CN114492946A - Method, device and equipment for performing fine management on energy consumption of electric appliances in park based on LSTM - Google Patents

Method, device and equipment for performing fine management on energy consumption of electric appliances in park based on LSTM Download PDF

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CN114492946A
CN114492946A CN202111683491.0A CN202111683491A CN114492946A CN 114492946 A CN114492946 A CN 114492946A CN 202111683491 A CN202111683491 A CN 202111683491A CN 114492946 A CN114492946 A CN 114492946A
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张轩铭
周文瑞
梁昆
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Hangzhou Tpson Technology Co ltd
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Abstract

The invention discloses a method, a device and equipment for carrying out fine management on energy consumption of electric appliances in a park based on LSTM, wherein the method comprises the following steps of 1, respectively acquiring energy consumption data of each line in a monitored park at each moment through monitoring equipment; step 2, preprocessing the energy consumption data, and splitting the preprocessed energy consumption data into a training set and a test set; step 3, constructing an LSTM model, and training the LSTM model based on a training set; step 4, testing the trained LSTM model based on the test set, if the expected test result is achieved, taking the trained LSTM model as an energy consumption prediction model, otherwise, returning to the step 3; and 5, predicting the energy consumption condition of the corresponding line in the monitoring park based on the energy consumption prediction model. According to the invention, the energy consumption prediction analysis model is established by training and learning the energy consumption data under each power utilization branch of the monitoring point according to the LSTM neural network, so that the power energy consumption of the park can be more finely acquired, analyzed and predicted, and the energy consumption prediction model has important significance for energy consumption management and energy saving scheme formulation of the park.

Description

Method, device and equipment for performing fine management on energy consumption of electric appliances in park based on LSTM
Technical Field
The invention belongs to the technical field of electric appliance energy consumption statistics, and particularly relates to a method for finely managing energy consumption of electric appliances in a park based on an LSTM.
Background
With the development of economic society and the increasing pressure of environmental resources in China and the severe situation of energy conservation and emission reduction, the park needs to manage energy consumption according to requirements and make a corresponding energy-saving scheme, but the park energy-saving management often lacks suitable indexes and cannot provide support for the pass of the decision of the refined energy-saving scheme of the park.
The existing energy consumption prediction means includes simple regression analysis of energy consumption data based on fixed time intervals in power consumption measurement, or prediction of energy consumption supply provided from power supply measurement levels such as a power grid; these predictions are either not fine enough or only solve the power declaration problem, making it difficult to support energy conservation decisions in a school or enterprise campus setting.
Disclosure of Invention
The invention aims to provide a method, a device and equipment for performing fine management on energy consumption of electric appliances in a park based on an LSTM, so as to solve the problems in the background art.
In order to achieve the purpose, the invention provides the following technical scheme:
a method for performing fine management on energy consumption of park electric appliances based on LSTM comprises the following steps: step 1, respectively acquiring energy consumption data of each line in a monitoring park at each moment through monitoring equipment; step 2, preprocessing the energy consumption data, and splitting the preprocessed energy consumption data into a training set and a test set; step 3, constructing an LSTM model, and training the LSTM model based on a training set; step 4, testing the trained LSTM model based on the test set, if the expected test result is achieved, taking the trained LSTM model as an energy consumption prediction model, otherwise, returning to the step 3; and 5, predicting the energy consumption condition of the corresponding line in the monitoring park based on the energy consumption prediction model.
Preferably, in step 2, the preprocessing of the energy consumption data includes the following steps: and dividing the energy consumption data into units of days, and normalizing the energy consumption data.
Preferably, after the energy consumption data is divided into units of days, the energy consumption data is firstly subjected to data cleaning and then normalized.
Preferably, the data cleansing includes missing value cleansing and outlier cleansing.
Preferably, the missing value is cleaned by taking the moment of the missing value as the moment to be supplemented, and taking the average energy consumption data of the moment before and after the moment to be supplemented as the energy consumption data of the moment to be supplemented.
Preferably, the outliers are cleaned up to find outliers based on the grubbs criterion, and the outliers are data corrected.
Preferably, the step 4 comprises the steps of:
step 4.1, testing the trained LSTM model based on the test set to obtain a test result;
step 4.2, calculating the standard error and the average absolute error of the trained LSTM model based on the test result and the test set;
and 4.3, judging whether the standard error and the average absolute error are respectively smaller than the first reference value and the second reference value, if so, executing the step 5, otherwise, executing the step 3.
A device for performing fine management on energy consumption of electrical appliances in a park based on LSTM comprises a data acquisition module, a data processing module and an energy consumption prediction module, wherein the data acquisition module is used for acquiring energy consumption data of each line at each moment; the data processing module is used for carrying out data preprocessing on the energy consumption data; the energy consumption prediction module is used for establishing an energy consumption prediction model and acquiring the energy consumption condition of the corresponding line at the next moment based on the energy consumption prediction model.
Preferably, the energy consumption prediction module comprises a model establishing unit, a model training unit and a model testing unit, wherein the model establishing unit is used for establishing an LSTM model; the model training unit is used for training the LSTM model; the model test unit is used for testing the trained LSTM model.
An electronic device, comprising: a memory for storing a computer executable program; and the data processing device is used for reading the computer executable program in the memory so as to execute the method for performing the energy consumption fine management on the garden electric appliances based on the LSTM.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, the energy consumption prediction analysis model is established by training and learning the energy consumption data under each power utilization branch of the monitoring point according to the LSTM neural network, so that the power energy consumption of the park can be more finely acquired, analyzed and predicted, and the energy consumption prediction model has important significance for energy consumption management and energy saving scheme formulation of the park.
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FIG. 1 is a flow chart of the operation of the present invention.
Fig. 2 is a schematic diagram of the LSTM model.
FIG. 3 is an architecture diagram of the device for the smart management of energy consumption of electric appliances in a campus based on LSTM according to the present invention.
Fig. 4 is an architecture diagram of the electronic device of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without any inventive step, are within the scope of the present invention.
A method for performing fine management on energy consumption of electrical appliances in a park based on LSTM specifically comprises the following 5 steps.
Step 1, respectively acquiring energy consumption data of each line in a monitoring park at each moment through monitoring equipment.
In the present invention, the energy consumption data is the hourly power consumption of the line.
And 2, preprocessing the energy consumption data, and splitting the preprocessed energy consumption data into a training set and a test set.
In the invention, the pretreatment of the energy consumption data specifically comprises 2 sub-steps:
and 2.1, after the energy consumption data are divided into units of days, respectively cleaning all the energy consumption data of the current day.
Data cleansing refers to the last procedure to find and correct recognizable errors in data files, including checking data consistency, processing invalid and missing values, etc. Unlike questionnaire review, cleaning of data after entry is typically done by computer rather than manually.
In the present invention, data cleansing includes deficiency value cleansing and outlier cleansing. More specifically, the missing value is cleaned by taking the time of the missing value as the time to be supplemented, and taking the average energy consumption data of the previous time and the next time of the time to be supplemented as the energy consumption data of the time to be supplemented. And the abnormal value is cleaned by finding the abnormal value based on the Grubbs criterion and carrying out data correction on the abnormal value.
If the absolute value | Vi | of the residual error of a certain measured value is greater than Gg, the larger error in the value is judged to be eliminated, namely the Grabbs criterion. As to how to find the abnormal value based on the Grubbs criterion is common knowledge in the field, the skilled person can set the abnormal value according to the actual situation, and the step of correcting the abnormal value is consistent with the step of cleaning the missing value.
And 2.2, respectively carrying out normalization processing on all energy consumption data of the current day.
Normalization is a dimensionless processing means to make the absolute value of the physical system value become some relative value relation. Simplifying the calculation and reducing the magnitude. The normalization method generally comprises two methods, namely min-max normalization and Z-score normalization, wherein the min-max normalization is also called dispersion normalization and is a linear transformation on original data, so that a result value is mapped between 0 and 1, but when new data is added, the maximum value and the minimum value of sample data need to be redefined; z-score normalization standardizes data by giving mean and standard deviation of raw data, and processed data conform to standard normal distribution, namely mean is 0 and standard deviation is 1; in the invention, a person skilled in the art can select the normalization method of the energy consumption data according to the actual situation.
In the invention, a training set and a test set jointly form a sample set, each sample in the sample set comprises a monitoring date, a monitoring time and energy consumption data of all lines under the monitoring date and the monitoring time, the energy consumption data of all lines under the monitoring date and the monitoring time is a sequence, the sequence sequentially comprises the energy consumption data of the line with the serial number of i from front to back, i is 1, 2, …, n, and the value of n is the value of a bus line. In the invention, the sample set is divided into a training set and a test set according to the proportion of 7: 3.
And 3, constructing an LSTM model, and training the LSTM model based on a training set.
In the invention, the LSTM model is a long-short term memory model (long-short term memory), is a special RNN model and is provided for solving the problem of gradient diffusion of the RNN model; in the conventional RNN, the training algorithm uses the BPTT, and when the time is long, the residual error required to be returned decreases exponentially, so that the network weight is updated slowly, and the effect of the long-term memory of the RNN cannot be reflected, so that a storage unit is required to store the memory, and therefore an LSTM model is proposed, and a schematic diagram of the LSTM model is shown in fig. 2.
In step 3 of the invention, the monitoring date and the monitoring time are used as target input, a sequence consisting of n energy consumption data is used as target output, and the LSTM model is trained, so that the parameters of the LSTM model are updated, and the loss value between the predicted output and the target output of the LSTM model after the parameters are updated is minimum. The setting of the loss function of the loss value is well known in the art and is set by the person skilled in the art according to the actual requirements.
And 4, testing the trained LSTM model based on the test set, taking the trained LSTM model as an energy consumption prediction model if an expected test result is achieved, and otherwise, returning to the step 3.
Specifically, the step 4 includes the following 3 sub-steps:
step 4.1, testing the trained LSTM model based on the test set to obtain a test result;
in step 4 of the invention, the monitoring date and the monitoring time in the test set are used as the input of the trained LSTM model to obtain the prediction output of the trained LSTM model.
Step 4.2, calculating the standard error and the average absolute error of the trained LSTM model based on the test result and the test set;
based on the predicted output and the target output in the test set, the standard error of the trained LSTM model is calculated through a standard deviation calculation formula, and the average absolute error is calculated by using an absolute error calculation formula and a mean calculation formula, wherein the average absolute error is the average of the absolute values of the deviations of all the single observed values and the arithmetic mean. Compared with the average error, the average absolute error is absolute value because of the dispersion, and the situation of negative and positive offsets does not occur, so the average absolute error can better reflect the actual situation of the error of the predicted value.
4.3, judging whether the standard error and the average absolute error are respectively smaller than a first reference value and a second reference value, if so, executing a step 5, otherwise, executing a step 3;
in the invention, if the test error is smaller than the preset value, the trained LSTM model is considered to achieve the expected effect, so that the trained LSTM model can be used as an energy consumption prediction model to predict the later energy consumption, otherwise, the trained LSTM model needs to be retrained.
And 5, predicting the energy consumption condition of the corresponding line in the monitoring park based on the energy consumption prediction model.
Embodiments of the apparatus of the present invention are described below, which may be used to perform method embodiments of the present invention. The details described in the device embodiments of the invention should be regarded as complementary to the above-described method embodiments; reference is made to the above-described method embodiments for details not disclosed in the apparatus embodiments of the invention.
FIG. 3 is a schematic diagram of an architecture of a device for performing fine management on energy consumption of electrical appliances in a campus based on LSTM according to the present invention. As shown in fig. 3, the apparatus of the present invention comprises: the energy consumption prediction system comprises a data acquisition module, a data processing module and an energy consumption prediction module, wherein the data acquisition module is used for acquiring energy consumption data of each line at each moment; the data processing module is used for carrying out data preprocessing on the energy consumption data; the energy consumption prediction module is used for establishing an energy consumption prediction model and acquiring the energy consumption condition of the corresponding line at the next moment based on the energy consumption prediction model. The energy consumption prediction module comprises a model establishing unit, a model training unit and a model testing unit, wherein the model establishing unit is used for establishing an LSTM model; the model training unit is used for training the LSTM model; the model test unit is used for testing the trained LSTM model.
In the following, embodiments of the electronic device of the present invention are described, which may be regarded as an implementation in physical form for the above-described embodiments of the method and apparatus of the present invention. Details described in the embodiments of the electronic device of the invention should be considered supplementary to the embodiments of the method or apparatus described above; for details which are not disclosed in embodiments of the electronic device of the invention, reference may be made to the above-described embodiments of the method or the apparatus.
In addition, the invention also provides electronic equipment which can finely manage the energy consumption of electric appliances in the garden based on the LSTM. Fig. 4 is a schematic structural framework diagram of an electronic device according to the present invention, and as shown in fig. 4, the electronic device includes a memory for storing a computer-executable program and a data processing device for reading the computer-executable program in the memory to execute the method for performing the LSTM-based energy consumption fine management on the campus appliances. The memory of the invention can be a local memory, and can also be a distributed storage system, such as a cloud storage system. The data processor includes at least one device with digital information processing capability, such as a CPU, GPU, multiprocessor system, or cloud processor.
It should be understood that the modules, units, components, and the like included in the device of one embodiment of the present invention may be adaptively changed to be provided in a device different from that of the embodiment. The different modules, units or components comprised by the apparatus of an embodiment may be combined into one module, unit or component or they may be divided into a plurality of sub-modules, sub-units or sub-components. The modules, units or components in the embodiments of the present invention may be implemented in hardware, or may be implemented in software running on one or more processors, or a combination thereof.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A method for finely managing energy consumption of electric appliances in a park based on LSTM is characterized by comprising the following steps:
step 1, respectively acquiring energy consumption data of each line in a monitoring park at each moment through monitoring equipment;
step 2, preprocessing the energy consumption data, and splitting the preprocessed energy consumption data into a training set and a test set;
step 3, constructing an LSTM model, and training the LSTM model based on a training set;
step 4, testing the trained LSTM model based on the test set, if the expected test result is achieved, taking the trained LSTM model as an energy consumption prediction model, otherwise, returning to the step 3;
and 5, predicting the energy consumption condition of the corresponding line in the monitoring park based on the energy consumption prediction model.
2. The LSTM-based method for fine management of energy consumption of park appliances according to claim 1, wherein the step 2 of preprocessing the energy consumption data comprises the steps of: and dividing the energy consumption data into units of days, and normalizing the energy consumption data.
3. The method for the energy consumption fine management of park appliances based on the LSTM according to claim 2, wherein after the energy consumption data is divided into units of days, the energy consumption data is firstly subjected to data cleaning and then is subjected to normalization.
4. The method for the fine management of energy consumption of electric appliances on the campus based on the LSTM of claim 3 wherein said data cleansing includes missing value cleansing and outlier cleansing.
5. The method for the energy consumption fine management of park electric appliances based on the LSTM according to claim 3, wherein the missing value is cleaned by taking the time of the missing value as the time to be supplemented, and taking the average energy consumption data of the previous time and the next time of the time to be supplemented as the energy consumption data of the time to be supplemented.
6. The method for the energy consumption fine management of park appliances based on the LSTM according to claim 3, wherein the outlier is cleaned by searching for the outlier based on the Grubbs criterion and performing data correction on the outlier.
7. The LSTM-based method for fine management of park appliance energy consumption according to claim 1, wherein the step 4 comprises the steps of:
step 4.1, testing the trained LSTM model based on the test set to obtain a test result;
step 4.2, calculating the standard error and the average absolute error of the trained LSTM model based on the test result and the test set;
and 4.3, judging whether the standard error and the average absolute error are respectively smaller than the first reference value and the second reference value, if so, executing the step 5, otherwise, executing the step 3.
8. The device for performing fine management on energy consumption of electric appliances in a park based on the LSTM is characterized by comprising a data acquisition module, a data processing module and an energy consumption prediction module, wherein the data acquisition module is used for acquiring energy consumption data of each line at each moment; the data processing module is used for carrying out data preprocessing on the energy consumption data; the energy consumption prediction module is used for establishing an energy consumption prediction model and acquiring the energy consumption condition of the corresponding line at the next moment based on the energy consumption prediction model.
9. The device for the energy consumption fine management of electric appliances in a garden based on the LSTM as claimed in claim 8, wherein the energy consumption prediction module comprises a model building unit, a model training unit and a model testing unit, the model building unit is used for building the LSTM model; the model training unit is used for training the LSTM model; the model test unit is used for testing the trained LSTM model.
10. An electronic device, comprising:
a memory for storing a computer executable program;
a data processing device for reading the computer executable program in the memory to execute the method for performing the LSTM-based park appliance energy consumption fine management in any one of claims 1 to 7.
CN202111683491.0A 2021-12-31 2021-12-31 Method, device and equipment for performing fine management on energy consumption of electric appliances in park based on LSTM Pending CN114492946A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116109011A (en) * 2023-04-10 2023-05-12 知鱼智联科技股份有限公司 Energy consumption management method and terminal for intelligent park

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116109011A (en) * 2023-04-10 2023-05-12 知鱼智联科技股份有限公司 Energy consumption management method and terminal for intelligent park

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