Weather forecasting based on data-driven and physics-informed reservoir computing models

Article Geosciences, Multidisciplinary

When Does the Lorenz 1963 Model Exhibit the Signal-To-Noise Paradox?

Bjoern Mayer et al.

Summary: This study investigates how the signal-to-noise paradox (SNP) can arise in a climate prediction system based on a simple dynamical model, showing that the SNP can be reproduced if the model overestimates observational uncertainty. The development of the SNP on subseasonal timescales and its clear dependency on lead-time of the prediction leads to an alternative hypothesis on the origin of the SNP.

GEOPHYSICAL RESEARCH LETTERS (2021)

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Article Mathematics, Applied

Erratum: On explaining the surprising success of reservoir computing forecaster of chaos? The universal machine learning dynamical system with contrasts to VAR and DMD [Chaos 31(1), 013108 (2021)]

Erik Bollt

CHAOS (2021)

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Review Physics, Applied

Physics-informed machine learning

George Em Karniadakis et al.

Summary: Physics-informed learning seamlessly integrates data and mathematical models through neural networks or kernel-based regression networks for accurate inference of realistic and high-dimensional multiphysics problems. Challenges remain in incorporating noisy data seamlessly, complex mesh generation, and addressing high-dimensional problems.

NATURE REVIEWS PHYSICS (2021)

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Article Engineering, Multidisciplinary

Forecasting of solar and wind power using LSTM RNN for load frequency control in isolated microgrid

Dhananjay Kumar et al.

Summary: The study discusses the use of deep learning methods to develop a multi-step forecasting model and the application of Long Short-Term Memory (LSTM) RNN for wind speed and solar irradiance prediction. The forecasted solar and wind power are utilized to analyze load frequency behavior, with a PI controller used to mitigate frequency deviation and ensure stability of the MG power system.

INTERNATIONAL JOURNAL OF MODELLING AND SIMULATION (2021)

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Article Engineering, Electrical & Electronic