Comprehensive Forecasting Method of Monthly Electricity Consumption Based on Time Series Decomposition and Regression Analysis

Changfeng Luan, Xinfu Pang, Yanbo Wang, Li Liu, Shi You

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Abstract

Power consumption prediction is the basis of implementing planned power consumption and preparing production plan. It is one of the main projects in the design of industrial and mining enterprises. It is also an important link to ensure the balance between national economic needs and power supply. Due to the influence of distributed energy and the change of power demand and load characteristics of the user side compared with the past, the power consumption prediction starts to face small-scale users and is more easily disturbed by various influencing factors, so the traditional prediction method is not fully suitable for today's power consumption prediction. Firstly, STL is used to decompose the power consumption sequence of corresponding month into trend component, season component and random component. Secondly, the BP neural network model is used to predict the seasonal component of the month when the seasonal mutation and major festivals are located. ARIMA model is used to predict the trend component. The average value is used to predict the random components. Then, the predicted values of the three components are reconstructed into the final predicted values. Finally, the algorithm is compiled by R language, and the validity of the proposed method is verified by the actual monthly electricity sales data of a University Park in the north. And further consider the prediction method of economic factors.
Original languageEnglish
Title of host publicationProceedings of 2nd International Conference on Industrial Artificial Intelligence
Number of pages6
PublisherIEEE
Publication date2020
ISBN (Electronic)978-1-7281-8216-2
DOIs
Publication statusPublished - 2020
Event2nd International Conference on Industrial Artificial
Intelligence
- Shenyang, China
Duration: 23 Oct 202025 Oct 2020
http://icaiic.org/

Conference

Conference2nd International Conference on Industrial Artificial
Intelligence
CountryChina
CityShenyang
Period23/10/202025/10/2020
Internet address

Keywords

  • Monthly electricity consumption forecasting
  • Electricity consumption characteristics
  • Personalized decomposition
  • Time series
  • STL model

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