Solar photovoltaic plants are widely integrated into most countries worldwide. Due to the
ever-growing utilization of solar photovoltaic plants, either via grid-connection or stand …

Nowadays in order to meet the increase in power demands and to reduce the global
warming, renewable energy sources based system is used. Out of the various renewable …

Deep artificial neural networks have been popular for time series forecasting literature in
recent years. The recurrent neural networks present more suitable architectures for …

Selecting the correct weather forecasting technique is a crucial task when planning an
efficient solar energy generation system. Estimating accurate solar photovoltaic systems …

The integration of solar photovoltaic (PV) into electricity networks introduces technical
challenges due to varying PV output. Rapid ramp events due to cloud movements are of …

The problem of forecasting hourly solar irradiance over a multi-step horizon is dealt with by
using three kinds of predictor structures. Two approaches are introduced: Multi-Model (MM) …

Electrical power systems which incorporate solar or wind energy sources, or electric
vehicles, must deal with the uncertainty about the availability of injected or demanded …

Recurrent neural networks (RNNs) are powerful tools for learning information from temporal
sequences. Designing an optimum deep RNN is difficult due to configuration and training …

The increasing demand for solar photovoltaic systems that generate electricity from sunlight
stems from their clean and renewable nature. These systems are often deployed in remote …

The high penetration of renewable sources of energy in electrical power systems implies an
increase in the uncertainty variables of the economic dispatch (ED). Uncertainty costs are a …