Underground data center energy efficiency grade determination method based on multi-index evaluation

Abstract

The invention discloses an underground data center energy efficiency grade determination method based on multi-index evaluation. The invention provides a comprehensive evaluation method integrating multiple energy efficiency evaluation indexes for solving the problem of multi-dimensional index difference of an underground data center in energy efficiency evaluation. Firstly, a system comprising five key energy efficiency evaluation indexes including PUE, pPUE, PLF, CLF and WUE is constructed, and a standard range and a good and bad threshold value of each index are determined. In view of the difference between the dimension and the numerical range of each index, the numerical value of each index is converted into a unified score by adopting a mathematical conversion method. The conversion method comprises but is not limited to a linear interpolation method, and a weight is set for each index to ensure the scientificity of an evaluation result. Finally, according to the converted score and weight, the energy efficiency level of the underground data center is obtained through comprehensive calculation, the limitation of traditional single index evaluation is solved, and the overall energy efficiency performance of the underground data center is scientifically reflected.

Description

Technical Field

[0001] This invention relates to the field of constructing an energy efficiency index system for underground data centers, and in particular to a method for determining the energy efficiency level of underground data centers based on multi-index evaluation. Background Technology

[0002] This invention discloses a method for determining the energy efficiency level of underground data centers based on multi-indicator fusion. With the continuous development of data centers, especially the emergence of underground data centers, their complex structures, significant differences in scale, and varying operating conditions and technical configurations make it difficult to establish a universal and unified energy efficiency rating standard. Existing data center energy efficiency rating methods typically rely on the classification analysis of single indicators, mainly classifying individual indicators according to industry standards or specifications to ultimately determine the energy efficiency level. However, this method fails to comprehensively consider the overall energy efficiency performance of the data center, only assessing one aspect of energy efficiency, failing to reflect the interaction between various operational links, and unable to comprehensively evaluate the energy efficiency of the data center across multiple dimensions such as energy consumption and equipment operation.

[0003] In existing technologies, traditional energy efficiency assessment methods such as PUE (Power Usage Effectiveness) and WUE (Water Usage Effectiveness) typically employ a single-indicator grading method. While this method is simple to operate and easy to implement, it lacks sufficient accuracy and cannot comprehensively evaluate the overall energy efficiency of a data center. Furthermore, existing methods are limited in that a single indicator cannot cover the multi-dimensional energy efficiency performance of a data center, especially in complex underground data center environments, where a single indicator often fails to reflect the data center's comprehensive performance across multiple dimensions.

[0004] To address the aforementioned problems, this invention proposes a method for determining the energy efficiency level of underground data centers based on multi-indicator fusion. By constructing an energy efficiency evaluation system that includes multiple key indicators such as Power Usage Effectiveness (PUE), Local Power Usage Effectiveness (pPUE), Power Supply and Distribution System Energy Efficiency (PLF), Cooling System Energy Efficiency (CLF), and Water Usage Effectiveness (WUE), this invention can more comprehensively and scientifically assess the overall energy efficiency level of underground data centers. Unlike existing single-indicator methods, this invention, by comprehensively considering multiple dimensions of energy efficiency indicators, can accurately reflect the comprehensive performance of underground data centers in terms of energy consumption, equipment operation, and environmental control, avoiding the one-sidedness of single-indicator evaluation methods.

[0005] The technical solution of this invention rationally integrates and weights the aforementioned multiple energy efficiency indicators, uses linear interpolation to convert the values ​​of each indicator into scores, and ultimately determines the energy efficiency level of the data center. This method effectively overcomes the limitations of traditional single-indicator methods and provides a more accurate and comprehensive energy efficiency assessment standard for underground data centers. Summary of the Invention

[0006] This invention relates to a method for determining the energy efficiency level of underground data centers based on multi-indicator fusion. It aims to overcome the limitations of existing single-indicator energy efficiency assessment methods by comprehensively and scientifically evaluating the overall energy efficiency level of underground data centers through multi-indicator fusion. The method provided by this invention can more accurately reflect the comprehensive energy efficiency of underground data centers in multiple dimensions such as energy consumption, equipment operating efficiency, and environmental control, and can be flexibly adjusted according to the characteristics of different underground data centers.

[0007] The specific implementation steps of this invention are as follows: Figure 1 As shown:

[0008] Step 1: Construct a multi-indicator energy efficiency evaluation system

[0009] First, construct an evaluation system that includes multiple key energy efficiency indicators, specifically including:

[0010] • Power Usage Effectiveness (PUE) index;

[0011] • Maximum local power utilization (pPUE) index;

[0012] • Power supply and distribution system energy efficiency index (PLF);

[0013] • Cooling system energy efficiency rating (CLF);

[0014] Water use efficiency index (WUE).

[0015] These indicators cover multiple aspects such as energy consumption, equipment operation, and environmental control, and can comprehensively reflect the energy efficiency performance of underground data centers.

[0016] Step 2: Research on Industry Standards and Norms

[0017] Based on the construction of the energy efficiency assessment system, this invention has conducted research on the industry standards and specifications of the above five energy efficiency indicators to ensure that the assessment system meets the existing industry requirements and can provide appropriate energy efficiency assessment standards for the actual operating conditions and technical configuration of underground data centers.

[0018] Step 3: Numerical Standardization and Interpolation Methods

[0019] Considering the differences in the actual numerical range and dimensions of the aforementioned indicators, this invention employs interpolation methods (including but not limited to linear interpolation) to convert the numerical values ​​of each energy efficiency indicator into scores. Through this method, the values ​​of each indicator can be uniformly converted into a standardized scoring range, facilitating subsequent weighted calculations and comprehensive evaluation.

[0020] Step 4: Assign weights to each indicator

[0021] Based on the importance of each energy efficiency indicator in the comprehensive evaluation, this invention assigns a certain weight to each indicator. The weighting is based on the actual operational needs of underground data centers, taking into account the degree of impact of each indicator on overall energy efficiency.

[0022] Step 5: Weighted Fusion of Multiple Indicators

[0023] After standardizing the numerical values ​​and determining the weights, this invention calculates a weighted comprehensive score to ultimately determine the energy efficiency level of the underground data center. This method comprehensively considers multiple energy efficiency dimensions to fully assess the overall energy efficiency level of the data center, rather than relying on a single indicator for energy efficiency evaluation.

[0024] Through the steps described above, this invention provides a more comprehensive and accurate method for assessing the energy efficiency of underground data centers, overcoming the limitations of existing single-indicator methods. By conducting multi-dimensional energy efficiency assessments, this invention can more accurately reflect the operational efficiency of underground data centers and provide effective decision support for energy efficiency management.

[0025] The technical solution of the present invention has the following beneficial effects:

[0026] This invention provides a method for determining the energy efficiency level of underground data centers based on multi-indicator fusion. By constructing an evaluation system encompassing key energy efficiency indicators such as Power Usage Effectiveness (PUE), Local Power Usage Effectiveness (pPUE), Power Distribution System Energy Efficiency (PLF), Cooling System Energy Efficiency (CLF), and Water Usage Effectiveness (WUE), it effectively converts each indicator into a unified score using linear interpolation and weighted calculation, overcoming the limitations of existing single-indicator evaluation methods. This method comprehensively reflects the overall energy efficiency performance of underground data centers in terms of energy consumption, equipment operation, and other aspects. It not only overcomes the challenges posed by differences in the dimensions and numerical ranges of different indicators but also improves the accuracy and reliability of energy efficiency assessment. Compared to traditional single-indicator methods, this invention simplifies the evaluation process, reduces implementation complexity, and has strong adaptability. It can provide efficient energy efficiency assessment results for underground data centers of different types and sizes, further supporting energy efficiency optimization and management decisions, and improving the overall operational efficiency and sustainable development capabilities of data centers. Attached Figure Description

[0027] Figure 1 This is a flowchart of an energy efficiency assessment method for underground data centers based on multi-index fusion disclosed in this invention;

[0028] Figure 2 This is a diagram showing the weighting percentage of each indicator in this invention. Detailed Implementation

[0029] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. The technical problems solved by the present invention and its beneficial effects are also described. It should be noted that the described embodiments are only intended to facilitate understanding of the present invention and do not constitute any limitation thereof.

[0030] This invention addresses the technical shortcomings in the field of energy efficiency rating for underground data centers by proposing a standardized evaluation method that integrates multiple indicators. It specifically solves the problem that traditional energy efficiency evaluation methods rely solely on single indicators for hierarchical analysis, failing to achieve comprehensive quantitative evaluation of multi-dimensional indicators and eliminate dimensional differences in multi-indicator fusion evaluation. Due to technical bottlenecks such as significant dimensional differences, large numerical ranges, and inconsistent industry standards among different energy efficiency indicators (e.g., PUE, pPUE, PLF, CLF, nWUE), direct weighted calculations lead to distorted evaluation results. This invention effectively eliminates the impact of dimensional differences on evaluation results by establishing a standardized numerical conversion mechanism and matching a linear interpolation conversion model. This method uniformly maps all indicator scores to a 0-100 score range, solving the quantification problem of nonlinear indicators while preserving the variation characteristics of different indicators. Finally, a comprehensive score is calculated through weight allocation, providing a scientific and quantifiable technical basis for energy efficiency rating, overcoming the limitations of the one-sidedness and subjectivity of traditional evaluation methods.

[0031] Step 1: Investigate the Standard Values ​​for Each Indicator. The first step is to conduct a comprehensive industry survey of each energy efficiency assessment indicator. The main purpose of this survey is to clarify the industry reference standards for these indicators, specifically including which numerical ranges represent good energy efficiency performance and which ranges reflect poor energy efficiency levels.

[0032] (1) Power Usage Effectiveness (PUE)

[0033] Data center energy efficiency is divided into three levels, with the corresponding PUE values ​​as follows: Level 1: Data center PUE should not exceed 1.20, indicating the highest energy efficiency; Level 2: Data center PUE should not exceed 1.30; Level 3: Data center PUE should not exceed 1.50. This is the maximum allowable value for data center energy efficiency and the basic requirement for data center energy efficiency level.

[0034] (2) Maximum local power utilization rate (pPUE) max )

[0035] pPUE ma× It is an indicator for measuring the energy efficiency of a local area or equipment in a data center. As an extension and generalization of the PUE concept, it is used for energy efficiency evaluation of a single or partial data center or area. Its indicator specifications can be found in reference to PUE.

[0036] (3) Power supply and distribution system energy efficiency index (PLF)

[0037] Energy efficiency is good when PLF is around 0.5-0.7; when PLF reaches 0.9-1, energy efficiency will decrease; if PLF is too high, such as greater than 1, it indicates that the power supply and distribution system has low energy efficiency and more energy is lost during transmission and conversion.

[0038] (4) Refrigeration system energy efficiency index (CLF)

[0039] Generally, a lower CLF value means that the cooling system is relatively more efficient because it indicates that the power consumption of the cooling equipment accounts for a lower proportion of the power consumption of the IT equipment. For example, a CLF value below 0.5 may be considered high energy efficiency, between 0.5 and 0.8 may be medium energy efficiency, and above 0.8 may be relatively low energy efficiency.

[0040] (5) Water Use Efficiency Index (WUE)

[0041] A WUE value between 1.2 and 1.5 is considered moderate, while a value greater than 1.5 indicates that the data center performs poorly in terms of water resource utilization efficiency.

[0042] Step 2: A numerical conversion to a score method was adopted. By setting a standard value range for each indicator and using linear interpolation as an example, the actual values ​​of different dimensions were uniformly converted into scores of 0-100. Standardization of the values ​​was then performed.

[0043] 1. PUE Score Conversion Method

[0044] Power Usage Effectiveness (PUE) is a core metric for measuring data center energy efficiency; a lower PUE indicates higher energy efficiency. Therefore, we defined a PUE-based scoring range: 90 points for PUE = 1.2, and 60 points for PUE = 1.5. Actual PUE values ​​falling between these key points are calculated using linear interpolation. The basic principle of linear interpolation is to gradually decrease the score based on the proportion of the PUE value within the scoring range. For example, when PUE = 1.1, the score will be between 100 and 90 points, with the specific score calculated based on the proportion within the range.

[0045] 2.pPUE max Score conversion method

[0046] Maximum local power utilization (pPUE) max The scoring conversion rules for ) are consistent with those for PUE, and the evaluation logic is also the same as that for PUE, so the conversion process is consistent.

[0047] 3. PLF Score Conversion Method

[0048] The partial load factor (PLF) of a power distribution system is an important indicator reflecting the load utilization efficiency of the system. Theoretically, the PLF value can approach 0 infinitely, but will never equal 0. We define a score of 100 points for PLF = 0 and 60 points for PLF ≥ 0.9. To handle the actual PLF values, we also use linear interpolation to convert PLF values ​​between 0 and 0.9 into scores. When the PLF value is in the middle range, the score will be between 100 and 60 points, decreasing linearly based on the specific PLF value.

[0049] 4. CLF Score Conversion Method

[0050] The scoring logic for the Cooling Load Factor (CLF) is similar to that of the PLF. The CLF value can also approach 0 infinitely, but it cannot actually reach 0. We set the score to 100 points when CLF = 0 and 60 points when CLF ≥ 0.8. For CLF values ​​between 0 and 0.8, a linear interpolation method is used to gradually decrease the score. The score approaches 100 when the CLF value is close to 0, and approaches 60 when the CLF value gradually increases to 0.8.

[0051] 5. WUE scoring conversion method

[0052] When WUE = 1, the score is 100 points; when the WUE index is ≥ 1.5, the score is 60 points. For WUE values ​​between 0 and 0.8, a linear interpolation method is used to gradually reduce the score.

[0053] Step 3: Determine the weight of each indicator and calculate the overall score. The indicator weight accounts for, for example, the percentage of the total score. Figure 2 As shown.

[0054] First, using the aforementioned interpolation method, the actual value of each indicator is converted into a score, with all scores uniformly ranging from 0 to 100 to facilitate subsequent weighted calculations. Then, according to the weighting scheme, the score of each indicator is weighted. For example, the PUE score is multiplied by its 40% weight, the pPUE score by its 17.1% weight, and so on. Finally, the weighted scores of all indicators are summed to obtain the comprehensive score of the underground data center, calculated using the following formula:

[0055] Overall score = (PUE score × 40%) + (pPUE) max (Score × 40%)

[0056] +(PLF score × 11.9%) +(CLF score × 23%)

[0057] +(nWUE score × 23%)

[0058] Step 4: Based on the overall score calculated in Step 3, assign it to the corresponding energy efficiency level.

[0059] The grading standards are as follows: Level 1 - Excellent (90 points and above); Level 2 - Good (80-90 points); Level 3 - Average (70-80 points); Level 4 - Pass (60-70 points); Level 5 - Fail (below 60 points).

Claims

1. A method for determining the energy efficiency level of underground data centers based on multi-index evaluation, characterized in that, Includes the following steps: Step 1: Construct a multi-index energy efficiency evaluation system, which includes indicators for evaluating energy efficiency in various aspects such as electricity use, power supply and distribution systems, refrigeration systems, and water resource use; Step 2: Obtain the actual value of the indicator and the range of the benchmark value used for conversion; Step 3: Use a unified scoring conversion method to convert the actual values ​​of the indicators into standard scores. The conversion method includes, but is not limited to, mathematical conversion methods such as linear interpolation and nonlinear interpolation, in order to achieve dimensionless representation of the indicators. Step 4: Assign weights to each indicator and perform a comprehensive calculation using a weighted scoring method to obtain the overall score; Step 5: Based on the preset energy efficiency level classification threshold, determine the calculated comprehensive score as a predetermined discrete energy efficiency level. The number of level classifications is at least two, thus completing the classification of energy efficiency levels.

2. The method according to claim 1, characterized in that, The energy efficiency evaluation indicators include Power Usage Effectiveness (PUE), Maximum Local Power Usage Effectiveness (pPUE), Power Supply and Distribution System Energy Efficiency (PLF), Cooling System Energy Efficiency (CLF), and Water Usage Effectiveness (WUE).

3. The method according to claim 1, characterized in that: The conversion method pre-sets a standard value range for each energy efficiency assessment indicator and combines interpolation or other mathematical conversion operations to convert the actual value of the indicator into a standard score, thereby enabling indicators with different dimensions to be processed into dimensionless values ​​within a unified score range.

4. The method according to claim 1, characterized in that, The weighting criteria include industry standards, actual operating data, and the degree of influence of different indicators on energy efficiency.

5. The method according to claim 1, characterized in that, The method described can dynamically adjust the weight of each indicator based on the operation or technical configuration of different underground data centers.

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