From Data-Rich to Data-Scarce: Spatiotemporal Evaluation of a Hybrid Wavelet-Enhanced Deep Learning Model for Day-Ahead Wind Power Forecasting Across Greece

Ioannis Laios, Dimitrios Zafirakis () and Konstantinos Moustris
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Ioannis Laios: Soft Energy Applications & Environmental Protection Laboratory, Mechanical Engineering Department, University of West Attica, 250 Thivon & P. Ralli Street, 12241 Athens, Greece
Dimitrios Zafirakis: Soft Energy Applications & Environmental Protection Laboratory, Mechanical Engineering Department, University of West Attica, 250 Thivon & P. Ralli Street, 12241 Athens, Greece
Konstantinos Moustris: Air Pollution Laboratory, Department of Mechanical Engineering, University of West Attica, 250 Thivon & P. Ralli Street, 12241 Athens, Greece

Energies, 2025, vol. 18, issue 21, 1-18

Abstract: Efficient wind power forecasting is critical in achieving large-scale integration of wind energy in modern electricity systems. On the other hand, limited availability of wealthy, long-term historical data of wind power generation for many sites of interest often challenges the training of tailored forecasting models, which, in turn, introduces uncertainty concerning the anticipated operational status of similar early-life, or even prospective, wind farm projects. To that end, this study puts forward a spatiotemporal, national-level forecasting exercise as a means of addressing wind power data scarcity in Greece. It does so by developing a hybrid wavelet-enhanced deep learning model that leverages long-term historical data from a reference site located in central Greece. The model is optimized for 24-h day-ahead forecasting, using a hybrid architecture that incorporates discrete wavelet transform for feature extraction, with deep neural networks for spatiotemporal learning. Accordingly, the model’s generalization is evaluated across a number of geographically distributed sites of different quality wind potential, each constrained to only one year of available data. The analysis compares forecasting performance between the original and target sites to assess spatiotemporal robustness of the model without site-specific retraining. Our results demonstrate that the developed model maintains competitive accuracy across data-scarce locations for the first 12 h of the day-ahead forecasting horizon, designating, at the same time, distinct performance patterns, dependent on the geographical and wind potential quality dimensions of the examined areas. Overall, this work underscores the feasibility of leveraging data-rich regions to inform forecasting in under-instrumented areas and contributes to the broader discourse on spatial generalization in renewable energy modeling and planning.

Keywords: wind power forecasting; spatiotemporal analysis; discrete wavelet transform; deep learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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