Load forecasting using a multivariate meta-learning system

Authors:
Marin Matijaš;Johan A. K. Suykens;Slavko Krajcar
Affiliations:
Department of Electrical Engineering, ESAT-SCD-SISTA, KU Leuven, B-3001, Leuven, Belgium and Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia;Department of Electrical Engineering, ESAT-SCD-SISTA, KU Leuven, B-3001, Leuven, Belgium;Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

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Abstract

Although over a thousand scientific papers address the topic of load forecasting every year, only a few are dedicated to finding a general framework for load forecasting that improves the performance, without depending on the unique characteristics of a certain task such as geographical location. Meta-learning, a powerful approach for algorithm selection has so far been demonstrated only on univariate time-series forecasting. Multivariate time-series forecasting is known to have better performance in load forecasting. In this paper we propose a meta-learning system for multivariate time-series forecasting as a general framework for load forecasting model selection. We show that a meta-learning system built on 65 load forecasting tasks returns lower forecasting error than 10 well-known forecasting algorithms on 4 load forecasting tasks for a recurrent real-life simulation. We introduce new metafeatures of fickleness, traversity, granularity and highest ACF. The meta-learning framework is parallelized, component-based and easily extendable.