M.R.A. Calado

TL;DR: Comparisons with other published methods demonstrate that the proposed GCPSO method produces very good results in the extraction of the PV model parameters, which can find highly accurate solutions while demanding a reduced computational cost.

TL;DR: A hybrid method that combines similar and recent day-based selection, correlation and wavelet analysis in a pre-processing stage is presented, revealing positive forecasting results in comparison with other state-of-the-art methods.

TL;DR: A new hybrid methodology that combines diversification and intensification mechanisms from different metaheuristics (MHs) to estimate PV parameters precisely has the capacity to adapt to the specific optimization problem and maintain diversity when building solutions, thus mitigating premature convergence and population stagnation.

TL;DR: A new enhanced method for one day ahead load forecast is presented, combing improved data selection and features extraction techniques, which brings more “regularity” to the load time-series, an important precondition for the successful application of neural networks.

TL;DR: This paper proposes using optimised deep learning neural networks to forecast the wave energy flux, and other wave parameters, using moth-flame optimisation as the central decision-making unit to configure the deep neural network structure and the proper input data selection.