Numerical computation and prediction of electricity consumption in tobacco industry
Abstract
Electricity is a key energy source in each country and an important condition for economic development. It is necessary to use modern methods and tools to predict energy consumption for different types of systems and weather conditions. In every industrial plant, electricity consumption presents one of the greatest operating costs. Monitoring and forecasting of this parameter provide the opportunity to rationalize the use of electricity and thus significantly reduce the costs. The paper proposes the prediction of energy consumption by a new time-series model. This involves time series models using a set of previously collected data to predict the future load. The most commonly used linear time series models are the AR (Autoregressive Model), MA (Moving Average) and ARMA (Autoregressive Moving Average Model). The AR model is used in this paper. Using the AR (Autoregressive Model) model, the Monte Carlo simulation method is utilized for predicting and analyzing the energy consumption cha...nge in the considered tobacco industrial plant. One of the main parts of the AR model is a seasonal pattern that takes into account the climatic conditions for a given geographical area. This part of the model was delineated by the Fourier transform and was used with the aim of avoiding the model complexity. As an example, the numerical results were performed for tobacco production in one industrial plant. A probabilistic range of input values is used to determine the future probabilistic level of energy consumption.
Keywords:
Seasonality Pattern / Monte Carlo Simulation / Forecasting of Electricity / Electricity Consumption / AR ModelSource:
Facta Universitatis-Series Mechanical Engineering, 2017, 15, 3, 457-465Publisher:
- Univerzitet u Nišu, Niš
DOI: 10.22190/FUME170927025L
ISSN: 0354-2025
WoS: 000424114700008
Scopus: 2-s2.0-85037687952
Collections
Institution/Community
Mašinski fakultetTY - JOUR AU - Laković, Mirjana S. AU - Pavlović, Ivan AU - Banjac, Miloš AU - Jović, Milica M. AU - Mančić, Marko PY - 2017 UR - https://machinery.mas.bg.ac.rs/handle/123456789/2578 AB - Electricity is a key energy source in each country and an important condition for economic development. It is necessary to use modern methods and tools to predict energy consumption for different types of systems and weather conditions. In every industrial plant, electricity consumption presents one of the greatest operating costs. Monitoring and forecasting of this parameter provide the opportunity to rationalize the use of electricity and thus significantly reduce the costs. The paper proposes the prediction of energy consumption by a new time-series model. This involves time series models using a set of previously collected data to predict the future load. The most commonly used linear time series models are the AR (Autoregressive Model), MA (Moving Average) and ARMA (Autoregressive Moving Average Model). The AR model is used in this paper. Using the AR (Autoregressive Model) model, the Monte Carlo simulation method is utilized for predicting and analyzing the energy consumption change in the considered tobacco industrial plant. One of the main parts of the AR model is a seasonal pattern that takes into account the climatic conditions for a given geographical area. This part of the model was delineated by the Fourier transform and was used with the aim of avoiding the model complexity. As an example, the numerical results were performed for tobacco production in one industrial plant. A probabilistic range of input values is used to determine the future probabilistic level of energy consumption. PB - Univerzitet u Nišu, Niš T2 - Facta Universitatis-Series Mechanical Engineering T1 - Numerical computation and prediction of electricity consumption in tobacco industry EP - 465 IS - 3 SP - 457 VL - 15 DO - 10.22190/FUME170927025L ER -
@article{ author = "Laković, Mirjana S. and Pavlović, Ivan and Banjac, Miloš and Jović, Milica M. and Mančić, Marko", year = "2017", abstract = "Electricity is a key energy source in each country and an important condition for economic development. It is necessary to use modern methods and tools to predict energy consumption for different types of systems and weather conditions. In every industrial plant, electricity consumption presents one of the greatest operating costs. Monitoring and forecasting of this parameter provide the opportunity to rationalize the use of electricity and thus significantly reduce the costs. The paper proposes the prediction of energy consumption by a new time-series model. This involves time series models using a set of previously collected data to predict the future load. The most commonly used linear time series models are the AR (Autoregressive Model), MA (Moving Average) and ARMA (Autoregressive Moving Average Model). The AR model is used in this paper. Using the AR (Autoregressive Model) model, the Monte Carlo simulation method is utilized for predicting and analyzing the energy consumption change in the considered tobacco industrial plant. One of the main parts of the AR model is a seasonal pattern that takes into account the climatic conditions for a given geographical area. This part of the model was delineated by the Fourier transform and was used with the aim of avoiding the model complexity. As an example, the numerical results were performed for tobacco production in one industrial plant. A probabilistic range of input values is used to determine the future probabilistic level of energy consumption.", publisher = "Univerzitet u Nišu, Niš", journal = "Facta Universitatis-Series Mechanical Engineering", title = "Numerical computation and prediction of electricity consumption in tobacco industry", pages = "465-457", number = "3", volume = "15", doi = "10.22190/FUME170927025L" }
Laković, M. S., Pavlović, I., Banjac, M., Jović, M. M.,& Mančić, M.. (2017). Numerical computation and prediction of electricity consumption in tobacco industry. in Facta Universitatis-Series Mechanical Engineering Univerzitet u Nišu, Niš., 15(3), 457-465. https://doi.org/10.22190/FUME170927025L
Laković MS, Pavlović I, Banjac M, Jović MM, Mančić M. Numerical computation and prediction of electricity consumption in tobacco industry. in Facta Universitatis-Series Mechanical Engineering. 2017;15(3):457-465. doi:10.22190/FUME170927025L .
Laković, Mirjana S., Pavlović, Ivan, Banjac, Miloš, Jović, Milica M., Mančić, Marko, "Numerical computation and prediction of electricity consumption in tobacco industry" in Facta Universitatis-Series Mechanical Engineering, 15, no. 3 (2017):457-465, https://doi.org/10.22190/FUME170927025L . .