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Nov 15, 2023 , 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 3, 2023 , This 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, 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.
Mar 18, 2024 , 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 ...
Aug 5, 2024 , The proposed BiLSTM forecasting model exhibits higher accuracy and can forecast user electricity consumption data that more accurately reflect real-life usage.
Oct 22, 2024 , In this paper, a forecasting method is proposed, which has a strong capability of predicting highly fluctuating demand data.
Nov 15, 2023 , 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)
Jun 19, 2024 , This paper introduces BiLSTM-MLAM, a novel multi-scale time series prediction model. Initially, the approach utilizes bidirectional long short-term memory.
Sep 4, 2024 , In 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, 2024 , We introduce the Multi-Channel Data Fusion Network (MCDFN), a novel hybrid deep learning architecture integrating multiple data modalities for superior demand ...