Results 81 to 90 of about 53,937 (302)
Evaluation of Machine Learning Techniques for Green Energy Prediction [PDF]
arXiv, 2014 We evaluate the following Machine Learning techniques for Green Energy (Wind, Solar) Prediction: Bayesian Inference, Neural Networks, Support Vector Machines, Clustering techniques (PCA). Our objective is to predict green energy using weather forecasts, predict deviations from forecast green energy, find correlation amongst different weather parameters
arxiv
Advanced Functional Materials, EarlyView.This work explores Li‐substituted P2 layered oxides for Na‐ion batteries by crystallographic and electrochemical studies. The effect of lithium on superstructure orderings, on phase transitions during synthesis and electrochemical cycling and on the interplay of O‐ versus TM‐redox is revealed via various advanced techniques, including semi‐simultaneous
Mingfeng Xu +5 more
wiley +1 more source
On the Performance of Forecasting Models in the Presence of Input Uncertainty [PDF]
arXiv, 2017 Nowadays, with the unprecedented penetration of renewable distributed energy resources (DERs), the necessity of an efficient energy forecasting model is more demanding than before. Generally, forecasting models are trained using observed weather data while the trained models are applied for energy forecasting using forecasted weather data.
arxiv
An experiment in weather forecast [PDF]
Journal of the Franklin Institute, 1885 n ...
openaire +2 more sources
THE GALACTIC CENTER WEATHER FORECAST [PDF]
The Astrophysical Journal, 2012 15 pages, 5 figures, accepted to ApJ ...
Joshua C. Dolence +3 more
openaire +2 more sources
Advanced Functional Materials, EarlyView.This review provides an in‐depth understanding of all theoretical reaction mechanisms to date concerning zinc–iodine batteries. It revisits the inherent issues and solutions of zinc–iodine batteries from the perspective of industrial application. By integrating existing examples of energy storage applications, it identifies the challenges faced on the ...
Haokun Wen +10 more
wiley +1 more source
Uncertainty quantification for data-driven weather models [PDF]
Artificial intelligence (AI)-based data-driven weather forecasting models have experienced rapid progress over the last years. Recent studies, with models trained on reanalysis data, achieve impressive results and demonstrate substantial improvements over state-of-the-art physics-based numerical weather prediction models across a range of variables and
arxiv +1 more source
Simple Baseline for Weather Forecasting Using Spatiotemporal Context Aggregation Network [PDF]
arXiv, 2022 Traditional weather forecasting relies on domain expertise and computationally intensive numerical simulation systems. Recently, with the development of a data-driven approach, weather forecasting based on deep learning has been receiving attention. Deep learning-based weather forecasting has made stunning progress, from various backbone studies using ...
arxiv
Advanced Healthcare Materials, EarlyView.Volumetric muscle loss (VML) due to trauma or surgery, often leads to physical impairments. Traditional treatments rely on autologous flaps, limited by muscle availability often leading to donor site morbidity. This study presents multimodal bioprinting as an innovative approach for fabricating vascularized muscle flaps with 3D‐printed macrovessels ...
Eliana O. Fischer +8 more
wiley +1 more source