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Energy consumption and carbon emissions forecasting for industrial processes: Status, challenges and perspectives
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 …
Machine learning and deep learning in energy systems: A review
With population increases and a vital need for energy, energy systems play an important
and decisive role in all of the sectors of society. To accelerate the process and improve the …
and decisive role in all of the sectors of society. To accelerate the process and improve the …
Photovoltaic power forecasting: A hybrid deep learning model incorporating transfer learning strategy
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 …
A short-term wind speed prediction method utilizing novel hybrid deep learning algorithms to correct numerical weather forecasting
The accuracy of the wind speed prediction is of crucial significance for the operation and
dispatch of the power grid system reasonably. However, wind speed is so random and …
dispatch of the power grid system reasonably. However, wind speed is so random and …
Ultra-short term wind power prediction applying a novel model named SATCN-LSTM
L **ang, J Liu, X Yang, A Hu, H Su - Energy Conversion and Management, 2022 - Elsevier
Accurate and reliable wind power forecasting has become very important to power system
scheduling and safely stable operating. In this paper, a novel self-attention temporal …
scheduling and safely stable operating. In this paper, a novel self-attention temporal …
PFVAE: a planar flow-based variational auto-encoder prediction model for time series data
Prediction based on time series has a wide range of applications. Due to the complex
nonlinear and random distribution of time series data, the performance of learning prediction …
nonlinear and random distribution of time series data, the performance of learning prediction …
Outlet water temperature prediction of energy pile based on spatial-temporal feature extraction through CNN–LSTM hybrid model
Energy pile is a novel ground heat exchanger for ground source heat pump (GSHP)
systems. Prediction of the energy pile outlet water temperature is essential for the efficient …
systems. Prediction of the energy pile outlet water temperature is essential for the efficient …
Hybrid Inception-embedded deep neural network ResNet for short and medium-term PV-Wind forecasting
Accurate and consistent forecasting of regional wind power is essential for efficient
scheduling and maximizing the utilization of renewable energy in the power grid. Medium …
scheduling and maximizing the utilization of renewable energy in the power grid. Medium …
Evolutionary quantile regression gated recurrent unit network based on variational mode decomposition, improved whale optimization algorithm for probabilistic short …
Wind energy, as clean energy, has attracted more and more attention. Wind power
generation is easily threatened by the irregular fluctuation of wind speed, which interferes …
generation is easily threatened by the irregular fluctuation of wind speed, which interferes …
Big data analytics deep learning techniques and applications: A survey
Deep learning (DL), as one of the most active machine learning research fields, has
achieved great success in numerous scientific and technological disciplines, including …
achieved great success in numerous scientific and technological disciplines, including …