In recent advancements in agricultural technology, quantum mechanics and deep learning
integration have shown promising potential to revolutionize rice yield forecasting methods …

In recent years, hybrid Solar-Wind energy system has emerged as a viable solution to
achieve sustainable energy generation and alleviate the burden on the power grid …

Merging machine learning with the power of quantum computing holds great potential for
data-driven decision making and the development of powerful models for complex datasets …

Conversion of solar energy using photovoltaic (PV) panels faces challenges due to snow
accumulation on PV surface in cold regions. Despite existing methods to assess this impact …

The stability of power system operations is being challenged by the rapid development of
renewable energy. A viable solution is to achieve accurate renewable energy forecasting. In …

Predicting solar panel power output is crucial for advancing the energy transition but is
complicated by the variable and non-linear nature of solar energy. This is influenced by …

The objective of this study is to investigate a novel hybrid model for the accurate prediction
of direct normal irradiance. For this purpose, a decomposition technique, a clustering …

Sustainability refers to a development approach that meets the needs of the present
generation without compromising the ability of future generations to meet their own needs …

Modern renewable power operations can be enhanced by integrating deep neural networks,
particularly for forecasting solar irradiance. Recent advancements in quantum computing …

The pursuit of energy transition necessitates the coordination of several technologies,
including more efficient and cost-effective distributed energy resources (DERs), smart grids …