Due to rapid development in information technology in both hardware and software, deep
neural networks for regression have become widely used in many fields. The optimization of …

Accurate prediction of photovoltaic power is of great significance to the safe operation of
power grids. In order to improve the prediction accuracy, a similar day clustering …

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

Air pollution threatens worldwide human health, ecosystems, and climate change.
Transportation is a major contributor to air pollution. However, the link between …

Short-term wind power forecasting has become a de facto tool to better facilitate the
integration of such renewable energy resources into modern power grids. Instead of point …

Wind speed interval prediction is one of the most long-standing challenges because of the
high uncertainty and the complex spatial–temporal correlation between wind turbines. In this …

Accurately predicting the dynamic behaviour of complex flow fields has always been a major
challenge in Computational Fluid Dynamics (CFD) research. This paper proposes an …

This paper aims to explore the relationship between deep learning and forecasting within
the context of the Sustainable Development Goals (SDGs). The primary objective is to …

This research introduces a creative strategy for addressing the challenges of wind power
predicting, crucial for effective renewable energy integration into the power grid. We propose …

A novel hybrid model is proposed to improve the accuracy of ultra-short-term wind speed
prediction by combining the improved complete ensemble empirical mode decomposition …