The learning process and hyper-parameter optimization of artificial neural networks (ANNs)
and deep learning (DL) architectures is considered one of the most challenging machine …

In a modern power system with an increasing proportion of renewable energy, wind power
prediction is crucial to the arrangement of power grid dispatching plans due to the volatility …

Spatio-temporal wind power forecasting is significant to the stability of electric power
systems. However, the accuracy of power forecasting results is easily impaired by the …

Regional forecasting is crucial for a balanced energy delivery system and for achieving the
global transition to clean energy. However, regional wind forecasting is challenging due to …

This article proposes a novel framework to improve the prediction accuracy of very short-
term (5-min) wind power generation. The framework consists of complete ensemble …

Fast and accurate wind power prediction is of great significance for grid planning. However,
wind power dataset tends to be highly stochastic and volatile, while showing more stable …

Wind power forecasting plays a significant role in regulating the peak and frequency of the
power system, which can improve the wind power receiving capacity. Despite plenty of …

Long short-term memory faces challenges in information mining and parameter selection
due to inherent uncertainty and randomness. In this study, we propose a novel hybrid model …

The transition to sustainable electricity generation depends heavily on renewable energy
sources, particularly wind power. Making precise forecasts, which calls for clever predictive …

The inherent randomness and volatility of wind power generation present significant
challenges to the reliable and secure operation of the power system. Therefore, it is crucial …