Electric load forecasting’s accuracy and reliability are pivotal for enhancing the dispatch efficiency of power systems and the integration of renewable energy into the grid. In response to this need, ...

With the acceleration of urbanization, a series of ‘urban diseases”, such as traffic congestion, caused by population aggregation have emerged. A well-developed subway transportation network has, to ...

A global team of researchers has made strides in refining weather forecasting methods, with a specific focus on addressing the persistent issue of "quantile crossing." This phenomenon disrupts the ...

New paired studies from the University of Minnesota Twin Cities show that machine learning can improve the prediction of floods. The studies, published in Water Resources Research and the Proceedings ...

Researchers in China have applied a machine learning technology based on temporal convolutional networks in PV power forecasting for the first time. The new model reportedly outperforms similar models ...

Electron density prediction for a four-million-atom aluminum system using machine learning, deemed to be infeasible using traditional DFT method. × Researchers from Michigan Tech and the University of ...

Picture a single forecasting mistake triggering a cascade of negative consequences, such as surplus inventory, strained supplier relationships and disappointed customers. In today's world, accurate ...

A scientist in Sweden has developed a new hybrid local features-based method using thermographs to identify faulty solar panels. A researcher from Sweden’s Jönköping University has proposed a machine ...

Conventional clustering techniques often focus on basic features like crystal structure and elemental composition, neglecting target properties such as band gaps and dielectric constants. A new study ...

Trajectories of the vehicle-borne experiment on Google earth. Panel a Presents the overall bird’s eye view of the experiment, while panels b and c are the snapshots of the vehicle starting and driving ...