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• W2920734573 abstract "To surmount the issue related to reactive part of produced power, it is imperative to predict its quantity of generation. Optimizing this back and forth energy flow will enlarge actual part of energy flow. Generating reactive part motives to gain the magnetic field voltage and conversely, no flow of reactive part impedes sending of requested energy. In this paper, two popular deep learning frameworks, Long Short-Term Memory (LSTM) and Artificial Neural Network (ANN) are adopted to forecast reactive part of generated power." @default.
• W2920734573 created "2019-03-11" @default.
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• W2920734573 date "2019-01-01" @default.
• W2920734573 modified "2023-10-14" @default.
• W2920734573 title "Univariate Time Series Prediction of Reactive Power Using Deep Learning Techniques" @default.
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• W2920734573 doi "https://doi.org/10.1109/icrest.2019.8644160" @default.
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