Analysing Impacts Responsible for South Africas Energy Consumption: LMDI Application Industry participation is pivotal to the economic growth of any nation. However, its energy demand, if not managed can cripple the economy. South Africa recently experienced load shedding resulting in the decreased of manufacturing and mining outputs among others dragging down the economic growth. This study focused on understanding those factors responsible for the energy consumption in the following industrial subsectors: basic chemicals, nonmetallic minerals, basic iron and steel, basic nonferrous metals and other manufacturing industries, between 1994 and 20016 through the application of Logarithmic Mean Divisia Index (LMDI), a form of Index Decomposition Analysis (IDA). These factors are the activity, structure and intensity. Among the three factors, activity was the most responsible for the increase in the amount of energy consumed whereas intensity factor contributed to minimizing energy consumption. Structural effect contributed minimally to the consumption of energy. The results implied concentrating more on policies that would affect the activity effect.

Predicting Industrial Sectors Energy Consumption: Application of Support Vector Machine To follow in Europeu0027s footstep in minimizing emission rate, much concentration should be on Africau0027s industrial energy consumption. South Africau0027s industrial sector is contributing immensely to the countryu0027s economic growth of which energy plays significant role. To reduce its consumption will mean planning accurately. Forecasting techniques have gained grounds when it comes to planning accessibility to energy demand to prevent incessant increase in emission rate. These techniques include traditional and machine learning techniques. This study applied support vector machine (SVM) of machine learning technique compared to regression analysis of traditional technique. The SVM is applied to forecast South Africau0027s energy consumption of five subsectors (manufacturing, basic nonferrous metals, basic iron and steel, nonmetallic minerals and basic chemicals), with activity, structure and intensity as inputs whereas energy consumed was the output from 1970 to 2016. In contrast to the traditional technique, results confirmed SVM to be a better modelling system in terms of visual inspection (Figures 2 and 3) and statistical measures of performance in Table 1 (correlation coefficient, RMSE and RRSE).