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

Gülfem Yeşil; Bersam Bolat

doi:10.1007/978-3-030-31343-2_5

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

Gülfem Yeşil
, Bersam Bolat^*

^*Bu çalışma için yazışmadan sorumlu yazar

İşletme Mühendisliği Bölümü

Istanbul Technical University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

1 Atıf (Scopus)

Özet

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.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Proceedings of the International Symposium for Production Research 2019
Editörler	Numan M. Durakbasa, Muhammed Nafis Osman Zahid, Radhiyah Abd. Aziz, Ahmad Razlan Yusoff, Nafrizuan Mat Yahya, Fazilah Abdul Aziz, Mohd Yazid Abu, M. Günes Gençyilmaz
Yayınlayan	Springer Science and Business Media Deutschland GmbH
Sayfalar	51-61
Sayfa sayısı	11
ISBN (Basılı)	9783030313425, 9789811509490
DOI'lar	https://doi.org/10.1007/978-3-030-31343-2_5
Yayın durumu	Yayınlandı - 2020
Etkinlik	19th International Symposium for Production Research, ISPR 2019 - Vienna, Austria Süre: 28 Ağu 2019 → 30 Ağu 2019

Yayın serisi

Adı	Lecture Notes in Mechanical Engineering
ISSN (Basılı)	2195-4356
ISSN (Elektronik)	2195-4364

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	19th International Symposium for Production Research, ISPR 2019
Ülke/Bölge	Austria
Şehir	Vienna
Periyot	28/08/19 → 30/08/19

Bibliyografik not

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

BM SKH

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Belgeye Erişim

10.1007/978-3-030-31343-2_5

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Yeşil, G., & Bolat, B. (2020). Electricity Consumption Forecasting with Artificial Neural Network for Fast-Moving Consumer Goods Sector. In N. M. Durakbasa, M. N. Osman Zahid, R. Abd. Aziz, A. R. Yusoff, N. Mat Yahya, F. Abdul Aziz, M. Yazid Abu, & M. G. Gençyilmaz (Eds.), Proceedings of the International Symposium for Production Research 2019 (pp. 51-61). (Lecture Notes in Mechanical Engineering). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-31343-2_5

Yeşil, Gülfem ; Bolat, Bersam. / Electricity Consumption Forecasting with Artificial Neural Network for Fast-Moving Consumer Goods Sector. Proceedings of the International Symposium for Production Research 2019. editör / Numan M. Durakbasa ; Muhammed Nafis Osman Zahid ; Radhiyah Abd. Aziz ; Ahmad Razlan Yusoff ; Nafrizuan Mat Yahya ; Fazilah Abdul Aziz ; Mohd Yazid Abu ; M. Günes Gençyilmaz. Springer Science and Business Media Deutschland GmbH, 2020. pp. 51-61 (Lecture Notes in Mechanical Engineering).

@inproceedings{b1c3d38d7cda45828f3f7f78a107ff04,

title = "Electricity Consumption Forecasting with Artificial Neural Network for Fast-Moving Consumer Goods Sector",

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{\textquoteright} electricity consumption of the one of the big fast moving goods company located in Turkey.",

keywords = "Artificial Neural Network (ANN), Electricity consumption, FMCG, Forecasting",

author = "G{\"u}lfem Ye{\c s}il and Bersam Bolat",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 19th International Symposium for Production Research, ISPR 2019 ; Conference date: 28-08-2019 Through 30-08-2019",

year = "2020",

doi = "10.1007/978-3-030-31343-2\_5",

language = "English",

isbn = "9783030313425",

series = "Lecture Notes in Mechanical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "51--61",

editor = "Durakbasa, \{Numan M.\} and \{Osman Zahid\}, \{Muhammed Nafis\} and \{Abd. Aziz\}, Radhiyah and Yusoff, \{Ahmad Razlan\} and \{Mat Yahya\}, Nafrizuan and \{Abdul Aziz\}, Fazilah and \{Yazid Abu\}, Mohd and Gen{\c c}yilmaz, \{M. G{\"u}nes\}",

booktitle = "Proceedings of the International Symposium for Production Research 2019",

address = "Germany",

}

Yeşil, G & Bolat, B 2020, Electricity Consumption Forecasting with Artificial Neural Network for Fast-Moving Consumer Goods Sector. in NM Durakbasa, MN Osman Zahid, R Abd. Aziz, AR Yusoff, N Mat Yahya, F Abdul Aziz, M Yazid Abu & MG Gençyilmaz (eds), Proceedings of the International Symposium for Production Research 2019. Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH, pp. 51-61, 19th International Symposium for Production Research, ISPR 2019, Vienna, Austria, 28/08/19. https://doi.org/10.1007/978-3-030-31343-2_5

Electricity Consumption Forecasting with Artificial Neural Network for Fast-Moving Consumer Goods Sector. / Yeşil, Gülfem; Bolat, Bersam.
Proceedings of the International Symposium for Production Research 2019. ed. / Numan M. Durakbasa; Muhammed Nafis Osman Zahid; Radhiyah Abd. Aziz; Ahmad Razlan Yusoff; Nafrizuan Mat Yahya; Fazilah Abdul Aziz; Mohd Yazid Abu; M. Günes Gençyilmaz. Springer Science and Business Media Deutschland GmbH, 2020. p. 51-61 (Lecture Notes in Mechanical Engineering).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Electricity Consumption Forecasting with Artificial Neural Network for Fast-Moving Consumer Goods Sector

AU - Yeşil, Gülfem

AU - Bolat, Bersam

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Artificial Neural Network (ANN)

KW - Electricity consumption

KW - FMCG

KW - Forecasting

UR - https://www.scopus.com/pages/publications/85076210065

U2 - 10.1007/978-3-030-31343-2_5

DO - 10.1007/978-3-030-31343-2_5

M3 - Conference contribution

AN - SCOPUS:85076210065

SN - 9783030313425

SN - 9789811509490

T3 - Lecture Notes in Mechanical Engineering

SP - 51

EP - 61

BT - Proceedings of the International Symposium for Production Research 2019

A2 - Durakbasa, Numan M.

A2 - Osman Zahid, Muhammed Nafis

A2 - Abd. Aziz, Radhiyah

A2 - Yusoff, Ahmad Razlan

A2 - Mat Yahya, Nafrizuan

A2 - Abdul Aziz, Fazilah

A2 - Yazid Abu, Mohd

A2 - Gençyilmaz, M. Günes

PB - Springer Science and Business Media Deutschland GmbH

T2 - 19th International Symposium for Production Research, ISPR 2019

Y2 - 28 August 2019 through 30 August 2019

ER -

Yeşil G, Bolat B. Electricity Consumption Forecasting with Artificial Neural Network for Fast-Moving Consumer Goods Sector. In Durakbasa NM, Osman Zahid MN, Abd. Aziz R, Yusoff AR, Mat Yahya N, Abdul Aziz F, Yazid Abu M, Gençyilmaz MG, editörler, Proceedings of the International Symposium for Production Research 2019. Springer Science and Business Media Deutschland GmbH. 2020. p. 51-61. (Lecture Notes in Mechanical Engineering). doi: 10.1007/978-3-030-31343-2_5

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

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