Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …

The development of deep learning technology has brought great improvements to the field
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …

With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …

The development of solar energy is crucial to combat the global climate change and fossil
energy crisis. However, the inherent uncertainty of solar power prevents its large-scale …

Accurate wind farm cluster power forecasting is of great significance for the safe operation of
the power system with high wind power penetration. However, most of the current neural …

Diffusion models, a family of generative models based on deep learning, have become
increasingly prominent in cutting-edge machine learning research. With distinguished …

Existing short-term bus load forecasting methods mostly use temporal domain features, such
as historical loads, to forecast and do not fully consider the influence of unstructured spatial …

Accurate regional distributed PV power forecasting provides data support for power grid
management and optimal operation. Distributed PV has the characteristics of large quantity …

Spatio-temporal graph neural networks (STGNN) have emerged as the dominant model for
spatio-temporal graph (STG) forecasting. Despite their success, they fail to model intrinsic …

Accurate forecasting of photovoltaic (PV) and wind production is crucial for the integration of
more renewable energy sources into the power grid. To address the limited resolution and …