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A spark-based short-term power consumption prediction method

A prediction method and electric power technology, applied in the direction of prediction, data processing applications, instruments, etc., can solve the problems of lack of computing resources, inability to achieve efficient training, etc., to reduce cross-effects, improve prediction accuracy, and improve the ability of massive data Effect

Active Publication Date: 2022-06-24
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

Under the massive data, the stand-alone environment cannot achieve efficient training due to the lack of computing resources. Therefore, it is necessary to realize the processing of large-scale training data through computer clusters.

Method used

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  • A spark-based short-term power consumption prediction method
  • A spark-based short-term power consumption prediction method
  • A spark-based short-term power consumption prediction method

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Embodiment Construction

[0021] The technical solutions of the present invention will be described in detail below with reference to the accompanying drawings.

[0022] like figure 1 As shown, it is the flow chart of the training and prediction stage of the present invention, in which, except for the high efficiency of STL time series decomposition, which is not parallelized, the rest of the steps are parallelized through the Spark distributed computing framework.

[0023] In the model training phase, historical power consumption data and weather data are used

[0024] The first step: power consumption data preprocessing and feature engineering processing, among which, the preprocessing includes a) missing data processing, which is completed by the adjacent number averaging method; b) outlier processing, which is judged by the standard deviation method, and then the same The method of missing data processing; c) Noise reduction, completed by the moving average method. The feature engineering process...

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Abstract

The invention discloses a Spark-based short-term power consumption prediction method. The method of the present invention mainly includes, according to historical power consumption data and weather information, using STL time series decomposition and support vector regression to predict the use of power consumption in the short term in the future, and using the Spark distributed computing framework to accelerate massive power consumption The model training under the data improves the ability of the model to process massive data. At the same time, due to the use of the STL time series decomposition algorithm, the cross influence between components is reduced and the prediction accuracy of the model is improved.

Description

technical field [0001] The invention relates to a Spark-based short-term power consumption prediction method. Background technique [0002] At present, energy conservation and emission reduction has become an important measure to achieve sustainable development in my country. However, as the main carrier of energy conservation and emission reduction technology application, some universities and parks have extensive energy consumption data statistics, and there is no scientific energy consumption supervision and prediction, which cannot rely on history. The energy consumption data assists management, improves the system and formulates corresponding energy saving strategies. The reason for this is the lack of effective supervision of energy consumption data. In theory, there is also a lack of research on energy consumption models. The analysis and prediction of power consumption can effectively help tap the potential of energy conservation and promote the optimization of energ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/27G06Q10/04G06Q50/06
CPCG06Q10/04G06Q50/06G06F30/20
Inventor 姜书艳赵云鹏左志宏
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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