Electricity Demand Forecasting: The Uruguayan Case

The development of new electricity generation technologies has given new opportunities to developing economies. These economies are often highly dependent on fossil sources and so on the price of petrol. Uruguay has finished the transformation of its energetic mix, presenting today a very large participation of renewable sources among its production mix. This rapid change has demanded new mathematical and computing methods for the administration and monitoring of the system load. In this work we present enercast, a R package that contains prediction models that can be used by the network operator. The prediction models are divided in two groups, exogenous and endogenous models, that respectively uses external covariates or not. Each model is used to produce daily prediction which are then combined using a sequential aggregation algorithm. We show by numerical experiments the appropriateness of our end-to-end procedure applied to real data from the Uruguayan electrical system.