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Load forecasting using a multivariate meta-learning system
- Source :
-
Expert Systems with Applications . Sep2013, Vol. 40 Issue 11, p4427-4437. 11p. - Publication Year :
- 2013
-
Abstract
- 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. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 09574174
- Volume :
- 40
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- Expert Systems with Applications
- Publication Type :
- Academic Journal
- Accession number :
- 86885743
- Full Text :
- https://doi.org/10.1016/j.eswa.2013.01.047