A review of deep learning with special emphasis on architectures, applications and recent trends

S Sengupta, S Basak, P Saikia, S Paul… - Knowledge-Based …, 2020‏ - Elsevier
Deep learning (DL) has solved a problem that a few years ago was thought to be intractable—
the automatic recognition of patterns in spatial and temporal data with an accuracy superior …

Machine learning and metaheuristic methods for renewable power forecasting: a recent review

H Alkabbani, A Ahmadian, Q Zhu… - Frontiers in Chemical …, 2021‏ - frontiersin.org
The global trend toward a green sustainable future encouraged the penetration of
renewable energies into the electricity sector to satisfy various demands of the market …

Wind power prediction using deep neural network based meta regression and transfer learning

AS Qureshi, A Khan, A Zameer, A Usman - Applied Soft Computing, 2017‏ - Elsevier
An innovative short term wind power prediction system is proposed which exploits the
learning ability of deep neural network based ensemble technique and the concept of …

[HTML][HTML] The application of ANFIS prediction models for thermal error compensation on CNC machine tools

AM Abdulshahed, AP Longstaff, S Fletcher - Applied soft computing, 2015‏ - Elsevier
Thermal errors can have significant effects on CNC machine tool accuracy. The errors come
from thermal deformations of the machine elements caused by heat sources within the …

Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks

A Zameer, J Arshad, A Khan, MAZ Raja - Energy conversion and …, 2017‏ - Elsevier
The inherent instability of wind power production leads to critical problems for smooth power
generation from wind turbines, which then requires an accurate forecast of wind power. In …

RETRACTED: Artificial neural networks applications in wind energy systems: A review

R Ata - 2015‏ - Elsevier
One of the conditions of submission of a paper for publication is that authors declare
explicitly that their work is original and has not been submitted to nor appeared in another …

Forecasting green roofs' potential in improving building thermal performance and mitigating urban heat island in the Mediterranean area: An artificial intelligence …

D Mazzeo, N Matera, G Peri, G Scaccianoce - Applied Thermal Engineering, 2023‏ - Elsevier
Green roofs are widely used in hot or cold climates mainly because they are capable to
improve the energy efficiency of buildings and, when implemented at a large scale, reducing …

Hourly forecasting of the photovoltaic electricity at any latitude using a network of artificial neural networks

N Matera, D Mazzeo, C Baglivo… - … Energy Technologies and …, 2023‏ - Elsevier
Nowadays, special attention is paid to the importance of using photovoltaic (PV) systems to
tackle the problem of climate change and the energy crisis. Artificial intelligence is currently …

Wind turbine power curve modeling based on Gaussian processes and artificial neural networks

B Manobel, F Sehnke, JA Lazzús, I Salfate, M Felder… - Renewable Energy, 2018‏ - Elsevier
An accurate estimation of the wind turbine power curve is a key issue to the provision of the
electricity that the wind farm will transfer to the grid and for a correct evaluation of the …

Modelling and analysis of real-world wind turbine power curves: Assessing deviations from nominal curve by neural networks

G Ciulla, A D'Amico, V Di Dio, VL Brano - Renewable energy, 2019‏ - Elsevier
The power curve of a wind turbine describes the generated power versus instantaneous
wind speed. Assessing wind turbine performance under laboratory ideal conditions will …