CN112884307A - Power consumption data-based standing population prediction model construction method - Google Patents
Power consumption data-based standing population prediction model construction method Download PDFInfo
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Abstract
The invention discloses a standing population prediction model construction method based on electricity consumption data, and belongs to the technical field of mathematical modeling. The method realizes the construction of the standing population prediction model by confirming the four steps of electric power behavior characteristics, data preprocessing, data training and model evaluation. According to the method, the electricity stealing samples are constructed, the prediction model is built on the voltage data set, and the prediction model is combined with the users with abnormal line loss, so that the recall rate and the accuracy of the lead-in line are improved, and a more accurate prediction effect is obtained. According to the method, the standing population is predicted, the accuracy rate of the incoming line loss rate is 52.69-74.11%, the recall rate of the incoming line loss rate is 71.37-99.49%, the metric value is 70.45-73.48%, and the prediction accuracy is high.
Description
Technical Field
The invention belongs to the technical field of mathematical modeling, and particularly relates to a standing population prediction model construction method based on electricity consumption data.
Background
The population prediction model is a comprehensive system and a quantitative method which are established on the basis of the current population condition and on the condition of prediction parameters and used for measuring and calculating the future population. Population prediction methods can be divided into dynamic and static predictions. Population prediction models fall into the category of dynamic population prediction. The basic idea of establishing a population prediction model is as follows: the method comprises the steps of grasping main factors among a plurality of factors influencing population development, establishing a dynamic mathematical model describing population development process, enabling the dynamic mathematical model to accurately reflect the dynamic change process of future population development, and reasonably defining and describing various variables, indexes and mutual dependency relations among the variables and the indexes in the population development process.
The invention discloses a user electricity consumption deviation analysis method based on an intelligent algorithm, which is found through the search of the existing documents, and is disclosed in the patent application of Chinese patent publication No. CN109460849A, publication No. 2019, 03, 12 and mainly comprises the steps of calculating a Pearson coefficient of each deviation influence factor and the total annual electricity consumption of a prediction area through a two-dimensional scatter diagram of the prediction area in which the total annual electricity consumption is combined, establishing an electricity consumption prediction model by utilizing a grey neural network, and predicting the total annual electricity consumption of the prediction area by adopting an electricity consumption prediction model. Although the invention can improve the accuracy of prediction to a certain extent, the detection range is narrow.
Disclosure of Invention
The invention aims to provide a power consumption data-based standing population prediction model construction method which improves prediction accuracy and has a wide detection range.
In order to achieve the purpose, the invention adopts the following technical scheme:
a standing population prediction model construction method based on electricity utilization data comprises the following steps:
1) determining characteristics reflecting the electricity consumption behavior of a user, extracting data from an acquisition system, and acquiring economic parameters related to electricity prediction;
2) preprocessing the data extracted in the step 1), detecting whether missing values and abnormal values exist in the data, then cleaning the data, and converting a data format by using read thermal coding to convert the data into numerical data;
3) training the data by using a classification algorithm, constructing an anti-electricity-stealing model, then performing error removal processing on the data in the step 2) by using an Adam algorithm, calculating an average value of line loss rates of days before and after each line, and calculating the growth rate of each two average values;
4) and comparing and analyzing the experimental result of each model, evaluating the anti-electricity-stealing early warning model, generating a random number of positive-phase and negative-phase distribution by using the cloud generator, and calculating a specific input value of the cloud generator.
Further, the CNN layer is used to extract the data hiding features in step 3).
Further, the evaluation index in step 4) includes a recall rate and a precision rate, and the recall rate and the precision rate are proportional, wherein the recall rate is npre _ real/nreal, and the precision rate is npre _ real/npre.
And further, the voltage in the power distribution network is monitored in real time by using the power distribution network control center, and when the voltage out-of-limit condition occurs, the voltage is adjusted by using the AVC function of the power distribution network by the control center.
Compared with the prior art, the invention has the beneficial effects that:
1) according to the method, the electricity stealing samples are constructed, the prediction model is built on the voltage data set, and the prediction model is combined with the users with abnormal line loss, so that the recall rate and the accuracy of the lead-in line are improved, and a more accurate prediction effect is obtained.
2) According to the method, the standing population is predicted, the accuracy rate of the incoming line loss rate is 52.69-74.11%, the recall rate of the incoming line loss rate is 71.37-99.49%, the metric value is 70.45-73.48%, and the prediction accuracy is high.
Detailed Description
The following describes in further detail embodiments of the present invention. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
A standing population prediction model construction method based on electricity utilization data comprises the following steps:
1) determining characteristics reflecting the electricity consumption behavior of a user, extracting data from an acquisition system, and acquiring economic parameters related to electricity prediction;
2) preprocessing the data extracted in the step 1), detecting whether missing values and abnormal values exist in the data, then cleaning the data, and converting a data format by using read thermal coding to convert the data into numerical data;
3) training the data by using a classification algorithm, constructing an anti-electricity-stealing model, then performing error removal processing on the data in the step 2) by using an Adam algorithm, calculating an average value of line loss rates of days before and after each line, calculating the growth rate of each two average values, and extracting data hiding characteristics by using a CNN layer.
4) And comparing and analyzing the experimental result of each model, evaluating the anti-electricity-stealing early warning model, generating a random number of positive-phase and negative-phase distribution by using the cloud generator, and calculating a specific input value of the cloud generator. The evaluation index includes a recall rate and a precision rate, which are proportional to the recall rate, wherein the recall rate is npre _ real/nreal, and the precision rate is npre _ real/npre. The voltage in the power distribution network is monitored in real time by using the power distribution network control center, and when the voltage out-of-limit condition occurs, the voltage is adjusted by using the AVC function of the power distribution network by the control center.
Where nreal is the number of real electricity stealing users, npre is the number of model-predicted electricity stealing users, and npre _ real is the intersection of nreal and npre.
When the recall rate and the precision rate cannot be simultaneously considered, the quality of the model is measured by a metric value f 1:
f1 is 2 × (recall rate × precision)/(recall rate + precision) × 100%,
the following predictions of the standing population were made according to the method of the present invention, and the results of a number of algorithmic model evaluations based on voltage data are shown in table 1:
as can be seen from Table 1, the accuracy rate of the incoming line loss rate is 52.69-74.11%, the recall rate of the incoming line loss rate is 71.37-99.49%, the metric value is 70.45-73.48% and the prediction accuracy rate is high when the standing population is predicted.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments or portions thereof without departing from the spirit and scope of the invention.
Claims (4)
1. A standing population prediction model construction method based on electricity utilization data is characterized by comprising the following steps:
1) determining characteristics reflecting the electricity consumption behavior of a user, extracting data from an acquisition system, and acquiring economic parameters related to electricity prediction;
2) preprocessing the data extracted in the step 1), detecting whether missing values and abnormal values exist in the data, then cleaning the data, and converting a data format by using read thermal coding to convert the data into numerical data;
3) training the data by using a classification algorithm, constructing an anti-electricity-stealing model, then performing error removal processing on the data in the step 2) by using an Adam algorithm, calculating an average value of line loss rates of days before and after each line, and calculating the growth rate of each two average values;
4) and comparing and analyzing the experimental result of each model, evaluating the anti-electricity-stealing early warning model, generating a random number of positive-phase and negative-phase distribution by using the cloud generator, and calculating a specific input value of the cloud generator.
2. The method for constructing a standing population prediction model based on electricity consumption data as claimed in claim 1, wherein the step 3) utilizes a CNN layer to extract data hidden features.
3. The method as claimed in claim 1, wherein the evaluation index in step 4) includes a recall rate and a precision rate, and the recall rate and the precision rate are proportional, wherein the recall rate is npre _ real/nreal and the precision rate is npre _ real/npre.
4. The method for constructing the standing population prediction model based on the electricity consumption data according to claim 1, wherein a power distribution network control center is used for monitoring the voltage in the power distribution network in real time, and when the voltage exceeds the limit, the control center adjusts the voltage by using an AVC function of the power distribution network.
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| CN115759430A (en) * | 2022-11-23 | 2023-03-07 | 国网商用大数据有限公司 | Method and device for predicting population mobility based on power data and government affair data |
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