Short-term and medium- and long-term electric power load prediction method based on machine learning model

A machine learning model, power load technology, applied in forecasting, instrumentation, complex mathematical operations, etc., can solve problems such as low forecasting accuracy

Inactive Publication Date: 2018-01-09
FOSHAN SHUNDE SUN YAT SEN UNIV RES INST +2
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

These algorithms are easy to implement on some datasets, but their predictive accuracy is usually lower compared with more complex machine learning algorithms, which can provide higher accuracy and stronger learning ability

Method used

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  • Short-term and medium- and long-term electric power load prediction method based on machine learning model
  • Short-term and medium- and long-term electric power load prediction method based on machine learning model
  • Short-term and medium- and long-term electric power load prediction method based on machine learning model

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Embodiment Construction

[0118] The present invention will be described in detail below in conjunction with the accompanying drawings, but the application of the present invention is not limited thereto.

[0119] figure 1 It is a flow chart of short-term and medium- and long-term electric load forecasting based on machine learning model provided by the present invention. Method of the present invention comprises the following steps:

[0120] S1: Preprocessing the input historical data, including smoothing abnormal data and filling missing values.

[0121] Abnormal data often use horizontal smoothing and vertical smoothing. When horizontally smoothing the data, when the fluctuation range of the data to be processed exceeds the maximum fluctuation range of the two moments before and after, the average value is used to replace the abnormal data. When the data is vertically smoothed, because different dates have similarities at the same moment, the load fluctuation occurs within a certain range, and wh...

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Abstract

The invention discloses a short-term and medium- and long-term electric power load prediction method based on machine learning model. Firstly, preprocessing is conducted on data, including smootheningabnormal data and filling missing data. Factors of affecting load changes will be analyzed, including historical data, time periodicity, and weather variable characteristics. Domestication will be conducted on all input variables for accelerating learning speed and raising prediction precision. The invention is advantageous in that linear regression is compared, and the performance of the vectorregression and gradient lifting regression in the short-term and medium- and long-term electric power load prediction is supported; with the prolongation of the prediction time, the performance of thegradient lifting regression model is better that that of the other two models; the AdaBoost algorithm which uses the gradient lifting tree as a basic classifier is brought forward, and load prediction is conducted, and the precision of electric power load prediction can be effectively raised.

Description

technical field [0001] The present invention relates to the field of power load forecasting, and more specifically, relates to a short-term and medium- and long-term power load forecasting method based on a machine learning model. Background technique [0002] Power load forecasting is an important part of power research and plays a key role in the effective operation of the power market. With the advancement of science and technology and the needs of economic development, the construction of smart grid has begun, thereby improving the utilization rate of energy and promoting the optimal allocation of resources. [0003] According to different forecast periods, power load forecasting can be divided into short-term, medium-term and long-term forecasting. Short-term load forecasting (STLF) includes load forecasting for the next few minutes, one hour, one day, and one week, and is used for economic dispatch and power system security assessment to ensure safe, economical, and s...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06K9/62G06F17/18G06F17/11
Inventor 苏方晨许银亮
Owner FOSHAN SHUNDE SUN YAT SEN UNIV RES INST
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