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Enterprise power consumption maximum demand prediction method based on ARIMA and SVM

A forecasting method and technology of electricity consumption, applied in forecasting, nuclear methods, data processing applications, etc., can solve the problems of forecast deviation of future monthly maximum demand, lack of enterprise electricity load characteristics, and insufficient enterprise users.

Pending Publication Date: 2019-11-15
SHANDONG INSPUR GENESOFT INFORMATION TECH CO LTD
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AI Technical Summary

Problems solved by technology

[0009] Zhongshen Technology Energy (Shenzhen) Co., Ltd. also introduced a plan to predict the maximum monthly demand in the future. In the specific steps of the plan, only the user's electricity consumption curve data and electricity consumption behavior are analyzed, and there is no need for enterprises The consideration of the characteristics of electricity load and the steps of data cleaning for the collected data will cause deviations in the forecast of the maximum demand in the future, so that it cannot fully save electricity bills for enterprise users

Method used

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  • Enterprise power consumption maximum demand prediction method based on ARIMA and SVM
  • Enterprise power consumption maximum demand prediction method based on ARIMA and SVM
  • Enterprise power consumption maximum demand prediction method based on ARIMA and SVM

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Embodiment 1

[0099] Such as figure 1 Shown, a kind of future monthly maximum demand prediction method based on differential integrated moving average autoregressive model (ARIMA) time series prediction and support vector machine (SVM), described method implementation process is as follows:

[0100] 1. To preprocess the data, first fill in the missing values ​​in the uploaded data, because the uploaded meter reading value records the total power displayed by the meter every 15 minutes, and the calculation of the time period has to be done poorly, and sometimes "0" will be uploaded value, resulting in data errors, we use the moving average method to fill in missing values;

[0101] 2. If Figure 5 As shown, the K-Means clustering method is used to remove outliers to make the curve converge better. The specific calculation process is as follows:

[0102] 1) Randomly select K center points;

[0103] 2) Assign each data point to its nearest center point;

[0104] 3) Recalculate the average...

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Abstract

The invention discloses an enterprise power consumption maximum demand prediction method based on ARIMA and SVM, and the method is characterized in that the method comprises the steps: reading from apower grid company charging table TSDB; supplementing missing values by using a moving average method; removing outliers by using a K-Means clustering algorithm; predicting power consumption by usingan ARIMA time sequence; in combination with prediction of weather factors and production conditions, using a trained SVM model to perform decision making; and taking the maximum value of the predictedmonth, and calculating the maximum demand of the future month. According to the invention, the time sequence of historical electric quantity is fully considered, which includes trends, periodic and seasonal; meanwhile, prediction of weather factors and production conditions is also considered; two factors are unified in one model by using a machine learning decision and a time sequence predictionalgorithm, an accurate result is obtained through training, and along with more and more data and longer and longer use years, the prediction error of the maximum demand of the future month is smaller, so that more electric charge is saved for enterprises.

Description

technical field [0001] The invention relates to the technical field of on-line monitoring, in particular to an ARIMA and SVM-based method for predicting the maximum electricity demand of an enterprise. Background technique [0002] According to Chinese law, users pay by metering point (there can be multiple transformers in one metering point), instead of according to transformer or electrical equipment, the payment of electricity charges includes capacity charges, electricity charges and power rate adjustment charges. According to experience, the situation that saves the most electricity bills is to adjust the declaration method of capacity charging. Capacity charging is the fixed electricity fee that users pay to the grid company every month according to the capacity of the transformer in operation. Even if the user does not consume electricity in the current month, the grid company will charge this fee. . my country's domestic industrial and commercial users can pay in tw...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N20/10G06K9/62
CPCG06Q10/04G06Q50/06G06N20/10G06F18/23213G06F18/2433G06F18/10Y04S10/50Y02E40/70Y02A30/00
Inventor 亓浩陈兆瑞
Owner SHANDONG INSPUR GENESOFT INFORMATION TECH CO LTD
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