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Nov 14, 2023This 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, 2023This 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, 2023This paper proposes an electricity forecasting method based on empirical mode decomposition (EMD) and bidirectional LSTM.
The objective of the present work is to compare two Deep Learning models, namely the Long Short-Term Memory (LSTM) model, and the Bi-directional LSTM (BLSTM)
Dec 13, 2021In this paper, BiLSTM short term traffic forecasting models have been developed and evaluated using data from a calibrated micro-simulation model for a ...
Aug 4, 2021To enhance the forecasting accuracy of daily teleconsultation demand, this study proposes an ensemble hybrid deep learning model.
Missing: Electronic | Show results with:Electronic
In this study, the authors propose a prediction framework for electric load forecasting using a recurrent neural network (RNN) and a meta-heuristic algorithm.
Aug 1, 2022The results indicate that the proposed deep bidirectional long short-term memory neural network-based approach improves the prediction accuracy ...
Aug 19, 2020In this paper we investigate to what extent long short-term memory neural networks (LSTMs) are suitable for demand forecasting in the e-grocery retail sector.
Missing: Bidirectional | Show results with:Bidirectional
Electric load forecasting process plays an extensive role in forecasting future electric load demand and peak load by understanding the previous data.