The industrial process consumes substantial energy and emits large amounts of carbon
dioxide. With the help of accurate energy consumption and carbon emissions forecasting …

Since the energy sector is the dominant contributor to global greenhouse gas emissions, the
decarbonization of energy systems is crucial for climate change mitigation. Two major …

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 …

The reliability of power supply in distribution network is vulnerable to extreme weather
events such as typhoon. Pre-event preparation can effectively mitigate the deterioration of …

Recent advancements in data science and artificial intelligence, as well as the development
of clean and sustainable energy sources, have created numerous opportunities for energy …

The Smart Grid (SG) is a critical energy infrastructure that collects real-time electricity usage
data to forecast future energy demands using information and communication technologies …

Wind power forecasting (WPF) is the crucial technology for power system operation with
large-scale grid-connected wind farms. A large number of related studies have emerged …

An ultra-short-term wind power forecasting method based on personalized robust federated
learning (PRFL) is proposed to exploit spatio-temporal correlation in a privacy-preserving …

The prediction of wind power output is part of the basic work of power grid dispatching and
energy distribution. At present, the output power prediction is mainly obtained by fitting and …

Wind power is a cheap renewable energy that plays an important role in the economic
development of a country. Identifying potential locations for energy production is challenging …