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Short‐term wind power forecasting based on two‐stage attention mechanism

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Outgoing claims: the papers this work cites, builds on, or argues with.

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The Wind Integration National Dataset (WIND) Toolkit
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Short-term prediction of wind power with a clustering approach
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Learning long-term dependencies with gradient descent is difficult
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Short -term wind power plant predicting with Artificial Neural Network
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A Data Mining Approach Combining $K$ -Means Clustering With Bagging Neural Network for Short-Term Wind Power Forecasting
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Short-Term Forecasting of Wind Power Generation Based on the Similar Day and Elman Neural Network
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Health Status Assessment and Failure Prediction for Hard Drives with Recurrent Neural Networks
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Short-Term Prediction of Wind Farm Power: A Data Mining Approach
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Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals
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Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network
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A Short-Term Wind Power Forecasting Approach With Adjustment of Numerical Weather Prediction Input by Data Mining
inferredcites →https://doi.org/10.1145/3097983.3098088openalex-snapshot
Dipole
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Global Conservative Solutions of a Generalized Two-Component Camassa-Holm System
inferredcites →https://doi.org/10.1155/2014/835791openalex-snapshot
Support Vector Regression Based on Grid-Search Method for Short-Term Wind Power Forecasting
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Long Short-Term Memory
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On Estimation of a Probability Density Function and Mode
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Remarks on Some Nonparametric Estimates of a Density Function
inferredcites →https://doi.org/10.1257/jep.15.4.143openalex-snapshot
Quantile Regression
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Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
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Power Transformer Operating State Prediction Method Based on an LSTM Network
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Neural Machine Translation by Jointly Learning to Align and Translate

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