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Abstract The transition to carbon-neutral energy has increased the reliance upon renewable sources of energy, e.g., wind power, placing added demands on resilience and stability of the power grid. Wind-to-hydrogen production systems can be a solution for addressing these demands. By converting excess wind energy into hydrogen via electrolysis, these systems can effectively store the intermittent energy generated by wind turbines. This study discusses the application of two time-series prediction models, i.e., long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), in forecasting the energy of wind farms, subsequently used for an assessment of hydrogen production means of a proton exchange membrane (PEM) electrolyzer. Using a dataset comprising wind speed, active power, and wind direction, inputs were normalized, and wind direction was transformed into sine and cosine components to retain circular characteristics. Bi-LSTM demonstrated superior accuracy with lower testing RMSE than LSTM. Integrating wind forecasts with a PEM electrolyzer model, incorporating critical electro-chemical parameters, revealed an optimal efficiency of 63.611% at a membrane thickness of 0.00254 cm and a temperature of 70°C. Bi-LSTM forecasts boosted hydrogen production by 5% to 8% compared to LSTM.
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This content will become publicly available on April 1, 2026
Enhanced Wind Energy Forecasting Using an Extended Long Short-Term Memory Model
This paper presents an innovative approach to wind energy forecasting through the implementation of an extended long short-term memory (xLSTM) model. This research addresses fundamental limitations in time-sequence forecasting for wind energy by introducing architectural enhancements to traditional LSTM networks. The xLSTM model incorporates two key innovations: exponential gating with memory mixing and a novel matrix memory structure. These improvements are realized through two variants, i.e., scalar LSTM and matrix LSTM, which are integrated into residual blocks to form comprehensive architectures. The xLSTM model was validated using SCADA data from wind turbines, with rigorous preprocessing to remove anomalous measurements. Performance evaluation across different wind speed regimes demonstrated robust predictive capabilities, with the xLSTM model achieving an overall coefficient of determination value of 0.923 and a mean absolute percentage error of 8.47%. Seasonal analysis revealed consistent prediction accuracy across varied meteorological patterns. The xLSTM model maintains linear computational complexity with respect to sequence length while offering enhanced capabilities in memory retention, state tracking, and long-range dependency modeling. These results demonstrate the potential of xLSTM for improving wind power forecasting accuracy, which is crucial for optimizing turbine operations and grid integration of renewable energy resources.
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- Award ID(s):
- 2429540
- PAR ID:
- 10652862
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Algorithms
- Volume:
- 18
- Issue:
- 4
- ISSN:
- 1999-4893
- Page Range / eLocation ID:
- 206
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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