Naslov
Load forecasting using a multivariate meta-learning system

Autori
Matijaš, Marin ; Suykens, Johan A.K. ; Krajcar, Slavko

Izvornik
Expert systems with applications (0957-4174) 40 (2013), 11; 4427-4437

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
electricity consumption prediction; energy expert systems; industrial applications; short-term electric load forecasting; meta-learning; power demand estimation

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo

POVEZANOST RADA