CN114781915A - A method, device, system and edge proxy device for acquiring energy consumption characteristics - Google Patents

Abstract

The present application discloses a method, device, system and edge proxy device for acquiring energy consumption characteristics, including: acquiring energy consumption data of industrial production equipment in energy consumption monitoring equipment and power data of new energy power generation equipment in inverters; The inverter status information is monitored and controlled, and the control process data is obtained; based on the control process data, the energy consumption data and power data are decomposed into information particle sequences, and the information particle sequence is sent to the monitoring center, and the monitoring center is based on the information particle sequence. Energy consumption characteristics are predicted. Due to the lack of analysis and evaluation methods, the existing energy consumption analysis methods based on artificial intelligence cannot guarantee to give a complete and real causal structure, and there are certain risks in application. The application improves the correlation between the analysis method and the actual equipment operation law, meets the high reliability and high stability required by industrial control, and further supports the planning, construction, and operation and maintenance of low-carbon industrial parks.

Description

A method, device, system and edge proxy device for acquiring energy consumption characteristics

technical field

The present application relates to the field of optimal configuration of an integrated energy system, and in particular, to a method, device, system and edge proxy device for acquiring energy consumption characteristics.

Background technique

Industrial production is an important main body of energy consumption. With the continuous improvement of technology and management level, the construction of distributed new energy sources in industrial parks, such as photovoltaic power generation, wind power generation, tidal power generation, etc., is used to realize the rational utilization and optimal dispatch of energy in the industrial production process, which is to reduce the energy consumption of industrial parks. The inevitable requirements and important ways of consumption.

In general, the energy consumption of industrial parks is closely related to the production process and the distributed new energy generation process. For example, affected by the production process and the operation status of industrial equipment, the energy consumption of the industrial park has the characteristics of high frequency fluctuation; affected by the adjustment of the production plan, the energy consumption of the industrial park will show periodic characteristics related to social factors; Affected by the intermittent characteristics of distributed new energy power generation, the energy consumption of industrial parks has the characteristics of periodic fluctuations. Therefore, obtaining effective energy consumption characteristics is very important for the low-carbon operation of industrial parks, and supports the planning, construction, and operation and maintenance of industrial parks.

However, due to the lack of analysis and evaluation methods, the existing energy consumption analysis methods based on artificial intelligence often judge the energy consumption of industrial parks through a certain rule, which cannot guarantee a complete and true causal structure, and industrial control requires With high reliability and high stability, the existing energy consumption analysis methods have certain risks in application.

SUMMARY OF THE INVENTION

In view of this, embodiments of the present invention provide a method, apparatus, system, and edge proxy device for acquiring energy consumption characteristics, so as to improve the reliability and stability of industrial control.

According to a first aspect, an embodiment of the present invention provides a method for acquiring energy consumption characteristics, the method is applied to an edge proxy device, and the method includes:

Obtain energy consumption data of industrial production equipment in energy consumption monitoring equipment;

Obtain the power data of the new energy power generation equipment in the inverter;

Monitor and control the status information of the inverter, and obtain the control process data;

The energy consumption data and the power data are decomposed into information particle sequences based on the control process data, and the information particle sequences are sent to the monitoring center, so that the monitoring center can predict the energy consumption according to the information particle sequences. consumption characteristics.

With reference to the first aspect, in a first implementation manner of the first aspect, the state information includes at least one of voltage, current, active power, and reactive power, and the control includes frequency modulation control and/or voltage modulation control;

The control process data includes a plurality of data samples, each of the data samples has a plurality of attribute values, the attribute values include voltage, current, active power, reactive power, phase control signal, frequency control signal and voltage control at least one of the signals.

With reference to the first embodiment of the first aspect, in the second embodiment of the first aspect, the decomposing the energy consumption data and the power data into a sequence of information particles based on the control process data includes:

performing fuzzy clustering processing on the control process data to generate a time window sequence of the control process data;

Based on the time window sequence, the energy consumption data and the power data are decomposed according to the time window sequence to obtain an information particle sequence of the energy consumption data and the power data.

With reference to the second embodiment of the first aspect, in the third embodiment of the first aspect, performing fuzzy clustering processing on the control process data to generate a time window sequence of the control process data includes:

dividing the control process data into a plurality of fuzzy prototypes, and constructing the divided fuzzy prototypes into a fuzzy prototype matrix;

Obtaining the degree values of the data samples belonging to each of the fuzzy class prototypes to construct a fuzzy partition matrix;

Solve the fuzzy partition matrix and the fuzzy class prototype matrix, and obtain the solution result;

Arrange the solution results in ascending order to obtain a time window series.

In combination with the third embodiment of the first aspect, in the fourth embodiment of the first aspect, the degree value is determined by the following formula:

Among them, u _im ∈[0,1], uim represents the degree value of the i-th fuzzy class prototype to which the m-th data sample belongs, i∈[1,c], c represents the number of the fuzzy class prototypes, M Indicates the number of data samples in the control process data;

The fuzzy partition matrix is constructed by the following formula:

U=[u _im ]∈R ^c×M ;

Wherein, U represents a fuzzy partition matrix, which is used to store the degree value corresponding to each of the data samples, and R represents that the result of the calculation is in the form of a matrix.

With reference to the fourth embodiment of the first aspect, in the fifth embodiment of the first aspect, the solution of the fuzzy partition matrix and the fuzzy class prototype matrix, and obtaining the solution result, includes:

The fuzzy partition matrix and the fuzzy class prototype matrix are solved by the following formulas:

Wherein, Q represents the objective function, dm represents the mth data sample in the control process data, vi represents the ith fuzzy class prototype, V represents the fuzzy class prototype matrix, f represents the fuzzification factor, f>1.

With reference to any of the second embodiment to the fifth embodiment of the first aspect, in the sixth embodiment of the first aspect, the energy consumption data and the power data are The time window sequence is decomposed to obtain the information grain sequence of the energy consumption data and the power data, including:

Based on the time window sequence, the energy consumption data and the power data are respectively divided into a plurality of energy consumption subsets and a plurality of power subsets, the energy consumption subsets and the power subsets are based on the time window sequence one-to-one correspondence;

Obtain the maximum value and minimum value of each of the energy subsets, and obtain the maximum value and minimum value of each of the power subsets, and use the combination of the maximum value and the minimum value as the corresponding energy consumption sub-set set and the information grain features of the power subset, and each set of the information grain features constitutes an information grain sequence.

According to a second aspect, an embodiment of the present invention provides a device for acquiring energy consumption characteristics, the device comprising:

The energy consumption data acquisition module is used to acquire the energy consumption data of the industrial production equipment in the energy consumption monitoring equipment;

The power data acquisition module is used to acquire the power data of the new energy power generation equipment in the inverter;

The control process data acquisition module is used to monitor and control the status information of the inverter, and obtain the control process data;

The energy consumption feature acquisition module is used to decompose the energy consumption data and the power data into a sequence of information granules based on the control process data, and send the sequence of information granules to a monitoring center, so that the monitoring center can The information particle sequence predicts energy consumption characteristics.

According to a third aspect, an embodiment of the present invention provides an edge proxy device, where the edge proxy device includes a processor and a memory for storing executable instructions of the processor;

Wherein, when the executable instructions are executed by the processor, the following functions are implemented:

Obtain energy consumption data of industrial production equipment in energy consumption monitoring equipment;

Obtain the power data of the new energy power generation equipment in the inverter;

Monitor and control the status information of the inverter, and obtain the control process data;

The energy consumption data and the power data are decomposed into information particle sequences based on the control process data, and the information particle sequences are sent to the monitoring center, so that the monitoring center can predict the energy consumption according to the information particle sequences. consumption characteristics.

According to a fourth aspect, an embodiment of the present invention provides a system for acquiring energy consumption characteristics, including: industrial production equipment, energy consumption monitoring equipment, new energy power generation equipment, combiner boxes, inverters, transformers, monitoring centers, and the present invention The edge proxy device provided by the third aspect of the embodiment;

Wherein, the edge proxy device and the energy consumption monitoring device are connected through a wireless network, connected with the inverter through a Modbus communication protocol, and connected with the monitoring center through an industrial switch.

The technical solution provided by this application can include the following beneficial effects:

Embodiments of the present application provide a method, device, system, and edge proxy device for acquiring energy consumption characteristics. By acquiring energy consumption data of industrial production equipment and power data of new energy power generation equipment, the energy consumption characteristics of industrial parks and the The correlation between the production process and the distributed new energy power generation process; through the acquisition of the control process data of the inverter, the high frequency fluctuation of the energy consumption of the production process and industrial equipment, and the intermittent fluctuation of the distributed energy power generation are taken into account. factor; decompose the energy consumption data and the power data into a sequence of information granules, introduce granular computing, give a complete and real causal structure, improve the correlation between the analysis method and the actual equipment operation law, and describe the energy more accurately The influence of different cycle characteristics of production and consumption on energy consumption characteristics, to meet the high reliability and high stability of industrial control requirements, further support the planning, construction and operation and maintenance of low-carbon industrial parks.

Description of drawings

In order to illustrate the technical solutions of the present application more clearly, the accompanying drawings required in the embodiments will be briefly introduced below. Obviously, for those of ordinary skill in the art, without creative work, the Additional drawings can be obtained from these drawings.

FIG. 1 is a method flowchart of a method for acquiring energy consumption characteristics disclosed in an embodiment of the present application.

FIG. 2 is a method flowchart of a method for acquiring energy consumption characteristics disclosed in an embodiment of the present application.

FIG. 3 is a structural block diagram of an apparatus for acquiring energy consumption characteristics disclosed in an embodiment of the present application.

FIG. 4 is a schematic diagram of an edge proxy device disclosed in an embodiment of the present application.

FIG. 5 is a schematic structural diagram of a system for acquiring energy consumption characteristics disclosed in an embodiment of the present application.

FIG. 6 is a schematic diagram of a long-short-term memory network disclosed in an embodiment of the present application.

Detailed ways

In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments It is a part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

It should be noted that the method, device, system and edge proxy device for obtaining energy consumption characteristics provided in this specification relate to the technical field of optimal configuration of comprehensive energy systems, such as energy consumption analysis of industrial parks, etc. The specific application field is not limited, and can be used according to the actual situation. In the embodiments described below, a detailed description is given by taking the energy consumption analysis field applied to an industrial park as an example.

Due to the lack of analysis and evaluation methods, the existing energy consumption analysis methods based on artificial intelligence often judge the energy consumption of industrial parks through a certain rule, which cannot guarantee a complete and real causal structure, and the industrial control requires high Reliability and high stability, the existing energy consumption analysis methods have certain risks in application.

Based on this, an embodiment of the present application proposes a method for acquiring energy consumption characteristics, which is applied to an edge proxy device. As shown in FIG. 1 , the method includes:

Step S101 , acquiring energy consumption data of industrial production equipment in the energy consumption monitoring equipment.

Specifically, the edge proxy device may be a dedicated computer for nearby data processing. The energy consumption monitoring device monitors and obtains the energy consumption data of the industrial production equipment, and the energy consumption data edge proxy device obtains the energy consumption data from the energy consumption monitoring device, and the energy consumption data can be defined as the time series data set E:

E={e ₁ ,e ₂ ....e _n };

It can be seen that the time series data set E (ie, energy consumption data) is composed of n time series data e, where the time series data refers to time series data. Time series data is a column of data recorded in chronological order according to a unified indicator. All data in the same data column must be of the same caliber, and comparability is required. Time series data can be either epochs or time points. The purpose of time series analysis is to find out the statistical characteristics and development regularity of the time series within the sample.

Step S102 , acquiring power data of the new energy power generation equipment in the inverter.

Specifically, the new energy power generation equipment can be photovoltaic power generation equipment, wind power generation equipment and tidal power generation equipment. Due to the advantages of power supply occasions and other advantages, the new energy power generation equipment in the industrial park is usually photovoltaic power generation equipment, which uses photovoltaic modules to obtain light energy and convert it into electrical energy. The inverter obtains the power data of the photovoltaic module (new energy power generation equipment), and the edge proxy device obtains the power data from the inverter. The power data can be defined as the time series data set P:

P={p ₁ ,p ₂ ... p _n };

It can be seen that the time series data set P (ie power data) is composed of n time series data p. By obtaining the energy consumption data of industrial production equipment and the power data of new energy power generation equipment, the energy consumption characteristics and production of industrial parks are considered The correlation between the process and the distributed new energy generation process.

Step S103 , monitor and control the state information of the inverter, and acquire control process data.

Specifically, the state information includes at least one of voltage, current, active power and reactive power, and the control includes frequency modulation control and/or voltage modulation control; the control process data includes a plurality of data samples, and each data sample is It has a plurality of attribute values, and the attribute value includes at least one of voltage, current, active power, reactive power, phase control signal, frequency control signal and voltage control signal. The edge agent device monitors and controls the status information of the inverter, and obtains the control process data. Among them, the frequency regulation control is to control the frequency to 50HZ alternating current, and the voltage regulation control is to control the voltage to a voltage level of 10 kV. The control process data can be defined as a data set D, including M data samples:

D={d ₁ , d ₂ ... d _M };

Among them, due to the different types of monitoring state quantities and control signals, each data sample can include s attribute values. Therefore, the target data sample d _m in the data set D can be expressed as:

d _m = {d _1m , d _2m ... d _sm };

It should be noted that the target data sample dm may be any data sample in the data set D.

Step S104 , decompose the energy consumption data and the power data into an information particle sequence based on the control process data, and send the information particle sequence to a monitoring center, so that the monitoring center predicts the energy consumption feature according to the information particle sequence.

Specifically, the edge proxy device performs fuzzy clustering processing on the control process data, and performs fuzzy C-means clustering on the data set D (control process data), that is, divides the data set D (control process data) into c fuzzy class prototypes, A fuzzy class prototype matrix is constructed, and the degree value of the data samples belonging to each fuzzy class prototype is obtained by the following formula to construct a fuzzy partition matrix:

Among them, u _im ∈[0,1], u _im represents the degree value of the i-th fuzzy class prototype to which the m-th data sample belongs, i∈[1,c], c represents the number of the fuzzy class prototypes, M Indicates the number of data samples in the control process data.

It should be noted that the fuzzy class prototype matrix is used to store each fuzzy class prototype and is presented in the form of a matrix, which can be expressed by the following formula:

V=[v ₁ , v ₂ . . . v _c ]∈R ^s×c ;

Among them, V represents the fuzzy class prototype matrix, which is used to store each fuzzy class prototype, v _i represents the i-th fuzzy class prototype, i∈[1,c], R represents the result in matrix form, and c represents the fuzzy class prototype , and s represents the number of attribute values in the data sample.

The fuzzy partition matrix is used to store the degree value corresponding to each data sample, and is presented in the form of a matrix, which can be expressed by the following formula:

U=[u _im ]∈R ^c×M ;

Among them, U represents the fuzzy partition matrix, and R represents the result of the calculation in the form of a matrix.

After constructing the fuzzy prototype matrix and the fuzzy partition matrix, the edge agent device solves the fuzzy partition matrix and the fuzzy prototype matrix by the following formula, and obtains the solution result:

Among them, Q represents the objective function, d _m represents the mth data sample in the control process data, vi represents the _ith fuzzy class prototype, V represents the fuzzy class prototype matrix, f represents the fuzzification factor, f>1.

After obtaining the solution results, the edge proxy device sorts the solution results in ascending order, obtains a time window series, and divides the energy consumption data and power data into several energy consumption subsets and several energy consumption subsets corresponding to one-to-one based on the time window series. Therefore, the number of time windows in the time window sequence is the same as the number of energy subsets and power subsets, and they correspond one-to-one.

After dividing the subsets, the edge proxy device obtains the maximum and minimum values of each of the energy consumption subsets, and obtains the maximum and minimum values of each of the power subsets, and assigns the corresponding energy consumption subsets and power subsets The combination of the maximum value and the minimum value of , as the information granule feature of the corresponding energy consumption subset and power subset, the set of each information granule feature constitutes the information granule sequence (that is, the time series data set E and the time series data set P are divided into Several energy consumption subsets Ex and power subsets Px, and then use the minimum and maximum values on each energy consumption subset Ex and power subset Px to describe the characteristics of information grains. For example, if there are ten time windows in the time window sequence, then The edge proxy device divides the energy consumption data and power data by the edge proxy device into ten energy subsets and ten power subsets corresponding to each other according to each time window. Therefore, the first information particle feature is The set composed of the respective minimum and maximum values of the first energy subset corresponding to the first time window and the first power subset, and so on, to obtain ten information particle features, these ten information particles The feature constitutes the final information grain sequence), and the target information grain feature can be represented by the following formula:

Ω _x =[min(E _x ),min(P _x ),max(E _x ),max(P _x )];

Among them, Ω _x represents the feature of the target information granule, and the target information granule feature is any information granule feature in the information granule sequence.

Finally, after obtaining the information granule sequence, the edge proxy device sends the information granule sequence to the monitoring center, and the monitoring center predicts the energy consumption feature according to the information granule sequence.

It should be noted that the monitoring center can use the information particle sequence as the input of a long-short-term memory network (LSTM, LongShort-Term Memory) to predict energy consumption characteristics. Long Short-Term Memory (LSTM) is a temporal recurrent neural network that learns features in sequential input data through a gating mechanism, including forget gates, input gates, output gates, and storage units. Among them, the storage unit can memorize information at any time interval, and the flow of information into the storage unit is regulated by three gates. Referring to the schematic diagram of the long short-term memory network provided in Figure 6, Figure 6 is a long short-term memory network (LSTM, Long Short-Term Memory) model with two-step sequence input, which can be implemented by the TensorFlow platform, where the gate control unit adopts sigmoid The function, denoted by σ, uses the tanh function in the input gate to generate alternatives for updating. Compared with the analysis method based on a single time dimension, the identification of energy consumption characteristics based on Long Short-Term Memory (LSTM) network can more accurately describe the influence of different periodic laws of energy production and consumption on energy consumption characteristics.

To sum up, the embodiments of the present application provide a method for acquiring energy consumption characteristics. By acquiring energy consumption data of industrial production equipment and power data of new energy power generation equipment, the energy consumption characteristics, production process and The correlation between distributed new energy generation processes; through the acquisition of inverter control process data, factors such as high-frequency fluctuations in energy consumption of production processes and industrial equipment, and intermittent fluctuations in distributed energy generation are taken into account; The energy consumption data and the power data are decomposed into a sequence of information granules, and granular computing is introduced to give a complete and real causal structure, improve the correlation between the analysis method and the actual equipment operation law, and more accurately describe the characteristics of different cycles of energy production and consumption The impact on energy consumption characteristics, high reliability and high stability to meet industrial control requirements, further support the planning, construction and operation and maintenance of low-carbon industrial parks.

FIG. 2 shows another flowchart of a method for acquiring energy consumption characteristics according to an embodiment of the present application, and the method may include the following steps:

Step S201 , acquiring energy consumption data of industrial production equipment in the energy consumption monitoring equipment.

In a possible implementation manner, the edge proxy device may be a dedicated computer for performing data processing nearby. The energy consumption monitoring device monitors and obtains the energy consumption data of the industrial production equipment, and the energy consumption data edge proxy device obtains the energy consumption data from the energy consumption monitoring device, and the energy consumption data can be defined as the time series data set E:

E={e ₁ ,e ₂ ....e _n };

It can be seen that the time series data set E (ie, energy consumption data) is composed of n time series data e, where the time series data refers to time series data. Time series data is a column of data recorded in chronological order by the same unified metric. All data in the same data column must be of the same caliber, and comparability is required. Time series data can be either epochs or time points. The purpose of time series analysis is to find out the statistical characteristics and development regularity of the time series within the sample.

Step S202, acquiring power data of the new energy power generation equipment in the inverter.

Specifically, the new energy power generation equipment can be photovoltaic power generation equipment, wind power generation equipment and tidal power generation equipment. Due to the advantages of power supply occasions and other advantages, the new energy power generation equipment in the industrial park is usually photovoltaic power generation equipment, which uses photovoltaic modules to obtain light energy and convert it into electrical energy. The inverter obtains the power data of the photovoltaic module (new energy power generation equipment), and the edge proxy device obtains the power data from the inverter. The power data can be defined as the time series data set P:

P={p ₁ ,p ₂ ... p _n };

It can be seen that the time series data set P (ie power data) is composed of n time series data p. By obtaining the energy consumption data of industrial production equipment and the power data of new energy power generation equipment, the energy consumption characteristics and production of industrial parks are considered The correlation between the process and the distributed new energy generation process.

Step S203 , monitor and control the state information of the inverter, and acquire control process data.

In a possible implementation manner, the status information includes at least one of voltage, current, active power and reactive power, and the control includes frequency modulation control and/or voltage modulation control; the control process data includes a plurality of data samples , each data sample has a plurality of attribute values, the attribute values include at least one of voltage, current, active power, reactive power, phase control signal, frequency control signal and voltage control signal. The edge agent device monitors and controls the status information of the inverter, and obtains the control process data. Among them, the frequency regulation control is to control the frequency to 50HZ alternating current, and the voltage regulation control is to control the voltage to a voltage level of 10 kV. The control process data can be defined as a data set D, including M data samples:

D={d ₁ , d ₂ ... d _M };

Among them, due to the different types of monitoring state quantities and control signals, each data sample can include s attribute values. Therefore, the target data sample dm in the data set D can be expressed as:

d _m = {d _1m , d _2m ... d _sm };

It should be noted that the target data sample dm may be any data sample in the data set D.

Step S204, performing fuzzy clustering processing on the control process data to generate a time window sequence of the control process data;

In a possible implementation manner, the control process data is divided into multiple fuzzy prototypes, and the divided fuzzy prototypes are constructed into a fuzzy prototype matrix; the degree to which the data samples belong to each fuzzy prototype is obtained. value to construct a fuzzy partition matrix; solve the fuzzy partition matrix and the fuzzy class prototype matrix, and obtain the solution results; arrange the solution results in ascending order to obtain a time window series.

In another possible implementation, the degree value is determined by the following formula:

Among them, u _im ∈[0,1], u _im represents the degree value of the i-th fuzzy class prototype to which the m-th data sample belongs, i∈[1,c], c represents the number of the fuzzy class prototypes, M Indicates the number of data samples in the control process data.

The fuzzy partition matrix is constructed by the following formula:

U=[u _im ]∈R ^c×M ;

Among them, U represents the fuzzy partition matrix, which is used to store the degree value corresponding to each data sample, and R represents that the result is in the form of a matrix.

It should be noted that the fuzzy class prototype matrix is used to store each fuzzy class prototype and is presented in the form of a matrix, which can be expressed by the following formula:

V=[v ₁ , v ₂ . . . v _c ]∈R ^s×c ;

Among them, V represents the fuzzy class prototype matrix, which is used to store each fuzzy class prototype, v _i represents the i-th fuzzy class prototype, i∈[1,c], R represents the result in matrix form, and c represents the fuzzy class prototype , and s represents the number of attribute values in the data sample.

In another possible implementation manner, the fuzzy partition matrix and the fuzzy class prototype matrix are solved by the following formula:

Among them, Q represents the objective function, d _m represents the mth data sample in the control process data, vi represents the _ith fuzzy class prototype, V represents the fuzzy class prototype matrix, f represents the fuzzification factor, f>1.

Step S205 , based on the time window sequence, decompose the energy consumption data and the power data according to the time window sequence to obtain an information particle sequence of the energy consumption data and the power data.

In a possible implementation manner, based on the time window sequence, the energy consumption data and power data are respectively divided into multiple energy consumption subsets and multiple power subsets, and the energy consumption subsets and power subsets are based on the time window sequence 1 A correspondence. Obtain the maximum value and minimum value of each of the energy subsets, and obtain the maximum value and minimum value of each of the power subsets, and use the combination of the maximum value and the minimum value as the corresponding energy consumption subset and power subset. Information granule features, the collection of each information granule feature constitutes an information granule sequence. (That is, the time series data set E and the time series data set P are divided into several energy consumption subsets Ex and power subsets Px, and then the minimum and maximum values on each energy consumption subset Ex and power subset Px are used to describe the characteristics of information particles , for example, there are ten time windows in the time window sequence, then the edge proxy device divides the energy consumption data and power data into ten energy consumption subsets corresponding one-to-one and ten energy consumption subsets respectively according to each time window. Power subset, therefore, the first information particle feature is the set composed of the first energy subset corresponding to the first time window and the respective minimum and maximum values of the first power subset. By analogy, ten information granule features are obtained, and these ten information granule features form the final information granule sequence), and the target information granule feature can be represented by the following formula:

Ω _x =[min(E _x ),min(P _x ),max(E _x ),max(P _x )];

Among them, Ω _x represents the feature of the target information granule, and the target information granule feature is any information granule feature in the information granule sequence.

Step S206: Send the information grain sequence to the monitoring center, so that the monitoring center predicts the energy consumption feature according to the information grain sequence.

It should be noted that the monitoring center can use the information particle sequence as the input of a long-short-term memory network (LSTM, LongShort-Term Memory) to predict energy consumption characteristics. Long Short-Term Memory (LSTM) is a temporal recurrent neural network that learns features in sequential input data through a gating mechanism, including forget gates, input gates, output gates, and storage units. Among them, the storage unit can memorize information at any time interval, and the flow of information into the storage unit is regulated by three gates. Referring to the schematic diagram of the long short-term memory network provided in Figure 6, Figure 6 is a long short-term memory network (LSTM, Long Short-Term Memory) model with two-step sequence input, which can be implemented by the TensorFlow platform, where the gate control unit adopts sigmoid The function, denoted by σ, uses the tanh function in the input gate to generate alternatives for updating. Compared with the analysis method based on a single time dimension, the identification of energy consumption characteristics based on Long Short-Term Memory (LSTM) network can more accurately describe the influence of different periodic laws of energy production and consumption on energy consumption characteristics.

To sum up, the embodiments of the present application provide a method for acquiring energy consumption characteristics. By acquiring energy consumption data of industrial production equipment and power data of new energy power generation equipment, the energy consumption characteristics, production process and The correlation between distributed new energy generation processes; through the acquisition of inverter control process data, factors such as high-frequency fluctuations in energy consumption of production processes and industrial equipment, and intermittent fluctuations in distributed energy generation are taken into account; The energy consumption data and the power data are decomposed into a sequence of information granules, and granular computing is introduced to give a complete and real causal structure, improve the correlation between the analysis method and the actual equipment operation law, and more accurately describe the characteristics of different cycles of energy production and consumption The impact on energy consumption characteristics, high reliability and high stability to meet industrial control requirements, further support the planning, construction and operation and maintenance of low-carbon industrial parks.

An embodiment of the present application proposes a device for acquiring energy consumption characteristics, as shown in FIG. 3 , including:

The energy consumption data acquisition module 101 is used for acquiring the energy consumption data of the industrial production equipment in the energy consumption monitoring equipment.

The power data acquisition module 102 is used for acquiring the power data of the new energy power generation equipment in the inverter.

The control process data acquisition module 103 is used to monitor and control the state information of the inverter, and acquire control process data.

It should be noted that the state information includes at least one of voltage, current, active power and reactive power, and the control includes frequency modulation control and/or voltage regulation control; the control process data includes a plurality of data samples, each of which Each of the data samples has a plurality of attribute values, and the attribute values include at least one of voltage, current, active power, reactive power, phase control signal, frequency control signal, and voltage control signal.

The energy consumption feature acquisition module 104 is configured to decompose the energy consumption data and the power data into a sequence of information granules based on the control process data, and send the sequence of information granules to the monitoring center, so that the monitoring center can perform the data according to the sequence of information granules Energy consumption characteristics are predicted.

The energy consumption feature acquisition module 104 includes:

The time window sequence acquisition sub-module is used to perform fuzzy clustering processing on the control process data to generate a time window sequence of the control process data.

The information particle sequence acquisition sub-module is configured to decompose the energy consumption data and the power data according to the time window sequence based on the time window sequence, so as to obtain the information particle sequence of the energy consumption data and the power data.

The time window sequence acquisition sub-module includes:

The fuzzy class prototype matrix construction unit is used for dividing the control process data into a plurality of fuzzy class prototypes, and constructing the plurality of fuzzy class prototypes obtained by division into a fuzzy class prototype matrix.

The fuzzy partition matrix construction unit is used to obtain the degree value of the data sample belonging to each of the fuzzy class prototypes, so as to construct the fuzzy partition matrix.

The solution result obtaining unit is used to solve the fuzzy partition matrix and the fuzzy class prototype matrix, and obtain the solution result.

The time window series obtaining unit is used to sort the solution results in ascending order to obtain the time window series.

The fuzzy partition matrix building unit includes:

The first calculation subunit is used to determine the degree value by the following formula:

Among them, u _im ∈[0,1], u _im represents the degree value of the i-th fuzzy class prototype to which the m-th data sample belongs, i∈[1,c], c represents the number of the fuzzy class prototypes, M Indicates the number of data samples in the control process data.

The second calculation subunit is used to construct the fuzzy partition matrix by the following formula:

U=[u _im ]∈R ^c×M ;

Among them, U represents the fuzzy partition matrix, which is used to store the degree value corresponding to each data sample, and R represents that the result is in the form of a matrix.

The solution result acquisition unit includes:

The third calculation subunit is used to solve the fuzzy partition matrix and the fuzzy class prototype matrix by the following formula:

Among them, Q represents the objective function, d _m represents the mth data sample in the control process data, vi represents the _ith fuzzy class prototype, V represents the fuzzy class prototype matrix, f represents the fuzzification factor, f>1.

The information particle sequence acquisition sub-module includes:

a subset dividing unit, configured to divide the energy consumption data and the power data into a plurality of energy consumption subsets and a plurality of power subsets respectively based on the time window sequence, and the energy consumption subsets and the power subsets are based on the time window The sequences correspond one-to-one.

The information particle sequence acquisition unit is used to acquire the maximum value and minimum value of each of the energy subsets, as well as the maximum value and minimum value of each of the power subsets, and use the combination of the maximum value and the minimum value as Corresponding information granule features of the energy consumption subset and the power subset, and each set of the information granule features constitutes an information granule sequence.

To sum up, the embodiments of the present application provide a device for acquiring energy consumption characteristics, by acquiring energy consumption data of industrial production equipment and power data of new energy power generation equipment, taking into account the energy consumption characteristics and production process of industrial parks. The correlation between distributed new energy generation processes; through the acquisition of inverter control process data, factors such as high-frequency fluctuations in energy consumption of production processes and industrial equipment, and intermittent fluctuations in distributed energy generation are taken into account; The energy consumption data and the power data are decomposed into a sequence of information granules, and granular computing is introduced to give a complete and real causal structure, improve the correlation between the analysis method and the actual equipment operation law, and more accurately describe the characteristics of different cycles of energy production and consumption The impact on energy consumption characteristics, high reliability and high stability to meet industrial control requirements, further support the planning, construction and operation and maintenance of low-carbon industrial parks.

An embodiment of the present application proposes an edge proxy device. As shown in FIG. 4 , the edge proxy device includes a processor and a memory for storing executable instructions of the processor.

Wherein, when the executable instructions are executed by the processor, the following functions are implemented:

Obtain energy consumption data of industrial production equipment in energy consumption monitoring equipment.

Obtain the power data of the new energy power generation equipment in the inverter.

Monitor and control the status information of the inverter, and obtain the control process data.

Based on the control process data, the energy consumption data and the power data are decomposed into a sequence of information granules, and the sequence of information granules is sent to a monitoring center, so that the monitoring center can predict energy consumption characteristics according to the sequence of information granules.

It should be noted that at least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor to implement a vehicle control method according to the embodiment of the present application.

The processor may be a central processing unit (Central Processing Unit, CPU). The processor may also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), Field-Programmable Gate Array (Field-Programmable Gate Array, FPGA) or other Chips such as programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above types of chips.

As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs, non-transitory computer-executable programs and modules, such as program instructions/modules corresponding to the methods in the embodiments of the present application. The processor executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions, and modules stored in the memory, ie, implements the methods in the above method embodiments.

The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system and an application program required by at least one function; the storage data area may store data created by the processor, and the like. Additionally, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory may optionally include memory located remotely from the processor, such remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

An embodiment of the present application proposes a system for acquiring energy consumption characteristics, as shown in FIG. 5, including: industrial production equipment, energy consumption monitoring equipment, new energy power generation equipment, combiner boxes, inverters, transformers, monitoring centers, and The edge proxy device proposed by the embodiments of this application.

Wherein, the edge proxy device and the energy consumption monitoring device are connected through a wireless network, connected with the inverter through a Modbus communication protocol, and connected with the monitoring center through an industrial switch.

In practical application scenarios, due to the advantages of extremely refined photovoltaic power generation equipment, long reliable and stable life, easy installation and maintenance, and can be used in any occasion that requires power supply, new energy power generation equipment in industrial parks usually uses photovoltaic modules to obtain light energy and use it converted into electricity. After the electric energy obtained by multiple photovoltaic modules is collected by the combiner box, AC and DC conversion is carried out in the inverter, and the converted AC power is converted into AC voltage and current through the transformer to provide electric energy for the industrial production equipment in the industrial park. In addition, the transformer is also connected to the power grid. When the solar energy is insufficient, the power grid can obtain electricity to supply power to the industrial production equipment in the industrial park. The energy consumption monitoring equipment monitors and obtains the energy consumption data of the industrial production equipment, and the edge proxy equipment obtains energy. The energy consumption data of the industrial production equipment in the power consumption monitoring equipment and the power data output by the photovoltaic modules in the inverter, at the same time, the edge agent equipment also monitors and controls the status information of the inverter, and obtains the control process data, based on The control process data decomposes the energy consumption data and the power data into a sequence of information granules, and sends the sequence of information granules to a monitoring center, and the monitoring center predicts energy consumption characteristics according to the sequence of information granules.

The embodiments of the present application also provide a computer-readable storage medium, where at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement a function as described in the embodiments of the present application. The method for acquiring a consumption feature is used to store at least one computer program, and the at least one computer program is loaded and executed by the processor to realize all or part of the steps in the above method. For example, the computer-readable storage medium may be Read-Only Memory (ROM), Random Access Memory (RAM), Compact Disc Read-Only Memory (CD-ROM), Tape, floppy disk, and optical data storage devices, etc.

The present application has been described in detail above with reference to the specific embodiments and exemplary examples, but these descriptions should not be construed as a limitation on the present application. Those skilled in the art understand that, without departing from the spirit and scope of the present application, various equivalent replacements, modifications or improvements can be made to the technical solutions of the present application and the embodiments thereof, which all fall within the scope of the present application.

Claims (10)

1. The method for acquiring the energy consumption characteristics is applied to edge proxy equipment, and comprises the following steps:

acquiring energy consumption data of industrial production equipment in energy consumption monitoring equipment;

acquiring power data of new energy power generation equipment in an inverter;

monitoring and controlling the state information of the inverter and acquiring control process data;

and decomposing the energy consumption data and the power data into information particle sequences based on the control process data, and sending the information particle sequences to a monitoring center so that the monitoring center can predict energy consumption characteristics according to the information particle sequences.

2. The method of claim 1, wherein the status information comprises at least one of voltage, current, active power, and reactive power, and wherein the controlling comprises frequency modulation control and/or voltage regulation control;

the control process data includes a plurality of data samples, each of the data samples having a plurality of attribute values including at least one of a voltage, a current, an active power, a reactive power, a phase control signal, a frequency control signal, and a voltage control signal.

3. The method of claim 2, wherein the decomposing the energy consumption data and the power data into a sequence of information particles based on the control process data comprises:

performing fuzzy clustering processing on the control process data to generate a time window sequence of the control process data;

and decomposing the energy consumption data and the power data according to the time window sequence based on the time window sequence to obtain an information particle sequence of the energy consumption data and the power data.

4. The method of claim 3, wherein the fuzzy clustering of the control process data to generate the sequence of time windows of the control process data comprises:

dividing the control process data into a plurality of fuzzy prototype types, and constructing the fuzzy prototype types into fuzzy prototype matrixes;

obtaining the degree value of the data sample attached to each fuzzy prototype to construct a fuzzy partition matrix;

solving the fuzzy partition matrix and the fuzzy prototype matrix, and obtaining a solving result;

and performing ascending arrangement on the solving results to obtain a time window series.

5. The method of claim 4, wherein the degree value is determined by the formula:

wherein u is_im∈[0,1]，u_imRepresenting the degree value of the ith fuzzy class prototype to which the mth data sample belongs, i ∈ [1, c ∈ [ ]]C represents the number of the fuzzy prototypes, and M represents the number of data samples in the control process data;

constructing a fuzzy partition matrix by the following formula:

U＝[u_im]∈R^c×M；

and U represents a fuzzy partition matrix and is used for storing the corresponding degree value of each data sample, and R represents that the calculation result is in a matrix form.

6. The method according to claim 5, wherein solving the fuzzy partition matrix and the fuzzy prototype matrix and obtaining a solution result comprises:

solving the fuzzy partition matrix and the fuzzy prototype matrix by the following formulas:

q represents an objective function, dm represents an mth data sample in the control process data, vi represents an ith fuzzy type prototype, V represents the fuzzy type prototype matrix, f represents a fuzzification factor, and f > 1.

7. The method according to any one of claims 3 to 6, wherein the decomposing the energy consumption data and the power data according to the time window sequence based on the time window sequence to obtain the information particle sequence of the energy consumption data and the power data comprises:

based on the time window sequence, dividing the energy consumption data and the power data into a plurality of energy consumption subsets and a plurality of power subsets, wherein the energy consumption subsets and the power subsets are in one-to-one correspondence based on the time window sequence;

and acquiring the maximum value and the minimum value of each energy consumption subset, acquiring the maximum value and the minimum value of each power subset, and taking the combination of the maximum value and the minimum value as the information particle characteristics of the corresponding energy consumption subset and the corresponding power subset, wherein the set of the information particle characteristics forms an information particle sequence.

8. An apparatus for obtaining a characteristic of energy consumption, the apparatus comprising:

the energy consumption data acquisition module is used for acquiring energy consumption data of industrial production equipment in the energy consumption monitoring equipment;

the power data acquisition module is used for acquiring power data of new energy power generation equipment in the inverter;

the control process data acquisition module is used for monitoring and controlling the state information of the inverter and acquiring control process data;

and the energy consumption characteristic acquisition module is used for decomposing the energy consumption data and the power data into information particle sequences based on the control process data and sending the information particle sequences to a monitoring center so that the monitoring center can predict the energy consumption characteristics according to the information particle sequences.

9. An edge proxy device comprising a processor and a memory for storing executable instructions for the processor;

wherein the executable instructions, when executed by the processor, implement the following functions:

acquiring energy consumption data of industrial production equipment in the energy consumption monitoring equipment;

acquiring power data of new energy power generation equipment in an inverter;

monitoring and controlling the state information of the inverter and acquiring control process data;

and decomposing the energy consumption data and the power data into information particle sequences based on the control process data, and sending the information particle sequences to a monitoring center so that the monitoring center predicts energy consumption characteristics according to the information particle sequences.

10. An energy consumption characteristic acquisition system, comprising: industrial production equipment, energy consumption monitoring equipment, new energy generation equipment, combiner boxes, inverters, transformers, monitoring centers and edge proxy equipment according to claim 9;

the edge agent equipment is connected with the energy consumption monitoring equipment through a wireless network, the inverter is connected with the inverter through a Modbus communication protocol, and the monitoring center is connected with the inverter through an industrial switch.

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