We review the latest modeling techniques and propose new hybrid SAELSTM framework
based on Deep Learning (DL) to construct prediction intervals for daily Global Solar …

The accurate prediction of global solar radiation (GSR) with remote sensing in metropolitan,
regional and remote, yet solar-rich sites, is a core requisite for cleaner energy utilization …

Predicting electricity demand data is considered an essential task in decisions taking, and
establishing new infrastructure in the power generation network. To deliver a high-quality …

Data-intelligent algorithms tailored for short-term energy forecasting can generate
meaningful information on the future variability of solar energy developments. Traditional …

Electricity consumption has stochastic variabilities driven by the energy market volatility. The
capability to predict electricity demand that captures stochastic variances and uncertainties …

Reliable models that can forecast energy demand (G) are needed to implement affordable
and sustainable energy systems that promote energy security. In particular, accurate G …

Forecast models of solar radiation incorporating cloud effects are useful tools to evaluate the
impact of stochastic behaviour of cloud movement, real-time integration of photovoltaic …

Dispatchable, reliable, and clean energy is essential for sustained economic growth and a
better future. This study develops a novel technique of empirical wavelet transform (EWT) to …

We propose a method for detecting and forecasting events of high energy demand, which
are managed at the national level in demand side response programmes, such as the UK …

Breath sound detection from speech recordings has wide-ranging applications, from high-
quality audio recordings to medical diagnostics. However, perceptual recognition of breath …