Energy consumption and carbon emissions forecasting for industrial processes: Status, challenges and perspectives
Y Hu, Y Man - Renewable and Sustainable Energy Reviews, 2023 - Elsevier
The industrial process consumes substantial energy and emits large amounts of carbon
dioxide. With the help of accurate energy consumption and carbon emissions forecasting …
dioxide. With the help of accurate energy consumption and carbon emissions forecasting …
Photovoltaic power forecasting: A hybrid deep learning model incorporating transfer learning strategy
Y Tang, K Yang, S Zhang, Z Zhang - Renewable and Sustainable Energy …, 2022 - Elsevier
Accurate forecasting of photovoltaic power is essential in the integration, operation, and
scheduling of hybrid grid systems. In particular, modeling for newly built photovoltaic sites is …
scheduling of hybrid grid systems. In particular, modeling for newly built photovoltaic sites is …
COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications
Power prediction is now a crucial part of contemporary energy management systems, which
is important for the organization and administration of renewable resources. Solar and wind …
is important for the organization and administration of renewable resources. Solar and wind …
A hybrid attention-based deep learning approach for wind power prediction
Renewable energy, especially wind power, is a practicable and promising solution to
mitigate the existing dilemma associated with climate change. Efficient and accurate …
mitigate the existing dilemma associated with climate change. Efficient and accurate …
Dual stream network with attention mechanism for photovoltaic power forecasting
The operations of renewable power generation systems highly depend on precise
Photovoltaic (PV) power forecasting, providing significant economic, and environmental …
Photovoltaic (PV) power forecasting, providing significant economic, and environmental …
Multi-timescale photovoltaic power forecasting using an improved Stacking ensemble algorithm based LSTM-Informer model
As more and more photovoltaic (PV) systems are integrated into the grid, the intelligent
operation of the grid system is facing significant challenges. Therefore, accurately …
operation of the grid system is facing significant challenges. Therefore, accurately …
A deep model for short-term load forecasting applying a stacked autoencoder based on LSTM supported by a multi-stage attention mechanism
Z Fazlipour, E Mashhour, M Joorabian - Applied Energy, 2022 - Elsevier
This paper presents an innovative univariate Deep LSTM-based Stacked Autoencoder
(DLSTM-SAE) model for short-term load forecasting, equipped with a Multi-Stage Attention …
(DLSTM-SAE) model for short-term load forecasting, equipped with a Multi-Stage Attention …
A hybrid photovoltaic/wind power prediction model based on Time2Vec, WDCNN and BiLSTM
D Geng, B Wang, Q Gao - Energy conversion and management, 2023 - Elsevier
Accurate prediction of photovoltaic (PV)/wind power is an effective solution for the grid
stability, reasonable dispatching and power supply reliability. Nowadays, various deep …
stability, reasonable dispatching and power supply reliability. Nowadays, various deep …
A deep learning approach to state of charge estimation of lithium-ion batteries based on dual-stage attention mechanism
K Yang, Y Tang, S Zhang, Z Zhang - Energy, 2022 - Elsevier
For lithium-ion batteries, the state of charge (SOC) estimation is one of the most important
tasks, and accurate estimation of SOC can provide a guarantee for the continuous operation …
tasks, and accurate estimation of SOC can provide a guarantee for the continuous operation …
[HTML][HTML] A review of data-driven smart building-integrated photovoltaic systems: Challenges and objectives
The smart building-integrated photovoltaic (SBIPV) systems have become the important
source of electricity in recent years. However, many sociological and engineering …
source of electricity in recent years. However, many sociological and engineering …