A novel weight-based ensemble method for emerging energy players: an application to electric vehicle load prediction

The emergence of new resources and services in the electricity system implies that more and more agents need to obtain more accurate forecasts to optimize their operations. It is common for these agents to have different sources of forecasts (from specialized consultants or meteorological services, among others).

The proposed approach aims to obtain more accurate predictions by optimally combining a set of predictions obtained by different techniques. In this way it is possible to obtain a resulting prediction that improves the error and uncertainty associated with each of the individual forecasts. The objective is achieved by the analytical minimization of the errors obtained by each of the individual predictors. This allows to obtain dynamically the optimized weights assigned to each of the algorithms so that the combination outperforms the individual behaviour of each of them. The proposed ensemble approach has been successfully tested on a real time series of electric vehicle charging.

Likewise, the results obtained have been compared exhaustively with other ensemble techniques consolidated in the literature based on different methods, including dynamic ensembles as machine learning approaches. The results obtained show an appreciable improvement of the errors obtained in the predictions using the proposed techniques.