Modelling industry energy demand using multiple linear regression analysis based on consumed quantity of goods

Mohamed Maaouane, Smail Zouggar, Goran Krajačić and Hassan Zahboune

Energy, 2021, vol. 225, issue C

Abstract: Forecasting energy demand for the industrial sector is both interesting and difficult due to the difference in energy demand specific to each industrial sub-sector. For an accurate prediction of the future, Industry Energy Demand model was developed based on multiple linear regression method, using five macroeconomic independent variables. This model was tested by considering Morocco as a study case. Energy demand forecast is based on a bottom-up approach. It is built by piecing together consumed quantity of goods of each sub-sector to give rise to total energy demand. This model produces results comparable to those of the International Energy Agency. Regarding demand forecast, it was found that 8.27 MToe will be needed in 2050 to meet energy demand. It was also found that the adoption of energy efficiency measures allow an energy saving of 1 MToe in 2050. This model was also used to test the impact of variation in import and export on final energy demand. Regarding the potential of the production of biogas from Municipal Solid Waste, it was found that only 36.4% of total Liquefied Petroleum Gas demand could be replaced by biogas.

Keywords: Industry; Energy demand; Linear regression; Consumed quantity; Biogas (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:225:y:2021:i:c:s0360544221005193

DOI: 10.1016/j.energy.2021.120270

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