CN115423301A - Intelligent electric power energy management and control method, device and system based on Internet of things - Google Patents
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Abstract
The invention relates to the technical field of energy management and control, solves the technical problem that the prediction of the power energy consumption of communities is inaccurate, and particularly relates to an intelligent power energy management and control method based on the Internet of things, which comprises the following steps: obtaining a total electric energy consumption predicted value through an electric energy consumption prediction model according to the effective temperature data of the current day, the community resident data and the date type data; calculating an actual value of the total electric energy consumption according to the collected real-time monitoring data set; and calculating an electric energy consumption prediction error according to the actual value and the predicted value of the total electric energy consumption, sending early warning information according to the prediction error and adopting a corresponding electric energy management and control strategy. According to the method, the grey neural network and the ant colony optimization algorithm are adopted to construct the electric energy consumption prediction model with high stability and high prediction precision, and the cleaned database is used for continuously optimizing the electric energy consumption prediction model, so that the prediction precision is improved, and the intelligent management and control of the electric energy of the community are facilitated.
Description
Technical Field
The invention relates to the technical field of energy management and control, in particular to an intelligent electric energy management and control method, device and system based on the Internet of things.
Background
With the development of the internet of things technology, the internet of things technology is combined with energy management and control, the use of community electric energy is efficiently, safely and intelligently controlled, the energy consumption level of the community is reduced, and the energy conservation and emission reduction of the whole society and the reduction of the energy consumption cost of the community are very necessary.
At present, the existing intelligent energy management and control method is generally based on historical data for analysis, so that intelligent management of power and energy production and consumption is realized, however, for massive historical data, the traditional power and energy consumption prediction method is very slow in processing, often needs to spend a long time, and even sometimes causes prediction distortion due to poor stability of a prediction model, so that reasonable management and control on power and energy cannot be performed, and further the power and energy utilization rate is low.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an intelligent power energy management and control method, device and system based on the Internet of things, solves the technical problem that the prediction of the power energy consumption of communities is inaccurate, and achieves the purposes of reducing the power energy consumption and improving the utilization rate on the premise of fully meeting and improving the power energy consumption requirements of communities.
In order to solve the technical problems, the invention provides the following technical scheme: an intelligent electric energy management and control method based on the Internet of things comprises the following steps:
s1, calculating the effective temperature data of the current day according to the meteorological data of the current day of the area position of the community;
s2, acquiring community household data and date type data, wherein the date type data comprises holidays and workdays;
s3, according to the effective temperature data of the current day, the community resident data and the date type data, obtaining a total electric energy consumption predicted value of the current day of the community through a pre-constructed electric energy consumption prediction model, wherein the electric energy consumption prediction model is constructed according to a historical data set stored in a database;
s4, acquiring a real-time monitoring data set acquired by the electric energy data acquisition equipment, wherein the real-time monitoring data comprises household energy consumption monitoring data, corridor lighting energy consumption monitoring data and street lamp energy consumption monitoring data;
s5, calculating an actual value of the total electric energy consumption of the community on the current day according to the real-time monitoring data set;
s6, calculating an electric energy consumption prediction error according to the electric energy total consumption actual value and the electric energy total consumption predicted value;
s7, judging whether the prediction error is within a preset range, and if the prediction error is within the preset range, judging that the community electric energy consumption is normal; if the prediction error is not within the preset range, judging that the community electric energy consumption is abnormal and sending early warning information to an energy management and control center;
and S8, adopting a corresponding energy management and control strategy by the energy management and control center according to the early warning information.
Further, between the steps S6 and S7, the method further includes:
s9, cleaning the data in the database according to the prediction error;
and S10, continuously optimizing the electric energy consumption prediction model according to the cleaned database.
Further, the step S3 specifically includes:
s31, constructing an electric energy consumption prediction model according to a historical energy consumption sample set stored in a database;
and S32, obtaining a total electric energy consumption predicted value of the community on the current day by adopting the electric energy consumption prediction model according to the effective temperature data of the current day, the community household data and the date type data.
Further, the step S31 specifically includes:
s311, obtaining a historical energy consumption sample set which is processed by a principal component analysis algorithm and stored in a database, wherein each historical sample in the historical energy consumption sample set consists of historical day effective temperature data, historical date type data, historical day community resident data and historical day total electric energy consumption;
s312, randomly dividing the historical energy utilization sample set into a training sample set and a testing sample set according to a preset proportion;
s313, determining a topological structure of the grey neural network according to the data characteristics of the training sample set to obtain an initial prediction model;
s314, training and optimizing the initial prediction model through a training sample set according to the mapping relation between the weight bias of the initial prediction model and the ant colony optimization algorithm to obtain an optimal electric energy consumption prediction model;
and S315, testing and verifying the performance of the electric energy consumption prediction model by adopting the test sample set.
Further, the step S9 specifically includes:
when the prediction error is not within a preset range, uploading the current day effective temperature data, the date type data and the real-time monitoring data set to a database and removing dirty data in the database;
and when the prediction error is within a preset range, uploading the current day effective temperature data, the date type data and the real-time monitoring data set to a database, and removing repeated historical data with the collection time interval larger than a time threshold value in the database.
Further, the expression of the output value of the electric energy usage prediction model is as follows:
in the above formula, W f The total electric energy consumption of the community on the current day is predicted, m is the number of community electric units, n is the number of factor indexes influencing the total electric energy consumption of the community, and x j Is the jth electricity utilization unit, alpha ij The weight of the jth power utilization unit in the ith factor index, F τ Is a date type, and τ values of 1 and 0 represent weekday and holiday, respectively.
The invention also provides a technical scheme that: the utility model provides an electric power energy intelligence management and control device based on thing networking, includes:
the effective temperature calculation module is used for calculating effective temperature data of the current day according to meteorological data of the current day of the regional position of the community;
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring community household data and date type data, and the date type data comprises holidays and workdays;
the predicted value calculation module is used for obtaining a predicted value of the total electric energy consumption of the community on the current day through a pre-constructed electric energy consumption prediction model according to the effective temperature data of the current day, the community household data and the date type data, wherein the electric energy consumption prediction model is constructed according to a historical data set stored in a database;
the second acquisition module is used for acquiring a real-time monitoring data set acquired by the electric energy data acquisition equipment, wherein the real-time monitoring data comprises household energy consumption monitoring data, corridor lighting energy consumption monitoring data and street lamp energy consumption monitoring data;
the actual value calculating module is used for calculating the actual value of the total electric energy consumption of the community on the current day according to the real-time monitoring data set;
the prediction error calculation module is used for calculating the prediction error of the electric energy consumption according to the actual value and the predicted value of the total electric energy consumption;
the judging module is used for judging whether the prediction error is within a preset range or not, and if the prediction error is within the preset range, judging that the community electric energy consumption is normal; if the prediction error is not within the preset range, judging that the community electric energy consumption is abnormal and sending early warning information to an energy management and control center;
and the intelligent management and control module is used for adopting a corresponding energy management and control strategy according to the early warning information energy management and control center.
Further, the method also comprises the following steps:
the database cleaning module is used for cleaning data in the database according to the prediction error e;
and the prediction model optimization module is used for continuously optimizing the electric energy consumption prediction model according to the cleaned database.
The invention also provides another technical scheme that: a system of an intelligent power energy management and control method based on the Internet of things comprises the following steps: the system comprises electric energy data acquisition equipment, computer equipment and an energy management and control center;
the electric energy data acquisition equipment is in data communication with the computer equipment, and is used for acquiring electric energy consumption data of a community and transmitting the electric energy consumption data to the computer equipment;
the computer equipment and the energy management and control center establish data transmission, and the computer equipment comprises at least one processor, a memory and a database, and is used for receiving the real-time monitoring data set acquired by the electric energy data acquisition equipment, uploading the real-time monitoring data set to the database, and then calling and operating an executable instruction in the memory through the processor to calculate a predicted value of the total electric energy consumption of the community on the same day according to the received real-time monitoring data set;
the energy management and control center is used for displaying community power consumption energy consumption information and giving an alarm to remind management and control center workers.
Furthermore, the electric energy data acquisition equipment comprises a plurality of internet of things sensors which are deployed on the site and used for acquiring electric energy consumption, and a power supply module which is used for providing electric energy required by work for the internet of things sensors.
By means of the technical scheme, the invention provides an intelligent electric energy management and control method, device and system based on the Internet of things, and the intelligent electric energy management and control method, device and system at least have the following beneficial effects:
1. according to the method, the topological structure of the grey neural network is used as an initial prediction model according to the grey level relevance among the factors influencing the electric energy, then the initial prediction model is trained and optimized by combining an ant colony optimization algorithm according to a training sample set extracted from a database, and an optimal electric energy prediction model is obtained, so that the stability of the electric energy prediction model is improved, data can be rapidly processed, the output predicted value of the electric energy can be more accurate, the supply and detailed use conditions of electric energy in each link of a community can be conveniently monitored in real time, visual and scientific basis is provided for energy conservation and consumption reduction, and intelligent management and control are performed according to a scientific energy efficiency evaluation method and an evaluation standard.
2. According to the method, the collected data are processed and analyzed according to the error between the predicted value and the actual monitoring value of the electric energy consumption prediction model, the dirty data stored in the database are cleaned according to the analysis result, valuable data are reserved, the cleaned database is used for optimizing the electric energy consumption prediction model, monitoring and optimization by maintenance personnel of an energy management and control center are not needed, the accuracy of the electric energy consumption prediction value is improved, meanwhile, the calculated amount in the prediction process is reduced, the prediction speed is improved, and therefore the method has high social value and application prospect.
3. According to the method, the grey neural network and the ant colony optimization algorithm are adopted to construct the electric energy consumption prediction model with strong stability and high prediction precision, the cleaned database is used for continuously optimizing the electric energy consumption prediction model, and the monitoring and optimization of maintenance personnel of an energy management and control center are not needed, so that the optimization efficiency and the prediction precision are improved, the electric energy consumption is reduced and the electric energy utilization rate is improved on the premise that the requirement of community electric energy consumption is fully met and improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a flowchart of an intelligent electric power management and control method according to an embodiment of the present invention;
fig. 2 is a flowchart of step S3 of the intelligent electric energy management and control method according to the present invention;
fig. 3 is a flowchart of constructing an electric energy consumption prediction model according to the intelligent electric energy management and control method of the present invention;
FIG. 4 is a topological structure diagram of a gray neural network of the intelligent electric power energy management and control method of the present invention;
fig. 5 is a schematic block diagram of an intelligent electric power management and control apparatus according to a first embodiment of the present invention;
fig. 6 is a flowchart of an intelligent electric power management and control method according to a second embodiment of the present invention;
fig. 7 is a schematic block diagram of an intelligent electric power management and control device according to a second embodiment of the present invention;
fig. 8 is a schematic block diagram of an intelligent management and control system for electric power energy according to the present invention.
In the figure: 101. an effective temperature calculation module; 102. a first acquisition module; 103. a predicted value calculation module; 104. a second acquisition module; 105. an actual value calculation module; 106. a prediction error calculation module; 107. a judgment module; 108. an intelligent management and control module; 109. a database cleaning module; 110. a prediction model optimization module; 100. an electric energy data acquisition device; 200. a computer device; 300. an energy management and control center.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below. Therefore, the realization process of solving the technical problems and achieving the technical effects by applying technical means can be fully understood and implemented.
It will be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by relevant hardware instructed by a program, and therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Summary of the application
The power and energy consumption of the community mainly comprises community household power consumption, corridor lighting power consumption and street lamp power consumption, wherein the community household power consumption is an important factor influencing the community power and energy consumption, the start and stop of an air conditioner is a main factor of the community household power consumption, massive historical data are oriented, the stability of a prediction model is poor, and the distortion of a predicted value is easily caused. Therefore, the method and the device have the advantages that the total community electric energy consumption prediction value is obtained through the electric energy consumption prediction model according to the effective temperature, the number of community residents and the date type, then the real-time monitoring data set is collected through the electric energy data collection equipment based on the internet of things technology, the total community electric energy consumption actual value is calculated, the total community electric energy consumption actual value is compared with the total community electric energy consumption prediction value, the database for constructing the electric energy consumption prediction model is cleaned according to the comparison result, valuable data are reserved, the electric energy consumption prediction model is optimized continuously, the prediction precision of the prediction model is improved, the operation time is shortened, energy management and control personnel can conveniently adopt the optimal management and control strategy according to the more accurate electric energy consumption prediction value, and the purpose of reducing electric energy consumption on the premise that the community energy consumption requirement is met is achieved.
Example one
Referring to fig. 1 to 4, an intelligent management and control method for electric energy based on the internet of things according to an embodiment of the present invention is shown, including the following steps:
s1, calculating the effective temperature data of the current day according to the meteorological data of the current day of the area position of the community.
Specifically, weather data of the day can be acquired according to weather forecast of a city where a community to be subjected to electric power energy management and control is located, and air temperature data and air relative humidity data of the day are extracted from the weather data of the dayAccording to the data, the air temperature is recorded as T, the relative air humidity is recorded as R, and the effective temperature data on the day is recorded as T a When R is present>0.13T 2 -13.5T +363 and T>At 30 hours, T a The calculation formula of (2) is as follows:
T a ＝32.99+0.00515T 2 -0.02314TR+0.00001T 3 R
if not, then,
T a ＝4.00+0.7664T-0.00087R 2 -0.00024T 2 R+0.00006TR 2 +0.00001T 3 R
wherein, the effective temperature data T of the day a And the air temperature T are in units of ℃.
It should be noted that, the community residents usually determine whether to start or stop the air conditioner according to their own indoor feelings, and therefore, in the embodiment, the wind speed is not considered when calculating the effective temperature on the day.
S2, acquiring community household data and date type data, wherein the date type data comprises holidays and workdays.
Specifically, factor indexes influencing the community electric power energy consumption are very many and too complex, and therefore three main factor indexes, namely the effective temperature in the current day, the number of community residents and whether the current day is a holiday are extracted by adopting a principal component analysis algorithm (PCA for short).
And S3, obtaining a total electric energy consumption predicted value of the community on the current day through a pre-constructed electric energy consumption prediction model according to the effective temperature data, the community resident data and the date type data on the current day. As shown in fig. 2, the method specifically includes the following steps:
s31, constructing an electric energy consumption prediction model according to the historical energy consumption sample set stored in the database, wherein as shown in fig. 3, the specific construction steps are as follows:
s311, acquiring a historical energy sample set which is processed by a principal component analysis algorithm and stored in a database.
Specifically, the data before the current day collected by the data collecting device is processed and analyzed and then stored in the database, so as to form a sample set related to the historical energy of the community, in this embodiment, the number of the sample sets of the historical energy is 200.
It should be noted that the factor indexes affecting the community electric energy consumption are very many and too complex, and therefore three main influence indexes are extracted by adopting a principal component analysis algorithm (PCA for short), that is, each historical sample in the historical energy consumption sample set is composed of historical day effective temperature data, historical date type data, historical day community household data and historical day electric energy total consumption, wherein the historical day electric energy total consumption is an actual value of the electric energy total consumption of the community on the historical day.
And S312, randomly dividing the historical energy utilization sample set into a training sample set and a testing sample set according to a preset proportion.
In this embodiment, 200 samples contained in the historical energy sample set are randomly divided into a training sample set and a test sample set according to a ratio of 4 to 1, that is, 160 training samples are contained in the training sample set, and 40 test samples are contained in the test sample set.
And S313, determining the topological structure of the grey neural network according to the data characteristics of the training sample set to obtain an initial prediction model.
It should be noted that the gray neural network is a mixed model fusing a gray model and a neural network model; the modeling idea of the grey neural network model is that a time response function of a grey differential equation provided based on a grey theory is mapped into an expanded neural network, so that parameters of the grey differential equation correspond to each weight of the neural network, then the neural network is trained, when the network converges, corresponding equation parameters are extracted from the trained neural network, a whitened differential equation is obtained, and the whitened differential equation can be used for predicting the total electric energy consumption of the community. Wherein the expression of the time response function is:
in the above equation, t represents the number of input parameters, a represents the coefficient of progression of the gray model, and b represents the amount of reactive force.
In this embodiment, according to the data characteristics of 160 training samples in the training sample set, the neural network mapped by the above formula may form a topology structure of a gray neural network, as shown in fig. 4, there are four layers: l1, L2, L3 and L4, which are input layer, hidden layer 1, hidden layer 2 and output layer, respectively, t represents the serial number of the input parameter, x n (t) represents a network input parameter, ω 11 、ω 2i 、ω 3i (i =1,2, \8230;, n) represents the connection weight of each neural unit of the network, y represents the predicted value of the network, and n represents the number of the total community electric energy consumption prediction factor indexes.
Inputting a t value into a node of a layer L1, and calculating nodes of layers L2, L3 and L4, wherein an activation function of a neuron of the layer L2 is a Sigmoid function:
the excitation functions of the neurons in the other layers are taken as linear functions f (x) = x, and then calculation is carried out according to the topology of the neural network, so that a gray differential equation can be mapped into the topology of the neural network.
As can be seen from FIG. 4, the initial prediction model constructed by the method introduces factor indexes influencing the community electric energy consumption as input variables of the model at the L3 layer, considers the effect of the factor indexes influencing the total community electric energy consumption on the predicted value, and simultaneously, the weight omega of the network 21 ,ω 22 ,…,ω 2n Different variables are respectively endowed, the weight can be adjusted in different degrees during training, but due to the fact that the initial weight and the bias of the initial prediction model constructed based on the grey neural network are greatly randomized, the initial prediction model is easy to fall into local optimization during training, and related parameters cannot be further adjusted, so that the problems of low convergence rate, low prediction precision, poor stability and the like of the initial prediction model are causedAnd (4) molding.
And S314, training and optimizing the initial prediction model through a training sample set according to the mapping relation between the weight bias of the initial prediction model and the ant colony optimization algorithm to obtain an optimal electric energy consumption prediction model.
Specifically, according to data characteristic initial ant colony algorithm parameters of a training sample set, weights and node offsets connected with each node of a gray neural network are randomly generated, each generated gray neural network is coded to correspond to ant individuals in the ant colony algorithm, an initial colony of the ant colony optimization algorithm is generated, after the initial colony is formed, optimization processing is carried out on each individual in the colony, and an optimal electric energy consumption prediction model is output after iteration times are reached.
In this embodiment, the initial ant colony algorithm parameters are: the number of the ant individuals is M ant Maximum number of iterations is I max The learning rate is mu, the initial prediction model is trained to obtain an optimal electric energy consumption prediction model, and the expression of the output value of the electric energy consumption prediction model is as follows:
in the above formula, W f The total electric energy consumption of the community on the current day is predicted, m is the number of community electric units, n is the number of factor indexes influencing the total electric energy consumption of the community, and x j Is the jth electricity utilization unit, alpha ij Is the weight of the ith factor index of the jth power utilization unit, F τ Is of the date type and τ values of 1 and 0 indicate weekday and holiday, respectively.
And S315, testing and verifying the performance of the electric energy consumption prediction model by adopting the test sample set.
Specifically, 40 test samples in the test sample set are input into the electric energy consumption prediction model for testing, and the accuracy of the electric energy consumption prediction model is verified.
And S32, obtaining a total electric energy consumption predicted value of the community on the current day by adopting an electric energy consumption prediction model according to the effective temperature data, the community household data and the date type data on the current day.
Specifically, the obtained effective temperature data of the current day, the community household data and the date type data are input into the electric energy consumption prediction model, and after a series of processing of the electric energy consumption prediction model, the total electric energy consumption predicted value W of the current day of the community can be obtained f At the moment, the community can carry out electric power energy scheduling and energy storage according to the total consumption predicted value of electric energy so as to ensure the normal life demand of community residents and avoid the waste of electric power energy.
And S4, acquiring a real-time monitoring data set acquired by the electric energy data acquisition equipment.
Specifically, the electric energy data acquisition equipment deployed on the internet of things sensing layer is used for acquiring the time power consumption of each power consumption unit of the community at the same day, namely, a real-time monitoring data set.
And S5, calculating the actual value of the total electric energy consumption of the community on the current day according to the real-time monitoring data set.
Specifically, the household energy consumption monitoring data, the corridor lighting energy consumption monitoring data and the street lamp energy consumption monitoring data collected by the electric energy data collecting device are accumulated, and the actual value of the total electric energy consumption of the community on the day can be obtained and recorded as W.
And S6, calculating an electric energy consumption prediction error according to the actual electric energy consumption value and the predicted electric energy consumption value.
Specifically, the actual value W of the total electric energy consumption and the predicted value W of the total electric energy consumption are used f If the prediction error of the electric energy consumption is e, the expression is
S7, judging whether the prediction error e is within a preset range, and if the prediction error e is within the preset range, judging that the community electric energy consumption is normal; and if the prediction error e is not within the preset range, judging that the community electric energy consumption is abnormal and sending early warning information to the energy management and control center.
In the embodiment, the preset range is set to be 0-0.03, if the prediction error e is within the preset range, the community electric energy consumption is judged to be normal, namely, only continuous monitoring is needed, and regulation and control are not needed; if the prediction error e is not within the preset range, judging that the community electric energy consumption is abnormal, namely sending early warning information to an energy management and control center through a network, wherein the early warning information is that the community electric energy total consumption is overloaded or is far lower than a predicted value.
And S8, the energy management and control center adopts a corresponding energy management and control strategy according to the early warning information.
Specifically, the staff of the energy management and control center takes a corresponding management and control strategy according to the received early warning information, if the received early warning information is overloaded for the total electric energy consumption of the community, the staff detects whether the electricity stealing and leakage of each power utilization unit occurs, if the electricity stealing and leakage does not occur, the electric energy consumption prediction model needs to be optimized, the power utilization units are managed and controlled, and the electric energy consumption is reduced.
For example, the lighting lamp of the central square in the community can be intelligently controlled according to the flow of people, the power energy consumption is reduced, the intelligent control method comprises the steps of snapshotting a target area image through a monitoring camera and transmitting the target area image to a target detector through the Internet of things, extracting a humanoid target in the target area image through the target detector, counting the humanoid quantity according to the humanoid target, when the humanoid quantity is greater than or equal to a pedestrian flow threshold value, starting the lighting lamp, when the humanoid quantity is smaller than the pedestrian flow threshold value, closing the lighting lamp, wherein the lighting lamp and a street lamp are controlled by different circuits, so that the power energy consumption is reduced on the premise that the travel playing requirements of community residents are met, and the purpose of intelligently controlling the power energy is achieved.
Referring to fig. 5, this embodiment further provides a technical solution: the utility model provides an electric power energy intelligence management and control device based on thing networking, includes:
the effective temperature calculation module 101 is used for calculating the effective temperature data of the current day according to the meteorological data of the current day of the area position of the community;
the first acquisition module 102 is used for acquiring community household data and date type data, wherein the date type data comprises holidays and workdays;
the predicted value calculating module 103 is used for obtaining a predicted value of the total electric energy consumption of the community on the current day through a pre-constructed electric energy consumption predicting model according to the effective temperature data of the current day, the community household data and the date type data, wherein the electric energy consumption predicting model is constructed according to a historical data set stored in a database;
the second acquisition module 104 is used for acquiring a real-time monitoring data set acquired by the electric energy data acquisition equipment, wherein the real-time monitoring data comprises household energy consumption monitoring data, corridor lighting energy consumption monitoring data and street lamp energy consumption monitoring data;
the actual value calculating module 105 is used for calculating the actual value of the total electric energy consumption of the community on the current day according to the real-time monitoring data set;
the prediction error calculation module 106 is used for calculating the prediction error of the electric energy consumption according to the actual value and the predicted value of the total electric energy consumption by the prediction error calculation module 106;
the judging module 107 is used for judging whether the prediction error is within a preset range, and if the prediction error is within the preset range, judging that the community electric energy consumption is normal; if the prediction error is not within the preset range, judging that the community electric energy consumption is abnormal and sending early warning information to an energy management and control center;
and the intelligent management and control module 108, the intelligent management and control module 108 is used for adopting a corresponding energy management and control strategy according to the early warning information energy management and control center.
According to the embodiment, the topological structure of the gray neural network is used as an initial prediction model according to the gray level relevance among the factors influencing the electric energy, then the initial prediction model is trained and optimized by combining an ant colony optimization algorithm according to a training sample set extracted from a database, and an optimal electric energy prediction model is obtained, so that the stability of the electric energy prediction model is improved, data can be processed quickly, the output predicted value of the electric energy can be ensured to be more accurate, the supply and the detailed use condition of electric energy in each link of a community can be monitored in real time conveniently, visual and scientific basis is provided for energy conservation and consumption reduction, and intelligent management and control are performed according to a scientific energy efficiency evaluation method and an evaluation standard.
Example two
Referring to fig. 2 to 4 and fig. 6, a method for intelligently managing and controlling electric energy based on the internet of things according to a second embodiment of the present invention is shown, where the method for intelligently managing and controlling electric energy of the second embodiment includes the steps in the method for intelligently managing and controlling electric energy of the first embodiment, and further includes steps S9 and S10 compared with the method for intelligently managing and controlling electric energy of the first embodiment, as shown in fig. 6.
And S9, cleaning the data in the database according to the prediction error.
Specifically, when the prediction error e is not within the preset range, the current-day effective temperature data, the current-day type data and the real-time monitoring data set are uploaded to the database, and in addition, the accuracy of the predicted value output by the electric energy consumption prediction model is low, namely, dirty data stored in the database needs to be removed, so that the prediction accuracy of the electric energy consumption prediction model can be improved, the prediction time is shortened, and the stability of the electric energy consumption prediction model is enhanced.
In this embodiment, the idea of removing the dirty data in the database is to perform descriptive statistical analysis on data similar to the current day effective temperature data and the current day type data in the database, execute the ETL workflow to perform data cleaning according to a preset data mapping rule, and remove the dirty data stored in the database, where the data mapping rule indicates whether a difference between the daily electricity consumption data of the household stored in the database and the average daily electricity consumption data of the household collected by the current electric energy data collection device is within a threshold range under the condition of the same effective temperature and the same date type, and if the difference is not within the threshold range, it indicates that the data is error data and needs to be removed in time.
Specifically, when the prediction error e is within a preset range, the current day effective temperature data, the date type data and the real-time monitoring data set are uploaded to a database, historical data which are the same as the uploaded data in the database and have a time interval larger than a time threshold value are removed, and repeated data in the database are removed.
Because the electric energy data acquisition equipment monitors the electric equipment and acquires data every day, the electric energy data acquisition equipment faces mass data, and needs to clean repeated data in time, so that data redundancy is reduced, the stability of an electric energy consumption prediction model is further enhanced, and the prediction speed is increased.
And S10, continuously optimizing the electric energy consumption prediction model according to the cleaned database.
Specifically, because community resident's power consumption not only is relevant with effective temperature of the day and date type, still is closely relevant with personnel constitution and a plurality of factor indexes such as economic level of resident's family, therefore draw partial data sample set from the database after the washing and continuously optimize electric energy quantity prediction model for electric energy quantity prediction model's output value accords with present stage community power consumption demand more, thereby the intelligent management and control to community's electric energy of the energy management and control center realization of being convenient for.
Referring to fig. 7, this embodiment further provides a technical solution: the utility model provides an electric power energy intelligence management and control device based on thing networking, includes:
the effective temperature calculation module 101 is used for calculating the effective temperature data of the current day according to the meteorological data of the current day of the area position of the community;
the first acquisition module 102 is used for acquiring community resident data and date type data, wherein the date type data comprises holidays and workdays;
the predicted value calculating module 103 is used for obtaining a predicted value of the total electric energy consumption of the community on the current day through a pre-constructed electric energy consumption predicting model according to the effective temperature data of the current day, the community household data and the date type data, wherein the electric energy consumption predicting model is constructed according to a historical data set stored in a database;
the second acquisition module 104 is used for acquiring a real-time monitoring data set acquired by the electric energy data acquisition equipment, wherein the real-time monitoring data comprises household energy consumption monitoring data, corridor lighting energy consumption monitoring data and street lamp energy consumption monitoring data;
the actual value calculating module 105 is used for calculating the actual value of the total electric energy consumption of the community on the current day according to the real-time monitoring data set;
the prediction error calculation module 106 is used for calculating the prediction error of the electric energy consumption according to the actual value and the predicted value of the total electric energy consumption by the prediction error calculation module 106;
the judging module 107 is used for judging whether the prediction error is within a preset range, and if the prediction error is within the preset range, judging that the community electric energy consumption is normal; if the prediction error is not within the preset range, judging that the community electric energy consumption is abnormal and sending early warning information to an energy management and control center;
the intelligent management and control module 108, the intelligent management and control module 108 is used for adopting a corresponding energy management and control strategy by the energy management and control center according to the early warning information;
the database cleaning module 109, the database cleaning module 109 is used for cleaning the data in the database according to the prediction error e;
and the prediction model optimization module 110, the prediction model optimization module 110 is configured to continuously optimize the electric energy usage prediction model according to the cleaned database.
According to the embodiment, the collected data are processed and analyzed according to the error between the predicted value and the actual monitoring value of the electric energy consumption prediction model, the dirty data stored in the database are cleaned according to the analysis result, and the valuable data are reserved, so that the calculation amount of the electric energy consumption prediction model is reduced, the prediction speed is increased, the accuracy of the predicted value is further improved, and the high social value and the application prospect are achieved.
Referring to fig. 8, this embodiment further provides a technical solution: an electric power energy intelligence management and control system based on thing networking includes: the system comprises electric energy data acquisition equipment 100, computer equipment 200 and an energy management and control center 300;
the electric energy data acquisition equipment 100 is in data communication with the computer equipment 200, the electric energy data acquisition equipment 100 is used for acquiring electric energy consumption data of communities and transmitting the electric energy consumption data to the computer equipment 200, and the electric energy data acquisition equipment 100 comprises a plurality of internet of things sensors which are deployed on the site and used for acquiring electric energy consumption and a power supply module which is used for providing electric energy required by the internet of things sensors during working;
the computer device 200 and the energy management and control center 300 establish data transmission, and the computer device 200 includes at least one processor, a memory and a database, and is configured to receive the real-time monitoring data set acquired by the electric energy data acquisition device 100, upload the real-time monitoring data set to the database, and then call and run an executable instruction in the memory through the processor to calculate a total electric energy consumption predicted value of the community on the same day according to the received real-time monitoring data set;
the energy management and control center 300 is used for displaying community power consumption and energy consumption information and giving an alarm to remind management and control center workers.
The internet of things is applied in the intelligent electric power energy management and control system to realize large-range communication, real-time monitoring data collected by the internet of things sensor which is arranged on the internet of things sensing layer and used for collecting electric energy consumption are convenient to be transmitted to the internet from an industrial network through the transmission layer, and data are transmitted to the data monitoring center and compared with predicted values, so that intelligent management and control of electric power energy are realized, and the application range is enlarged.
The intelligent electric energy management and control system based on the internet of things is used for realizing the corresponding intelligent electric energy management and control method based on the internet of things in the multiple method embodiments, has the beneficial effects of the corresponding method embodiments, and is not repeated herein.
According to the method, the grey neural network and the ant colony optimization algorithm are adopted to construct the electric energy consumption prediction model with strong stability and high prediction precision, the cleaned database is used for continuously optimizing the electric energy consumption prediction model, and the monitoring and optimization of maintenance personnel of an energy management and control center are not needed, so that the optimization efficiency and the prediction precision are improved, the electric energy consumption is reduced and the electric energy utilization rate is improved on the premise that the requirement of community electric energy consumption is fully met and improved.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For each of the above embodiments, since they are basically similar to the method embodiments, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiments.
The foregoing embodiments have described the present invention in detail, and the principle and embodiments of the present invention are explained by applying specific examples herein, and the descriptions of the foregoing embodiments are only used to help understand the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. An intelligent electric energy management and control method based on the Internet of things is characterized by comprising the following steps:
s1, calculating the effective temperature data of the current day according to the meteorological data of the current day of the area position of the community;
s2, acquiring community resident data and date type data, wherein the date type data comprises festivals and holidays and workdays;
s3, obtaining a total electric energy consumption predicted value of the community on the current day through an electric energy consumption prediction model according to the effective temperature data of the current day, the community resident data and the date type data;
s4, acquiring a real-time monitoring data set acquired by the electric energy data acquisition equipment, wherein the real-time monitoring data comprises household energy consumption monitoring data, corridor lighting energy consumption monitoring data and street lamp energy consumption monitoring data;
s5, calculating the actual value of the total electric energy consumption of the community on the current day according to the real-time monitoring data set;
s6, calculating an electric energy consumption prediction error according to the electric energy total consumption actual value and the electric energy total consumption predicted value;
s7, judging whether the prediction error is within a preset range, and if the prediction error is within the preset range, judging that the community electric energy consumption is normal; if the prediction error is not within the preset range, judging that the community electric energy consumption is abnormal and sending early warning information to an energy management and control center;
and S8, adopting a corresponding energy management and control strategy by the energy management and control center according to the early warning information.
2. The intelligent electric power and energy management and control method according to claim 1, wherein between the steps S6 and S7, the method further comprises:
s9, cleaning the data in the database according to the prediction error;
and S10, continuously optimizing the electric energy consumption prediction model according to the cleaned database.
3. The intelligent electric power and energy management and control method according to claim 1, wherein the step S3 specifically includes:
s31, constructing an electric energy consumption prediction model according to a historical energy consumption sample set stored in a database;
and S32, obtaining a total electric energy consumption predicted value of the community on the current day by adopting the electric energy consumption prediction model according to the effective temperature data of the current day, the community household data and the date type data.
4. The intelligent electric power and energy management and control method according to claim 3, wherein the step S31 specifically comprises:
s311, acquiring a historical energy sample set which is processed by a principal component analysis algorithm and stored in a database;
s312, randomly dividing the historical energy utilization sample set into a training sample set and a testing sample set according to a preset proportion;
s313, determining a topological structure of a grey neural network according to the data characteristics of the training sample set to obtain an initial prediction model;
s314, training and optimizing the initial prediction model through a training sample set according to the mapping relation between the weight bias of the initial prediction model and the ant colony optimization algorithm to obtain an optimal electric energy consumption prediction model;
and S315, testing and verifying the electric energy consumption prediction model by adopting the test sample set.
5. The intelligent electric power and energy management and control method according to claim 2, wherein the step S9 specifically includes:
when the prediction error is not within a preset range, uploading the current day effective temperature data, the date type data and the real-time monitoring data set to a database and removing dirty data in the database;
and when the prediction error is within a preset range, uploading the current-day effective temperature data, the date type data and the real-time monitoring data set to a database, and removing repeated historical data with the collection time interval larger than a time threshold value in the database.
6. The intelligent electric energy management and control method according to claim 1, wherein the expression of the output value of the electric energy usage prediction model is as follows:
in the above formula, W f The method comprises the steps of predicting the total electric energy consumption of a community on the day, wherein m is the number of community electricity utilization units, n is the number of factor indexes influencing the total electric energy consumption of the community, and x j Is the jth electricity utilization unit, alpha ij Is the weight of the ith factor index of the jth power utilization unit, F τ Is a date type, and τ values of 1 and 0 represent weekday and holiday, respectively.
7. The utility model provides an electric power energy intelligence management and control device based on thing networking which characterized in that includes:
the effective temperature calculation module (101), the effective temperature calculation module (101) is used for calculating the effective temperature data of the current day according to the meteorological data of the current day of the regional location of the community;
the system comprises a first acquisition module (102), a second acquisition module (102) and a third acquisition module, wherein the first acquisition module (102) is used for acquiring community household data and date type data, and the date type data comprises holidays and workdays;
the predicted value calculating module (103) is used for obtaining a predicted value of the total electric energy consumption of the community on the current day through an electric energy consumption predicting model according to the effective temperature data of the current day, the community household data and the date type data;
the second acquisition module (104) is used for acquiring a real-time monitoring data set acquired by the electric energy data acquisition equipment, wherein the real-time monitoring data comprises household energy consumption monitoring data, corridor lighting energy consumption monitoring data and street lamp energy consumption monitoring data;
the actual value calculating module (105) is used for calculating the actual value of the total electric energy consumption of the community on the current day according to the real-time monitoring data set;
the prediction error calculation module (106) is used for calculating the prediction error of the electric energy consumption according to the actual value and the predicted value of the total electric energy consumption;
the judging module (107) is used for judging whether the prediction error is within a preset range, and if the prediction error is within the preset range, judging that the community electric energy consumption is normal; if the prediction error is not within the preset range, judging that the community electric energy consumption is abnormal and sending early warning information to an energy management and control center;
and the intelligent management and control module (108) is used for adopting a corresponding energy management and control strategy by the energy management and control center according to the early warning information.
8. The intelligent electric power and energy management and control device according to claim 7, further comprising:
a database cleaning module (109), wherein the database cleaning module (109) is used for cleaning data in the database according to the prediction error e;
a prediction model optimization module (110), wherein the prediction model optimization module (110) is used for continuously optimizing the electric energy usage prediction model according to the cleaned database.
9. A system applying the intelligent Internet of things-based electric energy intelligent management and control method as claimed in any one of claims 1 to 6, characterized by comprising the following steps: the system comprises electric energy data acquisition equipment (100), computer equipment (200) and an energy management and control center (300);
the electric energy data acquisition equipment (100) is in data communication with the computer equipment (200), and the electric energy data acquisition equipment (100) is used for acquiring electric energy consumption data of a community and transmitting the electric energy consumption data to the computer equipment (200);
the computer equipment (200) and the energy management and control center (300) establish data transmission, the computer equipment (200) comprises at least one processor, a memory and a database, and the computer equipment is used for receiving the real-time monitoring data set acquired by the electric energy data acquisition equipment (100), uploading the real-time monitoring data set to the database, and then calling and operating an executable instruction in the memory through the processor to calculate the total electric energy consumption predicted value of the community on the same day according to the received real-time monitoring data set;
the energy management and control center (300) is used for displaying community power consumption and energy consumption information and giving an alarm to remind management and control center workers.
10. The intelligent management and control method for electric power energy according to claim 9, wherein the electric energy data acquisition device (100) comprises a plurality of internet of things sensors deployed on site for acquiring electric energy usage and a power supply module for providing electric energy required by the internet of things sensors during operation.
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| CN117369813A (en) * | 2023-10-13 | 2024-01-09 | 湖北华中电力科技开发有限责任公司 | Visual display method of energy consumption monitoring index system based on data center |
| CN117318051A (en) * | 2023-11-28 | 2023-12-29 | 江苏清智云能综合能源服务有限公司 | Intelligent energy storage management and control system and method based on Internet of things technology |
| CN117318051B (en) * | 2023-11-28 | 2024-02-06 | 江苏清智云能综合能源服务有限公司 | Intelligent energy storage management and control system and method based on Internet of things technology |
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