It is of fundamental importance for utility providers to model and forecast power loads in advance, to strike a balance between production and demand, to ...
Feb 27, 2023 ， In this paper, deep learning algorithms are used to forecast the electrical loads; namely: (1) Long Short-Term Memory (LSTM), (2) Gated ...
In this scenario, Deep Learning models are a good alternative to learn patterns from customer data and then forecast demand for different forecasting horizons.
Aug 1, 2022 ， The results indicate that the proposed deep bidirectional long short-term memory neural network-based approach improves the prediction accuracy ...
(Citation2018) presented a Gated Recurrent Unit (GRU) based recurrent neural network for electricity price forecasting. Hourly price data from 1 January 2013 to ...
Dec 29, 2022 ， The bidirectional BLSTM designs, convolutional neural networks (CNNs), and auto-encoders (AEs) with bidirectional long short-term memory (LSTM).
Feb 3, 2023 ， In this paper, a novel hybrid method that combines ensemble empirical mode decomposition (EEMD) algorithm and a bidirectional long short-term ...
A model using Long Short-Term Memory Networks, a type of Recurrent Neural Network, to estimate demand based on historical patterns to help supply chain ...
Missing: Bidirectional | Show results with:Bidirectional
Abbasimehr, An optimized model using LSTM network for demand forecasting, Computers & Industrial Engineering, ∇ 143 ， Amasyali, K., & El-Gohary, N. M. (2018).
A hybrid deep learning framework with CNN and Bi-directional LSTM for store item demand forecasting ， Abstract ， References ， Cited By ， Index Terms.