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Adaptively coping with concept drifts in energy time series forecasting using profiles.

, , , , and . e-Energy, page 459-470. ACM, (2022)

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Automating Value-Oriented Forecast Model Selection by Meta-learning: Application on a Dispatchable Feeder., , , , , and . EI.A, volume 14467 of Lecture Notes in Computer Science, page 95-116. Springer, (2023)Towards line-restricted dispatchable feeders using probabilistic forecasts for PV-dominated low-voltage distribution grids., , , and . e-Energy, page 395-400. ACM, (2022)Controlling non-stationarity and periodicities in time series generation using conditional invertible neural networks., , , , , , and . Appl. Intell., 53 (8): 8826-8843 (April 2023)Transformer training strategies for forecasting multiple load time series., , , , , , and . Energy Inform., 6 (1): 20 (January 2023)AutoPV: Automated photovoltaic forecasts with limited information using an ensemble of pre-trained models., , , , and . CoRR, (2022)pyWATTS: Python Workflow Automation Tool for Time Series., , , , , , , , , and . CoRR, (2021)Creating Probabilistic Forecasts from Arbitrary Deterministic Forecasts using Conditional Invertible Neural Networks., , , , , and . CoRR, (2023)AutoPV: Automated photovoltaic forecasts with limited information using an ensemble of pre-trained models., , , , and . e-Energy, page 386-414. ACM, (2023)Adaptively coping with concept drifts in energy time series forecasting using profiles., , , , and . e-Energy, page 459-470. ACM, (2022)Forecasting energy time series with profile neural networks., , , , and . e-Energy, page 220-230. ACM, (2020)