首页|Electric demand forecasting with neural networks and symbolic time series representations
Electric demand forecasting with neural networks and symbolic time series representations
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
Elsevier
This paper addresses the electric demand prediction problem using neural networks and symbolization techniques. Symbolization techniques provide a time series symbolic representation of a lower length than the original time series. In our methodology, we incorporate the use of encoding from ordinal regression, preserving the notation of order between the symbols and make extensive experimentation with different neural network architectures and symbolization techniques. In our experimentation, we used the total electric demand data in the Spanish peninsula electric network, taken from 2009 to 2019 with a granularity of 10 min. The best model found making use of the symbolization methodology offered us slightly worse quality metrics (1.3655 RMSE and 0.0390 MAPE instead of the 1.2889 RMSE and 0.0363 MAPE from the best numerical model) but it was trained 6826 times faster. (C) 2022 Elsevier B.V. All reserved.
Time seriesForecastingSymbolic representationEnergy demandArtificial neural networks
Criado-Ramon, D.、Ruiz, L. G. B.、Pegalajar, M. C.
展开 >
Periodista Daniel Saucedo Aranda Sn, Granada 18071, Spain
NSTL
Elsevier
