CN111429040A - Optimized deployment method for metering equipment of building energy consumption detection system - Google Patents
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Abstract
A method for optimizing and deploying metering equipment of a building energy consumption detection system comprises the following steps: step 1: determining a monitoring system branch circuit; step 2: respectively carrying out neighbor propagation clustering analysis; and step 3: estimating a building energy consumption score; and 4, step 4: determining a demand factor group; and 5: and judging the requirement of the metering device. The invention provides an optimized deployment method of metering equipment of a building energy consumption detection system. The AP clustering is performed based on power rating, runtime, and utilization, and the energy consumption of each branch circuit is scored to determine if a metering device needs to be installed. The method not only comprehensively considers rated power, running time and utilization rate, but also carries out scientific scoring and comprehensive evaluation. The method reduces the metering device of the BEMS, and has important significance for reducing initial investment cost and current maintenance cost and promoting the development of the BEMS.
Description
Technical Field
The invention belongs to the technical field of building energy consumption monitoring in a building system, and particularly relates to an optimized deployment method of metering equipment of a building energy consumption detection system.
Background
The energy problem of buildings is becoming the focus of debate of governments and scientific communities. Except for the industry and the department of transportation, the energy consumption of the building industry is one of three major energy consumption industries and is also an important source of greenhouse gas (GHG) emission, and the reduction of the energy consumption of buildings has gradually become an important strategic measure for energy conservation and carbon reduction in all countries. Statistics show that the Chinese building related industry accounts for 53.3% of the total energy consumption. For the european union, the energy consumption of the construction industry accounts for about 40% of its total energy consumption. In the united states, residential and commercial buildings consume around 40% of the TEC of the country. Building energy consumption is affected by a variety of factors, such as the building's own characteristics, the amount of equipment power and operating time (e.g., lighting, heating, ventilation and air conditioning systems), and so forth.
The narrow definition of building energy consumption, namely the operation energy consumption of a building, refers to daily energy consumption of people, such as energy consumption for heating, air conditioning, lighting, cooking, clothes washing and the like, and is a leading part of the building energy consumption. The general definition of building energy consumption refers to the energy consumption of the whole process from building material manufacture, building construction to building use. Obviously, with the improvement of the quality of life of human beings and the increase of economic income, the building energy consumption will continue to increase in the future. Therefore, the development of building energy-saving work has extremely important guiding significance.
The BECMS (building Energy control Management System) technology mainly optimizes and modifies the operation of the existing equipment in a building and provides supervision and control. Environmental data, equipment information and past energy consumption data in the intelligent building are collected in the building energy closed-loop management architecture. The intelligent control system integrates a sensor, an instrument and equipment, can control a building in real time, and reduces energy consumption. Furthermore, according to the description of David t.harrje by princeton university, the goal of BECMS is an efficient design that allows for smooth building operation and maximizes energy savings while maintaining user comfort.
In the research field of the BECMS technology, researchers have made many researches on the aspects of BECMS architecture, monitoring parameters, data transmission, building energy consumption database, data visualization and the like. In the aspect of acquisition and statistics of building energy consumption, Cao and the like adopt Zigbee technology to carry out data communication between a data acquisition unit and an intelligent instrument. Piette et al propose to improve the real-time energy efficiency level of a building by collecting and analyzing energy consumption data in real time. Jang and the like design a building energy online monitoring system based on a WEB technology and have the functions of energy consumption statistics, analysis and the like. Figueiredo et al first proposed incorporation of renewable energy regulation into building energy consumption regulation systems. Detailed databases are established in the uk through the NDBS project for industrial and commercial buildings in the uk. Yasuhiro et al established a low energy building database of 66 residential buildings in 17 countries including regions in europe, america, asia, etc. for systematizing active and passive low energy technologies.
However, few scholars are currently concerned with the optimal deployment strategy of BECMS metering devices. It is estimated that up to 30% to 50% of building energy is wasted due to misuse and non-optimal management of BECMS. Over the past few years, a great deal of research work has been done by many researchers on the modern Building Energy Management System (BEMS). In fact, monitoring of the fire-fighting equipment or the reserved branch circuits is of little interest, since they are idle for most of the time and consume less power. How to perform index weight calculation on the metering devices and reduce the number of the metering devices on the basis of realizing energy consumption monitoring and energy consumption metering analysis, so that the cost reduction of the system is a complex and urgent problem to be solved.
Disclosure of Invention
The invention aims to provide an optimized deployment method of metering equipment of a building energy consumption detection system, which solves the problem of metering equipment waste in the existing building energy consumption monitoring system.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for optimizing and deploying metering equipment of a building energy consumption detection system comprises the following steps:
step 1: determining a branch circuit of the monitoring system according to a design drawing of a building power distribution system;
step 2: respectively carrying out neighbor propagation clustering analysis on the rated power, the running time and the utilization rate of the branch circuit by using a neighbor propagation algorithm;
and step 3: estimating a building energy consumption score through equipment parameter fusion calculation and weighting score;
and 4, step 4: a single target optimization equation and constraint conditions are given and defined to calculate a minimum metering device with an energy consumption score value and a threshold value, and then a demand factor group is determined through the equation according to the threshold value;
and 5: and judging the requirement of the metering device.
Further, in step 1, according to the design drawing of the building power distribution system, the use information of branch circuits is obtained, and each branch circuit is defined as ki(ii) a Then, all branch circuits are defined as a set K ═ K1，k2，...，kn}。
Further, in step 2, the neighbor propagation algorithm:
1) input data similarity and preference s (i, j) }i,j∈{1,...,N}；
2) Initializing iteration times and a damping factor lambda;
3) updating responsibility value r (i, j)
rnew(i,k)＝λrold(i,k)+(1-λ)(s(i,k)-max{a(i,k')+s(i,k')}) (1)
i is the cluster center sought; k is the candidate cluster center assigned to i; s reflects similarity; a is the reputation;
update confidence a (i, j)
anew(i,j)＝λaold(i,j)+(1-λ)min(0,r(j,j)+∑max(0,r(k,j))),i≠k (2)
anew(i,i)＝λaold(i,i)+(1-λ)∑max(0,r(k,j)),j≠k,j≠i (3)
The default value of the damping factor λ is 0.75, mainly used to converge λ ∈ (0, 1);
5) updating responsibility and credit until the algorithm converges;
6) and outputting the cluster allocation.
Further, in step 2, performing AP cluster analysis on the rated power, operation time and utilization rate parameters of the device:
the AP is used for respectively carrying out cluster analysis on rated power, running time and utilization rate, classifying the final cluster results from small to large, and then classifying the cluster results into a first class, a second class, a third class and the like; if the rated power, the running time and the equipment use efficiency of the equipment are taken as three judgment standards, each branch circuit is respectively scored; the cluster analysis result of the three parameters of all the devices is the basis of the scores of the three judgment standards; when rating the rated power of the device, if the final clustering result of the rated power of the device is classified into m categories, the larger the rated power is, the larger the influence on the score is, and the influence from the first category to the m-th category is gradually increasing.
Further, in step 3, the device parameters are fused and calculated:
clustering an important parameter into m classes and clustering equipment branch circuits into s class, then calculating branch energy consumption score y according to the following basic formulaScore：
Specific calculations are described herein: assuming that the rated power, running time and utilization are respectively aggregated at np，nt，nClass (c);
in the ith branch circuit, the rated power is defined as that
Class, set runtime to
Class, clustering utilization as such
Class, expressed as
And the branch circuits in the three clustering criteria are scored as: [ p ]i,ti,i]；
Further, in step 3, the energy consumption calculated by the weighted score is:
the influence of the three evaluation standards is also different; the largest impact on the rating is the rated power, the run time and the efficiency are almost the same; using weight value a1，a2And a3The three parameters were scored with weight values of 0.4, 0.3 and 0.3, respectively. Weighting the score of each parameter as the influence factors of the three judgments;
calculated wiIs a power consumption scoring circuit corresponding to one branch. Then we can obtain energy diversity: w ═ W1，w2，w3，....，wn}。
Further, in step 4, a demand threshold is calculated, and a single objective optimization equation and constraints are given and defined to calculate the energy consumption score value W and the threshold value WλM;
m represents the number of branch circuits that need to be installed with the metering apparatus; m represents the number of all branch circuits; mλRepresents the minimum metering requirement of the branch circuit; wminAnd WmaxRepresenting the minimum and maximum energy score value sets for all branch circuits, respectively. Threshold value wλBelong to WminAnd WmaxThe area in between. When W and WλWhen the minimum requirement is met, the function f (-) represents the value of m; next, the step size may be based on W with a constant step sizeminAnd WmaxTo calculate the threshold value wλTo obtain an optimum value m;
then, according to the threshold value wλDetermining a demand factor group D by equation 1;
di denotes the selection factor of the optimized branch circuit i, diIs a logical value.
Further, in step 5, if the coefficient d is selectediIf the value is equal to 1, installing a branch circuit metering device; if the demand factor diEqual to 0 indicates that no metering device is installed on the branch circuit.
Compared with the prior art, the invention has the following technical effects:
the invention provides an optimized deployment method of metering equipment of a building energy consumption detection system. The AP clustering is performed based on power rating, runtime, and utilization, and the energy consumption of each branch circuit is scored to determine if a metering device needs to be installed. The method not only comprehensively considers rated power, running time and utilization rate, but also carries out scientific scoring and comprehensive evaluation. The method reduces the metering device of the BEMS, and has important significance for reducing initial investment cost and current maintenance cost and promoting the development of the BEMS.
The introduction of deep learning may lead to better results and clustering may be more accurate when analyzing the demand for measurement devices in a building energy monitoring system. When building energy sub-score scores are used, it may be more objective to score the results using more branching factors.
Drawings
FIG. 1 is a flow chart of an implementation of the present invention;
FIG. 2 is a power rating diagram for a power rating;
FIG. 3 is a graph of weighted mixed fraction calculations for each branch circuit;
fig. 4 shows the number of four branch metering devices after the office area is optimized by the method provided by the invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings:
referring to fig. 1 to 4, the method for optimizing and deploying metering devices of a building energy consumption detection system provided by the invention includes the following steps:
step 1: determining a branch circuit of the monitoring system according to a design drawing of a building power distribution system;
step 2: respectively carrying out neighbor propagation clustering analysis on three important parameters of the branch circuit by using a neighbor propagation algorithm;
and step 3: estimating a building energy consumption score through equipment parameter fusion calculation and weighting score;
and 4, step 4: a single objective optimization equation and constraints are given and defined to calculate the energy consumption score value W and the threshold value WλM, in turn according to a threshold value wλThe demand factor group D can be determined by an equation;
and 5: and judging the requirement of the metering device.
Referring to the attached fig. 1, by taking a petroleum technology center of a certain base as an example, a part of steps of the optimized deployment method of the metering equipment of the building energy consumption detection system provided by the invention are described:
the total area of the building is 51000 square meters, and the building consists of 5 floors above the ground and 1 floor below the ground. The building includes an office, a laboratory, a conference room, a fire center and a distribution station. The air source heat pump is used as a cold source and a heat source, and the air conditioning system is an all-air system. The building power supply and distribution system has four dry-type transformers, and a total of 133 branch circuits.
Calculation of energy consumption score
The required experimental data can be obtained by design drawings and field investigation. This data is used to classify the power ratings of all branch circuits and then to score them.
As can be seen from fig. 2, each branch circuit can calculate the class to which the cluster belongs and perform power consumption scoring.
Then, the same processing is performed on the use time and the utilization rate of the branch circuit. Table 1 lists the power consumption ratings for all branch circuits.
Calculation of demand factors
According to the actual use condition of the equipment, MAC，MPAnd MSMust meet the following conditions.
MAC≥25,MP≥6,MS≥8
MACIndicating the installation requirement of the metering device of the air conditioner, MPIndicating installation requirements of metering devices of electrical apparatus, MSIndicating the installation requirements of the metering device in a particular area.
Then, when M satisfies the following condition, M can be calculatedλThe parameter (c) of (c).
Office area limitation conditions:
fig. 3 is a weighted mixing fraction calculation for each branch circuit. The demand score for each branch circuit is known by a threshold score and a composite analysis.
From equation (9), the threshold, office block w, can be calculatedi0.55. When the threshold value is greater than 0.55, the measurement device must be installed, and on the other hand, if the threshold value is less than 0.55, it is not necessary. For detailed information, see table 1. Set of demand factors D.
TABLE 1 estimation of energy consumption scores for branch circuits of office buildings
Discussion of the related Art
To verify and evaluate the performance of the proposed method, the rate of decrease of the load η of the metering device was calculated and the method effect was evaluated η was calculated as follows.
Where M is the number of metering devices that need to be installed after scoring and M is the number of all branches. PiRepresenting the power rating of the branch circuit.
As can be seen in fig. 4, the number of four branch meters was reduced by the method proposed herein to optimize the office area by 30.43%, 3.70%, 50.00%, 4.00% and 100.00% respectively. It can cover 92.87%, 95.73%, 58.03%, 97.87% and 100% of the load capacity. The branch with the largest number is an auxiliary branch, and then a power supply device and a lamp holder; although the power rating of a single lamp is not large, the connection circuit of the lamp usually connects many lamps, the total power rating is large, the service time is long, the usage rate is higher, the total score is higher, and a metering device needs to be installed. After the score is synthesized, the backup branch is the largest in the dip because the backup circuit is usually not working, the usage time and usage rate are small, which makes the total score very low and no metering device is installed. The number of the optimized metering devices of the air conditioner is reduced because the rated power of the air conditioner is large, the operation time and the utilization rate are high, the score is high, and the metering devices need to be installed. A large number of laboratories with a plurality of special electrical devices are arranged in the technical center, the rated power is high, the experimental frequency is high, the score is high, and an electric meter needs to be installed.
In summary, the method achieves good results, and is analyzed as follows:
(1) parameters such as rated power, operation time and utilization rate are used for branch energy consumption estimation in consideration of the actual operation condition of the equipment.
(2) High power equipment operates for a short time without monitoring. Also, if the runtime is long, the low power device needs to be monitored as well.
(3) Important equipment, such as air conditioners, must be monitored for high power ratings, long operating times and high usage.
(4) Since no backup circuit is used at all, there is no need to install a backup circuit for the metering device.
Claims (8)
1. A method for optimizing and deploying metering equipment of a building energy consumption detection system is characterized by comprising the following steps:
step 1: determining a branch circuit of the monitoring system according to a design drawing of a building power distribution system;
step 2: respectively carrying out neighbor propagation clustering analysis on the rated power, the running time and the utilization rate of the branch circuit by using a neighbor propagation algorithm;
and step 3: estimating a building energy consumption score through equipment parameter fusion calculation and weighting score;
and 4, step 4: a single target optimization equation and constraint conditions are given and defined to calculate a minimum metering device with an energy consumption score value and a threshold value, and then a demand factor group is determined through the equation according to the threshold value;
and 5: and judging the requirement of the metering device.
2. The method for optimizing the deployment of the metering equipment of the building energy consumption detection system according to claim 1, wherein in step 1, the use information of branch circuits is obtained according to a design drawing of a building power distribution system, and each branch circuit is defined as ki(ii) a Then, all branch circuits are defined as a set K ═ K1，k2，...，kn}。
3. The optimized deployment method of the metering equipment of the building energy consumption detection system according to claim 1, characterized in that in step 2, a neighbor propagation algorithm:
1) input data similarity and preference s (i, j) }i,j∈{1,...,N}；
2) Initializing iteration times and a damping factor lambda;
3) updating responsibility value r (i, j)
rnew(i,k)＝λrold(i,k)+(1-λ)(s(i,k)-max{a(i,k')+s(i,k')}) (1)
i is the cluster center sought; k is the candidate cluster center assigned to i; s reflects similarity; a is the reputation;
update confidence a (i, j)
anew(i,j)＝λaold(i,j)+(1-λ)min(0,r(j,j)+∑max(0,r(k,j))),i≠k (2)
anew(i,i)＝λaold(i,i)+(1-λ)∑max(0,r(k,j)),j≠k,j≠i (3)
The default value of the damping factor λ is 0.75, mainly used to converge λ ∈ (0, 1);
5) updating responsibility and credit until the algorithm converges;
6) and outputting the cluster allocation.
4. The optimal deployment method of the metering equipment of the building energy consumption detection system according to claim 1, wherein in the step 2, the AP cluster analysis is performed on the rated power, the operation time and the utilization rate parameters of the equipment:
the AP is used for respectively carrying out cluster analysis on rated power, running time and utilization rate, classifying the final cluster results from small to large, and then classifying the cluster results into a first class, a second class, a third class and the like; if the rated power, the running time and the equipment use efficiency of the equipment are taken as three judgment standards, each branch circuit is respectively scored; the cluster analysis result of the three parameters of all the devices is the basis of the scores of the three judgment standards; when rating the rated power of the device, if the final clustering result of the rated power of the device is classified into m categories, the larger the rated power is, the larger the influence on the score is, and the influence from the first category to the m-th category is gradually increasing.
5. The optimal deployment method for metering equipment of the building energy consumption detection system according to claim 1, characterized in that in step 3, the equipment parameters are fused and calculated:
clustering an important parameter into m classes and clustering equipment branch circuits into s class, then calculating branch energy consumption score y according to the following basic formulaScore：
Specific calculations are described herein: assuming that the rated power, running time and utilization are respectively aggregated at np，nt，nClass (c);
in the ith branch circuit, the rated power is defined as that
Class, set runtime to
Class, clustering utilization as such
Class, expressed as
And the branch circuits in the three clustering criteria are scored as: [ p ]i,ti,i]；
6. The method for optimizing the deployment of the metering equipment of the building energy consumption detection system according to claim 1, wherein in the step 3, the energy consumption calculated by the weighted score is as follows:
the influence of the three evaluation standards is also different; the largest impact on the rating is the rated power, the run time and the efficiency are almost the same; using weight value a1，a2And a3Scoring the three parameters, wherein the weighted values are 0.4, 0.3 and 0.3 respectively; weighting the score of each parameter as the influence factors of the three judgments;
calculated wiIs a power consumption scoring circuit corresponding to one branch; then we can obtain energy diversity: w ═ W1，w2，w3，....，wn}。
7. The building energy consumption detection system of claim 1The optimized deployment method of the system metering equipment is characterized in that in step 4, a demand threshold is calculated, a single target optimization equation and constraint conditions are given and defined, and an energy consumption score value W and a threshold value W are calculatedλM;
m represents the number of branch circuits that need to be installed with the metering apparatus; m represents the number of all branch circuits; mλRepresents the minimum metering requirement of the branch circuit; wminAnd WmaxRespectively representing the minimum and maximum energy score value sets of all branch circuits; threshold value wλBelong to WminAnd WmaxThe area in between; when W and WλWhen the minimum requirement is met, the function f (-) represents the value of m; next, the step size may be based on W with a constant step sizeminAnd WmaxTo calculate the threshold value wλTo obtain an optimum value m;
then, according to the threshold value wλDetermining a demand factor group D by equation 1;
di denotes the selection factor of the optimized branch circuit i, diIs a logical value.
8. The method for optimizing the deployment of the metering equipment of the building energy consumption detection system according to claim 1, wherein in the step 5, if the coefficient d is selected, the optimal deployment method is characterized in thatiIf the value is equal to 1, installing a branch circuit metering device; if the demand factor diEqual to 0 indicates that no metering device is installed on the branch circuit.
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