized Operation layer；The control layer on the spot is respectively set in regions such as energy source station, terminals, completes relevant device
Operation control, while the load instruction that coordinated control layer issues is received, the server in Optimized Operation layer completes energy resource system
Modeling and optimization scheduling, while multidimensional evaluation is carried out to airport building efficiency, i.e., in actual application, in the present invention
Efficiency various dimensions evaluation method is in the server being arranged in Optimized Operation layer as program.
Embodiment 2
Based on inventive concept same as Example 1, the embodiment of the invention provides a kind of efficiency various dimensions evaluating apparatus,
Include:
Sample database obtains module, for calculating energy consumption index and thermal comfort index based on relevant historical data, obtains
The sample database of efficiency evaluation characteristic quantity；
Preprocessing module, for being commented based on the energy consumption index, the binding occurrence of thermal comfort index and guidance value efficiency
The sample database of valence characteristic quantity is normalized；
Cluster Analysis module, for passing through clustering algorithm, to the efficiency evaluation characteristic quantity by normalized
Sample database carries out clustering, obtains typical characterization collection；
Energy efficiency evaluation characteristic quantity obtains module, for obtaining the real-time running data of system, and is based on the real time execution
Data calculate energy consumption index and thermal comfort index, form energy efficiency evaluation characteristic quantity to be assessed；
Pattern Matching Module, for use Fuzzy Pattern Recognition Method, by energy efficiency evaluation characteristic quantity to be assessed with it is described
Typical case's characterization collection carries out pattern match, completes the efficiency various dimensions overall merit of system.
Rest part is same as Example 1.
Embodiment 3
Based on inventive concept same as Example 1, the embodiment of the invention provides a kind of efficiency various dimensions evaluation system,
Include:
Processor is adapted for carrying out each instruction；And
Equipment is stored, is suitable for storing a plurality of instruction, described instruction is suitable for being loaded by processor and being executed any in embodiment 1
Step described in.
Rest part is same as Example 1.
The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention.The technology of the industry
Personnel are it should be appreciated that the present invention is not limited to the above embodiments, and the above embodiments and description only describe this
The principle of invention, without departing from the spirit and scope of the present invention, various changes and improvements may be made to the invention, these changes
Change and improvement all fall within the protetion scope of the claimed invention.The claimed scope of the invention by appended claims and its
Equivalent thereof.
Claims (9)
1. a kind of efficiency various dimensions evaluation method, which comprises the following steps:
Energy consumption index and thermal comfort index are calculated based on relevant historical data, obtains the sample database of efficiency evaluation characteristic quantity；
It is carried out based on the sample database of the energy consumption index, the binding occurrence of thermal comfort index and guidance value to efficiency evaluation characteristic quantity
Normalized；
By clustering algorithm, clustering is carried out to the sample database of the efficiency evaluation characteristic quantity by normalized, is obtained
To typical case's characterization collection；
The real-time running data of acquisition system, and energy consumption index is calculated and thermal comfort refers to based on the real-time running data
Mark, forms energy efficiency evaluation characteristic quantity to be assessed；
Using Fuzzy Pattern Recognition Method, by energy efficiency evaluation characteristic quantity to be assessed and the typical characterization collection carry out mode
Match, completes the efficiency various dimensions overall merit of system.
2. a kind of efficiency various dimensions evaluation method according to claim 1, it is characterised in that: the energy consumption index is boat station
The hot and cold load index of building unit area；The thermal comfort index is estimated average hotness index.
3. a kind of efficiency various dimensions evaluation method according to claim 1, it is characterised in that: the thermal comfort index
Calculating process are as follows:
According to the thermal environment test data of setting, thermal comfort index value is calculated；
Indoor thermal environment parameters of temperature t is established by machine learning algorithma, humidityMean radiant temperature θmr, wind speed VaWith heat
The statistical model of comfort index；
The indoor thermal environment parameter that will acquire is input in the statistical model, obtains thermal comfort index value.
4. a kind of efficiency various dimensions evaluation method according to claim 3, it is characterised in that: the machine learning algorithm is
Neural network algorithm or algorithm of support vector machine.
5. a kind of efficiency various dimensions evaluation method according to claim 3, it is characterised in that: the thermal comfort index point
It is respectively hot, warm, micro- warm, moderate, micro- cool, cool and cold for 7 grades.
6. a kind of efficiency various dimensions evaluation method according to claim 1, it is characterised in that: described to be referred to based on the energy consumption
The normalization that the sample database of efficiency evaluation characteristic quantity is normalized in mark, the binding occurrence of thermal comfort index and guidance value
Formula are as follows:
Wherein, ω0For index guidance value, ωmaxFor Index Constraints value；
It include several groups index feature vector ω=[ω in the sample database of the efficiency evaluation characteristic quantity by normalizedx
ωy], wherein ωxTo normalize energy consumption index, ωyTo normalize thermal comfort index.
7. a kind of efficiency various dimensions evaluation method according to claim 1, it is characterised in that: it is described by clustering algorithm,
Clustering is carried out to the sample database of the efficiency evaluation characteristic quantity by normalized, obtains typical characterization collection, specifically
The following steps are included:
(1) Fuzzy Weighting Exponent m, cluster numbers k (2≤k≤n), iteration stopping threshold values ε and the number of iterations b, initialization cluster are set
Center v(0)；
(2) subordinated-degree matrix is calculatedAnd update ith cluster center
(3) it transfinites judgement: if | | vi (b)-vi (b+1)| | < ε or the number of iterations then stop clustering, otherwise turn more than stipulated number
Enter step (2)；
(4) cluster result is exported.
8. a kind of efficiency various dimensions evaluating apparatus characterized by comprising
Sample database obtains module, for calculating energy consumption index and thermal comfort index based on relevant historical data, obtains efficiency
The sample database of evaluating characteristic amount；
Preprocessing module, for special to efficiency evaluation based on the energy consumption index, the binding occurrence of thermal comfort index and guidance value
The sample database of sign amount is normalized；
Cluster Analysis module, for passing through clustering algorithm, to the sample of the efficiency evaluation characteristic quantity by normalized
Library carries out clustering, obtains typical characterization collection；
Energy efficiency evaluation characteristic quantity obtains module, for obtaining the real-time running data of system, and is based on the real-time running data
Energy consumption index and thermal comfort index are calculated, energy efficiency evaluation characteristic quantity to be assessed is formed；
Pattern Matching Module, for using Fuzzy Pattern Recognition Method, by energy efficiency evaluation characteristic quantity to be assessed and the typical case
Characterization collection carries out pattern match, completes the efficiency various dimensions overall merit of system.
9. a kind of efficiency various dimensions evaluation system characterized by comprising
Processor is adapted for carrying out each instruction；And
Equipment is stored, is suitable for storing a plurality of instruction, described instruction is suitable for by processor load and perform claim requires to appoint in 1~7
Step described in one.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111397117A (en) * | 2020-03-10 | 2020-07-10 | 珠海派诺科技股份有限公司 | Big data-based comfort prediction method, intelligent terminal and storage device |
| CN115545507A (en) * | 2022-10-17 | 2022-12-30 | 哈尔滨工业大学 | Indoor space thermal comfort evaluation method, device and system |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111397117A (en) * | 2020-03-10 | 2020-07-10 | 珠海派诺科技股份有限公司 | Big data-based comfort prediction method, intelligent terminal and storage device |
| CN115545507A (en) * | 2022-10-17 | 2022-12-30 | 哈尔滨工业大学 | Indoor space thermal comfort evaluation method, device and system |
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