A multivariate ensemble learning method for medium-term energy forecasting

In the contemporary context, both production and consumption of energy, being concepts intertwined through a condition of synchronicity, are pivotal for the orderly functioning of society, with their management being a building block in maintaining regularity. Hence, the pursuit to develop reliable computational tools for modeling such serial and time-dependent phenomena becomes similarly crucial. This paper investigates the use of ensemble learners for medium-term forecasting of the Greek energy system load using additional information from injected energy production from various sources. Through an extensive experimental process, over 435 regression schemes and 64 different modifications of the feature inputs were tested over five different prediction time frames, creating comparative rankings regarding two case studies: one related to methods and the other to feature setups. Evaluations according to six widely used metrics indicate an aggregate but clear dominance of a specific efficient and low-cost ensemble layout. In particular, an ensemble method that incorporates the orthogonal matching pursuit together with the Huber regressor according to an averaged combinatorial scheme is proposed. Moreover, it is shown that the use of multivariate setups improves the derived predictions.