CN114374616A - Energy consumption evaluation method, device, equipment and medium - Google Patents
Energy consumption evaluation method, device, equipment and medium Download PDFInfo
- Publication number
- CN114374616A CN114374616A CN202111650494.4A CN202111650494A CN114374616A CN 114374616 A CN114374616 A CN 114374616A CN 202111650494 A CN202111650494 A CN 202111650494A CN 114374616 A CN114374616 A CN 114374616A
- Authority
- CN
- China
- Prior art keywords
- energy
- data
- saving
- base station
- 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.)
- Granted
Links
- 238000005265 energy consumption Methods 0.000 title claims abstract description 139
- 238000011156 evaluation Methods 0.000 title claims abstract description 25
- 238000000034 method Methods 0.000 claims abstract description 48
- 230000002159 abnormal effect Effects 0.000 claims description 52
- 238000003860 storage Methods 0.000 claims description 15
- 238000010219 correlation analysis Methods 0.000 claims description 5
- 238000012417 linear regression Methods 0.000 claims description 5
- 238000000513 principal component analysis Methods 0.000 claims description 5
- 238000004590 computer program Methods 0.000 claims description 2
- 230000000694 effects Effects 0.000 abstract description 9
- 238000010586 diagram Methods 0.000 description 11
- 238000012545 processing Methods 0.000 description 11
- 230000008569 process Effects 0.000 description 10
- 230000009467 reduction Effects 0.000 description 6
- 238000009826 distribution Methods 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 238000005520 cutting process Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 238000012937 correction Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 239000013307 optical fiber Substances 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013145 classification model Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000001932 seasonal effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/142—Network analysis or design using statistical or mathematical methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/24323—Tree-organised classifiers
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/08—Testing, supervising or monitoring using real traffic
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Mathematical Physics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Probability & Statistics with Applications (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Pure & Applied Mathematics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Algebra (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The embodiment of the disclosure provides an energy consumption evaluation method, an energy consumption evaluation device, energy consumption evaluation equipment and an energy consumption evaluation medium, and relates to the technical field of big data. The method comprises the following steps: acquiring energy consumption data of the base station from the characteristic data set of the base station in a preset time period; classifying the energy consumption data of the base station to obtain the energy-saving-free data in the energy-saving-free time period and the energy-saving data in the energy-saving time period of the base station; generating non-energy-saving data corresponding to the energy-saving time period of the base station through a variational self-encoder based on the non-energy-saving data of the non-energy-saving time period; and evaluating the energy consumption of the base station based on the energy-saving data and the energy-saving data corresponding to the energy-saving time period. According to the technical scheme of the embodiment of the disclosure, the non-energy-saving data corresponding to the energy-saving time period of the base station can be accurately generated, so that the energy-saving effect of the base station can be accurately evaluated.
Description
Technical Field
The disclosure relates to the technical field of big data, in particular to an energy consumption evaluation method and device, an electronic device and a computer readable medium.
Background
With the development of 5G technology, 5G base stations are beginning to be widely deployed throughout the country. At present, the main ways of energy saving of a base station include symbol turning off, channel turning off, carrier turning off, deep sleep, etc., and the time period and the effect of each energy saving way are different, so how to evaluate the energy saving effect of the base station becomes the focus of attention.
In one technical scheme, after energy-saving processing is performed on a base station, energy consumption changes of the base station before and after energy saving are monitored to estimate the energy-saving effect of the base station. However, in this technical solution, because the energy consumption of the base station has seasonal fluctuations, it is not possible to obtain the non-energy-saving data in the energy-saving period, so that the energy-saving effect of the base station cannot be accurately evaluated.
Therefore, how to accurately evaluate the energy saving effect of the base station becomes a technical problem to be solved urgently.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
An object of the embodiments of the present disclosure is to provide an energy consumption evaluation method, an energy consumption evaluation device, an electronic device, and a computer readable medium, so as to accurately evaluate an energy saving effect of a base station at least to a certain extent.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to a first aspect of the embodiments of the present disclosure, there is provided an energy consumption evaluation method, including: acquiring energy consumption data of a base station from a characteristic data set of the base station in a preset time period, wherein the characteristic data set comprises the energy consumption data of the base station; classifying the energy consumption data of the base station to obtain the energy-saving-free data in the energy-saving-free time period and the energy-saving data in the energy-saving time period of the base station; generating non-energy-saving data corresponding to the energy-saving time period of the base station through a variational self-encoder based on the non-energy-saving data of the non-energy-saving time period; and evaluating the energy consumption of the base station based on the energy-saving data and the energy-saving data corresponding to the energy-saving time period.
According to the first aspect, in some example embodiments, the variational self-encoder comprises an encoder and a decoder, and the generating, by the variational self-encoder, non energy saving data corresponding to the energy saving period of the base station based on the non energy saving data of the non energy saving period comprises: generating a corresponding non-energy-saving time sequence based on the non-energy-saving data of the non-energy-saving time period; inputting the non-energy-saving time sequence into the encoder, and encoding the non-energy-saving time sequence into an implicit vector through the encoder; reconstructing, by the decoder, non-energy-saving data corresponding to the energy-saving period of the base station based on the implicit vector.
According to the first aspect, in some example embodiments, the non-energy-saving time series is a time series of unequal lengths, the method further comprising: and changing the time sequences with different lengths into time sequences with equal lengths by sliding the time window.
According to the first aspect, in some example embodiments, the feature data set further comprises: the classifying of the energy consumption data of the base station includes: classifying the energy consumption data through an independent forest model based on the dimensionality of the energy consumption data to obtain non-energy-saving data and abnormal data, wherein the abnormal data comprises abnormal point data and energy-saving data; and classifying the abnormal data based on the cell key performance index and the dimension of the external data index to obtain abnormal point data and energy-saving data.
According to the first aspect, in some example embodiments, the method further comprises: performing correlation analysis on the abnormal point data based on the cell key performance index and the external data index; and performing linear fitting on the abnormal point data through a linear regression model to obtain corrected abnormal point data.
According to the first aspect, in some example embodiments, the method further comprises: and carrying out principal component analysis on the cell key performance index and the external data index so as to reduce the dimension of the cell key performance index and the external data index.
According to the first aspect, in some example embodiments, the evaluating the energy consumption of the base station based on the energy-saving-period-corresponding data and the energy-saving-period-corresponding data includes: arranging the non-energy-saving data and the energy-saving data corresponding to the energy-saving time period according to a time sequence to obtain an energy consumption reference basic value of the base station; evaluating the energy consumption of the base station based on the energy consumption reference base value.
According to a second aspect of the embodiments of the present disclosure, there is provided an energy consumption evaluation apparatus, including: the data acquisition module is used for acquiring the energy consumption data of the base station from a characteristic data set of the base station in a preset time period, wherein the characteristic data set comprises the energy consumption data of the base station; the data classification module is used for classifying the energy consumption data of the base station to obtain the energy-saving-free data in the energy-saving-free time period and the energy-saving-available data in the energy-saving time period of the base station; the non-energy-saving data generation module is used for generating non-energy-saving data corresponding to the energy-saving time interval of the base station through a variational self-encoder based on the non-energy-saving data of the non-energy-saving time interval; and the energy consumption evaluation module is used for evaluating the energy consumption of the base station based on the energy-saving-period-corresponding data and the energy-saving data.
According to a second aspect, in some example embodiments, the variational self-encoder comprises an encoder and a decoder, the non-power-saving data generation module is further configured to: generating a corresponding non-energy-saving time sequence based on the non-energy-saving data of the non-energy-saving time period; inputting the non-energy-saving time sequence into the encoder, and encoding the non-energy-saving time sequence into an implicit vector through the encoder; reconstructing, by the decoder, non-energy-saving data corresponding to the energy-saving period of the base station based on the implicit vector.
According to the second aspect, in some example embodiments, the non-energy-saving time series is a time series of unequal lengths, the apparatus further comprising: and the sliding window module is used for changing the time sequences with different lengths into time sequences with equal lengths through a sliding time window.
According to a second aspect, in some example embodiments, the feature data set further comprises: the data classification module is further configured to: classifying the energy consumption data through an independent forest model based on the dimensionality of the energy consumption data to obtain non-energy-saving data and abnormal data, wherein the abnormal data comprises abnormal point data and energy-saving data; and classifying the abnormal data based on the cell key performance index and the dimension of the external data index to obtain abnormal point data and energy-saving data.
According to a second aspect, in some example embodiments, the apparatus further comprises: the data correction module is used for carrying out correlation analysis on the abnormal point data based on the cell key performance index and the external data index; and performing linear fitting on the abnormal point data through a linear regression model to obtain corrected abnormal point data.
According to a second aspect, in some example embodiments, the apparatus further comprises: and the dimension reduction processing module is used for carrying out principal component analysis on the cell key performance index and the external data index so as to carry out dimension reduction on the cell key performance index and the external data index.
According to a second aspect, in some example embodiments, the energy consumption assessment module is further configured to: arranging the non-energy-saving data and the energy-saving data corresponding to the energy-saving time period according to a time sequence to obtain an energy consumption reference basic value of the base station; evaluating the energy consumption of the base station based on the energy consumption reference base value.
According to a third aspect of the embodiments of the present disclosure, there is provided a computer-readable medium, on which a computer program is stored, which when executed by a processor, implements the energy consumption assessment method as described in the first aspect of the embodiments above.
According to a fourth aspect of the embodiments of the present disclosure, there is provided an electronic apparatus including: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the method for energy consumption assessment as described in the first aspect of the embodiments above.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
in some embodiments of the present disclosure, on one hand, energy consumption data of a base station is classified, and energy-non-saving data of an energy-non-saving period and energy-saving data of an energy-saving period of the base station can be accurately obtained; on the other hand, the non-energy-saving data corresponding to the energy-saving time period of the base station is generated through the variational self-encoder based on the non-energy-saving data in the non-energy-saving time period, so that the non-energy-saving data corresponding to the energy-saving time period of the base station can be accurately generated, and the problem that the non-energy-saving data in the energy-saving time period cannot be acquired is solved; on the other hand, the energy consumption of the base station is evaluated based on the non-energy-saving data and the energy-saving data corresponding to the energy-saving time period, so that the energy-saving effect of the base station can be accurately evaluated.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty. In the drawings:
FIG. 1 shows a schematic diagram of an application scenario implementing the energy consumption assessment method of some example embodiments of the present disclosure;
FIG. 2 shows a flow diagram of a method of energy consumption assessment, according to some example embodiments of the present disclosure;
FIG. 3 shows a flow diagram of a method of energy consumption assessment implementing further example embodiments of the present disclosure;
FIG. 4 shows a schematic diagram of a variational auto-encoder, according to some example embodiments of the present disclosure;
FIG. 5 shows a schematic structural diagram of an energy consumption assessment apparatus according to an embodiment of the present disclosure;
fig. 6 shows a schematic structural diagram of an electronic device in an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
Fig. 1 shows a schematic diagram of a network architecture implementing the energy consumption assessment method of some example embodiments of the present disclosure.
Referring to fig. 1, the application scenario 100 may include a network device 110, a terminal device 120, and a core network 130. Network device 110 may be a device that communicates with terminal device 120 (otherwise known as a communication terminal, user terminal). Network device 110 may provide communication coverage for a particular geographic area and may communicate with terminal devices located within that coverage area. Optionally, the network device 110 may be an evolved Node B (eNB) or eNodeB in an LTE (Long Term Evolution) system, or a network device in a 5G network, such as a base station.
The application scenario 100 further comprises at least one terminal device 120 located within the coverage area of the network device 110. The terminal device 120 is a user terminal in a 4G wireless network or a 5G wireless network. It should be noted that the terminal device 120 may be an industrial device, a monitoring camera, a polling robot, a portable computer, a smart phone, a vehicle-mounted terminal, a tablet computer, or the like.
Technical solutions in example embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.
Fig. 2 shows a flow diagram of a method of energy consumption assessment according to some example embodiments of the present disclosure. The execution subject of the energy consumption assessment method provided by the embodiment of the present disclosure may be a computing device with computing capability, for example, a network element of a core network. The energy consumption evaluation method includes steps S210 to S240, and the energy consumption evaluation method in the example embodiment is described in detail below with reference to the drawings.
In step S210, energy consumption data of the base station is obtained from the characteristic data set of the base station for the predetermined time period.
In an example embodiment, the characteristic data set includes energy consumption data of the base station, and the energy consumption data may be energy consumption data uploaded by the base station for a predetermined period of time, for example, energy consumption data uploaded by the base station every hour for approximately 7 days. The energy consumption data includes non-energy saving data for a non-energy saving period and energy saving data for an energy saving period.
In step S220, the energy consumption data of the base station is classified to obtain the non-energy-saving data of the non-energy-saving time period and the energy-saving data of the energy-saving time period of the base station.
In some example embodiments, the energy consumption data of the base station is classified by using a data classification model, so that the energy-saving-time-period-nonexistent data and the energy-saving-time-period-already-energy data of the base station are obtained. For example, the energy consumption data of the base station is classified by adopting an isolated forest model, and the non-energy-saving data and the energy-saving data are obtained.
In further example embodiments, the feature data set further comprises: the energy consumption data also comprises abnormal point data, the energy consumption data is classified through an independent forest model based on the dimensionality of the energy consumption data to obtain non-energy-saving data and abnormal data, and the abnormal data comprises the abnormal point data and the energy-saving data; and classifying the abnormal data based on the cell key performance index and the dimension of the external data index to obtain abnormal point data and energy-saving data.
In step S230, non-energy saving data corresponding to the energy saving period of the base station is generated by a variational self-encoder based on the non-energy saving data of the non-energy saving period.
In an example embodiment, the variational self-encoder includes an encoder and a decoder that generate a corresponding non-power-saving time series based on non-power-saving data of a non-power-saving period; inputting the time sequence without energy saving into an encoder, and encoding the time sequence without energy saving into a hidden vector through the encoder; and reconstructing the non-energy-saving data corresponding to the energy-saving time interval of the base station through a decoder based on the implicit vector.
In step S240, the energy consumption of the base station is evaluated based on the non-energy-saving data and the energy-saving data corresponding to the energy-saving time period.
In the example embodiment, the non-energy-saving data and the energy-saving data corresponding to the energy-saving time period are arranged according to the time sequence to obtain an energy consumption reference basic value of the base station; and evaluating the energy consumption of the base station based on the energy consumption reference basic value. For example, the difference between the non-energy-saving data and the energy-saving data of the corresponding time is determined, and the energy consumption of the base station is evaluated according to the difference.
In other exemplary embodiments, the energy consumption data further includes abnormal point data, and the corrected abnormal point data, the non-energy-saving data and the energy-saving data corresponding to the energy-saving time period are arranged according to a time sequence to obtain an energy consumption reference basic value of the base station; and evaluating the energy consumption of the base station based on the energy consumption reference basic value.
According to the technical solution in the example embodiment of fig. 2, on one hand, energy consumption data of a base station is classified, and energy-non-saving data of a base station in a time period of no energy saving and energy-saving data of a base station in a time period of energy saving can be accurately obtained; on the other hand, the non-energy-saving data corresponding to the energy-saving time period of the base station is generated through the variational self-encoder based on the non-energy-saving data in the non-energy-saving time period, so that the non-energy-saving data corresponding to the energy-saving time period of the base station can be accurately generated, and the problem that the non-energy-saving data in the energy-saving time period cannot be acquired is solved; on the other hand, the energy consumption of the base station is evaluated based on the non-energy-saving data and the energy-saving data corresponding to the energy-saving time period, so that the energy-saving effect of the base station can be accurately evaluated.
FIG. 3 shows a flow diagram of a method of energy consumption assessment implementing further example embodiments of the present disclosure.
Referring to fig. 3, in step S310, a feature data set that needs to be input is obtained, and the feature data set includes three types of data.
In an example embodiment, the feature data set contains the following three types of data: a) energy consumption data of each hour uploaded by the base station in nearly 7 days; b) a cell KPI trend index corresponding to a base station in nearly 7 days; c) external data index of the base station. The cell KPI trend index comprises: the minimum RRC (Radio Resource Control) user number, the maximum RRC user number, the flow, the uplink PRB (physical Resource block) and the downlink PRB (physical Resource block) of each hour in 7 days; the external data indexes of the base station comprise: the temperature of the city where the base station is located, the type of the base station, the season, whether the weekend is weekend or not and other time characteristics.
In step S320, the feature data set is preprocessed.
In an example embodiment, principal component analysis is performed on the cell KPI trend indicator and the external data indicator, so as to perform dimension reduction on the cell KPI trend indicator and the external data indicator, and count a time point of missing energy consumption data in the energy consumption data of the base station.
In step S330, the uploaded base station energy consumption data is classified by using an isolated forest model.
Isolated forests are a very efficient unsupervised anomaly detection method. The specific process is as follows, a random hyperplane is used to cut a data space (data space), and two subspaces can be generated by cutting once. And then continuing to cut each subspace by using a random hyperplane, and circulating until only one data point is in each subspace. Intuitively, it can be seen that the high density clusters can be cut many times before cutting ceases, but the low density points quickly reach the leaves from the root, which are the outliers.
In an example embodiment, the first dimension is specified as energy consumption data, the obtained normal data is data which is not energy-saving, the abnormal data is abnormal points and energy-saving data, and at this time, the b-type and c-type features subjected to dimension reduction are used as cutting dimensions, so that the abnormal points and the energy-saving data can be separated, and finally the required three types of data are obtained.
In step S340, the abnormal data is processed.
In an example embodiment, the exception data includes exception point data and energy consumption missing data. Outputting the base stations with the missing energy consumption and the abnormal base stations to an alarm module, and then checking specific reasons by related support personnel; and meanwhile, performing correlation analysis by using the b-type and c-type characteristics of the base station, and performing linear fitting on abnormal data by using a linear regression model to obtain corrected data.
In step S350, an equal-length non-energy-saving time series is generated using a sliding time window.
In an example embodiment, since the energy-saving period is not fixed, the length of the input vector required by us to train the model is unequal; in order to solve the problem, the non-energy-saving time sequences with different lengths are changed into equal-length subsequences by using a sliding time window, the sequences with shorter self lengths are eliminated, and the accuracy of the generated model can be ensured by the residual sequences.
In step S360, an energy consumption base value of the energy saving period is generated using the variational self-encoder.
Variational Autocoder (VAE) is an important class of generative models, and a graph model of VAE is shown in fig. 4: the observed data is X, z represents a hidden vector, and from the perspective of the self-encoder, when X is generated by a hidden variable z, z → X is the generative model Pθ(x | z), i.e., the decoder; and X → z is the recognition model
I.e., the encoder from the encoder, N is a normal distribution. Identification model for VAEs
De-approximating the true posterior probability Pθ(x | z), which measures the similarity of two distributions, the KL divergence is generally used, as shown in equation (1) below:
wherein z is a hidden variable, X is target data such as non-energy-saving data of an energy-saving period,
theta is a parameter of the probability distribution,
is an encoder, Pθ(x | z) is the decoder and KL divergence is the degree of similarity used to measure the two distributions.
Further, in an example embodiment, the variational autoencoder is trained on the processed feature data set, and the energy consumption base value of the energy-saving period is generated by the trained variational autoencoder.
In step S370, the energy consumption of the base station is evaluated.
In the exemplary embodiment, the modified abnormal point data, the non-energy-saving data and the energy-saving data corresponding to the energy-saving time period are arranged according to the time sequence to obtain the energy consumption reference basic value of the base station; and evaluating the energy consumption of the base station based on the energy consumption reference basic value.
According to the technical scheme in the example embodiment of fig. 6, on one hand, classification of data of non-energy-saving points, data of energy-saving points and data of abnormal points is realized at one time by using an improved isolated forest algorithm, and meanwhile labels of various types of data are generated without manual marking; on the other hand, abnormal points can be found in time, and the problem of more data is avoided; on the other hand, for the energy-saving point, the theoretical non-energy-saving data is generated by using a variational self-encoder (VAE), and because only energy consumption data are used in the process, the training requirement is low, continuous cycle iteration can be realized, the robustness is higher, and the accuracy of the data is improved.
According to the technical scheme of some embodiments of the disclosure, firstly, an improved isolated forest algorithm is utilized to classify energy consumption data uploaded by each base station, and the energy consumption data are divided into non-energy-saving data, energy-saving data and abnormal data; firstly, kpi related data are used for linear fitting and filling of abnormal data, then a sliding time window is used for changing an uneconomical time sequence with unequal length into an equal-length subsequence which is used as the input of a variational self-encoder (VAE), and energy consumption data in an energy-saving time period are reconstructed through the training of the variational self-encoder, so that an accurate uneconomical energy consumption value of a base station is obtained; the energy consumption evaluation method can distinguish energy-saving data from non-energy-saving data, generate an energy consumption reference value in an energy-saving time period, and realize optimization of energy-saving energy consumption evaluation of the base station.
According to the technical scheme in other exemplary embodiments of the disclosure, on one hand, energy-saving points, non-energy-saving points and abnormal points of a base station can be successfully classified by using an isolated forest method and by specifying a discrimination dimension; on the other hand, the non-energy-saving time sequences with different lengths are changed into equal-length subsequences by utilizing a sliding time window, so that the input vector of the variational self-encoder is optimized, and a complete energy consumption standard value of the base station is trained and generated; on the other hand, the optimization of the energy-saving and energy-consumption evaluation of the base station is realized based on the complete energy-consumption standard value of the base station.
It is noted that the above-mentioned figures are merely schematic illustrations of processes involved in methods according to exemplary embodiments of the present disclosure and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
Embodiments of the disclosed apparatus are described below, which can be used to perform the above-described energy consumption assessment method of the present disclosure.
Fig. 5 shows a schematic structural diagram of a power consumption evaluation apparatus according to an embodiment of the present disclosure.
Referring to fig. 5, the energy consumption evaluation apparatus 500 includes: a data obtaining module 510, configured to obtain energy consumption data of a base station in a predetermined time period from a feature data set of the base station, where the feature data set includes the energy consumption data of the base station; a data classification module 520, configured to classify the energy consumption data of the base station to obtain non-energy-saving data in a non-energy-saving time period and energy-saving data in an energy-saving time period of the base station; an energy-saving-period-nonexistent data generating module 530, configured to generate energy-saving-period-nonexistent data corresponding to the energy saving period of the base station through a variational self-encoder based on the energy-saving-period-nonexistent data; an energy consumption evaluation module 540, configured to evaluate energy consumption of the base station based on the non-energy-saving data and the energy-saving data corresponding to the energy-saving time period.
In some example embodiments, the variational self-encoder includes an encoder and a decoder, and the non-power-saving data generation module 530 is further configured to: generating a corresponding non-energy-saving time sequence based on the non-energy-saving data of the non-energy-saving time period; inputting the non-energy-saving time sequence into the encoder, and encoding the non-energy-saving time sequence into an implicit vector through the encoder; reconstructing, by the decoder, non-energy-saving data corresponding to the energy-saving period of the base station based on the implicit vector.
In some example embodiments, the non-energy-saving time series is a time series of unequal lengths, and the apparatus 500 further includes: and the sliding window module is used for changing the time sequences with different lengths into time sequences with equal lengths through a sliding time window.
In some example embodiments, the feature data set further comprises: the data classification module 520 is further configured to: classifying the energy consumption data through an independent forest model based on the dimensionality of the energy consumption data to obtain non-energy-saving data and abnormal data, wherein the abnormal data comprises abnormal point data and energy-saving data; and classifying the abnormal data based on the cell key performance index and the dimension of the external data index to obtain abnormal point data and energy-saving data.
In some example embodiments, the apparatus 500 further comprises: the data correction module is used for carrying out correlation analysis on the abnormal point data based on the cell key performance index and the external data index; and performing linear fitting on the abnormal point data through a linear regression model to obtain corrected abnormal point data.
In some example embodiments, the apparatus 500 further comprises: and the dimension reduction processing module is used for carrying out principal component analysis on the cell key performance index and the external data index so as to carry out dimension reduction on the cell key performance index and the external data index.
In some example embodiments, the energy consumption assessment module 540 is further configured to: arranging the non-energy-saving data and the energy-saving data corresponding to the energy-saving time period according to a time sequence to obtain an energy consumption reference basic value of the base station; evaluating the energy consumption of the base station based on the energy consumption reference base value.
As each functional module of the energy consumption evaluation apparatus in the exemplary embodiment of the present disclosure corresponds to a step in the exemplary embodiment of the energy consumption evaluation method, please refer to the embodiment of the energy consumption evaluation method in the present disclosure for details that are not disclosed in the embodiment of the network device in the present disclosure.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Moreover, although the steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a UDM in a core network) to execute the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, there is also provided a computer storage medium capable of implementing the above method. On which a program product capable of implementing the above-described method of the present specification is stored. In some possible embodiments, various aspects of the present disclosure may also be implemented in the form of a program product including program code for causing a terminal device to perform the steps according to various exemplary embodiments of the present disclosure described in the "exemplary methods" section above of this specification when the program product is run on the terminal device.
The program product may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product described above may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
As will be appreciated by one skilled in the art, aspects of the present disclosure may be embodied as a system, method or program product. Accordingly, various aspects of the present disclosure may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device 600 according to this embodiment of the disclosure is described below with reference to fig. 6. The electronic device 600 shown in fig. 6 is only an example and should not bring any limitations to the function and scope of use of the embodiments of the present disclosure.
As shown in fig. 6, the electronic device 600 is embodied in the form of a general purpose computing device. The components of the electronic device 600 may include, but are not limited to: the at least one processing unit 610, the at least one memory unit 620, and a bus 630 that couples the various system components including the memory unit 620 and the processing unit 610.
Wherein the storage unit stores program codes, and the program codes can be executed by the processing unit 610, so that the processing unit 610 executes the steps according to various exemplary embodiments of the present disclosure described in the above section "exemplary method" of this specification. For example, the processing unit 610 may perform the following as shown in fig. 2: step S210, acquiring energy consumption data of the base station from the characteristic data set of the base station in a preset time period; step S220, classifying the energy consumption data of the base station to obtain the non-energy-saving data of the non-energy-saving time period and the energy-saving data of the energy-saving time period of the base station; step S230, generating non-energy-saving data corresponding to the energy-saving time period of the base station through a variational self-encoder based on the non-energy-saving data of the non-energy-saving time period; step S240, evaluating the energy consumption of the base station based on the non-energy-saving data and the energy-saving data corresponding to the energy-saving time period.
For example, the processing unit 610 may further perform the energy consumption evaluation method in the embodiment of the above-described manner.
The storage unit 620 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)6201 and/or a cache memory unit 6202, and may further include a read-only memory unit (ROM) 6203.
The memory unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 600 may also communicate with one or more external devices 690 (other network elements of the core network), one or more devices that enable a user to interact with the electronic device 600, and/or any device (e.g., router, modem, etc.) that enables the electronic device 600 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 650. Also, the electronic device 600 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 660. As shown, the network adapter 660 communicates with the other modules of the electronic device 600 over the bus 630. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
Furthermore, the above-described figures are merely schematic illustrations of processes included in methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (10)
1. A method for energy consumption assessment, comprising:
acquiring energy consumption data of a base station from a characteristic data set of the base station in a preset time period, wherein the characteristic data set comprises the energy consumption data of the base station;
classifying the energy consumption data of the base station to obtain the energy-saving-free data in the energy-saving-free time period and the energy-saving data in the energy-saving time period of the base station;
generating non-energy-saving data corresponding to the energy-saving time period of the base station through a variational self-encoder based on the non-energy-saving data of the non-energy-saving time period;
and evaluating the energy consumption of the base station based on the energy-saving data and the energy-saving data corresponding to the energy-saving time period.
2. The method of claim 1, wherein the variational self-encoder comprises an encoder and a decoder, and wherein the generating of the non-energy-saving data corresponding to the energy-saving period of the base station by the variational self-encoder based on the non-energy-saving data of the non-energy-saving period comprises:
generating a corresponding non-energy-saving time sequence based on the non-energy-saving data of the non-energy-saving time period;
inputting the non-energy-saving time sequence into the encoder, and encoding the non-energy-saving time sequence into an implicit vector through the encoder;
reconstructing, by the decoder, non-energy-saving data corresponding to the energy-saving period of the base station based on the implicit vector.
3. The method of claim 2, wherein the non-energy conserving time series are unequal length time series, the method further comprising:
and changing the time sequences with different lengths into time sequences with equal lengths by sliding the time window.
4. The method of claim 1, wherein the feature data set further comprises: the classifying of the energy consumption data of the base station includes:
classifying the energy consumption data through an independent forest model based on the dimensionality of the energy consumption data to obtain non-energy-saving data and abnormal data, wherein the abnormal data comprises abnormal point data and energy-saving data;
and classifying the abnormal data based on the cell key performance index and the dimension of the external data index to obtain abnormal point data and energy-saving data.
5. The method of claim 4, further comprising:
performing correlation analysis on the abnormal point data based on the cell key performance index and the external data index;
and performing linear fitting on the abnormal point data through a linear regression model to obtain corrected abnormal point data.
6. The method of claim 4, further comprising:
and carrying out principal component analysis on the cell key performance index and the external data index so as to reduce the dimension of the cell key performance index and the external data index.
7. The method of claim 5, wherein the evaluating the energy consumption of the base station based on the energy-saving data and the energy-saving data corresponding to the energy-saving time period comprises:
arranging the non-energy-saving data and the energy-saving data corresponding to the energy-saving time period according to a time sequence to obtain an energy consumption reference basic value of the base station;
evaluating the energy consumption of the base station based on the energy consumption reference base value.
8. An apparatus for evaluating energy consumption, comprising:
the data acquisition module is used for acquiring the energy consumption data of the base station from a characteristic data set of the base station in a preset time period, wherein the characteristic data set comprises the energy consumption data of the base station;
the data classification module is used for classifying the energy consumption data of the base station to obtain the energy-saving-free data in the energy-saving-free time period and the energy-saving-available data in the energy-saving time period of the base station;
the non-energy-saving data generation module is used for generating non-energy-saving data corresponding to the energy-saving time interval of the base station through a variational self-encoder based on the non-energy-saving data of the non-energy-saving time interval;
and the energy consumption evaluation module is used for evaluating the energy consumption of the base station based on the energy-saving-period-corresponding data and the energy-saving data.
9. A computer-readable medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the energy consumption assessment method according to any one of claims 1 to 7.
10. An electronic device, comprising:
one or more processors;
a storage device to store one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the energy consumption assessment method of any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111650494.4A CN114374616B (en) | 2021-12-30 | 2021-12-30 | Energy consumption evaluation method, device, equipment and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111650494.4A CN114374616B (en) | 2021-12-30 | 2021-12-30 | Energy consumption evaluation method, device, equipment and medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114374616A true CN114374616A (en) | 2022-04-19 |
| CN114374616B CN114374616B (en) | 2024-04-26 |
Family
ID=81141528
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111650494.4A Active CN114374616B (en) | 2021-12-30 | 2021-12-30 | Energy consumption evaluation method, device, equipment and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114374616B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115291108A (en) * | 2022-06-27 | 2022-11-04 | 东莞新能安科技有限公司 | Data generation method, device, equipment and computer program product |
| CN116528270A (en) * | 2023-06-27 | 2023-08-01 | 杭州电瓦特科技有限公司 | Base station energy saving potential evaluation method, device, equipment and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103854120A (en) * | 2012-12-05 | 2014-06-11 | 中国移动通信集团广西有限公司 | Method and device for evaluating performance of energy-saving equipment |
| CN107392445A (en) * | 2017-07-03 | 2017-11-24 | 中国联合网络通信集团有限公司 | A kind of appraisal procedure and device of base station energy-saving project |
| CN109862587A (en) * | 2018-12-18 | 2019-06-07 | 中国移动通信集团云南有限公司 | Mobile network quality appraisal procedure based on multiple features time series and self-encoding encoder |
| CN112418482A (en) * | 2020-10-26 | 2021-02-26 | 南京邮电大学 | Cloud computing energy consumption prediction method based on time series clustering |
| WO2021126500A1 (en) * | 2019-12-18 | 2021-06-24 | Google Llc | Processing multi-horizon forecasts for time series data |
| CN113055904A (en) * | 2019-12-27 | 2021-06-29 | 中国电信股份有限公司 | Base station data analysis method, base station data analysis device and computer readable storage medium |
-
2021
- 2021-12-30 CN CN202111650494.4A patent/CN114374616B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103854120A (en) * | 2012-12-05 | 2014-06-11 | 中国移动通信集团广西有限公司 | Method and device for evaluating performance of energy-saving equipment |
| CN107392445A (en) * | 2017-07-03 | 2017-11-24 | 中国联合网络通信集团有限公司 | A kind of appraisal procedure and device of base station energy-saving project |
| CN109862587A (en) * | 2018-12-18 | 2019-06-07 | 中国移动通信集团云南有限公司 | Mobile network quality appraisal procedure based on multiple features time series and self-encoding encoder |
| WO2021126500A1 (en) * | 2019-12-18 | 2021-06-24 | Google Llc | Processing multi-horizon forecasts for time series data |
| CN113055904A (en) * | 2019-12-27 | 2021-06-29 | 中国电信股份有限公司 | Base station data analysis method, base station data analysis device and computer readable storage medium |
| CN112418482A (en) * | 2020-10-26 | 2021-02-26 | 南京邮电大学 | Cloud computing energy consumption prediction method based on time series clustering |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115291108A (en) * | 2022-06-27 | 2022-11-04 | 东莞新能安科技有限公司 | Data generation method, device, equipment and computer program product |
| CN116528270A (en) * | 2023-06-27 | 2023-08-01 | 杭州电瓦特科技有限公司 | Base station energy saving potential evaluation method, device, equipment and storage medium |
| CN116528270B (en) * | 2023-06-27 | 2023-10-03 | 杭州电瓦特科技有限公司 | Base station energy saving potential evaluation method, device, equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114374616B (en) | 2024-04-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114374616B (en) | Energy consumption evaluation method, device, equipment and medium | |
| WO2020177377A1 (en) | Machine learning-based data prediction processing method and apparatus, and computer device | |
| CN111723209A (en) | Semi-supervised text classification model training method, text classification method, system, device and medium | |
| EP3136297A1 (en) | System and method for determining information and outliers from sensor data | |
| Zhai et al. | Degradation in common dynamic environments | |
| US20230401834A1 (en) | Image processing method, apparatus and device, and readable storage medium | |
| CN115759658B (en) | Enterprise energy consumption data management system suitable for smart city | |
| CN110826803A (en) | Electricity price prediction method and device for electric power spot market | |
| US20200090643A1 (en) | Speech recognition method and device | |
| CN116579618B (en) | Data processing method, device, equipment and storage medium based on risk management | |
| US20230308381A1 (en) | Test script generation from test specifications using natural language processing | |
| US9454457B1 (en) | Software test apparatus, software test method and computer readable medium thereof | |
| US20230252139A1 (en) | Efficient transformer for content-aware anomaly detection in event sequences | |
| CN115834433A (en) | Data processing method and system based on Internet of things technology | |
| CN116596556A (en) | Beef cattle traceability management system and method | |
| CN116307236A (en) | Fault prediction method, device, equipment and storage medium | |
| US20140324760A1 (en) | Synthetic time series data generation | |
| CN116785967B (en) | Automatic batching system for electronic grade lithium hexafluorophosphate preparation | |
| CN116910592A (en) | Log detection method and device, electronic equipment and storage medium | |
| CN116841808A (en) | Multi-core processor abnormality detection method and device, electronic equipment and storage medium | |
| CN113627514A (en) | Data processing method and device of knowledge graph, electronic equipment and storage medium | |
| CN113297045B (en) | Monitoring method and device for distributed system | |
| US20230376846A1 (en) | Information processing apparatus and machine learning method | |
| CN116561696B (en) | Multi-dimensional user adjustable load rapid aggregation method and system thereof | |
| US20230342644A1 (en) | Method for enhanced sampling from a probability distribution |
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 |