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Method for achieving EMS load prediction based on decision tree and linear regression

A linear regression and load forecasting technology, applied in forecasting, instrumentation, data processing applications, etc., can solve problems such as difficult to satisfy users, complex method models, large forecasting errors, etc., to achieve simple model and simple linear regression interpolation logic, reduce Training time, effect of reducing data load

Inactive Publication Date: 2015-06-24
NANJING TIANSU AUTOMATION CONTROL SYST
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AI Technical Summary

Problems solved by technology

The general load forecasting method, due to the complexity of the method model, poor real-time performance and large forecasting error, it is difficult to meet the requirements of users in practical applications

Method used

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  • Method for achieving EMS load prediction based on decision tree and linear regression
  • Method for achieving EMS load prediction based on decision tree and linear regression

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0056] Implemented in the EMS system, the application of the present invention configures according to the needs of the site, the specific steps of creating a forecasting model and continuing load forecasting are:

[0057] Step 1: Configure the city where the site is located in the configuration file of the EMS system environment, and the external network interface obtains the weather forecast for the current day and the next three days according to the configuration;

[0058] Step 2: Organize historical energy consumption and temperature into a structure to prepare for linear regression;

[0059] Step 3: Calculate the regression coefficients and constants of the linear regression equation based on historical data;

[0060] Step 4: Calculate the predicted value and store it in the database;

[0061] Step 5: Obtain the predicted value from the database according to the query conditions of the load forecast (energy consumption node id, classification item id, forecast date).

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Abstract

The invention discloses a method for achieving EMS load prediction based on a decision tree and linear regression. According to the method, the characteristics of the high algorithm classification accuracy of the decision tree, the simple decision tree generation mode and the simple linear regression interpolation logic are sufficiently utilized, energy consumption values which are recorded on time at certain energy consumption nodes every day and historical information formed with schedule days (from Monday to Sunday) and temperature which are highly related to energy consumption are added into the system, and the best time span of historical data adopted for the decision tree is obtained by using the natural selection method through the historical data. The method has the advantage of better meeting the requirement for prediction of short, medium and long term loads of an energy consumption unit.

Description

technical field [0001] The invention relates to the realization technology of EMS load forecasting, in particular to a method for realizing EMS load forecasting based on decision tree and linear regression interpolation. Background technique [0002] At present, my country's economic operation has entered a new normal mode, and the extensive growth that relies on a large amount of resource consumption and ultimately leads to environmental damage has become a thing of the past. According to the work objectives of the State Council's "2014-2015 Energy Conservation, Emission Reduction and Low-Carbon Development Action Plan": from 2014 to 2015, the unit GDP energy consumption, chemical oxygen demand, sulfur dioxide, ammonia nitrogen, and nitrogen oxide emissions decreased by 3.9% and 2%, 2%, 2%, 5%, and the carbon dioxide emissions per unit of GDP will drop by 4% and 3.5% respectively in two years. Therefore, the development direction of enterprises in the future should be envi...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/00
Inventor 戴新宇沈慧强
Owner NANJING TIANSU AUTOMATION CONTROL SYST
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