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Nov 14, 2023 ， This study builds on previous research focused on the application of artificial neural networks to achieve accurate electrical load forecasting.
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Aug 9, 2023 ， This work proposes load forecasting models that rely on deep neural networks (DNNs). These models are applied to a demand-side load database for analysis.
Jun 1, 2023 ， This paper proposes an electricity forecasting method based on empirical mode decomposition (EMD) and bidirectional LSTM.
The results indicate that the proposed deep bidirectional long short-term memory neural network-based approach improves the prediction accuracy by nearly 95% in ...
Dec 13, 2021 ， In this paper, BiLSTM short term traffic forecasting models have been developed and evaluated using data from a calibrated micro-simulation model for a ...
The results show that additional training of data and thus BiLSTM-based modeling offers better predictions than regular LSTM-based models.
Aug 4, 2021 ， To enhance the forecasting accuracy of daily teleconsultation demand, this study proposes an ensemble hybrid deep learning model.
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Feb 24, 2024 ， This study presents a new framework to long term load forecasting in the world of electricity power with the help of historical load trends.
The model can predict according to the trend of energy use data properly due to the data ratio of 100:100. Precise prediction of energy demand can be utilized ...
The aim of this type of LSTM network is to analyze sequences from both front-to-back and back-to-front, i.e., the sequence information flows in both directions ...
