ArticleEnvironmental science and pollution research international2022
Weather forecasting based on data-driven and physics-informed reservoir computing models.
Article in Environmental science and pollution research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Time series learning in a many-body Rydberg system with emergent collective amplification.Nature communications · 2026Article
- Triple-Mode Ferroelectric Thin-Film Transistor for Hybrid Electrical-Optical Reservoir Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Efficient optimisation of physical reservoir computers using only a delayed input.Communications engineering · 2025Article
- 3D-integrated multilayered physical reservoir array for learning and forecasting time-series information.Nature communications · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
In response to the growing demand for the global energy supply chain, wind power has become an important research subject among studies in the advancement of renewable energy sources. The major concern is the stochastic volatility of weather conditions that hinder the development of wind power forecasting approaches. To address this issue, the current study proposes a weather prediction method divided into two models for wind speed and atmospheric system forecasting. First, the data-based model incorporated with wavelet transform and recurrent neural networks is employed to predict the wind speed. Second, the physics-informed echo state network was used to learn the chaotic behavior of the atmospheric system. The findings were validated with a case study conducted on wind speed data from Turkmenistan. The results suggest the outperformance of physics-informed model for accurate and reliable forecasting analysis, which indicates the potential for implementation in wind energy analysis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.