To enhance the forecasting of solar radiation strength on horizontals, an ensemble learning
approach is proposed. Two types of machine learning models are arranged to predict solar …

Solar radiation data play an important role in solar energy study. These data are not
available for location of interest due to lack of meteorological stations. Therefore, it is all …

Photo Voltaic (PV) generation is intermittent which cannot carry out the constant electric
power. The amount of solar irradiance received for a particular area is one of the most …

This paper presents predictive models based on dynamic recurrent neural networks DRNNs
with short term delay units to predict daily solar radiation intensity. The proposed approach …

This research focuses on addressing active power losses in radial distribution networks,
particularly in regions like Kashmir, India, where energy demand is rising. To enhance the …

For the prediction of solar radiation, various models have been developed in literature with
sunshine hours as prime parameter. However, an extensive amount of work is still required …

Grid-connected photovoltaic (PV) power systems have the benefit of being rapid and
dependable sources of electricity. The power industry has been obliged to transition over to …

The use of solar energy is increasing for power generation and other uses. In order to meet
these demands and make better predictions, it is necessary to understand and explain the …

Photovoltaic (PV) power systems that are linked to the grid have the advantage of being
quick and dependable sources of electricity. The power industry has been obliged to …

Modern power networks are more dependent on smaller dispersed generating units as a
result of the increased focus on renewable energy. The identification of islanding …