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Electricity usage prediction using developed human evolutionary optimization algorithm and Xception neural network.

Dongxian Yu1, Di Wu2, Chongyang Liao3

  • 1College of Modern Information technology, Henan Polytechnic, Zhengzhou, 450046, Henan, China.

Scientific Reports
|July 9, 2025
PubMed
Summary
This summary is machine-generated.

This study presents a new method for forecasting electricity usage using the Developed human evolutionary optimization (DHEO) algorithm and Xception Neural Network (Xception-NN). Energy conservation strategies significantly reduce consumption, particularly in the industrial sector.

Keywords:
Developed human evolutionary optimization (DHEO) algorithmElectricity consumptionOptimizationPredictionsXception-NN

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Area of Science:

  • Artificial Intelligence
  • Energy Systems
  • Computational Optimization

Background:

  • Accurate electricity usage forecasting is crucial for energy grid management and resource allocation.
  • Traditional forecasting models often struggle with complex, non-linear patterns in energy consumption data.
  • Deep learning and evolutionary algorithms offer potential for improved predictive accuracy.

Purpose of the Study:

  • To introduce a novel hybrid model combining the Xception Neural Network (Xception-NN) with the Developed human evolutionary optimization (DHEO) algorithm for enhanced electricity usage forecasting.
  • To analyze electricity consumption patterns under baseline and energy conservation scenarios.
  • To identify sectors with the most significant impact from energy conservation measures.

Main Methods:

  • Development of a modified Xception Neural Network (Xception-NN) deep learning model for time-series electricity consumption data.
  • Application of the Developed human evolutionary optimization (DHEO) algorithm to refine Xception-NN parameters for improved pattern recognition.
  • Simulation of electricity consumption under Base Line (BL) and Energy Conservation (EC) scenarios, considering factors like weather, demographics, and economics.

Main Results:

  • The DHEO-Xception-NN model demonstrated improved accuracy in capturing intricate dependencies and patterns in electricity consumption.
  • The Energy Conservation (EC) scenario resulted in a significant 6.54% overall reduction in electricity consumption.
  • The industrial sector exhibited the most substantial decrease in electricity consumption under the EC scenario.

Conclusions:

  • The hybrid DHEO-Xception-NN approach offers a powerful tool for energy companies to enhance demand forecasting and optimize energy production.
  • Energy conservation initiatives can lead to substantial reductions in electricity usage, with targeted impacts across different economic sectors.
  • This research provides valuable insights for policymakers and utility providers aiming to improve energy efficiency and manage future energy demands.