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

Journal: Scientific reports
Published Date:

Abstract

The research paper introduces a novel technique for forecasting electricity usage by utilizing the Developed human evolutionary optimization (DHEO) algorithm and the Xception Neural Network (Xception-NN) model. The Xception-NN model, which is a modified deep learning framework, processes time-series data and incorporates various factors such as weather conditions, demographic insights, and economic indicators. By refining the model's parameters, the DHEO algorithm, inspired by human evolutionary principles, enables a more accurate capture of intricate dependencies and patterns in electricity consumption data. This approach provides energy companies and utilities with a means to enhance their predictions, optimize energy production, and effectively anticipate future demand. Additionally, the study investigates electricity consumption under two scenarios: Base Line (BL) and Energy Conservation (EC), with a focus on the volume of electricity consumed across different sectors. The EC scenario leads to a notable 6.54% reduction in electricity consumption, with the industry sector experiencing the most significant decline.

Authors

  • Dongxian Yu
    College of Modern Information Technology, Henan Polytechnic, Zhengzhou, Henan, China.
  • Di Wu
    University of Melbourne, Melbourne, VIC 3010 Australia.
  • Chongyang Liao
    Informat Commun Branch, CSG Power Generat Co, Guangzhou, 511400, China.
  • Zaihui Cao
    College of Art Design, Zhengzhou University of Aeronautics, Zhengzhou, Henan, 450015, China. czh@zua.edu.cn.
  • Somayeh Pouramini
    Firoozabad Branch, Islamic Azad University, Firoozabad, Iran. s.pouramini1986@gmail.com.

Keywords

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