Abstract
In
recent years, some enterprises are generating the energy they need to reduce
their energy supply costs. The ideal energy supply plan for enterprises
provides more accurate decisions for energy companies on the specific issues
such as commitment, production and maintenance planning and enterprises are
leading to prevent production losses caused by energy cuts. Nowadays, some
enterprises produce some part of the energy they need to reduce their energy
supply costs. However, especially in food industry which they produce seasonal
products, the amount of produced energy is sufficient for low seasons, while it
is insufficient for high seasons; since the capacity of production varies
widely between seasons. When the amount of energy produced is not sufficient,
the national distribution companies have supplied the energy demand for those
enterprises. In this study, a statistical model to forecast energy consumption
in a food processing company with
seasonal different production capacities , which tries to minimize total energy
production and supply cost is presented, and the results have been analyzed. . The
parameters, effects the energy
consumption are determined, and they have proceeded with a statistical method
to explain the relationship between those parameters and the amount of energy
consumption. Since the presented model requires daily data, energy consumption forecasting
is performed by using statistical model depending on the possible
changes in daily production quantities The presented model reduces the the uncertainty associated
with the amount of energy consumption for the food processing company.
Moreover, the company will be able to develop different energy supply
strategies for energy supply agreements by using the developed energy
consumption forecasting model.
References
- Altındağ,İ.,(2010), “Quantile Regresyon ve Bir Uygulama”, Selçuk Üniversitesi, Fen Bilimleri Enstitüsü,İstatistik Bölümü, Yüksek Lisans Tezi, Konya.
- Altındağ,İ.,(2010), “Quantile Regresyon ve Bir Uygulama”, Selçuk Üniversitesi, Fen Bilimleri Enstitüsü,İstatistik Bölümü, Yüksek Lisans Tezi, Konya.
- Badri,A.,Ameli,Z.,Birjandi,A.M., (2012), “Application of Artificial Neural Networks and Fuzzy Logic Methods for Short Term Load Forecasting”.Energy Procedia, 14. doi.org/10.1016/j.egypro.2011.12.1183
- Chai, J., Lai,K.K., Lu, Q.Y.,Wang, S.Y., (2016), “Analysis of road transportation energy consumption demand in China”, Energy,pp.112-124. doi.org/10.1016/j.trd.2016.08.009
- Elmalı, K.,(2014), “Kantil Regresyon ve Negatif Binomial Regresyon İle İllerde Kullanılan İlaç Sayısına Etki Eden Faktörlerin İncelenmesi”, Atatürk Üniversitesi, Sosyal Bilimler Enstitüsü, Ekonometri Bölümü, Yüksek Lisans Tezi, Erzurum.
- Fumon,N.,RafeBiswas,M.A.,(2015), “Regression Analysis For Prediction Of Residential Energy Consumption” , Renewable And Sustainable Energy Reviews,47,pp. 332–343. doi.org/10.1016/j.rser.2015.03.035
- Hagfors, L.I.,Bunn,D., Kristoffersen, E., Staver, T.T.,Westgaard, S., (2016), “Modeling The UK Electricity Price Distributions Using Quantile Regression”, Energy, 102, pp. 231-243. doi.org/10.1016/j.energy.2016.02.025
- Hamzaçebi, C., Kutay, F. (2004), “Yapay Sinir Ağları ile Türkiye Elektrik Enerjisi Tüketiminin 2010 Yılına Kadar Tahmini”, Gazi Üniv Müh Mim Fak Der, 19 (3), s.227-233.
- Hsu, C.,Chen, C.,(2003), “Regional Load Forecasting in Taiwan––Applications of Artificial Neural Networks”, Energy Conversion and Management, 44, pp.1941–1949. doi.org/10.1016/S0196-8904(02)00225-X
- Kialashaki, A., Reisel, J.R., (2014), “Development And Validation Of Artificial Neural Network Models Of The Energy Demand In The Industrial Sector Of The United State”, Energy, 76,pp. 749-760. doi.org/10.1016/j.energy.2014.08.072
- Kutlar,A.,(2009), Uygulamalı Ekonometri,3.Baskı, Nobel Yayınları, Ankara.
- Li, Z., Hurn, A.S., Clements, A.E., (2017), “Forecasting Quantiles Of Day-Ahead Electricity Load”, Energy Economics, 67 September 2017, pp.60–7. doi.org/10.1016/j.eneco.2017.08.002
- Niu, S., Jia, Y., Ye, L., Dai, R., Li, N., (2016), “ Does Electricity Consumption Improve Residential Living Status In Less Developed Regions? An Empirical Analysis Using The Quantile Regression Approach”, Energy,95, pp.550-560. doi.org/10.1016/j.energy.2015.12.029
- Pino-Mejías, R. , Perez-Fargallo, A. , Rubio-Bellido, C. , Pulido-Arcas, C., (2017), “Comparison of linear regression and artificial neural networks models to predict heating and cooling energy demand, energy consumption and CO2 emissions” , Energy 118, pp. 24-36. doi.org/10.1016/j.energy.2016.12.022
- Wang,S., Yu, Y., Zou,Z.,(2018), “Statistical regression modeling for energy consumption in wastewater treatment”, Journal of Environmental Sciences. doi.org/10.1016/j.jes.2018.03.023
Abstract
Günümüzde bazı işletmeler enerji tüketim maliyetlerini
düşürmek için ihtiyacı olan enerjinin bir miktarını kendisi üretmektedir. Ancak
özellikle mevsimsel ürün üretimi gerçekleştiren gıda işletmelerinde, üretim
kapasitesinin mevsimler arasında yüksek değişkenlik göstermesi nedeniyle,
üretilen enerji miktarı üretimin az olduğu dönemlerde yeterli olurken, üretimin
yoğun olduğu dönemlerde yeterli olmamaktadır. Üretilen enerji miktarının
yeterli olmaması durumunda, enerji ihtiyacı, ulusal dağıtım firmalarından
tedarik edilmektedir. Bu çalışmada dönemsel farklı üretim kapasitesine sahip bir
gıda işletmesi için, işletmenin toplam enerji tüketim miktarını tahmin eden
istatistiksel bir model geliştirilmiş ve sonuçlar analiz edilmiştir. Çalışmada
öncelikle enerji tüketim miktarını etkileyen parametreler belirlenmiş ve bu
parametrelerin enerji tüketim miktarı ile ilişkisini ortaya çıkaran sayısal bir
yöntem geliştirilmiştir. Çalışmada geliştirilen modelde günlük üretim
miktarlarındaki olası değişime bağlı olarak enerji tüketim tahmini
istatistiksel yöntemlerle gerçekleştirilmekte, bu durum da işletme için enerji
tedarik miktarı ile ilişkili belirsizliği azaltmaktadır. Ayrıca, işletme
geliştirilen enerji tüketim tahmin modelini kullanarak, ileride yapacağı enerji
tedarik anlaşmalarında farklı tedarik stratejileri de geliştirebilecektir.
References
- Altındağ,İ.,(2010), “Quantile Regresyon ve Bir Uygulama”, Selçuk Üniversitesi, Fen Bilimleri Enstitüsü,İstatistik Bölümü, Yüksek Lisans Tezi, Konya.
- Altındağ,İ.,(2010), “Quantile Regresyon ve Bir Uygulama”, Selçuk Üniversitesi, Fen Bilimleri Enstitüsü,İstatistik Bölümü, Yüksek Lisans Tezi, Konya.
- Badri,A.,Ameli,Z.,Birjandi,A.M., (2012), “Application of Artificial Neural Networks and Fuzzy Logic Methods for Short Term Load Forecasting”.Energy Procedia, 14. doi.org/10.1016/j.egypro.2011.12.1183
- Chai, J., Lai,K.K., Lu, Q.Y.,Wang, S.Y., (2016), “Analysis of road transportation energy consumption demand in China”, Energy,pp.112-124. doi.org/10.1016/j.trd.2016.08.009
- Elmalı, K.,(2014), “Kantil Regresyon ve Negatif Binomial Regresyon İle İllerde Kullanılan İlaç Sayısına Etki Eden Faktörlerin İncelenmesi”, Atatürk Üniversitesi, Sosyal Bilimler Enstitüsü, Ekonometri Bölümü, Yüksek Lisans Tezi, Erzurum.
- Fumon,N.,RafeBiswas,M.A.,(2015), “Regression Analysis For Prediction Of Residential Energy Consumption” , Renewable And Sustainable Energy Reviews,47,pp. 332–343. doi.org/10.1016/j.rser.2015.03.035
- Hagfors, L.I.,Bunn,D., Kristoffersen, E., Staver, T.T.,Westgaard, S., (2016), “Modeling The UK Electricity Price Distributions Using Quantile Regression”, Energy, 102, pp. 231-243. doi.org/10.1016/j.energy.2016.02.025
- Hamzaçebi, C., Kutay, F. (2004), “Yapay Sinir Ağları ile Türkiye Elektrik Enerjisi Tüketiminin 2010 Yılına Kadar Tahmini”, Gazi Üniv Müh Mim Fak Der, 19 (3), s.227-233.
- Hsu, C.,Chen, C.,(2003), “Regional Load Forecasting in Taiwan––Applications of Artificial Neural Networks”, Energy Conversion and Management, 44, pp.1941–1949. doi.org/10.1016/S0196-8904(02)00225-X
- Kialashaki, A., Reisel, J.R., (2014), “Development And Validation Of Artificial Neural Network Models Of The Energy Demand In The Industrial Sector Of The United State”, Energy, 76,pp. 749-760. doi.org/10.1016/j.energy.2014.08.072
- Kutlar,A.,(2009), Uygulamalı Ekonometri,3.Baskı, Nobel Yayınları, Ankara.
- Li, Z., Hurn, A.S., Clements, A.E., (2017), “Forecasting Quantiles Of Day-Ahead Electricity Load”, Energy Economics, 67 September 2017, pp.60–7. doi.org/10.1016/j.eneco.2017.08.002
- Niu, S., Jia, Y., Ye, L., Dai, R., Li, N., (2016), “ Does Electricity Consumption Improve Residential Living Status In Less Developed Regions? An Empirical Analysis Using The Quantile Regression Approach”, Energy,95, pp.550-560. doi.org/10.1016/j.energy.2015.12.029
- Pino-Mejías, R. , Perez-Fargallo, A. , Rubio-Bellido, C. , Pulido-Arcas, C., (2017), “Comparison of linear regression and artificial neural networks models to predict heating and cooling energy demand, energy consumption and CO2 emissions” , Energy 118, pp. 24-36. doi.org/10.1016/j.energy.2016.12.022
- Wang,S., Yu, Y., Zou,Z.,(2018), “Statistical regression modeling for energy consumption in wastewater treatment”, Journal of Environmental Sciences. doi.org/10.1016/j.jes.2018.03.023
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | December 31, 2018 |
| Submission Date | August 13, 2018 |
| Acceptance Date | October 19, 2018 |
| Published in Issue | Year 2018 Volume: 23 Issue: 4 |
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