Deep-learning forecasting method for electric power load via attention-based encoder-decoder with bayesian optimization
Short-term electrical load forecasting plays an important role in the safety, stability, and
sustainability of the power production and scheduling process. An accurate prediction of …
sustainability of the power production and scheduling process. An accurate prediction of …
Specialized convolutional transformer networks for estimating battery health via transfer learning
J Zhao, Z Wang - Energy Storage Materials, 2024 - Elsevier
Despite continuous advancements, modeling and predicting nonlinear, multiscale, and
multiphysics battery systems, which feature inherently inhomogeneous cascades of scales …
multiphysics battery systems, which feature inherently inhomogeneous cascades of scales …
[HTML][HTML] Assessing the performance of deep learning models for multivariate probabilistic energy forecasting
Deep learning models have the potential to advance the short-term decision-making of
electricity market participants and system operators by capturing the complex dependences …
electricity market participants and system operators by capturing the complex dependences …
The importance of short lag-time in the runoff forecasting model based on long short-term memory
It is still very challenging to enhance the accuracy and stability of daily runoff forecasts,
especially several days ahead, owing to the non-linearity of the forecasted processes. Here …
especially several days ahead, owing to the non-linearity of the forecasted processes. Here …
[HTML][HTML] Evaluation of transformer model and self-attention mechanism in the Yangtze River basin runoff prediction
Abstract Study region In the Yangtze River basin of China. Study focus We applied a
recently popular deep learning (DL) algorithm, Transformer (TSF), and two commonly used …
recently popular deep learning (DL) algorithm, Transformer (TSF), and two commonly used …
Performance evaluation of multivariate deep-time convolution neural architectures for short-term electricity forecasting: Findings and failures
FE Sapnken, AK Tazehkandgheshlagh, M Hamaidi… - Energy 360, 2024 - Elsevier
Deep learning (DL) models hold great promise in enhancing the decision-making abilities of
electricity market participants and system operators in the short term, as they excel at …
electricity market participants and system operators in the short term, as they excel at …
Deep learning-based positioning of visually impaired people in indoor environments
Wayfinding and navigation can present substantial challenges to visually impaired (VI)
people. Some of the significant aspects of these challenges arise from the difficulty of …
people. Some of the significant aspects of these challenges arise from the difficulty of …
Probabilistic forecasting of battery energy storage state-of-charge under primary frequency control
Multi-service market optimization of battery energy storage system (BESS) requires
assessing the forecasting uncertainty arising from coupled resources and processes. For the …
assessing the forecasting uncertainty arising from coupled resources and processes. For the …
Learning from Very Little Data: On the Value of Landscape Analysis for Predicting Software Project Health
When data is scarce, software analytics can make many mistakes. For example, consider
learning predictors for open source project health (eg, the number of closed pull requests in …
learning predictors for open source project health (eg, the number of closed pull requests in …
Multi-timescale forecasting of battery energy storage state-of-charge under frequency containment reserve for normal operation
Forecasting the state-of-charge changes of battery energy storage, anticipated from a
provision of different services, can facilitate planning of its market participation strategy and …
provision of different services, can facilitate planning of its market participation strategy and …