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 …
[HTML][HTML] Advances in solar forecasting: Computer vision with deep learning
Renewable energy forecasting is crucial for integrating variable energy sources into the grid.
It allows power systems to address the intermittency of the energy supply at different …
It allows power systems to address the intermittency of the energy supply at different …
Achieving wind power and photovoltaic power prediction: An intelligent prediction system based on a deep learning approach
Accurately predicting wind and photovoltaic power is one of the keys to improving the
economy of wind-solar complementary power generation system, reducing scheduling costs …
economy of wind-solar complementary power generation system, reducing scheduling costs …
Accurately forecasting solar radiation distribution at both spatial and temporal dimensions simultaneously with fully-convolutional deep neural network model
Accurately forecasting solar radiation is of great significance to solar energy utilization. To
forecast the spatial and temporal distributions of solar radiation simultaneously, a deep …
forecast the spatial and temporal distributions of solar radiation simultaneously, a deep …
Spatio-temporal interpretable neural network for solar irradiation prediction using transformer
Deep learning models have been increasingly applied in the field of solar radiation
prediction. However, the characteristics of a deep learning black box model restrict its …
prediction. However, the characteristics of a deep learning black box model restrict its …
Impact of climate changes on the stability of solar energy: Evidence from observations and reanalysis
Climate change alters the amount and spatiotemporal characteristics of solar radiation at the
surface. How this affects the stability of solar energy has not yet been explored on a global …
surface. How this affects the stability of solar energy has not yet been explored on a global …
Adversarial discriminative domain adaptation for solar radiation prediction: A cross-regional study for zero-label transfer learning in Japan
Deep learning models are increasingly applied in the field of solar radiation prediction.
However, the substantial demand for labeled data limits their rapid application in newly …
However, the substantial demand for labeled data limits their rapid application in newly …
Enabling coordination in energy communities: A Digital Twin model
Starting from the EU vision for Energy Communities (EC), our purpose is to support them by
proposing a Digital Twin (DT) that includes a bi-level optimization model to deliver …
proposing a Digital Twin (DT) that includes a bi-level optimization model to deliver …
[HTML][HTML] Improving cross-site generalisability of vision-based solar forecasting models with physics-informed transfer learning
Forecasting solar energy from cloud cover observations is crucial to truly anticipate future
changes in power supply. On an intra-hour timescale, ground-level sky cameras located …
changes in power supply. On an intra-hour timescale, ground-level sky cameras located …
DEST-GNN: A double-explored spatio-temporal graph neural network for multi-site intra-hour PV power forecasting
Accurate forecasting of photovoltaic (PV) power is crucial for real-time grid balancing and
storage system optimization. However, due to the intermittent and fluctuating nature of PV …
storage system optimization. However, due to the intermittent and fluctuating nature of PV …