CN117313952A - Load prediction method, device, equipment and storage medium - Google Patents
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Abstract
The application relates to a load prediction method, a load prediction device, a load prediction apparatus and a storage medium. The method comprises the following steps: acquiring target behavior data corresponding to each target time period of each family member in a target time interval, wherein the target behavior data corresponds to the household condition of each family member in the target time period; and obtaining load prediction data corresponding to the target time period according to the target time period, the target behavior data and a preset load prediction model. By adopting the method, the accuracy of the load prediction data corresponding to the target time period can be improved, so that the accuracy of the load prediction method is improved.
Description
Technical Field
The present disclosure relates to the field of power technologies, and in particular, to a load prediction method, apparatus, device, and storage medium.
Background
In the peak period of residential power consumption, the power system can appear the condition that the load is overweight for better coping power system's load pressure in the peak period of power consumption for power system can stable operation, need to predict home load data.
In the conventional technology, a target control terminal of each household in a power system determines electric equipment used in the household according to power consumption data acquired by an ammeter connected with the target control terminal, and predicts target load data of a target time period according to the use condition of the electric equipment corresponding to each historical time period in the household.
However, the conventional load prediction method is low in accuracy.
Disclosure of Invention
Based on this, it is necessary to provide a load prediction method, apparatus, device, and storage medium with high accuracy in view of the above-described technical problems.
In a first aspect, the present application provides a load prediction method. The method comprises the following steps:
acquiring target behavior data corresponding to each target time period of each family member in a target time interval, wherein the target behavior data corresponds to the household condition of each family member in the target time period;
obtaining load prediction data corresponding to a target time period according to the target time period, the target behavior data and a preset load prediction model;
in one embodiment, the method further comprises:
acquiring the integral data of the use of the electric appliance corresponding to each historical time period in the historical time interval and the historical behavior data of each family member in each historical time period; the historical behavior data corresponds to the household condition of each family member in the historical time period;
and obtaining a load prediction model based on the electric appliance usage overall data corresponding to each historical time period and the historical behavior data of each family member in each historical time period.
In one embodiment, obtaining the load prediction model based on the appliance usage overall data corresponding to each historical time period and the historical behavior data of each family member in each historical time period includes:
For each historical time period, integrating the whole data of the electric appliance use corresponding to the historical time period with the historical behavior data of each family member to obtain the member data of the electric appliance use corresponding to the historical time period;
and obtaining a load prediction model according to the appliance use member data corresponding to each historical time period.
In one embodiment, integrating the whole data of appliance usage corresponding to the historical time period with the historical behavior data of each family member to obtain the data of the appliance usage member corresponding to the historical time period includes:
according to the historical behavior data, obtaining historical behavior member data of each family member in a historical time period;
aiming at each family member, according to the historical behavior member data and the electric appliance use integral data corresponding to the family member, obtaining first electric appliance use member data of each family member in a historical time period,
aiming at the member combinations of all the family members, obtaining second electric appliance use member data of each member combination in a historical time period according to historical behavior member data and electric appliance use integral data corresponding to the family members, wherein the member combinations correspond to a plurality of family members;
and obtaining the electric appliance use member data corresponding to the historical time period according to the first electric appliance use member data of each family member in the historical time period and the second electric appliance use member data of each member in the historical time period.
In one embodiment, obtaining load prediction data corresponding to a target time period according to the target time period, the target behavior data and a preset load prediction model includes:
determining a reference time period corresponding to the target time period from each of the historical time periods within the historical time interval;
if the target behavior data corresponds to each member combination corresponding to the reference time period, determining load prediction data corresponding to the target time period by using the member data according to the second electric appliance in each reference time period;
and if the target behavior data corresponds to each family member corresponding to the reference time period, determining load prediction data corresponding to the target time period by using the member data according to the first electric appliance in each reference time period.
In one embodiment, obtaining the load prediction model based on the appliance usage overall data corresponding to each historical time period and the historical behavior data of each family member in each historical time period includes:
aiming at each historical time period, according to the integral data of the use of the electric appliance corresponding to the historical time period and the historical behavior data of each family member, obtaining the use combination data of the electric appliance corresponding to each family member in the historical time period;
And carrying out iterative learning on the initial prediction model by using the combined data by using the electric appliance to obtain a load prediction model.
In one embodiment, acquiring the overall data of appliance usage corresponding to each historical time period in the historical time interval includes:
for each historical time period, acquiring ammeter reading data corresponding to the historical time period;
and carrying out recognition analysis processing on waveform characteristics of the ammeter reading data to obtain integral data of the electric appliance.
In a second aspect, the present application also provides a load prediction apparatus. The device comprises:
the data acquisition module is used for acquiring target behavior data of each family member in a target time period, wherein the target behavior data corresponds to the household condition of each family member in the target time period;
the load prediction module is used for obtaining load prediction data corresponding to the target time period according to the target time period, the target behavior data and a preset load prediction model.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the steps of the method according to the first aspect when the processor executes the computer program.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method according to the first aspect.
According to the load prediction method, the load prediction device, the load prediction equipment and the load prediction storage medium, the target behavior data corresponding to each target time period of each family member in the target time period are obtained, and the target behavior data correspond to the household condition of each family member in the target time period; obtaining load prediction data corresponding to a target time period according to the target time period, the target behavior data and a preset load prediction model; thus, based on the behavior data of each family member, the load prediction data of the target time period is obtained by using the load prediction model, and the problem of low prediction accuracy caused by the fact that the load prediction data of the target time period is only obtained according to the use condition of the electrical equipment corresponding to each historical time period in the traditional technology is avoided; according to the method and the device, the influence of the behavior data of each family member on the use condition of the electrical equipment is considered, and the accuracy of the load prediction data corresponding to the target time period can be improved, so that the accuracy of the load prediction method is improved.
Drawings
FIG. 1 is a diagram of an application environment for a load prediction method in one embodiment;
FIG. 2 is a flow diagram of a load prediction method in one embodiment;
FIG. 3 is a flow chart illustrating steps for obtaining a load prediction model in one embodiment;
FIG. 4 is a flow chart illustrating steps for obtaining a load prediction model in one embodiment;
FIG. 5 is a flowchart illustrating steps for obtaining appliance usage membership data in one embodiment;
FIG. 6 is a flowchart illustrating steps for obtaining load prediction data in one embodiment;
FIG. 7 is a flow chart illustrating steps for obtaining a load prediction model in one embodiment;
FIG. 8 is a flowchart illustrating steps for obtaining overall appliance usage data in one embodiment;
FIG. 9 is a flowchart of a load prediction method according to another embodiment;
FIG. 10 is a flowchart of a load prediction method according to another embodiment;
FIG. 11 is a block diagram showing a structure of a load predicting apparatus in one embodiment;
fig. 12 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The load prediction method provided by the embodiment of the application can be applied to an application environment shown in fig. 1. The target control terminal 102 is connected with the intelligent ammeter 104, the intelligent ammeter 104 is connected with each electrical equipment in the household, and the overall data of the electrical equipment is formed and sent to the target control terminal 102; the target control terminal 102 is connected with the input device 106 and receives target behavior data sent by the input device 106; the target control terminal 102 is connected with the acquisition device 108 and receives historical behavior data sent by the acquisition device 108; the target control terminal 102 is also connected to the cloud end through a network. The target control terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, which may be smart watches, smart bracelets, headsets, etc.; the input device 106 may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices; the acquisition device 108 may be, but is not limited to, various smart door locks, fingerprint collectors.
In one embodiment, as shown in fig. 2, a load prediction method is provided, which is described by taking an example that the method is applied to the target control terminal 102 in fig. 1, and includes:
Step 202, obtaining target behavior data corresponding to each target time period of each family member in the target time period.
The target time interval refers to a time interval to be predicted by using the load prediction method in the embodiment, and the target time period refers to a time period of a plurality of uniform durations divided into by the target time interval.
The target behavior data corresponds to the household condition of each family member in the target time period.
Here, a home is understood to be a house that is occupied in units of a home, one home including at least one family member.
Illustratively, the target time interval is from 0 to 24 hours a day, and each target time period is each hour in the target time interval.
For example, the target behavior data of each target time period may be determined by acquiring, by the entry device 106, the departure time and the return time of each family member in the target time period. The information input by the input device 106 by the family member a includes that the family is separated by 20 at 8 times and returned by 20 at 10 times, so that it can be determined that the target behavior data of the family member a at 8 to 9 times is in a home state, the target behavior data at 9 to 10 times is in a leave state, and the target behavior data at 10 to 11 times is in a home state.
And 204, obtaining load prediction data corresponding to the target time period according to the target time period, the target behavior data and a preset load prediction model.
The load prediction method comprises the steps of inputting a target time period and target behavior data into a preset load prediction model, predicting according to the target behavior data based on the load prediction model, and outputting load prediction data corresponding to the target time period.
In the load prediction method, target behavior data corresponding to each target time period of each family member in the target time period are obtained, and the target behavior data correspond to the household condition of each family member in each target time period; obtaining load prediction data corresponding to a target time period according to the target time period, the target behavior data and a preset load prediction model; thus, based on the target behavior data of each family member, the load prediction data of the target time period is obtained by using the load prediction model, and the problem of low prediction accuracy caused by the fact that the load prediction data of the target time period is only obtained according to the use condition of the electrical equipment corresponding to each historical time period in the traditional technology is avoided; in the application, the behavior data of each family member can reflect the household condition of each family member in the target time period, the household condition of each family member can influence the use condition of the electrical equipment, the influence is incorporated into the prediction process of the load prediction data, and the accuracy of the load prediction data corresponding to the target time period can be improved, so that the accuracy of the load prediction method is improved.
In one embodiment, based on the embodiment shown in fig. 2, as shown in fig. 3, the method further comprises:
step 302, obtaining the whole data of the appliance usage corresponding to each history time period in the history time interval and the history behavior data of each family member in each history time period.
Wherein the appliance usage overall data is used to indicate each appliance device used by the home for each historical time period within the historical time period.
Illustratively, the historical time period T j,k A period k representing the j-th day, wherein k has a value ranging from 1 to 24, T 1,1 Time period 1 of the first day corresponds to 0 to 1 time of the first day. The appliance usage overall data corresponding to each history period within the history period may be represented as shown in table 1.
Table 1 overall data of appliance usage corresponding to each historical time period
Wherein the historical behavior data corresponds to household conditions of each household member in a historical time period.
For example, the collection device 108 may obtain the time when each family member returns to the family and the time when each family member leaves the family in each history period, and determine the historical behavior data of each family member in each history period according to the time when each family member returns to the family and the time when each family member leaves the family.
Illustratively, the family includes Zhang Mou, li Mou and some 3 family members, and the historical behavior data of each family member over each historical period of time can be represented as shown in Table 2.
Table 2 historical behavior data of family members over historical time periods
The historical behavior data corresponding to the period 1 of the first day is Zhang Mou, li Mou or some other one, which means that the family members Zhang Mou, li Mou or some other one are in the home state in the period.
And step 304, obtaining a load prediction model based on the electric appliance use integral data corresponding to each historical time period and the historical behavior data of each family member in each historical time period.
The load prediction model is used for predicting the load data corresponding to the target time period based on the electric appliance usage overall data corresponding to each historical time period and the historical behavior data of each family member in each historical time period, and the load prediction model is used for obtaining the load prediction data corresponding to the target time period.
The load prediction model may be a statistical model or a machine learning model, which is not limited in this application.
By way of example, the load prediction model may be a statistical model, such as a linear regression, logistic regression, time series analysis model, etc.; machine learning models such as decision trees, random forests, support vector machines, neural networks, etc. are also possible.
In the embodiment, based on the whole data of the electric appliance usage corresponding to each historical time period in the historical time period and the historical behavior data of each family member in each historical time period, a load prediction model is obtained, the load condition of the target time period is predicted based on the whole data of the electric appliance usage and the historical behavior data, and the influence of the behavior data of each family member on the use condition of the electric appliance is considered, so that the accuracy of the load prediction data corresponding to the target time period is improved, and the accuracy of the load prediction method is improved.
In one embodiment, based on the embodiment shown in fig. 3, as shown in fig. 4, based on the appliance usage overall data corresponding to each history period and the history behavior data of each family member in each history period, a load prediction model is obtained, including:
step 402, for each historical time period, integrating the whole data of the appliance usage corresponding to the historical time period with the historical behavior data of each family member to obtain the data of the appliance usage member corresponding to the historical time period.
The electric appliance use member data refers to the electric appliance use condition of each family member in each history period, wherein the electric appliance use member data refers to the integrated processing of the electric appliance use integral data corresponding to the history period and the history behavior data of each family member.
For example, according to the overall data of appliance usage corresponding to each historical time period shown in table 1 and the household situation corresponding to the historical behavior data of each family member in each historical time period shown in table 2, the appliance usage data corresponding to each family member in each historical time period can be obtained, that is, the appliance usage situation of Zhang Mou, li Mou and some person in each historical time period can be obtained.
And step 404, obtaining a load prediction model according to the appliance use member data corresponding to each historical time period.
The load prediction model is used for predicting the load data corresponding to the target time period by using the load prediction model based on the appliance use member data corresponding to each historical time period, so as to obtain the load prediction data corresponding to the target time period.
In this embodiment, a load prediction model is obtained based on the appliance usage member data corresponding to each historical time period, the load condition of the target time period is predicted based on the appliance usage condition corresponding to each historical time period of each family member, the condition of each family member using the appliance in the historical time period is fully utilized to predict, and the accuracy of the load prediction data corresponding to the target time period can be effectively improved.
In one embodiment, based on the embodiment shown in fig. 4, as shown in fig. 5, the integrating processing is performed on the integral data of appliance usage corresponding to the historical time period and the historical behavior data of each family member, so as to obtain the data of the appliance usage member corresponding to the historical time period, including:
step 502, according to the historical behavior data, obtaining historical behavior member data of each family member in a historical time period.
The historical behavior data corresponds to the household condition of each family member in the historical time period, and the historical member data corresponds to the household condition of one family member in the historical time period.
Illustratively, according to the historical behavior data of each family member in each historical period shown in table 2, one of the family members is provided with the historical behavior member data in each historical period as shown in table 3.
TABLE 3 historical behavior Member data for a certain time period of histories
| Period 1 | Period 2 | Period 3 | … | Period 24 | |
| First day | Stretch somewhere | Stretch somewhere | Stretch somewhere | … | - |
| Day of the year | Stretch somewhere | Stretch somewhere | Stretch somewhere | … | - |
| … | … | … | … | … | … |
| Seventh day | - | - | - | … | - |
Step 504, for each family member, obtaining first electric appliance use member data of each family member in a historical time period according to historical behavior member data and electric appliance use overall data corresponding to the family member.
The first appliance use member data of each family member in the historical time period is obtained by integrating the historical behavior member data of the family member with the integral appliance use data.
For example, the household condition corresponding to the member data of the history behavior in each history period shown in table 3 and the integral data of the appliance usage corresponding to each history period shown in table 1 are integrated, so that the first member data of the appliance usage in each history period can be obtained, and can be represented as shown in table 4.
TABLE 4 first appliance usage Member data for a certain time period of histories
| Period 1 | Period 2 | Period 3 | … | Period 24 | |
| First day | Electric appliance 1, 6 | Electrical appliance 1, electrical applianceDevice 2 | Electric appliance 1, electric appliance 2 and electric appliance 3 | … | - |
| The next day | Electric appliance 1, 7 | Electrical appliance 1, electrical appliance 2 | Electric appliance 1, electric appliance 2 and electric appliance 3 | … | - |
| … | … | … | … | … | … |
| Seventh day | - | - | - | … | - |
Illustratively, the first appliance usage member data also includes Zhang Mou first appliance usage member data over each historical period, as shown in table 5, and Li Mou first appliance usage member data over each historical period, as shown in table 6.
TABLE 5 first appliance usage Member data for each historic time period
Table 6 Li Mou first appliance usage Member data over various historic time periods
Step 506, for the member combination of each family member, obtaining second electric appliance use member data of each member combination in the history time period according to the history behavior member data and the electric appliance use integral data corresponding to the family member.
Wherein the member combinations correspond to a plurality of family members.
Illustratively, for a family that includes Zhang Mou, li Mou and 3 family members, the member combinations include Zhang Mou and Li Mou, zhang Mou and Zhang, li Mou and Zhang, zhang Mou and Li Mou and Zhang.
For example, for the 4 member combinations described above, the second appliance usage member data for each member combination over each history period may be represented as shown in tables 7, 8, 9 and 10, respectively.
Tables 7 Zhang Mou and Li Mou second appliance usage Member data over historic time periods
Table 8 Zhang Mou and second appliance usage member data for a certain time period of each history
| Period 1 | Period 2 | Period 3 | … | Period 24 | |
| First day | Electric appliance 1, 6 | Electrical appliance 1, electrical appliance 2 | Electric appliance 1, electric appliance 2 and electric appliance 3 | … | - |
| The next day | Electric appliance 1, 7 | Electrical appliance 1, electrical appliance 2 | Electric appliance 1, electric appliance 2 and electric appliance 3 | … | - |
| … | … | … | … | … | … |
| Seventh day | - | - | - | … | - |
Table 9 Li Mou and the second appliance usage member data for a certain time period in each history period
| Period 1 | Period 2 | Period 3 | … | Period 24 | |
| First day | Electric appliance 1, 6 | Electrical appliance 1, electrical appliance 2 | Electric appliance 1, electric appliance 2 and electric appliance 3 | … | - |
| The next day | Electric appliance 1, 7 | Electrical appliance 1, electrical appliance 2 | Electric appliance 1, electric appliance 2 and electric appliance 3 | … | - |
| … | … | … | … | … | … |
| Seventh day | - | - | - | … | - |
Table 10, sum Li Mou and Sum second appliance usage Member data for historic time periods
| Period 1 | Period 2 | Period 3 | … | Period 24 | |
| First day | Electric appliance 1, 6 | Electrical appliance 1, electrical appliance 2 | Electric appliance 1, electric appliance 2 and electric appliance 3 | … | - |
| The next day | Electric appliance 1, 7 | Electrical appliance 1, electrical appliance 2 | Electric appliance 1, electric appliance 2 and electric appliance 3 | … | - |
| … | … | … | … | … | … |
| Seventh day | - | - | - | … | - |
And step 508, obtaining the electric appliance use member data corresponding to the historical time period according to the first electric appliance use member data of each family member in the historical time period and the second electric appliance use member data of each member in the historical time period.
In this embodiment, through the whole electrical appliance usage data corresponding to the historical time period and the historical behavior data of each family member, the first electrical appliance usage member data and the second electrical appliance usage member data can be simply, conveniently and rapidly obtained, so that the electrical appliance usage member data can be obtained, the load data corresponding to the target time period can be rapidly predicted according to the load prediction model obtained from the electrical appliance usage member data, and the efficiency of the load prediction method is improved.
In one embodiment, based on the embodiment shown in fig. 5, as shown in fig. 6, according to the target time period, the target behavior data and the preset load prediction model, load prediction data corresponding to the target time period is obtained, including:
step 602, determining a reference time period corresponding to the target time period from each historical time period in the historical time interval.
Exemplary, target time period G a,i Time period i representing day a, the reference time period corresponding to the target time period is T j,i I.e. the period i of each day within the historical time interval.
In step 604, if the target behavior data corresponds to each member combination corresponding to the reference time period, the load prediction data corresponding to the target time period is determined according to the second appliance usage member data in each reference time period.
Illustratively, according to the target time period G of each family member a,i Target behavior data of (1), target period of time G a,i Target behavior data of Zhang Mou and Li Mouchu in the home state, corresponding to the member combinations shown in Table 7, according to the reference period T shown in the ith column in Table 7 j,i The corresponding second appliance uses the member data to determine load prediction data corresponding to the target time period.
In step 606, if the target behavior data corresponds to each member combination corresponding to the reference time period, the load prediction data corresponding to the target time period is determined according to the second appliance usage member data in each reference time period.
Illustratively, according to the target time period G of each family member a,b Target behavior data of (1), target period of time G a,b Target behavior data of (1) is that something is in a home state, corresponding to the combination of members shown in Table 4, according to the reference period T shown in column b of Table 4 j,b The corresponding first appliance uses the member data to determine load prediction data corresponding to the target time period.
In one possible implementation manner, there are a plurality of first appliance usage member data corresponding to the target behavior data, average processing may be performed on the plurality of first appliance usage member data, and load prediction data corresponding to the target time period may be determined according to the average value of the plurality of first appliance usage member data.
In this embodiment, the reference time period is determined according to the target time period, and the electrical appliance usage member data of each family member in the reference time period is rapidly determined according to the target behavior data, so as to obtain the load prediction data corresponding to the target time period, thereby improving the efficiency of the load prediction method and the accuracy of the load prediction method.
In one embodiment, based on the embodiment shown in fig. 3, as shown in fig. 7, based on the appliance usage overall data corresponding to each history period and the history behavior data of each family member in each history period, a load prediction model is obtained, including:
Step 702, for each historical time period, obtaining the electric appliance use combination data corresponding to each family member in the historical time period according to the electric appliance use integral data corresponding to the historical time period and the historical behavior data of each family member.
The appliance use combination data refers to combination data formed by combining the appliance use integral data corresponding to each historical time period and the historical behavior data of each family member.
For example, the appliance usage combination data of the historical behavior data corresponding to each historical period may be represented as shown in table 11.
Table 11 appliance usage combination data for each historical time period
And step 704, performing iterative learning on the initial prediction model by using the combined data by using the electric appliance to obtain a load prediction model.
And inputting the electric appliance use combination data into an initial prediction model, wherein the electric appliance use combination data comprises the historical behaviors of family members in each historical time period and electric appliance use data, and updating parameters in the initial model through iterative learning to obtain a load prediction model.
The initial prediction model may be a machine learning model, such as a decision tree, a random forest, a support vector machine, a neural network, and the like, which is not limited in this application.
In this embodiment, an initial prediction model is trained according to appliance usage combination data obtained by combining appliance usage overall data corresponding to a historical time period and historical behavior data of each family member, and behavior data of each family member and appliance usage conditions corresponding to a historical time period are considered when load data corresponding to a target time period is predicted, so that accuracy of load prediction data corresponding to the target time period is improved, and accuracy of a load prediction method is improved.
In one embodiment, as shown in fig. 8, acquiring the overall data of appliance usage corresponding to each history period in the history period includes:
step 802, for each historical time period, acquiring ammeter reading data corresponding to the historical time period.
Step 804, the waveform characteristics of the ammeter reading data are identified, analyzed and processed to obtain the integral data of the electric appliance.
The electric appliance usage overall data corresponding to each historical time period are obtained by identifying and analyzing ammeter reading data corresponding to each historical time period. The switching states and the generated load data of different electrical equipment are identified by monitoring waveform characteristics and change conditions of current and voltage passing through the ammeter in each historical time period in real time, and each electrical equipment corresponding to ammeter reading data is obtained.
In this embodiment, the waveform characteristics of the meter reading data are identified, analyzed and processed to obtain the overall data of the use of the electrical appliance, so that the use condition of the electrical appliance corresponding to each historical time period can be identified quickly and accurately without substantial modification or intervention on the electrical appliance.
In one embodiment, as shown in fig. 9, there is provided a load prediction method, the method comprising:
step 902, obtaining the whole data of the use of the electric appliance corresponding to each history time period in the history time interval and the history behavior data of each family member in each history time period.
Optionally, for each historical time period, acquiring ammeter reading data corresponding to the historical time period, and performing recognition analysis processing on waveform characteristics of the ammeter reading data to obtain integral data of the electrical appliance.
Step 904, for each historical time period, obtaining historical behavior member data of each family member in the historical time period according to the historical behavior data.
Step 906, for each family member, obtaining first electric appliance use member data of each family member in a historical time period according to historical behavior member data and electric appliance use overall data corresponding to the family member.
Step 908, for each member combination of family members, obtaining second electric appliance use member data of each member combination in a history time period according to the history behavior member data and the electric appliance use overall data corresponding to the family members.
Step 910, obtaining the appliance use member data corresponding to the historical time period according to the first appliance use member data of each family member in the historical time period and the second appliance use member data of each member combination in the historical time period.
Step 912, obtaining a load prediction model according to the appliance usage member data corresponding to each historical time period.
Step 914, obtaining target behavior data corresponding to each target time period of each family member in the target time interval.
Step 916, determining a reference time period corresponding to the target time period from each of the historical time periods within the historical time interval.
Step 918, if the target behavior data corresponds to each member combination corresponding to the reference time period, determining load prediction data corresponding to the target time period according to the second appliance usage member data in each reference time period.
In step 920, if the target behavior data corresponds to each family member corresponding to the reference time period, the load prediction data corresponding to the target time period is determined according to the first appliance usage member data in each reference time period.
In one embodiment, as shown in fig. 10, there is provided a load prediction method including:
step 1002, obtaining integral data of appliance usage corresponding to each history period in the history period, and historical behavior data of each family member in each history period.
Wherein the historical behavior data corresponds to household conditions of each household member in a historical time period.
Optionally, for each historical time period, acquiring ammeter reading data corresponding to the historical time period; and carrying out recognition analysis processing on waveform characteristics of the ammeter reading data to obtain integral data of the electric appliance.
Step 1004, for each historical time period, obtaining the electric appliance use combination data corresponding to each family member in the historical time period according to the electric appliance use integral data corresponding to the historical time period and the historical behavior data of each family member.
And step 1006, performing iterative learning on the initial prediction model by using the combined data by using the electric appliance to obtain a load prediction model.
Step 1008, obtaining target behavior data corresponding to each target time period of each family member in the target time interval.
The target behavior data corresponds to the household condition of each family member in the target time period.
And step 1010, obtaining load prediction data corresponding to the target time period according to the target time period, the target behavior data and a preset load prediction model.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a load prediction device for realizing the load prediction method. The implementation of the solution provided by the apparatus is similar to the implementation described in the above method, so the specific limitation in the embodiments of the load prediction apparatus provided below may be referred to the limitation of the load prediction method hereinabove, and will not be repeated here.
In one embodiment, as shown in fig. 11, there is provided a load predicting apparatus including: a data input module 1102 and a load prediction module 1104, wherein:
the data input module 1102 is configured to obtain target behavior data corresponding to each target time period of each family member in a target time period, where the target behavior data corresponds to a household condition of each family member in the target time period;
the load prediction module 1104 is configured to obtain load prediction data corresponding to the target time period according to the target time period, the target behavior data, and a preset load prediction model.
In one embodiment, the data input module 1102 is further configured to obtain overall data of appliance usage corresponding to each historical time period in the historical time interval, and historical behavior data of each family member in each historical time period; the historical behavior data corresponds to the household condition of each family member in the historical time period; the load prediction device further comprises a model generation module, wherein the model generation module is used for obtaining a load prediction model based on the whole data of the electric appliance usage corresponding to each historical time period and the historical behavior data of each family member in each historical time period.
In one embodiment, the model generating module is configured to integrate, for each historical time period, integral electrical appliance usage data corresponding to the historical time period with historical behavior data of each family member, to obtain electrical appliance usage member data corresponding to the historical time period; and obtaining a load prediction model according to the appliance use member data corresponding to each historical time period.
In one embodiment, the model generating module is configured to obtain historical behavior member data of each family member in a historical time period according to the historical behavior data; for each family member, according to historical behavior member data and electric appliance use integral data corresponding to the family member, obtaining first electric appliance use member data of each family member in a historical time period, and for member combinations of each family member, according to historical behavior member data and electric appliance use integral data corresponding to the family member, obtaining second electric appliance use member data of each member combination in the historical time period, wherein the member combinations correspond to a plurality of family members; and obtaining the electric appliance use member data corresponding to the historical time period according to the first electric appliance use member data of each family member in the historical time period and the second electric appliance use member data of each member in the historical time period.
In one embodiment, the load prediction module 1104 is configured to determine a reference time period corresponding to the target time period from each of the historical time periods within the historical time period; if the target behavior data corresponds to each member combination corresponding to the reference time period, determining load prediction data corresponding to the target time period by using the member data according to the second electric appliance in each reference time period; and if the target behavior data corresponds to each family member corresponding to the reference time period, determining load prediction data corresponding to the target time period by using the member data according to the first electric appliance in each reference time period.
In one embodiment, the model generating module obtains, for each historical time period, electric appliance usage combination data corresponding to each family member in the historical time period according to the electric appliance usage overall data corresponding to the historical time period and historical behavior data of each family member; and carrying out iterative learning on the initial prediction model by using the combined data by using the electric appliance to obtain a load prediction model.
In one embodiment, the data input module 1102 is configured to obtain meter reading data corresponding to each historical time period in the historical time period; and carrying out recognition analysis processing on waveform characteristics of the ammeter reading data to obtain integral data of the electric appliance.
The respective modules in the load prediction apparatus described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure thereof may be as shown in fig. 12. The computer device includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input means. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a load prediction method. The display unit of the computer device is used for forming a visual picture, and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be a key, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 12 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, implements the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data are required to comply with the related laws and regulations and standards of the related countries and regions.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. A method of load prediction, the method comprising:
acquiring target behavior data corresponding to each target time period of each family member in a target time interval, wherein the target behavior data corresponds to the household condition of each family member in the target time period;
and obtaining load prediction data corresponding to the target time period according to the target time period, the target behavior data and a preset load prediction model.
2. The method according to claim 1, wherein the method further comprises:
acquiring the whole data of the use of the electric appliance corresponding to each historical time period in the historical time interval and the historical behavior data of each family member in each historical time period; the historical behavior data corresponds to the household condition of each family member in the historical time period;
and obtaining the load prediction model based on the whole data of the electric appliance usage corresponding to each historical time period and the historical behavior data of each family member in each historical time period.
3. The method according to claim 2, wherein the obtaining the load prediction model based on the appliance usage overall data corresponding to each historical time period and the historical behavior data of each family member in each historical time period includes:
for each historical time period, integrating the whole data of the appliance use corresponding to the historical time period with the historical behavior data of each family member to obtain the data of the appliance use member corresponding to the historical time period;
and obtaining the load prediction model according to the electric appliance use member data corresponding to each historical time period.
4. The method of claim 3, wherein the integrating the appliance usage overall data corresponding to the historical time period with the historical behavior data of each family member to obtain appliance usage member data corresponding to the historical time period includes:
according to the historical behavior data, obtaining historical behavior member data of each family member in the historical time period;
for each family member, obtaining first electric appliance using member data of each family member in the historical time period according to historical behavior member data corresponding to the family member and the electric appliance using integral data,
aiming at the member combinations of all the family members, obtaining second electric appliance use member data of all the member combinations in the history time period according to the history behavior member data corresponding to the family members and the electric appliance use overall data, wherein the member combinations correspond to a plurality of family members;
and obtaining the electric appliance use member data corresponding to the historical time period according to the first electric appliance use member data of each family member in the historical time period and the second electric appliance use member data of each member combination in the historical time period.
5. The method according to claim 4, wherein the obtaining load prediction data corresponding to the target time period according to the target time period, the target behavior data and a preset load prediction model includes:
determining a reference time period corresponding to the target time period from each of the historical time periods within the historical time interval;
if the target behavior data corresponds to each member combination corresponding to the reference time period, determining load prediction data corresponding to the target time period by using member data according to the second electric appliance in each reference time period;
and if the target behavior data corresponds to each family member corresponding to the reference time period, determining load prediction data corresponding to the target time period according to the first electric appliance using member data in each reference time period.
6. The method according to claim 2, wherein the obtaining the load prediction model based on the appliance usage overall data corresponding to each historical time period and the historical behavior data of each family member in each historical time period includes:
For each historical time period, obtaining electric appliance use combination data corresponding to each family member in the historical time period according to the electric appliance use integral data corresponding to the historical time period and the historical behavior data of each family member;
and performing iterative learning on the initial prediction model by using the combined data by using the electric appliance to obtain the load prediction model.
7. The method according to claim 2, wherein the acquiring the appliance usage overall data corresponding to each history period in the history period includes:
acquiring ammeter reading data corresponding to the historical time periods for each historical time period;
and carrying out recognition analysis processing on the waveform characteristics of the ammeter reading data to obtain the integral data of the electric appliance.
8. A load predicting apparatus, the apparatus comprising:
the data acquisition module is used for acquiring target behavior data of each family member in the target time period, wherein the target behavior data corresponds to the household condition of each family member in the target time period;
and the load prediction module is used for obtaining load prediction data corresponding to the target time period according to the target time period, the target behavior data and a preset load prediction model.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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