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 …

Accurate wind power forecast is critical to the efficient and safe running of power systems. A
hybrid model that combines complementary ensemble empirical mode decomposition …

This study proposes a new approach for short-term power load forecasting using a
combination of convolutional neural networks (CNN), long short-term memory (LSTM), and …

Accurate state of health estimation of lithium-ion batteries is imperative for reliable and safe
operations of electric vehicles. This study presents a hybrid attention and deep learning …

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 …

Reservoir level control in hydroelectric power plants has importance for the stability of the
electric power supply over time and can be used for flood control. In this sense, this paper …

Accurate ultra-short-term wind power forecasting is a prerequisite for decision making
related to the management of power systems. Existing approaches used to forecast wind …

Accurate prediction of photovoltaic (PV)/wind power is an effective solution for the grid
stability, reasonable dispatching and power supply reliability. Nowadays, various deep …

Accurate wind speed forecasting is capable of increasing the stability of wind power system.
Notably, there are numerous factors affecting wind speed, thus causing wind speed …

Abstract Space cooling in buildings is responsible for massive energy consumption and
carbon emissions. Accurate cooling load prediction can facilitate the implementation of …