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Short-term multi-energy consumption forecasting for integrated energy system based on interactive multi-scale

Fang Liu1,2, Yucong Huang3, Yalin Wang3,4

  • 1School of Automation, Central South University, Changsha, 410083, Hunan, China. csuliufang@csu.edu.cn.

Scientific Reports
|September 13, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for forecasting energy consumption in integrated energy systems (IES). The approach enhances prediction accuracy by analyzing multi-scale energy data and seasonal patterns.

Keywords:
Consumption forecastingIntegrated energy systemMulti-energy interactive learningMulti-scale feature fusionSeasonal and coupling characteristics

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Area of Science:

  • Energy Systems Engineering
  • Artificial Intelligence
  • Computational Science

Background:

  • Accurate energy consumption forecasting is crucial for the stable and efficient operation of integrated energy systems (IES).
  • Understanding consumer energy habits is key to optimizing energy distribution and resource management.
  • Existing forecasting methods may not fully capture the complex interactions and multi-scale characteristics of energy consumption.

Purpose of the Study:

  • To propose an advanced short-term multi-energy consumption forecasting method for integrated energy systems (IES).
  • To develop a novel interactive multi-scale convolutional module for enhanced feature extraction and fusion.
  • To improve prediction performance by leveraging seasonal variations and inter-energy coupling characteristics.

Main Methods:

  • Development of an interactive multi-scale convolutional module for multi-scale feature fusion and interactive learning.
  • Implementation of a seasonal forecasting approach utilizing different network structures for distinct seasons.
  • Application of a Laplace distribution-based loss function for robust optimization of joint forecast tasks.

Main Results:

  • The proposed interactive multi-scale convolutional module effectively extracts and shares coupling information between energy consumptions at different scales without increasing network parameters.
  • The seasonal forecasting method demonstrated enhanced prediction performance by capitalizing on seasonal and coupling characteristics.
  • Comparative simulation experiments validated the effectiveness and superiority of the proposed method over existing approaches.

Conclusions:

  • The developed method offers a robust and effective solution for short-term multi-energy consumption forecasting in IES.
  • The novel convolutional module and seasonal adaptation significantly improve forecasting accuracy.
  • This research contributes to the stable and efficient operation of integrated energy systems through enhanced predictive capabilities.