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Nov 15, 2023The 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 3, 2023This paper aims to suggest a deep-learning framework for large-scale renewable demands using 1) variational auto-encoder (VAE) algorithm to generate a number ...
Feb 24, 2024This study presents a new framework to long term load forecasting in the world of electricity power with the help of historical load trends.
Mar 18, 2024The 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 ...
Aug 5, 2024The proposed BiLSTM forecasting model exhibits higher accuracy and can forecast user electricity consumption data that more accurately reflect real-life usage.
Oct 22, 2024In this paper, a forecasting method is proposed, which has a strong capability of predicting highly fluctuating demand data.
Nov 15, 2023The 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)
Jun 19, 2024This paper introduces BiLSTM-MLAM, a novel multi-scale time series prediction model. Initially, the approach utilizes bidirectional long short-term memory.
Sep 4, 2024In 2022, Mahendran and Raj have implemented an advanced deep learning model called the Enhanced Deep Recurrent Neural Network (EDRNN) in order to detect AD at ...
May 26, 2024We introduce the Multi-Channel Data Fusion Network (MCDFN), a novel hybrid deep learning architecture integrating multiple data modalities for superior demand ...
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