Deep learning shows immense potential for strengthening the cyber-resilience of renewable
energy supply chains. However, research gaps in comprehensive benchmarks, real-world …

This paper focuses on integrating two popular signal processing techniques, ie, variational
mode decomposition (VMD) and discrete wavelet transform (DWT), with deep learning (DL) …

Merging machine learning with the power of quantum computing holds great potential for
data-driven decision making and the development of powerful models for complex datasets …

Floods are one of the most lethal natural disasters. It is crucial to forecast the timing and
evolution of these events and create an advanced warning system to allow for the proper …

The inherent uncertainty of solar energy brings great difficulties to the grid connection and
short-term energy planning and dispatching. Deep learning method makes it possible to …

This study delves into the application of hybrid extreme machine-based techniques for solar
radiation prediction in Adrar, Algeria. The models under evaluation include the Extreme …

This paper addresses the challenge of accurately forecasting solar power generation (SPG)
across multiple sites using a single common model. The proposed deep learning-based …

In cold-climate regions, road salt is used as a deicer for winter road maintenance. The
applied road salt melts ice and snow on roads and can be washed off through storm sewer …

To tackle environmental and increasing energy demand issues, different energy transition
options have been investigated. Solar power has vast resources and is environment …

This paper proposes to analyze the performance increase in the forecasting of solar
irradiance and wind speed by implementing a dynamic ensemble architecture for intra-hour …