Albayram, MehmetYilmaz, AlperBayrak, GokayBasaran, KivancPopescu, Luminita Georgeta2026-02-082026-02-0820260960-14811879-0682https://doi.org/10.1016/j.renene.2025.124062https://hdl.handle.net/20.500.12885/5778This study explored the effectiveness of Un-Decimated Wavelet Transform (UWT) in time-series applications, using photovoltaic (PV) calculation as a case study. Real-time measurements of irradiance, ambient temperature, module temperature, and humidity were collected at 5-min intervals from a 1.2 kW rooftop PV system at Bursa Technical University. Wavelet-based features extracted with both UWT and the conventional Discrete Wavelet Transform (DWT) were combined with regression and tree-based learners to build 16 hybrid models. The results show that the shift-invariant UWT significantly improves both feature extraction and prediction accuracy compared to the DWT approach. The UWT-DT model achieved the highest accuracy, with the lowest MSE (0.0001), the lowest RMSE (0.0118) and the highest R-2 coefficient (0.9986). A Wilcoxon signed-rank test applied to paired RMSE values confirmed that these improvements were statistically significant (p value < 0.05 for UWTDT vs DWT-DT). In terms of computational complexity, the 'a` trous' algorithm used in UWT requires convolution operations at every level, resulting in higher processing costs than DWT (12 ms feature extraction per 1024-sample input). However, the full-resolution features provided by UWT significantly reduced the error rates of treebased models, raising R-2 above 0.99.eninfo:eu-repo/semantics/closedAccessUn-decimated wavelet transformMachine learningRegressionEnsemble learningPV power forecastingArticle10.1016/j.renene.2025.124062256WOS:0015435732000012-s2.0-105011590709Q1Q1