The use of wind power, a pollution-free and renewable form of energy, to generate electricity
has attracted increasing attention. However, intermittent electricity generation resulting from …

Numerous deep learning architectures have been developed to accommodate the diversity
of time-series datasets across different domains. In this article, we survey common encoder …

Traffic flow prediction plays a key role in intelligent transportation systems. However, since
traffic sensors are typically manually controlled, traffic flow data with varying length, irregular …

Accurate forecasting of solar power generation with fine temporal and spatial resolution is
vital for the operation of the power grid. However, state-of-the-art approaches that combine …

Large-scale and multidimensional spatiotemporal data sets are becoming ubiquitous in
many real-world applications such as monitoring urban traffic and air quality. Making …

With the quick advancement of urbanization and industrialization, air pollution has become a
serious issue in developing countries. Governments and natives have raised their …

Accurate and reliable wind speed forecasting is important for the dispatch and management
of wind power generation systems. However, existing forecasting models based on the data …

This paper addresses the problem of time series forecasting for non-stationary signals and
multiple future steps prediction. To handle this challenging task, we introduce DILATE …

The accurate forecasting of wind speed plays a vital role in the transformation of wind
energy and the dispatching of electricity. However, the inherent intermittence of wind makes …

The world is rapidly urbanizing, and the energy intensive built environment is becoming
increasingly responsible for the world's energy consumption and associated environmental …