CN116528270B - Base station energy saving potential evaluation method, device, equipment and storage medium - Google Patents
Base station energy saving potential evaluation method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN116528270B CN116528270B CN202310763712.8A CN202310763712A CN116528270B CN 116528270 B CN116528270 B CN 116528270B CN 202310763712 A CN202310763712 A CN 202310763712A CN 116528270 B CN116528270 B CN 116528270B
- Authority
- CN
- China
- Prior art keywords
- base station
- energy
- influence factor
- factor data
- energy consumption
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000011156 evaluation Methods 0.000 title claims abstract description 41
- 238000005265 energy consumption Methods 0.000 claims abstract description 66
- 238000004134 energy conservation Methods 0.000 claims abstract description 21
- 238000007781 pre-processing Methods 0.000 claims abstract description 21
- 238000000034 method Methods 0.000 claims description 26
- 238000012545 processing Methods 0.000 claims description 14
- 238000004590 computer program Methods 0.000 claims description 8
- 238000012795 verification Methods 0.000 claims description 8
- 238000012549 training Methods 0.000 claims description 7
- 238000000605 extraction Methods 0.000 claims description 4
- 238000005457 optimization Methods 0.000 claims description 4
- 238000000926 separation method Methods 0.000 claims description 4
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 238000004140 cleaning Methods 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 230000007613 environmental effect Effects 0.000 claims description 2
- 238000012854 evaluation process Methods 0.000 abstract description 2
- 238000005516 engineering process Methods 0.000 description 4
- 239000011159 matrix material Substances 0.000 description 4
- 238000002790 cross-validation Methods 0.000 description 3
- 238000004378 air conditioning Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000012417 linear regression Methods 0.000 description 2
- 238000005057 refrigeration Methods 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/02—Power saving arrangements
- H04W52/0203—Power saving arrangements in the radio access network or backbone network of wireless communication networks
- H04W52/0206—Power saving arrangements in the radio access network or backbone network of wireless communication networks in access points, e.g. base stations
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The application discloses a base station energy saving potential evaluation method, a device, equipment and a storage medium, which relate to the technical field of energy saving evaluation and comprise the following steps: acquiring original influence factor data of energy conservation of a base station under various energy conservation measures in target time; preprocessing and extracting features of original influence factor data under various energy-saving measures to obtain a plurality of groups of first main influence factor data which are in one-to-one correspondence with the various energy-saving measures; and respectively inputting a plurality of groups of first main influence factor data into an energy consumption model to obtain energy consumption values of the base station under various energy saving measures in a target time period, and outputting an energy saving potential evaluation result of the base station according to the energy consumption values. By establishing a comprehensive, accurate and objective base station energy consumption model, the scheme not only improves the accuracy of the base station energy saving potential evaluation result, but also reduces the interference of human factors on the evaluation process and ensures the objectivity of the evaluation result.
Description
Technical Field
The present application relates to the field of energy saving evaluation technologies, and in particular, to a method, an apparatus, a device, and a storage medium for evaluating energy saving potential of a base station.
Background
With the rapid development of mobile communication technology, the number of base stations is gradually increased, and the energy consumption of the base stations is gradually increased year by year, so that in order to reduce the energy consumption of the base stations, save energy sources and reduce carbon emission, the energy saving technology of the base stations is also the focus of attention in the industry.
The existing base station energy-saving potential evaluation methods mainly depend on historical energy consumption data and operator experience, so that the accuracy and objectivity of evaluation results have certain limitations, and meanwhile, the methods also lack comprehensive analysis on a plurality of energy-saving influence factors, so that the energy-saving potential of the base station cannot be fully mined.
Disclosure of Invention
Aiming at the defects of the existing base station energy-saving potential evaluation method, the application provides the base station energy-saving potential evaluation method, which can improve the accuracy of the base station energy-saving potential evaluation and provide scientific basis for the formulation of base station energy-saving measures.
In order to achieve the above purpose, the present application adopts the following technical scheme:
the application discloses a base station energy-saving potential evaluation method, which comprises the following steps:
acquiring original influence factor data of energy conservation of a base station under various energy conservation measures in target time;
preprocessing and extracting features of original influence factor data under various energy-saving measures respectively to obtain a plurality of groups of first main influence factor data corresponding to the various energy-saving measures one by one;
and respectively inputting the plurality of groups of first main influence factor data into an energy consumption model to obtain energy consumption values of the base station under various energy saving measures in a target time period, and outputting an energy saving potential evaluation result of the base station according to the energy consumption values.
Preferably, the energy saving measures comprise any one or a combination of two or more of no measures or equipment upgrade, network optimization and intelligent control.
Preferably, the original impact factors include base station type, device parameters, environmental data, and device operating status.
Preferably, the preprocessing and feature extraction are performed on the original influence factor data under each energy-saving measure to obtain a plurality of groups of first main influence factor data corresponding to each energy-saving measure, including:
carrying out data cleaning, missing value processing and abnormal value processing on original influence factor data of energy saving of a base station under each energy saving measure to obtain a plurality of groups of first influence factor data which are in one-to-one correspondence with various energy saving measures;
and extracting first main influence factor data in each group of the first influence factor data by utilizing a characteristic engineering to obtain a plurality of groups of first main influence factor data, wherein the characteristic engineering comprises normalization and barrel separation processing.
Preferably, the method for establishing the energy consumption model includes:
collecting historical energy consumption values of the base station under various energy-saving measures and historical influence factor data corresponding to the historical energy consumption values;
preprocessing and extracting features of historical influence factor data under various energy-saving measures respectively to obtain a plurality of groups of second main influence factor data which are in one-to-one correspondence with the various energy-saving measures;
and training a ridge regression model by using a cross verification method based on all the historical energy consumption values and the second main influence factor data corresponding to the historical energy consumption values respectively to obtain the energy consumption model of the base station.
Preferably, the initial value of the model parameter in the ridge regression model is a solution of a normal equation.
Preferably, the energy saving potential evaluation result of the base station includes the energy saving potential of the base station and the priority ordering of various energy saving measures.
A base station energy saving potential assessment apparatus, comprising:
the acquisition module is used for acquiring original influence factor data of energy conservation of the base station under various energy conservation measures in target time;
the preprocessing module is used for respectively preprocessing and extracting features of original influence factor data under various energy-saving measures to obtain a plurality of groups of first main influence factor data which are in one-to-one correspondence with the various energy-saving measures;
the evaluation module is used for respectively inputting the plurality of groups of first main influence factor data into an energy consumption model to obtain energy consumption values of the base station under various energy saving measures in a target time period, and outputting an energy saving potential evaluation result of the base station according to the energy consumption values.
An electronic device comprising a memory and a processor, the memory to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement a base station energy saving potential assessment method as claimed in any one of the preceding claims.
A computer readable storage medium storing a computer program which, when executed by a computer, causes the computer to implement a base station energy saving potential assessment method as claimed in any one of the preceding claims.
The application has the following beneficial effects:
according to the scheme, through establishing a comprehensive, accurate and objective base station energy consumption model, the accuracy of the base station energy saving potential evaluation result can be improved, interference of human factors on the evaluation process is reduced, the objectivity of the evaluation result is guaranteed, and meanwhile, the complexity of manual operation is reduced by adopting an automatic data acquisition and processing method, and the scheme is simple and efficient.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the application, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
Fig. 1 is a flowchart of a base station energy saving potential evaluation method implemented in embodiment 1 of the present application;
fig. 2 is a schematic diagram of a base station energy saving potential evaluation device implemented in embodiment 2 of the present application;
fig. 3 is a schematic diagram of an electronic device for implementing a base station energy saving potential evaluation method according to embodiment 3 of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The terms "first," "second," and the like in the claims and the description of the application, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order, and it is to be understood that the terms so used may be interchanged, if appropriate, merely to describe the manner in which objects of the same nature are distinguished in the embodiments of the application by the description, and furthermore, the terms "comprise" and "have" and any variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of elements is not necessarily limited to those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
As shown in fig. 1, a base station energy saving potential evaluation method includes the following steps:
s110, acquiring original influence factor data of energy conservation of a base station under various energy conservation measures in target time;
s120, preprocessing and extracting features of original influence factor data under various energy-saving measures to obtain a plurality of groups of first main influence factor data corresponding to the various energy-saving measures one by one;
s130, respectively inputting the plurality of groups of first main influence factor data into an energy consumption model to obtain energy consumption values of the base station under various energy saving measures in a target time period, and outputting an energy saving potential evaluation result of the base station according to the energy consumption values.
In this embodiment, before original impact factor data of energy saving of a base station in various energy saving measures in a target time is collected, a corresponding energy consumption model is first constructed according to historical energy consumption data of the base station, so that energy consumption values of the base station in different energy saving measures in the target time are predicted through the energy consumption model.
Further, collecting historical energy consumption values of the base station under various energy-saving measures and historical influence factor data corresponding to the historical energy consumption values;
preprocessing and extracting features of historical influence factor data under various energy-saving measures respectively to obtain a plurality of groups of second main influence factor data which are in one-to-one correspondence with the various energy-saving measures;
and training a ridge regression model by using a cross verification method based on all the historical energy consumption values and the second main influence factor data corresponding to the historical energy consumption values respectively to obtain the energy consumption model of the base station.
Firstly, determining factors influencing energy consumption of a base station, namely, factors influencing energy conservation of the base station, wherein the factors generally comprise base station types (such as macro base stations, micro base stations and indoor substations), equipment parameters, environment data (such as temperature and humidity), equipment operation states (such as equipment switching states and equipment operation time and the like), the equipment parameters further comprise equipment energy efficiency coefficients, air conditioning refrigeration efficiency and equipment loads (such as traffic and signal quality), and then collecting historical energy consumption values of the base station under different energy conservation measures and historical influence factor data corresponding to the historical energy consumption values respectively, wherein the energy conservation measures are any one or combination of two or more of no-measure or equipment upgrading, network optimization and intelligent control, and preferably, the equipment upgrading further comprises air conditioning refrigeration efficiency improvement, switching power supply conversion efficiency improvement and base transceiver equipment transformation, that is, the same base station can take a plurality of energy-saving measures at the same time, or can take only one energy-saving measure, or does not take energy-saving measures at all, collected historical data contains all conditions, all collected historical energy consumption data, equipment parameters, equipment running state and other information are preprocessed, the preprocessing comprises data cleaning, missing value processing and abnormal value processing, the effectiveness of the data is improved, the preprocessed data is screened by utilizing characteristic engineering to extract representative influencing factor data therefrom, the characteristic engineering comprises normalization processing and barrel separation processing as preferred, the preprocessed temperature and humidity data can be normalized according to the data characteristics of the influencing factors, the preprocessed equipment load data is barrel separation processing, finally, a plurality of groups of second main influence factor data are obtained, the combination condition of how many energy-saving measures are provided, and how many groups of second main influence factor data are provided, so that the correlation among influence factors is reduced through characteristic engineering, and the generalization capability of the model can be improved.
Because the base station energy consumption may be affected by factors such as the base station type, environment (temperature and humidity), and equipment operation state, in this embodiment, a Ridge Regression (Ridge Regression) model is selected as a training model, and meanwhile, a loss function of the Ridge Regression model is expressed as: loss=rss+α Σ (β i 2 ) Where RSS represents the sum of residual squares, in particular rss=Σ (e i 2 )，e i =y i -y_hat i ，y i For actual observations, y_hat i E is the predicted value of the model i To correspond to the residual error of the sample, beta i Representing model parameters, wherein alpha represents regularization parameters, and the variance of the model parameters can be reduced through the introduction of regularization terms, so that the stability of the model is improved, and particularly, the ridge regression can effectively reduce the deviation of parameter estimation under the condition of multiple collinearity; the regularization term can limit the increase of parameters, so that the model is prevented from being excessively fitted, and the generalization capability of the model is improved; the regularization parameter alpha can also control the balance of the model on the data fitting and regularization terms, and different model complexity can be selected by adjusting the value of alpha, so that the requirements of different problems are met.
The initial value of the model parameter is the solution of the normal equation, β= (X) T X+αI) (-1) X T Y, where I is an identity matrix and beta represents a parameter estimation vector, also known as a feature coefficient or weight, for a linear regression model, each beta i Corresponding to a characteristic coefficient for describing the relationship between the characteristic and the target variable, X represents an input characteristic matrix, which is a matrix containing sample data, each row of which represents a sample, each column of which represents a characteristic, and X has dimensions m X n, where m is the number of samples, n is the number of characteristics, X T The transposed matrix of X is represented, alpha is represented as regularized parameter, Y is represented as target variable vector which contains actual observed value of sample, the dimension of Y is m multiplied by 1, m is sample number, by substituting these parameters and variables into normal equation formula, the value of beta can be solved, the parameter estimation result of linear regression model can be obtained, further used for prediction and analysis, then based on all collected historical energy consumption values and their fractionsThe corresponding second main influence factor data is trained by using a cross validation method, the cross validation method is a technology for evaluating the performance of the model and selecting super parameters, the performance of the model can be evaluated on the verification set by dividing the data set into a training set and a verification set, and the optimal super parameter setting is selected, in the k-fold cross validation, the data set, namely the historical data after feature engineering processing, is randomly divided into k folds (folds) with similar sizes, one fold is selected as the verification set each time, the rest k-1 folds are used as the training set, the process is repeated for k times, each time different folds are selected as the verification set, and finally the evaluation result of the performance of k models is obtained.
After the energy consumption model is constructed, the energy saving potential of the base station can be estimated by using the model, because the energy saving measures are used in various situations, in order to improve the accuracy of estimating the energy saving potential, the energy saving potential of the base station under each situation can be predicted, namely, the original influence factor data of the base station under different situations need to be obtained.
According to the method, the energy consumption model is built through the multidimensional parameters, and then the energy consumption value of the base station is estimated by using the model to predict the energy consumption value of the base station in a future period without using energy-saving measures and using various energy-saving measures, so that the accuracy and objectivity of an estimated result are improved, and the working efficiency is improved.
Example 2
As shown in fig. 2, a base station energy saving potential evaluation device includes:
the acquisition module 10 is used for acquiring original influence factor data of energy conservation of the base station under various energy conservation measures in target time;
the preprocessing module 20 is configured to respectively perform preprocessing and feature extraction on the original influence factor data obtained by the obtaining module 10 under various energy-saving measures, so as to obtain a plurality of groups of first main influence factor data corresponding to the various energy-saving measures one by one;
the evaluation module 30 is configured to input the plurality of sets of first main impact factor data obtained by the preprocessing module 20 into an energy consumption model to obtain energy consumption values of the base station under various energy saving measures in a target time period, and output an energy saving potential evaluation result of the base station according to the energy consumption values.
One embodiment of the above device may be: the acquisition module 10 acquires original influence factor data of energy conservation of the base station under various energy conservation measures in target time; the preprocessing module 20 respectively performs preprocessing and feature extraction on the original influence factor data under various energy-saving measures obtained by the obtaining module 10 to obtain a plurality of groups of first main influence factor data corresponding to the various energy-saving measures one by one; the evaluation module 30 inputs the plurality of sets of first main impact factor data obtained by the preprocessing module 20 into an energy consumption model to obtain energy consumption values of the base station under various energy saving measures in a target time period, and outputs an energy saving potential evaluation result of the base station according to the energy consumption values.
Example 3
As shown in fig. 3, an electronic device includes a memory 301 and a processor 302, where the memory 301 is configured to store one or more computer instructions, and the one or more computer instructions are executed by the processor 302 to implement a base station energy saving potential evaluation method described above.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the electronic device described above may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
A computer readable storage medium storing a computer program which, when executed by a computer, causes the computer to implement a base station energy saving potential assessment method as described above.
By way of example, a computer program may be divided into one or more modules/units stored in the memory 301 and executed by the processor 302 and completed by the input interface 305 and the output interface 306 to complete the present application, and one or more modules/units may be a series of computer program instruction segments capable of performing specific functions for describing the execution of the computer program in a computer device.
The computer device may be a desktop computer, a notebook computer, a palm computer, a cloud server, or the like. The computer device may include, but is not limited to, a memory 301, a processor 302, it will be understood by those skilled in the art that the present embodiment is merely an example of a computer device and is not limiting of a computer device, may include more or fewer components, or may combine certain components, or different components, e.g., a computer device may also include an input 307, a network access device, a bus, etc.
The processor 302 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors 302, digital signal processors 302 (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor 302 may be a microprocessor 302 or the processor 302 may be any conventional processor 302 or the like.
The memory 301 may be an internal storage unit of the computer device, such as a hard disk or a memory of the computer device. The memory 301 may also be an external storage device of a computer device, such as a plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash memory Card (Flash Card) or the like, which is provided on a computer device, and further, the memory 301 may also include an internal storage unit of a computer device and an external storage device, the memory 301 may also be used to store computer programs and other programs and data required by a computer device, the memory 301 may also be used to temporarily store the programs and data in the output 308, and the aforementioned storage Media include a U disk, a removable hard disk, a read-only memory ROM303, a random access memory RAM304, a disk or an optical disk and other various Media that can store program codes.
The foregoing is merely illustrative of specific embodiments of the present application, and the scope of the present application is not limited thereto, but any changes or substitutions within the technical scope of the present application should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. The base station energy saving potential evaluation method is characterized by comprising the following steps of:
acquiring original influence factor data of energy conservation of a base station under various energy conservation measures in target time, wherein the energy conservation measures comprise any one or combination of two or more of no-measure or equipment upgrade, network optimization and intelligent control;
preprocessing and extracting features of original influence factor data under various energy-saving measures respectively to obtain a plurality of groups of first main influence factor data corresponding to the various energy-saving measures one by one;
respectively inputting the plurality of groups of first main influence factor data into an energy consumption model to obtain energy consumption values of the base station under various energy saving measures in a target time period, and outputting an energy saving potential evaluation result of the base station according to the energy consumption values;
the method for establishing the energy consumption model comprises the following steps:
collecting historical energy consumption values of the base station under various energy-saving measures and historical influence factor data corresponding to the historical energy consumption values;
preprocessing and extracting features of historical influence factor data under various energy-saving measures respectively to obtain a plurality of groups of second main influence factor data which are in one-to-one correspondence with the various energy-saving measures;
and training a ridge regression model by using a cross verification method based on all the historical energy consumption values and the second main influence factor data corresponding to the historical energy consumption values respectively to obtain the energy consumption model of the base station.
2. The method of claim 1, wherein the primary impact factors include base station type, device parameters, environmental data, and device operating status.
3. The method for evaluating energy saving potential of a base station according to claim 1, wherein the preprocessing and feature extraction are performed on the original influence factor data under each energy saving measure, respectively, to obtain a plurality of groups of first main influence factor data corresponding to each energy saving measure one by one, and the method comprises the following steps:
carrying out data cleaning, missing value processing and abnormal value processing on original influence factor data of energy saving of a base station under each energy saving measure to obtain a plurality of groups of first influence factor data which are in one-to-one correspondence with various energy saving measures;
and extracting first main influence factor data in each group of the first influence factor data by utilizing a characteristic engineering to obtain a plurality of groups of first main influence factor data, wherein the characteristic engineering comprises normalization and barrel separation processing.
4. The base station energy saving potential evaluation method according to claim 1, wherein the initial value of the model parameter in the ridge regression model is a solution of a normal equation.
5. The base station energy saving potential evaluation method according to claim 1, wherein the energy saving potential evaluation result of the base station includes an energy saving potential size of the base station and a priority ranking of the various energy saving measures.
6. A base station energy saving potential evaluation device, comprising:
the acquisition module is used for acquiring original influence factor data of energy conservation of the base station under various energy conservation measures in target time, wherein the energy conservation measures comprise any one or combination of two or more of no-measure or equipment upgrade, network optimization and intelligent control;
the preprocessing module is used for respectively preprocessing and extracting features of original influence factor data under various energy-saving measures to obtain a plurality of groups of first main influence factor data which are in one-to-one correspondence with the various energy-saving measures;
the evaluation module is used for respectively inputting the plurality of groups of first main influence factor data into an energy consumption model to obtain energy consumption values of the base station under various energy saving measures in a target time period, and outputting an energy saving potential evaluation result of the base station according to the energy consumption values;
the energy consumption model building module comprises:
collecting historical energy consumption values of the base station under various energy-saving measures and historical influence factor data corresponding to the historical energy consumption values;
preprocessing and extracting features of historical influence factor data under various energy-saving measures respectively to obtain a plurality of groups of second main influence factor data which are in one-to-one correspondence with the various energy-saving measures;
and training a ridge regression model by using a cross verification method based on all the historical energy consumption values and the second main influence factor data corresponding to the historical energy consumption values respectively to obtain the energy consumption model of the base station.
7. An electronic device comprising a memory and a processor, the memory for storing one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement a base station energy saving potential assessment method according to any one of claims 1 to 5.
8. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a computer, implements a base station energy saving potential evaluation method according to any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310763712.8A CN116528270B (en) | 2023-06-27 | 2023-06-27 | Base station energy saving potential evaluation method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310763712.8A CN116528270B (en) | 2023-06-27 | 2023-06-27 | Base station energy saving potential evaluation method, device, equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116528270A CN116528270A (en) | 2023-08-01 |
| CN116528270B true CN116528270B (en) | 2023-10-03 |
Family
ID=87397914
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310763712.8A Active CN116528270B (en) | 2023-06-27 | 2023-06-27 | Base station energy saving potential evaluation method, device, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116528270B (en) |
Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004038269A (en) * | 2002-06-28 | 2004-02-05 | Toshiba Corp | Design support apparatus, method, and program |
| JP2012027536A (en) * | 2010-07-20 | 2012-02-09 | Yamatake Corp | Energy saving evaluation support device and energy saving evaluation support method |
| CN103530706A (en) * | 2013-10-23 | 2014-01-22 | 国家电网公司 | Analysis method for comprehensive energy-saving potential of power distribution network |
| CN106849792A (en) * | 2017-01-24 | 2017-06-13 | 中国农业大学 | The energy consumption calculation and conservation measures appraisal procedure of motor device and group system |
| CN109376437A (en) * | 2018-10-30 | 2019-02-22 | 中车南京浦镇车辆有限公司 | A kind of vehicle-mounted energy storage optimal control method of new energy based on multi-objective genetic algorithm |
| CN111861246A (en) * | 2020-07-28 | 2020-10-30 | 国电南瑞科技股份有限公司 | Rail transit energy-saving assessment method and system based on space-time big data |
| CN111950738A (en) * | 2020-08-10 | 2020-11-17 | 中国平安人寿保险股份有限公司 | Machine learning model optimization effect evaluation method and device, terminal and storage medium |
| CN113379093A (en) * | 2020-03-09 | 2021-09-10 | 中国石油化工股份有限公司 | Energy consumption analysis and optimization method for oil gas gathering and transportation system |
| CN114374616A (en) * | 2021-12-30 | 2022-04-19 | 中国电信股份有限公司 | Energy consumption evaluation method, device, equipment and medium |
| CN114449550A (en) * | 2020-11-05 | 2022-05-06 | 中国电信股份有限公司 | Parameter optimization method and device of base station energy-saving system |
| CN114662852A (en) * | 2022-02-24 | 2022-06-24 | 国网山东省电力公司电力科学研究院 | Active power distribution network multi-measure loss reduction effect evaluation method and system |
| WO2022143621A1 (en) * | 2020-12-29 | 2022-07-07 | 阿里巴巴集团控股有限公司 | Data processing method and apparatus, computing device, and test simplification device |
| CN114970357A (en) * | 2022-06-01 | 2022-08-30 | 新奥数能科技有限公司 | Energy-saving effect evaluation method, system, device and storage medium |
| CN115459309A (en) * | 2022-09-20 | 2022-12-09 | 国网甘肃省电力公司电力科学研究院 | Efficiency evaluation method and device for energy storage system participating in multi-link regulation and control of power grid |
| CN115915237A (en) * | 2021-08-09 | 2023-04-04 | 中国移动通信集团设计院有限公司 | Energy-saving measuring and calculating method and device for base station equipment and calculating equipment |
| CN116249186A (en) * | 2022-12-30 | 2023-06-09 | 中国电信股份有限公司 | Data processing method and device of wireless network equipment, storage medium and electronic equipment |
-
2023
- 2023-06-27 CN CN202310763712.8A patent/CN116528270B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004038269A (en) * | 2002-06-28 | 2004-02-05 | Toshiba Corp | Design support apparatus, method, and program |
| JP2012027536A (en) * | 2010-07-20 | 2012-02-09 | Yamatake Corp | Energy saving evaluation support device and energy saving evaluation support method |
| CN103530706A (en) * | 2013-10-23 | 2014-01-22 | 国家电网公司 | Analysis method for comprehensive energy-saving potential of power distribution network |
| CN106849792A (en) * | 2017-01-24 | 2017-06-13 | 中国农业大学 | The energy consumption calculation and conservation measures appraisal procedure of motor device and group system |
| CN109376437A (en) * | 2018-10-30 | 2019-02-22 | 中车南京浦镇车辆有限公司 | A kind of vehicle-mounted energy storage optimal control method of new energy based on multi-objective genetic algorithm |
| CN113379093A (en) * | 2020-03-09 | 2021-09-10 | 中国石油化工股份有限公司 | Energy consumption analysis and optimization method for oil gas gathering and transportation system |
| CN111861246A (en) * | 2020-07-28 | 2020-10-30 | 国电南瑞科技股份有限公司 | Rail transit energy-saving assessment method and system based on space-time big data |
| CN111950738A (en) * | 2020-08-10 | 2020-11-17 | 中国平安人寿保险股份有限公司 | Machine learning model optimization effect evaluation method and device, terminal and storage medium |
| CN114449550A (en) * | 2020-11-05 | 2022-05-06 | 中国电信股份有限公司 | Parameter optimization method and device of base station energy-saving system |
| WO2022143621A1 (en) * | 2020-12-29 | 2022-07-07 | 阿里巴巴集团控股有限公司 | Data processing method and apparatus, computing device, and test simplification device |
| CN115915237A (en) * | 2021-08-09 | 2023-04-04 | 中国移动通信集团设计院有限公司 | Energy-saving measuring and calculating method and device for base station equipment and calculating equipment |
| CN114374616A (en) * | 2021-12-30 | 2022-04-19 | 中国电信股份有限公司 | Energy consumption evaluation method, device, equipment and medium |
| CN114662852A (en) * | 2022-02-24 | 2022-06-24 | 国网山东省电力公司电力科学研究院 | Active power distribution network multi-measure loss reduction effect evaluation method and system |
| CN114970357A (en) * | 2022-06-01 | 2022-08-30 | 新奥数能科技有限公司 | Energy-saving effect evaluation method, system, device and storage medium |
| CN115459309A (en) * | 2022-09-20 | 2022-12-09 | 国网甘肃省电力公司电力科学研究院 | Efficiency evaluation method and device for energy storage system participating in multi-link regulation and control of power grid |
| CN116249186A (en) * | 2022-12-30 | 2023-06-09 | 中国电信股份有限公司 | Data processing method and device of wireless network equipment, storage medium and electronic equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116528270A (en) | 2023-08-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Huang et al. | Predicting execution time of computer programs using sparse polynomial regression | |
| WO2021129086A1 (en) | Traffic prediction method, device, and storage medium | |
| DE102018001535A1 (en) | DURATION PROCESSOR OPTIMIZATION | |
| CN111680841B (en) | Short-term load prediction method, system and terminal equipment based on principal component analysis | |
| CN107730097B (en) | Bus load prediction method and device and computing equipment | |
| KR102468316B1 (en) | Time series prediction method and apparatus based on past prediction data | |
| CN110781595B (en) | Method, device, terminal and medium for predicting energy use efficiency (PUE) | |
| CN113449919B (en) | Power consumption prediction method and system based on feature and trend perception | |
| CN112257928A (en) | Short-term power load probability prediction method based on CNN and quantile regression | |
| CN114041031A (en) | Combined solution determination system | |
| KR102495494B1 (en) | Method and apparatus for predicting home energy usage | |
| CN111179108A (en) | Method and device for predicting power consumption | |
| Yang et al. | Developing data-driven models to predict BEMS energy consumption for demand response systems | |
| US20210099894A1 (en) | Forcasting time series data | |
| CN116739152A (en) | New energy power prediction model construction method and new energy power prediction method | |
| CN113110961B (en) | Equipment abnormality detection method and device, computer equipment and readable storage medium | |
| CN116528270B (en) | Base station energy saving potential evaluation method, device, equipment and storage medium | |
| CN114139803A (en) | Method, system, equipment and medium for predicting short-term power of wind power station | |
| CN111473494A (en) | Air conditioner control method, device and equipment based on machine learning | |
| CN110648248A (en) | Control method, device and equipment for power station | |
| CN117220286B (en) | Risk assessment method, device and medium for water-wind-solar multi-energy complementary system | |
| CN112801372B (en) | Data processing method, device, electronic equipment and readable storage medium | |
| CN114115150B (en) | Online modeling method and device for heat pump system based on data | |
| CN117094453B (en) | Scheduling optimization system and method for virtual power plant | |
| CN117272212B (en) | Method for selecting optimal operation parameters of refrigeration system based on real-time data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |