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Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time-Series Forecast., , и . CoRR, (2022)PrOuD: Probabilistic Outlier Detection Solution for Time Series Analysis on Real-world Photovoltaic Inverters, , , и . Energies (MDPI), 17 (1): 64 (2024)Quantile Surfaces -- Generalizing Quantile Regression to Multivariate Targets, , , , и . arXiv e-prints, (2020)Application of numerical weather prediction in wind power forecasting: Assessment of the diurnal cycle, , и . Meteorologische Zeitschrift, 26 (3): 319--331 (2017)Deep Learning Based Failure Prediction in Wind Turbines Using SCADA Data, , , и . Conference for Wind Power Drives (CWD), стр. 391--404. (2019)Evaluation of Anomaly Detection of an Autoencoder Based on Maintenace Information and Scada-Data, , , , , , , и . Energies, 13 (5): 1063 (2020)Uncertainty and Utility Sampling with Pre-Clustering., , , и . IAL@PKDD/ECML, том 3079 из CEUR Workshop Proceedings, стр. 21-34. CEUR-WS.org, (2021)Unified Autoencoder with Task Embeddings for Multi-Task Learning in Renewable Power Forecasting., , и . ICMLA, стр. 1530-1536. IEEE, (2022)Targeted adversarial attacks on wind power forecasts., , , и . Mach. Learn., 113 (2): 863-889 (февраля 2024)Synthetic Photovoltaic and Wind Power Forecasting Data., , и . CoRR, (2022)