Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization

Energy

Volumn 116, Issue , 2016, Pages 1031-1038

  Sen, Parag ^a   Roy, Mousumi ^a   Pal, Parimal ^a  

^a NATIONAL INSTITUTE OF TECHNOLOGY   (India)

Author keywords

ARIMA; Energy consumption; Environmentally conscious manufacturing programs; Forecasting; GHG emission; Pig iron manufacturing

Indexed keywords

ENERGY POLICY; ENERGY UTILIZATION; ENVIRONMENTAL MANAGEMENT; ENVIRONMENTAL PROTECTION; FORECASTING; MAMMALS; MANUFACTURE; PIG IRON;

ARIMA; AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE; ENVIRONMENT FRIENDLY; ENVIRONMENTAL MANAGEMENT PRACTICES; ENVIRONMENTAL POLICY; ENVIRONMENTALLY CONSCIOUS MANUFACTURING; GHG EMISSION; MANUFACTURING ORGANIZATIONS;

GREENHOUSE GASES;

ENVIRONMENTAL ECONOMICS; ENVIRONMENTAL MANAGEMENT; ENVIRONMENTAL POLICY; FORECASTING METHOD; FUEL CONSUMPTION; GREENHOUSE GAS; IRON AND STEEL INDUSTRY; MANUFACTURING; SEASONAL VARIATION;

INDIA;

SUIDAE;

EID: 84993993175     PISSN: 03605442     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.energy.2016.10.068     Document Type: Article

References (37)

1. [1] Marchi, V., Environmental innovation and R&D cooperation: empirical evidence from Spanish manufacturing firms. Res Policy 41 (2012), 614–623.
2. [2] Li, G.C., Huang, G.H., Lin, Q.G., Zhang, X.D., Tan, Q., Chen, Y.M., Development of a GHG-mitigation oriented inexact dynamic model for regional energy system management. Energy 36:5 (2011), 3388–3398.
3. [3] Curkovic, S., Environmentally Responsible Manufacturing: the development and validation of a measurement model. Eur J Oper Res 146 (2003), 130–155.
4. [4] Pukšec, T., Mathiesen, B.V., Novosel, T., Duic, N., Assessing the impact of energy saving measures on the future energy demand and related GHG (greenhouse gas) emission reduction of Croatia. Energy 76:1 (2014), 198–209.
5. [5] Delarue, E., Lamberts, H., D'haeseleer, W., Simulating greenhouse gas (GHG) allowance cost and GHG emission reduction in Western Europe. Energy 32:8 (2007), 1299–1309.
6. [6] Fang, K., Uhan, N., Zhao, F., Sutherland, J.W., A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction. J Manuf Syst 30 (2011), 234–240.
7. [7] Garg, A., Bhattacharya, S., Shukla, P.R., Dadhwal, V.K., Regional and sectoral assessment of greenhouse gas emissions in India. Atmos Environ 35:15 (2001), 2679–2695.
8. [8] Lee, A.H.I., Kang, H.Y., Hsu, C.F., Hung, H.C., A green supplier selection model for high-tech industry. Expert Syst Appl 36 (2009), 7917–7927.
9. [9] Madu, C.N., Kuei, C., Madu, I.E., A hierarchic metric approach for integration of green issues in manufacturing: a paper recycling application. J Environ Manag 64 (2002), 261–272.
10. [10] Zhu, Q., Geng, Y., Sarkis, J., Lai, K., Evaluating green supply chain management among Chinese manufacturers from the ecological modernization perspective. Transp Res Part E 47 (2011), 808–821.
11. [11] Hidalgo, L.M.R., Peralta-Suárez, L.M., Yera, Y.B., Development of a technology for treating wastewater contaminated with nitric acid. J Waste Manag, 2013, 1–7.
12. [12] Sundarakani, B., Souza, R., Goh, M., Wagner, S.M., Manikandan, S., Modeling carbon footprints across the supply chain. Int J Prod Econ 128 (2010), 43–50.
13. [13] Branker, K., Jeswiet, J., Kim, I.Y., Greenhouse gases emitted in manufacturing a product—A new economic model. CIRP Ann Manuf Technol 60 (2011), 53–56.
14. [14] Doushanov, D.L., Control of pollution in the iron and steel industry. Pollut Control Tecnol 3 (2010), 32–34 (accessed on 14.August.2014.) www.eolss.net/sample-chapters/c09/e4-14-04-04.pdf.
15. [15] Bassi, A.M., Tan, Z., Mbi, A., Estimating the impact of investing in a resource efficient, resilient global energy-intensive manufacturing industry. Technol Forecast Soc 79 (2012), 69–84.
16. [16] Ion, V.I., Popescu, F., Georgescu, L., Prediction of the pollutants generation in natural gas/residual steel gases co-combustion. Int J Energ & Environ 2:1 (2007), 79–84.
17. 2 capture. Energy Procedia 37 (2013), 7139–7151.
18. [18] Yeboah, S., Ohene, M., Wereko, T.B., Forecasting aggregate and disaggregate energy consumption using ARIMA models: a literature survey. J Stat Econ Methods 1:2 (2012), 71–79.
19. [19] Siew, L.Y., Chin, L.Y., Wee, M.J., ARIMA and integrated ARFIMA models for forecasting air pollution index in Shah Alam, Selangor. Malays J Anal Sci 12:1 (2008), 257–263.
20. 2 emissions of an integrated UCG-CCS process. Energy 66:1 (2014), 779–790.
21. [21] Nixon, J.D., Dey, P.K., Davies, P.A., Which is the best solar thermal collection technology for electricity generation in north-west India? Evaluation of options using the analytical hierarchy process. Energy 35:1 (2010), 5230–5240.
22. [22] Hsu, D., Identifying key variables and interactions in statistical models of building energy consumption using regularization. Energy 83:1 (2015), 144–155.
23. [23] Ediger, V.S., Akar, S., ARIMA forecasting of primary energy demand by fuel in Turkey. Energ Policy 35:3 (2007), 1701–1708.
24. [24] Liu, C., Wu, K., Tsao, M., Energy efficient information collection with the ARIMA model in wireless sensor networks. Proceedia GLOBECOM (global telecommunications conference), 2005, 2470–2474.
25. [25] Mohamed, Z., Bodger, P., Forecasting electricity consumption in New Zealand using economic and demographic variables. Energy 30 (2005), 1833–1843.
26. [26] Abdel-Aal, R.E., Al-Garni, A.Z., Forecasting monthly electric energy consumption in eastern Saudi Arabia using univariate time-series analysis. Energy 22:11 (1997), 1059–1069.
27. [27] Saab, S., Badr, E., Nasr, G., Univariate modeling and forecasting of energy consumption: the case of electricity in Lebanon. Energy 26:1 (2001), 1–14.
28. [28] Albayrak, A.S., ARIMA forecasting of primary energy production and consumption in Turkey: 1923–2006. Enerji, Piyasa ve Düzenleme 1:1 (2010), 24–50.
29. 2 emissions, energy consumption, and economic growth in Brazil. Energy 36:5 (2011), 2450–2458.
30. [30] Sandvall, A.F., Borjesson, M., Ekvall, T., Ahlgren, E.O., Modelling environmental and energy system impacts of large-scale excess heat utilisation - a regional case study. Energy 79:1 (2015), 68–79.
31. [31] Ang, C.T., Morad, N., Ismail, M.T., Ismail, N., Projection of carbon dioxide emissions by energy consumption and transportation in Malaysia: a time series approach. J Energy Technol Policy 3:1 (2013), 63–75.
32. 2 emissions in the United States. Int J Energy Statistics 1:4 (2013), 269–279.
33. [33] Liu, L., Zong, H., Zhao, E., Chen, C., Wang, J., Can China realize its carbon emission reduction goal in 2020: from the perspective of thermal power development. Appl Energy 124 (2014), 199–212.
34. 2 emissions in Iran using grey and ARIMA models. Int J Energy Econ Policy 3:3 (2013), 229–237.
35. [35] Derwent, R.G., Stevenson, D.S., Doherty, R.M., Collins, W.J., Sanderson, M.G., Johnson, C.E., Clim Change 88:3–4 (2007), 385–401.
36. [36] Abeysinghe, T., Balasooriya, U., Tsui, A., Small-sample forecasting: regression or ARIMA models?. J Quant Econ New Ser 1 (2003), 103–113.
37. [37] Yurekli, K., Kurunc, A., Testing the residuals of an ARIMA model on the cekerek stream watershed in Turkey. Turkish J Eng Environ S. C 29 (2005), 61–74.