Nov 14, 2023 ¡¤ The primary aim of the proposed bidirectional LSTM network is to enhance predictive performance by capturing intricate temporal patterns and interdependencies ...

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)

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 ...

Dec 1, 2023 ¡¤ In this study, we develop a deep learning hybrid model for predicting electricity demand ( that combines Artificial Neural Network (ANN), Encoder and Decoder ...

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.

Jun 14, 2024 ¡¤ This paper proposes a computational approach to address these challenges in short-term power load forecasting and energy information management

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)

Aug 29, 2024 ¡¤ This paper provides a comprehensive survey on deep-learning-based STELF over the past ten years. It examines the entire forecasting process.

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.

May 6, 2024 ¡¤ In this paper, a forecasting method is proposed, which has a strong capability of predicting highly fluctuating demand data.