Energy Consumption Forecast Procedure for an Industrial Facility
We regard forecasting of energy consumption by
private production areas of a large industrial facility as well as by the
facility itself. As for production areas, the forecast is made based on
empirical dependencies of the specific energy consumption and the
production output. As for the facility itself, implementation of the
task to minimize the energy consumption forecasting error is based
on adjustment of the facility’s actual energy consumption values
evaluated with the metering device and the total design energy
consumption of separate production areas of the facility. The
suggested procedure of optimal energy consumption was tested based
on the actual data of core product output and energy consumption by
a group of workshops and power plants of the large iron and steel
facility. Test results show that implementation of this procedure gives
the mean accuracy of energy consumption forecasting for winter
2014 of 0.11% for the group of workshops and 0.137% for the power
plants.
[1] W.C. Turner, S. Doty, Energy Management Handbook, Library of
Congress Cataloging in Publication Data, 2007.
[2] D. Niu, J. Wang, D.D. Wu, Power load forecasting using support vector
machine and ant colony optimization, Expert Systems with Applications,
2010, vol. 37, pp. 2531–2539.
[3] D. Niu, J. Li, J. Li, D. Liu, Middle long power load forecasting based on
particle swarm optimization, Compt. Mathem. Appl. 2009, vol. 57, pp.
1883–1889.
[4] K. Nolde, M. Morari, Electrical load tracking scheduling of a steel
plant, Compt. Chem. Eng., 2010, vol. 34, pp. 1899–1903.
[5] M. Amina, V.S. Kodogiannis, I. Petrounias, D. Tomtsis, A hybrid
intelligent approach for the prediction of electricity consumption, Electr.
Power Energy Syst., 2012, vol. 43, pp. 1–108.
[6] F.M Andersen, H.V. Larsen, T.K. Boomsma, Longterm forecasting of
hourly electricity load: Identification of consumption profiles and
segmentation of customers, Energy Conver. Manag., 2013, vol. 68, pp.
244–252.
[7] L.S. Kazarinov, D.A. Shnayder, T.A. Barbasova, Automated Control
Systems in Energy Saving (Development Experience), Chelyabinsk:
Publisher SUSU, 2010, 228 p.
[8] T.A .Barbasova, A.A. Zakharova, The Introduction of an Energy
Management System at the Metallurgical Enterprises of the Chelyabinsk
Region in Order to Increase Energy Efficiency in the Region.
Ekonomika v promyshlennosti, vol.3, 2012, pp. 42-46
[9] T.A Barbasova, A.A. Zakharova, Ways to Increase the Energy
Efficiency of the Chelyabinsk Region. Innov. Vestnik Region, 2012, vol.
2, pp. 69-75
[10] Yu.A. Bodyayev, Yu.P. Zhuravlev, L.A. Koptsev, S.V Prokhorov., I.D.
Novitskiy, Energy Balance Optimization and Operating Mode Selection
of Arc Steel Furnace. Steel, 2010, vol. 2, pp. 29-31.
[11] A.G. Ivakhnenko, Long-Term Forecasting and Control of Complex
Systems. Publisher Tekhnika, 1975, 312 p.
[12] L.S. Kazarinov, System Studies and Management / Cognitive Approach.
– Chelyabinsk: Publisher SUSU, 2011, 524 p.
[13] L.S. Kazarinov, T.A. Barbasova, A.A. Zakharova, Optimal Prediction of
Energy Resources Consumption, in Value Criterion. Vestn. Yuzh._Ural.
Gos. Univ. Ser. Komp. Tekhn., Upravl., Radioelektr.,, 2013, Volume 13,
vol.1, pp. 90–94.
[14] L.S Kazarinov., T.A. Barbasova, A.A Zakharova, Automated
Information Decision Support System on Control and Planning Energy
Resources Usage. Vestn. Yuzh._Ural. Gos. Univ. Ser. Komp. Tekhn.,
Upravl., Radioelektr., 2012, vol.23, pp. 118–122.
[15] L.A. Koptsev, Energy Saving and Economic Efficiency Increase of the
Enterprise by Loading Control of Production. Industrial energy. – 2011,
vol. 11, pp. 14-21.
[16] L. A. Koptsev, Rationing and Electricity Consumption Forecasting
Depending on Outputs. Industrial energy, 1996, vol. 3, pp. 5-7.
[17] L. A. Koptsev, Yu. P. Zhuravlev, V. V. Zuyevskiy, Energy Balance
Optimization of Arc Steel Furnaces on the Basis of a Linear
Programming Method. Steel, 2008, vol. 9, pp. 92-95.
[1] W.C. Turner, S. Doty, Energy Management Handbook, Library of
Congress Cataloging in Publication Data, 2007.
[2] D. Niu, J. Wang, D.D. Wu, Power load forecasting using support vector
machine and ant colony optimization, Expert Systems with Applications,
2010, vol. 37, pp. 2531–2539.
[3] D. Niu, J. Li, J. Li, D. Liu, Middle long power load forecasting based on
particle swarm optimization, Compt. Mathem. Appl. 2009, vol. 57, pp.
1883–1889.
[4] K. Nolde, M. Morari, Electrical load tracking scheduling of a steel
plant, Compt. Chem. Eng., 2010, vol. 34, pp. 1899–1903.
[5] M. Amina, V.S. Kodogiannis, I. Petrounias, D. Tomtsis, A hybrid
intelligent approach for the prediction of electricity consumption, Electr.
Power Energy Syst., 2012, vol. 43, pp. 1–108.
[6] F.M Andersen, H.V. Larsen, T.K. Boomsma, Longterm forecasting of
hourly electricity load: Identification of consumption profiles and
segmentation of customers, Energy Conver. Manag., 2013, vol. 68, pp.
244–252.
[7] L.S. Kazarinov, D.A. Shnayder, T.A. Barbasova, Automated Control
Systems in Energy Saving (Development Experience), Chelyabinsk:
Publisher SUSU, 2010, 228 p.
[8] T.A .Barbasova, A.A. Zakharova, The Introduction of an Energy
Management System at the Metallurgical Enterprises of the Chelyabinsk
Region in Order to Increase Energy Efficiency in the Region.
Ekonomika v promyshlennosti, vol.3, 2012, pp. 42-46
[9] T.A Barbasova, A.A. Zakharova, Ways to Increase the Energy
Efficiency of the Chelyabinsk Region. Innov. Vestnik Region, 2012, vol.
2, pp. 69-75
[10] Yu.A. Bodyayev, Yu.P. Zhuravlev, L.A. Koptsev, S.V Prokhorov., I.D.
Novitskiy, Energy Balance Optimization and Operating Mode Selection
of Arc Steel Furnace. Steel, 2010, vol. 2, pp. 29-31.
[11] A.G. Ivakhnenko, Long-Term Forecasting and Control of Complex
Systems. Publisher Tekhnika, 1975, 312 p.
[12] L.S. Kazarinov, System Studies and Management / Cognitive Approach.
– Chelyabinsk: Publisher SUSU, 2011, 524 p.
[13] L.S. Kazarinov, T.A. Barbasova, A.A. Zakharova, Optimal Prediction of
Energy Resources Consumption, in Value Criterion. Vestn. Yuzh._Ural.
Gos. Univ. Ser. Komp. Tekhn., Upravl., Radioelektr.,, 2013, Volume 13,
vol.1, pp. 90–94.
[14] L.S Kazarinov., T.A. Barbasova, A.A Zakharova, Automated
Information Decision Support System on Control and Planning Energy
Resources Usage. Vestn. Yuzh._Ural. Gos. Univ. Ser. Komp. Tekhn.,
Upravl., Radioelektr., 2012, vol.23, pp. 118–122.
[15] L.A. Koptsev, Energy Saving and Economic Efficiency Increase of the
Enterprise by Loading Control of Production. Industrial energy. – 2011,
vol. 11, pp. 14-21.
[16] L. A. Koptsev, Rationing and Electricity Consumption Forecasting
Depending on Outputs. Industrial energy, 1996, vol. 3, pp. 5-7.
[17] L. A. Koptsev, Yu. P. Zhuravlev, V. V. Zuyevskiy, Energy Balance
Optimization of Arc Steel Furnaces on the Basis of a Linear
Programming Method. Steel, 2008, vol. 9, pp. 92-95.
@article{"International Journal of Business, Human and Social Sciences:71572", author = "Tatyana Aleksandrovna Barbasova and Lev Sergeevich Kazarinov and Olga Valerevna Kolesnikova and Aleksandra Aleksandrovna Filimonova", title = "Energy Consumption Forecast Procedure for an Industrial Facility", abstract = "We regard forecasting of energy consumption by
private production areas of a large industrial facility as well as by the
facility itself. As for production areas, the forecast is made based on
empirical dependencies of the specific energy consumption and the
production output. As for the facility itself, implementation of the
task to minimize the energy consumption forecasting error is based
on adjustment of the facility’s actual energy consumption values
evaluated with the metering device and the total design energy
consumption of separate production areas of the facility. The
suggested procedure of optimal energy consumption was tested based
on the actual data of core product output and energy consumption by
a group of workshops and power plants of the large iron and steel
facility. Test results show that implementation of this procedure gives
the mean accuracy of energy consumption forecasting for winter
2014 of 0.11% for the group of workshops and 0.137% for the power
plants.", keywords = "Energy consumption, energy consumption
forecasting error, energy efficiency, forecasting accuracy,
forecasting.", volume = "9", number = "12", pages = "4211-4", }
