Results 191 to 200 of about 7,885 (219)

Global solar energy potential forecasting through machine learning and deep learning models. [PDF]

Sci Rep
Raza MA, Karim A, Altayeb M, Masud MI, Faheem M, Jumani TA, Aman M.   +6 more
europepmc   +1 more source

Thermally Robust Spin–Orbit Torque Switching in Low‐Resistivity Mo‐Alloyed β‐W Heterostructures

Advanced Functional Materials, EarlyView.
Mo‐alloyed β‐W heterostructures exhibit enhanced spin‐orbit torque efficiency, reduced resistivity and switching current density, and reliable operation from −50 to +150°C. The optimized W87.3Mo12.7 device achieves high spin Hall conductivity with thermal robustness, highlighting its potential for energy‐efficient magnetic memory and automotive ...
Han Seok Ko, Hyun Ju Lee, Jeong Kyu Lee, Han Sol Kim, Min Seok Kim, Quynh Anh T. Nguyen, Sonny H. Rhim, Young Keun Kim   +7 more
wiley   +1 more source

Machine learning-based wind speed forecasting: a comparative study. [PDF]

Sci Rep
Zabihi A, B Manur V, Dintakurthy Y, Prasad K, Rene P, Krishna V.   +5 more
europepmc   +1 more source

Machine Learning Assisted Hybrid Cuckoo Search for Predictive Optimization in Renewable Energy Systems. [PDF]

F1000Res
Dalaf N, Abbas IT.
europepmc   +1 more source

A genetic algorithm-based ensemble framework for wind speed forecasting. [PDF]

Sci Rep
Barchi TM, Dos Santos JLF, Alves TA, de Mattos Neto PSG, Stevan SL, Corrêa FC, Medina MCC, Siqueira HV, de Oliveira JFL.   +8 more
europepmc   +1 more source

Reconstructing fine-scale 3D wind fields with terrain-informed machine learning. [PDF]

Nat Commun
Lin C, Tie R, Yi S, Liu D, Zhong X, Hu Z, Li H.   +6 more
europepmc   +1 more source

AI-enhanced multi-timescale optimization strategy for virtual power plants: Advancing losad forecasting and dynamic demand response integration. [PDF]

PLoS One
Xu G, Yang G, Bao J, Feng H, Zhang F, Zheng H.   +5 more
europepmc   +1 more source

A stacked Gradient Boosting-XGBoost ensemble with ridge meta-learner for accurate short-term solar PV power forecasting in smart grids. [PDF]

Sci Rep
Rohini G, Mariprasth T, Bustanji SA, Zaitsev I.   +3 more
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

A review of wind speed and wind power forecasting with deep neural networks

Applied Energy, 2021
Yun Wang, Qianyi Liu, Runmin Zou
exaly