Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization. (1st December 2016)

• Record Type: Journal Article
• Authors: Sen, Parag; Roy, Mousumi; Pal, Parimal
• Is Part Of: Energy. Volume 116:Part 1(2016)
• Page Start: 1031
• Page End: 1038
• Publication Date: 2016-12-01
• Subjects: Environmentally conscious manufacturing programs -- ARIMA -- Forecasting -- Energy consumption -- GHG emission -- Pig iron manufacturing; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2016.10.068
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Environmentally conscious manufacturing (ECM) has become an important strategy and proactive approach for the iron and steel sector of India to produce environment friendly and to reduce manufacturing cost. There are several environmentally conscious manufacturing indicators to evaluate ECM programs. Among those indicators, energy consumption and greenhouse (GHG) emission may be considered critical environmentally conscious manufacturing indicators (CECMI) for Indian iron and steel sector. This paper focuses on forecasting energy consumption and GHG emission for a pig iron manufacturing organization of India because the managers are interested to know the current and future trends of these indicators for better environmental policy. For forecasting purpose, autoregressive integrated moving average (ARIMA) is applied to reveal that ARIMA (1, 0, 0) × (0, 1, 1) is the best fitted model for energy consumption. Regarding GHG emission, ARIMA (0, 1, 4) × (0, 1, 1) is the best fitted model. In both cases, the forecasts resemble those of the seasonal random trend model, however they appear smoother because the seasonal pattern and the trend are efficiently averaged for energy consumption and as well as GHG emission. Selection of the correct ARIMA models for these indicators will help in accurate forecasting in order to achieve better environmental management practice. Highlights: Discuss case study for an Indian pig iron manufacturing organization. Find out best fitted ARIMA model for energy consumption. Find out best fitted ARIMA model for GHG emission. Find out forecasting results. May help to develop better environmental policy.