2026-04-27T12:59:41Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/541572025-02-13T09:20:10Zcom_10324_1191com_10324_931com_10324_894col_10324_1379

López Martín, Manuel Sánchez Esguevillas, Antonio Javier Hernández Callejo, Luis Arribas Sánchez, Juan Ignacio Carro Martínez, Belén 2022-07-22T08:31:23Z 2022-07-22T08:31:23Z 2021 Applied Sciences, 2021, vol. 11, n. 12, p. 5708 2076-3417 https://uvadoc.uva.es/handle/10324/54157 10.3390/app11125708 5708 12 Applied Sciences 11 2076-3417 This work brings together and applies a large representation of the most novel forecasting techniques, with origins and applications in other fields, to the short-term electric load forecasting problem. We present a comparison study between different classic machine learning and deep learning techniques and recent methods for data-driven analysis of dynamical models (dynamic mode decomposition) and deep learning ensemble models applied to short-term load forecasting. This work explores the influence of critical parameters when performing time-series forecasting, such as rolling window length, k-step ahead forecast length, and number/nature of features used to characterize the information used as predictors. The deep learning architectures considered include 1D/2D convolutional and recurrent neural networks and their combination, Seq2seq with and without attention mechanisms, and recent ensemble models based on gradient boosting principles. Three groups of models stand out from the rest according to the forecast scenario: (a) deep learning ensemble models for average results, (b) simple linear regression and Seq2seq models for very short-term forecasts, and (c) combinations of convolutional/recurrent models and deep learning ensemble models for longer-term forecasts. eng info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ © 2021 The Author(s) Atribución 4.0 Internacional Novel data-driven models applied to short-term electric load forecasting info:eu-repo/semantics/article