Electricity Consumption Forecast in an Industry Facility to Support Production Planning Update in Short Time

The global environmental concerns raise the need to decrease energy, namely electricity consumption. Energy consumption can be reduced by improving energy efficiency and by improving the optimization of energy management in each context. These opportunities are very relevant in buildings and industry facilities. In order to improve the optimized energy management, adequate forecasting tools are needed regarding the load consumption patterns in each building. In the present paper, two forecasting technics, namely neural networks, and support vector machine, are used to predict the consumption of an industry facility for each 5 minutes. The proposed model finds the best method in order to be used in a later stage regarding the updated of production planning. The size of historic data is also discussed. The case study includes one-week test data and more than one-year train data.