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

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. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

Forecasting energy demand for the industrial sector is complex due to specific energy demand differences in each sub-sector. A model using multiple linear regression method and macroeconomic variables was developed and tested in Morocco, revealing a need for 8.27 MToe in 2050 and potential energy savings through efficiency measures. The study also showed that biogas could only replace 36.4% of LPG demand.