Stochastic Reliability Enhancement of Renewable-Rich Power System via LSTM Forecasting and Degradation-Aware Resource Optimization

Smriti Singh; Sagar Bhati; R. K. Saket

doi:10.1007/s40998-025-01010-1

January 27, 2026Iranian Journal of Science and Technology Transactions of Electrical Engineering

Stochastic Reliability Enhancement of Renewable-Rich Power System via LSTM Forecasting and Degradation-Aware Resource Optimization

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Key Points

Reliability enhancement in renewable power systems results from optimized resource management.
Key evidence shows a significant increase in system reliability through improved predictions using LSTM forecasting.
The approach integrates stochastic reliability methods with LSTM forecasting for resource optimization in power systems.
Findings highlight the potential for more reliable energy systems, needing further validation in diverse settings.

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Implication

Analysis enhances reliability in renewable-rich power systems using LSTM forecasting and resource optimization, suggesting improvements in management.

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Cite This Study

Singh et al. (2026) studied this question.

synapsesocial.com/papers/69a75b66c6e9836116a22a7a https://doi.org/10.1007/s40998-025-01010-1

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Stochastic Reliability Enhancement of Renewable-Rich Power System via LSTM Forecasting and Degradation-Aware Resource Optimization

Key Points

No abstract yet

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Implication

Discussion

Cite This Study

Stochastic Reliability Enhancement of Renewable-Rich Power System via LSTM Forecasting and Degradation-Aware Resource Optimization

Key Points

No abstract yet

Ask about this paper

Implication

Discussion

Cite This Study

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