Predicting electricity consumption and cost for South African mines

Abstract

Electricity costs in South Africa have risen steeply; there are a number of factors that have contributed to this increase. The increased costs have a considerable infuence on the mines and mining sector in general. It requires considerable planning to assist mines in such management. The present study addresses the development of a way to predict both electricity consumption and costs, which general involves a large range of personnel. The majority of planning personnel can be more usefully employed in other ways. The goal is not to replace such planners but make them more task effective. Automation, which will reduce their workload, may have little or no effect on performance. In some cases, however, automation may produce better results. There is a complex system to be analysed in the prediction of electricity consumption and costs. The existing prediction methodology is investigated in this study; the investigation highlights the need for a new methodology. The new method should be automated, easier to use and more accurate. Such a model is developed. The new prediction methodology extracts data from the monthly Eskom bills and stores it in a database. The data is grouped according to a new model and then normalised. An artificial neural network is used to "learn" the dynamics of the data to calculate new future electricity consumption. Electricity costs are predicted by multiplying the predicted electrical consumption with a calculated factor based on cost per electricity unit of the previous year with the expected increase added. The new methodology is integrated in a commercial energy management platform named Management Toolbox, which offers a range of functionality. In this study the prediction of electricity consumption and costs are implemented. The implementation is executed with simplicity in mind and care is taken to present the user with the optimal amount of data. The performance of the electricity consumption prediction is sensitive to production changes and the quality of the data history. Performance of the electricity costs prediction model is an improvement over the existing prediction method. The proposed methodology has greater accuracy and uses less personnel, which can lead to using most of the personnel on more important tasks.