Using big data for insights into sustainable energy consumption in industrial and mining sectors

Human activities related to the production and consumption of energy are negatively impacting the environment and need to be addressed by focusing on cleaner production. With this perspective, this study investigates sustainable energy consumption in the industrial and mining sectors. Although these sectors are experiencing exponential data growth, their adoption of big data technologies is limited. Hence, big data methods were applied to design a big data system that makes novel use of microservices and containers for modularity and extensibility. A hybrid architecture incorporating characteristics from polyglot persistence, data lakes and Lambda architecture was used. MongoDB served as the primary data store. The system was implemented for an engineering services company. The implementation yielded seven case studies, which illustrate the impact of big data on decision-making at the various levels of the hierarchy within an organisation. Valuable insights were delivered to personnel at the different levels of the organisational hierarchy. By developing these insights, directed at the appropriate personnel, improved decisions were taken and suitable actions initiated to achieve sustainable energy consumption reductions. The overall impact of the case studies was an energy saving of 75 622 MWh, a reduction of 74111 Mg of carbon-dioxide-emission, and a saving of 108 141 m(3) of water. Sustained reductions in energy consumption result in cleaner production and will eventually aid in addressing the various energy challenges confronting the economy. (C) 2018 Elsevier Ltd. All rights reserved.