Electricity Consumption Forecasting with Artificial Neural Network for Fast-Moving Consumer Goods Sector

Gülfem Yeşil, Bersam Bolat*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Nowadays, it is evident that electricity is an indispensable source of energy in the production sectors when industry 4.0 transformation and sustainability become important at the same time. Electricity consumption forecast has crucial importance for effective energy planning in many production sectors. It is important to predict the total consumption of energy consumption and to make a production plan according to it and therefore to make all the functions in the supply chain cost and optimization plans. In this study, Artificial Neural Networks (ANN) method is used for electricity demand estimation for production processes of cold chain product in the fast moving consumer goods sector (FMCG). The impact of the observed independent variables is analyzed on electricity consumption. Estimates in the model are made for the following periods based on the last three years’ electricity consumption of the one of the big fast moving goods company located in Turkey.

Original languageEnglish
Title of host publicationProceedings of the International Symposium for Production Research 2019
EditorsNuman M. Durakbasa, Muhammed Nafis Osman Zahid, Radhiyah Abd. Aziz, Ahmad Razlan Yusoff, Nafrizuan Mat Yahya, Fazilah Abdul Aziz, Mohd Yazid Abu, M. Günes Gençyilmaz
PublisherSpringer Science and Business Media Deutschland GmbH
Pages51-61
Number of pages11
ISBN (Print)9783030313425, 9789811509490
DOIs
Publication statusPublished - 2020
Event19th International Symposium for Production Research, ISPR 2019 - Vienna, Austria
Duration: 28 Aug 201930 Aug 2019

Publication series

NameLecture Notes in Mechanical Engineering
ISSN (Print)2195-4356
ISSN (Electronic)2195-4364

Conference

Conference19th International Symposium for Production Research, ISPR 2019
Country/TerritoryAustria
CityVienna
Period28/08/1930/08/19

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

  • Artificial Neural Network (ANN)
  • Electricity consumption
  • FMCG
  • Forecasting

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