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

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Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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.

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References

  1. Tiwari, M.K.: An empirical analysis of effect of advertising on marketing of FMCG product. Int. J. Mark. Technol. 2(2012), 167, 200 (2012)

    Google Scholar 

  2. Bodyanskiy, Y., Popov, S.: Neural network approach to forecasting of quasiperiodic financial time series. Eur. J. Oper. Res. 175(3), 1357–1366 (2006)

    Article  MathSciNet  Google Scholar 

  3. Flood, I., Kartam, N.: Neural networks in civil engineering II: systems and application. Comput. Civ. Eng. 8(2), 149–162 (1994)

    Google Scholar 

  4. Flood, I., Christophilos, P.: Modeling construction processes using artificial neural networks. Autom. Constr. 4, 307–320 (1996)

    Google Scholar 

  5. Szoplik, J.: Forecasting of natural gas consumption with artificial neural networks. Energy 85, 208–220 (2015). https://doi.org/10.1016/j.energy.2015.03.084

    Article  Google Scholar 

  6. Androjića, I., Dolaček-Aldukc, Z.: Artificial neural network model for forecasting energy consumption in hot mix asphalt (HMA) production. Constr. Build. Mater. 170, 424–432 (2018). https://doi.org/10.1016/j.conbuildmat.2018.03.086

    Article  Google Scholar 

  7. Li, G., Shi, J.: On comparing three artificial neural networks for wind speed forecasting. Appl. Energy 87(7), 2313–2320 (2010). https://doi.org/10.1016/j.apenergy.2009.12.013

    Article  Google Scholar 

  8. Biswas, M.R., Robinson, M.D., Fumo, N.: Prediction of residential building energy consumption: a neural network approach. Energy 117, 84–92 (2016). https://doi.org/10.1016/j.energy.2016.10.066

    Article  Google Scholar 

  9. Keles, D., Scelle, J., Paraschiv, F., Fichtner, W.: Extended forecast methods for day ahead electricity spot prices applying artificial neural networks. Appl. Energy 162, 218–230 (2016). https://doi.org/10.1016/j.apenergy.2015.09.087

    Article  Google Scholar 

  10. Panapakidis, I., Dagoumas, A.S.: Day-ahead electricity price forecasting via the application of artificial neural network based models. Appl. Energy 172, 132–151 (2016). https://doi.org/10.1016/j.apenergy.2016.03.089

    Article  Google Scholar 

  11. Chae, Y.T., Horesh, R., Hwang, Y., Lee, Y.M.: Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings. Energy Build. 111, 184–194 (2016). https://doi.org/10.1016/j.enbuild.2015.11.045

    Article  Google Scholar 

  12. Ceylan, H., Ozturk, H.K.: Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach. Energy Convers. Manag. 45, 2525–2537 (2004)

    Article  Google Scholar 

  13. Zhang, G., Patuwo, B.E., Hu, M.Y.: Forecasting with artificial neural networks: the state of the art. Int. J. Forecast. 14, 35–62 (1998)

    Article  Google Scholar 

  14. Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of ICML, vol. 30, pp. S807–S814 (2010)

    Google Scholar 

  15. Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks (2017). arXiv preprint arXiv:1706.02515

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Correspondence to Bersam Bolat .

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Yeşil, G., Bolat, B. (2020). Electricity Consumption Forecasting with Artificial Neural Network for Fast-Moving Consumer Goods Sector. In: Durakbasa, N., Gençyılmaz, M. (eds) Proceedings of the International Symposium for Production Research 2019. ISPR ISPR 2019 2019. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-31343-2_5

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  • DOI: https://doi.org/10.1007/978-3-030-31343-2_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31342-5

  • Online ISBN: 978-3-030-31343-2

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