Installed capacities of wind and solar power have grown rapidly over recent years, and the
pool of literature on very short-term (minutes-to hours-ahead) wind and solar forecasting has …

As renewable energy becomes increasingly popular in the global electric energy grid,
improving the accuracy of renewable energy forecasting is critical to power system planning …

The forecasting and estimation of wind power is a challenging problem in renewable energy
generation due to the high volatility of wind power resources, inevitable intermittency, and …

This paper proposes an effective computing framework for Short-Term Load Forecasting
(STLF). The proposed technique copes with the stochastic variations of the load demand …

Spiking neural networks (SNNs) are regarded as a promising candidate to deal with the
major challenges of current machine learning techniques, including the high energy …

Renewable energy is essential for planet sustainability. Renewable energy output
forecasting has a significant impact on making decisions related to operating and managing …

Nowadays, various wind power forecasting methods have been developed to improve wind
power utilization. Most of these techniques are designed based on the mean square error …

The use of renewable energy to reduce the effects of climate change and global warming
has become an increasing trend. In order to improve the prediction ability of renewable …

Wind power generation is always associated with uncertainties as a result of fluctuations of
wind speed. Accurate predictions of wind power generation are important for the efficient …

Wind power forecasting plays a vital role in enhancing the efficiency of power grid operation
and increasing the competitiveness of power market. In this paper, a novel hybrid …