Energy energy efficiency comprehensive management and control method and management and control system thereof

Abstract

The invention relates to the technical field of energy management and control, and discloses an energy efficiency comprehensive management and control method and a management and control system thereof, and the technical scheme is characterized in that an evaluation system is established, and evaluation indexes are screened; based on weighting of an AHP method, analyzing a non-sequential relationship between target standards; the consistency index is calculated, and the credibility is ensured; fuzzy comprehensive evaluation is constructed, and factors without clear boundaries are quantified. Energy consumption evaluation indexes are provided, and an analytic hierarchy process and a fuzzy comprehensive evaluation method are combined, so that the method for comprehensively evaluating the energy consumption is realized. Meanwhile, the weights of the indexes are determined through an analytic hierarchy process (AHP), and extra strength and credibility are provided for the result.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management and control, and more specifically, to an integrated energy efficiency management and control method and its management and control system. Background Art

[0002] Existing energy management and control standards only define the minimum standards, so the evaluation results are always whether the equipment meets or does not meet the standards. These existing methods cannot comprehensively reflect the energy consumption level of the equipment.

[0003] Therefore, the present invention provides an integrated energy efficiency management and control method and its management and control system, which improves the above technical problems. Summary of the Invention

[0004] The embodiments of the present disclosure aim at the deficiencies of the prior art and provide an integrated energy efficiency management and control method and its management and control system. The present invention proposes evaluation indicators of energy consumption and combines the analytic hierarchy process (AHP) and the fuzzy comprehensive evaluation method to realize a method for comprehensively evaluating energy consumption. At the same time, the weight of the indicators is determined by the analytic hierarchy process (AHP), providing additional strength and credibility for the results.

[0005] The above technical objectives of the present invention are achieved through the following technical solutions: An integrated energy efficiency management and control method and its management and control system, including the following steps:

[0006] S1. Establish an evaluation system and screen the evaluation indicators;

[0007] S2. Based on the weighting of the AHP method, analyze the non-sequential relationship between the target criteria;

[0008] S3. Calculate the consistency index to ensure credibility;

[0009] S4. Construct a fuzzy comprehensive evaluation to quantify factors without clear boundaries.

[0010] As a preferred technical solution of the present invention, an evaluation index system is established to reflect the hierarchical structure of the evaluated object, as follows:

[0011]

[0012] Wherein, Y i (i = 1, 2, 3, 4) respectively represent the index sets of the maximum type, minimum type, medium type and interval type.

[0013] As a preferred technical solution of the present invention, the conditional generalized variance minimization method is used to screen the evaluation indicators: First, calculate the mean χ i , variance S ii and covariance S ij :

[0014]

[0015]

[0016] Then, the generalized variance matrix S p×p is expressed as:

[0017]

[0018] where the P index can be divided into two parts (X1, X2,..., X P1 ) and (X P+1 , X P+2 ,..., X P ), denoted as X (1) and X (2) respectively;

[0019] X (1) and X (2) have the following conditional covariance:

[0020]

[0021] According to Equation (6) etc., the value t p is obtained to obtain other values t i (i = 1, 2,..., P); if the condition that t i is less than the defined critical value C is satisfied, the indicator χ i can be deleted.

[0022] As a preferred technical solution of the present invention, the process of analyzing the non-sequential relationship between target criteria based on the weighting of the AHP method is:

[0023] Construct a judgment matrix W n×n :

[0024]

[0025] where δ ij is the relative importance between two indicators and also satisfies the following conditions:

[0026]

[0027] As a preferred technical solution of the present invention, the root mean square is used to calculate the maximum eigenvalue and eigenvector of the matrix: First, calculate the product b j for each row element in the judgment matrix:

[0028]

[0029] Then calculate the nth root of b j ​

[0030]

[0031] Finally, normalize the vector:

[0032] In the formula:

[0033] As a preferred technical solution of the present invention, the process of calculating the consistency index to ensure credibility is as follows:

[0034] Calculate the maximum eigenvalue λ of the judgment matrix max :

[0035]

[0036] In the formula,

[0037] where (AW) j is the j-th element of the vector AW;

[0038] To apply the consistency check method of judgment, calculate CI:

[0039]

[0040] In the formula, CI is an index used to measure the degree of deviation of the judgment matrix from consistency;

[0041] Calculate the random consistency ratio CR:

[0042]

[0043] where CR is the consistency index, λ max is the maximum eigenvalue of the judgment matrix, n is the order of the matrix, and RI is the average random consistency index.

[0044] As a preferred technical solution of the present invention, the process of constructing a fuzzy comprehensive evaluation to quantify factors without clear boundaries is as follows:

[0045] Determine the evaluation criteria and levels: Let U = {u1, u2,..., u m} contain m factors for describing the target; Let V = {v1, v2,..., v n} be n judgments for describing the states of the m factors;

[0046] Determine the fuzzy relation matrix: The processing factor u of the factor set i (i = 1, 2,..., m) has a single-factor evaluation; From the perspective of u i , the membership degree of the target to the judgment level is r ij , ui The single-factor evaluation set is r i =(r i1 , r i2 , ···, r in ); Therefore, the evaluation sets of m factors form the total evaluation matrix R; thus, the fuzzy relationship R of each target from the index set U to the judgment level set V is determined, and the evaluation matrix R is as follows:

[0047]

[0048] As a preferred technical solution of the present invention, a weighted average fuzzy operator is adopted to enable the evaluation result to balance the weights of all indicators:

[0049] First-order fuzzy evaluation vector:

[0050] B i =A i ·R i =(b i1 , b i2 , …, b in ) i = 1, 2, …, 7 (17)

[0051] Second-order fuzzy evaluation vector:

[0052] B = A·R = (b1, b2, …, b n ) n = 1, 2, …, 5 (18)

[0053] Among them, A is the fuzzy subset of the importance (weight) of the indicators in the standard layer; A i is the fuzzy subset of the importance degree of the indicators in the factor layer; R and R i are the comprehensive evaluation matrices;

[0054] As a preferred technical solution of the present invention, the evaluation index score: the score set Z is defined as follows:

[0055] Z = (Z1, Z2, Z3, Z4, Z5) T (19)

[0056] According to the result of the fuzzy comprehensive evaluation, the total score of the target layer and the scores of each layer of indicators F i can be determined;

[0057] F = B·Z, F i =B i ·Z (20);

[0058] An integrated energy efficiency control and management system, the system includes: a perception layer and a network layer;

[0059] The perception layer consists of intelligent meters and sensors; the sensors are used to detect various parameters, such as temperature, pressure, flow rate, voltage, and pollutant concentration;

[0060] The network layer sends the collected data to the gateway, and the gateway transmits the data to the cloud server for storage and analysis through a standard communication protocol.

[0061] In summary, the present invention has the following beneficial effects: An evaluation index for energy consumption is proposed and combined with the analytic hierarchy process and fuzzy comprehensive evaluation method to achieve a method for comprehensively evaluating energy consumption. At the same time, the analytic hierarchy process (AHP) is used to determine the weights of the indicators, providing additional strength and credibility for the results. Description of the Drawings

[0062] Figure 1 It is a flowchart of an integrated energy efficiency control method and its control system provided by an embodiment of the present invention. Detailed Embodiments

[0063] The following describes the present application in detail with reference to specific embodiments. The following embodiments will help those skilled in the art to further understand the present application, but do not limit the present application in any form. It should be noted that those of ordinary skill in the art can make several modifications and improvements without departing from the concept of the present application. These all belong to the protection scope of the present application.

[0064] In order to make the purpose, technical solution, and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0065] It should be noted that if there is no conflict, the various features in the embodiments of the present application can be combined with each other, and all are within the protection scope of the present application. In addition, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order from the module division in the device or the flowchart. In addition, the terms "first", "second", "third", etc. used herein do not limit the data and execution order, but only distinguish the same items or similar items with basically the same function and role.

[0066] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used in this specification in the description of the present application are only for the purpose of describing specific embodiments and are not used to limit the present application. The term "and / or" used in this specification includes any and all combinations of one or more of the related listed items.

[0067] In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0068] Please refer to Figure 1 , Figure 1 which shows the flowchart of the comprehensive energy efficiency control method described in the embodiments of the present disclosure. The overall process mainly includes the following 4 steps:

[0069] Step 1: Establish an evaluation system and screen the evaluation indicators;

[0070] Specifically, an evaluation index system is established to reflect the hierarchical structure of the object to be evaluated, as follows:

[0071]

[0072] where Y i (i = 1, 2, 3, 4) respectively represent the index sets of the maximum type, minimum type, medium type, and interval type.

[0073] To establish a representative and non-repetitive evaluation system, the conditional generalized variance minimization method is used to screen the evaluation indicators: First, the mean χ i , variance S ii and covariance S ij are calculated based on equations (2)-(4):

[0074]

[0075]

[0076] Then, the generalized variance matrix S p×p is expressed as

[0077]

[0078] where the P index can be divided into two parts (X1, X2,..., X P1 ) and (X P+1 , X P+2 ,..., X P ), which are respectively represented as X (1) and X (2) , so the conditional covariance of X (1) and X (2) is as follows:

[0079]

[0080] According to equation (6) and so on, the value t p is obtained to obtain other values t i(i = 1, 2, …, P). If it satisfies t i the condition of being less than the defined critical value C, the indicator χ i can be deleted.

[0081] Step 2: Analyze the non-sequential relationship between the objective criteria based on the weighting of the AHP method

[0082] Specifically, it is supported by pairwise comparisons from the overall objective to the criteria and sub-criteria based on the AHP method, where the judgment matrix W n×n is as shown in Equation (7):

[0083]

[0084] where δ ij is the relative importance between two indicators and also satisfies the following conditions:

[0085]

[0086] According to Equations (9)-(11), the root mean square is used to calculate the maximum eigenvalue and eigenvector of the matrix: First, calculate the product b j of each row element in the judgment matrix:

[0087]

[0088] Then calculate the nth root of b j :

[0089]

[0090] Finally, apply normalization to the vector:

[0091] In the formula:

[0092] Step 3: Since the judgment matrix is affected by expert knowledge and preferences, it is necessary to check the judgment matrix through a consistency test to ensure the credibility based on the equation. If CR < 0.1, the judgment matrix meets the consistency requirement. Otherwise, the judgment matrix needs to be reallocated until the consistency check is passed.

[0093] Specifically, calculate the maximum eigenvalue λ max of the judgment matrix:

[0094]

[0095] In the formula,

[0096] where, (AW) jis the j-th element of vector AW.

[0097] To apply the consistency check method of judgment, calculate CI:

[0098]

[0099] where CI is an index used to measure the degree of deviation of the judgment matrix from consistency.

[0100] Calculate the random consistency ratio CR:

[0101]

[0102] where CR is the consistency index, λ max is the maximum eigenvalue of the judgment matrix, n is the order of the matrix, and RI is the average random consistency index.

[0103] Step 4: Construct a fuzzy comprehensive evaluation to quantify factors without clear boundaries.

[0104] 1) Determine the evaluation criteria and levels: Let U = {u1, u2, …, u m} contain m factors used to describe the target, called evaluation indicators (the lowest level of evaluation indicators).

[0105] Let V = {v1, v2, …, v n} be n judgments used to describe the states of m factors, called evaluation levels.

[0106] 2) Determine the fuzzy relation matrix: The processing factor u i (i = 1, 2, …, m) of the factor set has a single-factor evaluation; from the perspective of u i , the membership degree of the target to the judgment level is r ij , and the single-factor evaluation set of u i is r i = (r i1 , r i2 , ···, r in ). Therefore, the evaluation sets of m factors form the total evaluation matrix R. Thus, the fuzzy relation R of each target from the index set U to the judgment level set V is determined, and the evaluation matrix R is as follows:

[0107]

[0108] 3) Fuzzy composition: The fuzzy comprehensive evaluation (obtaining the vector of the fuzzy comprehensive evaluation) is a fuzzy combination operation composed of the membership degree vector and the index weight vector. Various fuzzy operators are used in the fuzzy composition. In this paper, the weighted average fuzzy operator is adopted to make the evaluation result balance the weights of all indicators. This choice makes the evaluation process more complete.

[0109] First-order fuzzy evaluation vector:

[0110] B i = A i ·R i = (b i1 ,b i2 ,…,B in ) i = 1, 2, …·, 7 (17)

[0111] Second-order fuzzy evaluation vector:

[0112] B = A·R = (b1, b2, …, b n ) n = 1, 2, …, 5 (18)

[0113] In the above formulas, A is the fuzzy subset of the importance degree (weight) of the indicators in the standard layer. A i is the fuzzy subset of the importance degree of the indicators in the factor layer. R and R i are the comprehensive evaluation matrices.

[0114] 4) Evaluation index score: The score set Z is defined as follows:

[0115] Z = (Z1, Z2, Z3, Z4, Z5) T (19)

[0116] According to the results of the fuzzy comprehensive evaluation, the total score of the target layer and the scores of the indicators of each layer F i can be determined.

[0117] F = B·Z, F i = B i ·Z (20).

[0118] The above is only the preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the idea of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art, several improvements and refinements made without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A comprehensive energy efficiency management method, characterized in that: The method comprises the following steps: S1. Establish an evaluation system and screen the evaluation indicators; S2, based on the weighting of the AHP method, analyze the non-sequential relationship between the target criteria; S3, calculate consistency indicators to ensure credibility; S4. Construct fuzzy comprehensive evaluation to quantify factors without clear boundaries.

2. The energy efficiency comprehensive management and control method according to claim 1 is characterized in that: Establish an evaluation index system to reflect the hierarchical structure of the evaluated object, as follows: Among them, Y i (i=1, 2, 3, 4) represent the index sets of maximum type, minimum type, medium type and interval type respectively.

3. The energy efficiency comprehensive management and control method according to claim 1 is characterized in that: Use the conditional generalized variance minimization method to screen the evaluation indicators: First calculate the mean x i , variance S ij and covariance S ij : Then, the generalized variance matrix S p×p It is expressed as: Among them, the P index can be divided into two parts (X1, X2, ..., X P1 ) and (X P+1 , X P+2 , …, X P ), respectively represented by X (1) and X (2) ; X (1) and X (2) The conditional covariance of is as follows: According to equation (6), the value t is obtained p , to obtain other values ​​of t i (i=1,2,…,P); if t i If the condition is less than the defined critical value C, the indicator x can be deleted. i .

4. The energy efficiency comprehensive management and control method according to claim 1 is characterized in that: Based on the weighting of the AHP method, the process of analyzing the non-sequential relationship between target criteria is as follows: Construct the judgment matrix W n×n : Among them, δ ij is the relative importance between the two indicators, and also satisfies the following conditions:

5. The method for comprehensive energy efficiency management and control according to claim 4, characterized in that: Use the root mean square to calculate the maximum eigenvalue and eigenvector of the matrix: First calculate the product b j Determine each row element in the matrix: Then calculate b j The nth root of Finally, normalization is applied to the vector: Where:

6. The energy efficiency comprehensive management and control method according to claim 1 is characterized in that: The process of calculating consistency indicators and ensuring credibility is: Calculate the maximum eigenvalue λ of the judgment matrix max : In the formula, Among them, (AW) j is the jth element of the vector AW; To apply the judgmental consistency check method, calculate the CI: In the formula, CI is an indicator used to measure the degree of deviation of the judgment matrix from consistency; Calculate the random consistency ratio CR: Among them, CR is the consistency index, λ max is the maximum eigenvalue of the judgment matrix, n is the order of the matrix, and RI is the average random consistency index.

7. The energy efficiency comprehensive management and control method according to claim 1 is characterized in that: The process of constructing fuzzy comprehensive evaluation and quantifying factors without clear boundaries is as follows: Determine the evaluation criteria and levels: Let U = {u1,u2,…,u m } contains m factors used to describe the target; let V = {v1,v2,…,v n } is n judgments used to describe the status of m factors; Determine the fuzzy relationship matrix: the processing factor u of the factor set i (i=1,2,…,m) has a single factor evaluation; from u i From the perspective of ij ,u i The single factor evaluation set is r i =(r i1 , r i2 ,··,r in ); therefore, the evaluation set of m factors forms the total evaluation matrix R; thus, the fuzzy relationship R of each target from the indicator set U to the judgment level set V is determined, and the evaluation matrix R is as follows:

8. The energy efficiency comprehensive management and control method according to claim 7 is characterized in that: The weighted average fuzzy operator is used to balance the weights of all indicators in the evaluation results: First-order fuzzy evaluation vector: B i =A i ·R i =(b i1 ,b i2 ,…,b in ) i=1,2,…,7 (17) Second-order fuzzy evaluation vector: B=A·R=(b1,b2,…,b n )n=1,2,…,5 (18) Among them, A is the fuzzy subset of the importance (weight) of the indicators in the standard layer; A i is a fuzzy subset of the importance of indicators in the factor layer; R and R i It is a comprehensive evaluation matrix.

9. The energy efficiency comprehensive management and control method according to claim 7, characterized in that: Evaluation index score: The score set Z is defined as follows: Z=(Z1,Z2,Z3,Z4,Z5) T (19) According to the results of fuzzy comprehensive evaluation, the total score of the target layer and the index F of each layer can be determined. i score; F=B·Z,F i =B i ·Z (20)。 10. An energy efficiency integrated management and control system, characterized in that: The system is used to implement a comprehensive energy efficiency management method as described in any one of claims 1 to 9, and the system includes: a perception layer and a network layer; The perception layer is composed of smart meters and sensors; the sensors are used to detect various parameters, such as temperature, pressure, flow, voltage and pollutant concentration; The network layer sends the collected data to the gateway, and the gateway transmits the data to the cloud server for storage and analysis through a standard communication protocol.

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