A Power Load Forecasting Method Based on Intelligent Data Analysis

Abnormal electricity consumption behavior not only affects the safety of power supply but also damages the infrastructure of the power system, posing a threat to the secure and stable operation of the grid. Predicting future electricity consumption plays a crucial role in resource management in the...

Full description

Saved in:
Bibliographic Details
Published inElectronics (Basel) Vol. 12; no. 16; p. 3441
Main Authors Liu, He, Xiong, Xuanrui, Yang, Biao, Cheng, Zhanwei, Shao, Kai, Tolba, Amr
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.08.2023
Subjects
Accuracy
Algorithms
Alternative energy sources
Artificial intelligence
China
Data analysis
Data processing
Deep learning
Design
Electric power systems
Electricity
Electricity consumption
Electricity distribution
Empirical analysis
Energy consumption
Forecasting
Forecasting techniques
Internet of Things
Mathematical models
Methods
Neural networks
Power supply
Prediction models
Prediction theory
Resource management
Time series
China
Online AccessGet full text

Cover

More Information
Summary:Abnormal electricity consumption behavior not only affects the safety of power supply but also damages the infrastructure of the power system, posing a threat to the secure and stable operation of the grid. Predicting future electricity consumption plays a crucial role in resource management in the energy sector. Analyzing historical electricity consumption data is essential for improving the energy service capabilities of end-users. To forecast user energy consumption, this paper proposes a method that combines adaptive noise-assisted complete ensemble empirical mode decomposition (CEEMDAN) with long short-term memory (LSTM) networks. Firstly, considering the challenge of directly applying prediction models to non-stationary and nonlinear user electricity consumption data, the adaptive noise-assisted complete ensemble empirical mode decomposition algorithm is used to decompose the signal into trend components, periodic components, and random components. Then, based on the CEEMDAN decomposition, an LSTM prediction sub-model is constructed to forecast the overall electricity consumption by using an overlaying approach. Finally, through multiple comparative experiments, the effectiveness of the CEEMDAN-LSTM method is demonstrated, showing its ability to explore hidden temporal relationships and achieve smaller prediction errors.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12163441