Two-stage decomposition and temporal fusion transformers for interpretable wind speed forecasting

Binrong Wu and Lin Wang

Energy, 2024, vol. 288, issue C

Abstract: Contemporary wind speed prediction research methodologies often employ two-stage decomposition preprocessing techniques to leverage the temporal correlation of wind speed. However, they frequently neglect to investigate the interpretability within the wind speed prediction model. To this end, a novel and interpretable hybrid forecasting model that combines two-layer decomposition, adaptive differential evolution with optional external archive (JADE), and temporal fusion transformers (TFT) is proposed. Primarily, on the basis of the linear-nonlinear decomposition criterion, a set of subcomponents is obtained using two-stage decomposition to fully extract the wind speed series prediction information for high-fluctuation and multi-resolution modes. Utilizing the JADE algorithm for intelligent and efficient optimization of parameter combinations in the TFT model guarantees the stability and reliability of the prediction model. Later, the obtained two-layer decomposition subseries are used as historical variables, and the meteorological and temporal data are entered into the TFT model as future known inputs. Empirical studies show that the proposed model demonstrates remarkable suitability and effectiveness in short-term wind speed forecasting. The utilization of the interpretable model has catalyzed significant advancements in wind speed prediction, while the analysis of its interpretable results empowers managers in formulating effective policies.

Keywords: Wind speed forecasting; Interpretable forecasting; Deep learning; Multisource data (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:288:y:2024:i:c:s0360544223031225

DOI: 10.1016/j.energy.2023.129728

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