Single and multi-sequence deep learning models for short and medium term electric load forecasting
Time series analysis using long short term memory (LSTM) deep learning is a very attractive
strategy to achieve accurate electric load forecasting. Although it outperforms most machine …
strategy to achieve accurate electric load forecasting. Although it outperforms most machine …
Hybrid CNN-LSTM model for short-term individual household load forecasting
M Alhussein, K Aurangzeb, SI Haider - Ieee Access, 2020 - ieeexplore.ieee.org
Power grids are transforming into flexible, smart, and cooperative systems with greater
dissemination of distributed energy resources, advanced metering infrastructure, and …
dissemination of distributed energy resources, advanced metering infrastructure, and …
Hybrid ensemble deep learning-based approach for time series energy prediction
The energy manufacturers are required to produce an accurate amount of energy by
meeting the energy requirements at the end-user side. Consequently, energy prediction …
meeting the energy requirements at the end-user side. Consequently, energy prediction …
Multi-step short-term power consumption forecasting with a hybrid deep learning strategy
Electric power consumption short-term forecasting for individual households is an important
and challenging topic in the fields of AI-enhanced energy saving, smart grid planning …
and challenging topic in the fields of AI-enhanced energy saving, smart grid planning …
Deep-learning-based short-term electricity load forecasting: A real case application
The rising popularity of deep learning can largely be attributed to the big data phenomenon,
the surge in the development of new and novel deep neural network architectures, and the …
the surge in the development of new and novel deep neural network architectures, and the …
Multivariate deep learning approach for electric vehicle speed forecasting
YN Malek, M Najib, M Bakhouya… - Big Data Mining and …, 2021 - ieeexplore.ieee.org
Speed forecasting has numerous applications in intelligent transport systems' design and
control, especially for safety and road efficiency applications. In the field of electromobility, it …
control, especially for safety and road efficiency applications. In the field of electromobility, it …
Research on short-term load prediction based on Seq2seq model
G Gong, X An, NK Mahato, S Sun, S Chen, Y Wen - Energies, 2019 - mdpi.com
Electricity load prediction is the primary basis on which power-related departments to make
logical and effective generation plans and scientific scheduling plans for the most effective …
logical and effective generation plans and scientific scheduling plans for the most effective …
Dynamic pricing for fast charging stations with deep reinforcement learning
L Cui, Q Wang, H Qu, M Wang, Y Wu, L Ge - Applied Energy, 2023 - Elsevier
With the rapid development of electric vehicles (EVs) and charging infrastructures, the
unbalanced utilization rate of fast charging stations (FCSTs) and the long waiting time for …
unbalanced utilization rate of fast charging stations (FCSTs) and the long waiting time for …