Load forecasting using a multivariate meta-learning system (CROSBI ID 192172)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Matijaš, Marin ; Suykens, Johan A.K. ; Krajcar, Slavko
engleski
Load forecasting using a multivariate meta-learning system
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.
electricity consumption prediction; energy expert systems; industrial applications; short-term electric load forecasting; meta-learning; power demand estimation
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Podaci o izdanju
40 (11)
2013.
4427-4437
objavljeno
0957-4174
10.1016/j.eswa.2013.01.047
Povezanost rada
Elektrotehnika, Računarstvo