Results 151 to 160 of about 3,700 (260)

Feasibility of Wind‐Powered Green Hydrogen Production via a Hybrid Graph Neural Network‐Transformer Forecasting Model

open access: yesEnergy Science &Engineering, EarlyView.
ABSTRACT Accurate long‐term wind speed forecasting is pivotal for the strategic planning of renewable energy infrastructure, particularly for assessing the techno‐economic feasibility of wind‐powered green hydrogen facilities. However, capturing the complex spatiotemporal dependencies in climate data remains a significant challenge. This study proposes
Iman Baghaei   +2 more
wiley   +1 more source

Energy Consumption and CO2 Emissions Forecasting of Transport Sector Using Machine Learning

open access: yesEnergy Science &Engineering, EarlyView.
The transport sector accounts for approximately one‐quarter of Iran's final energy consumption. The energy demand in this sector has the least variation, with petroleum products accounting for more than 85% of the demand. Furthermore, the accelerated growth of energy consumption and the sector's reliance on fossil fuels, which are the main cause of ...
Amir Hossein Akbari   +2 more
wiley   +1 more source

Triboelectric nanogenerators for vehicle intelligent cockpits

open access: yesFlexMat, EarlyView.
Converting mechanical stimuli from drivers and vehicle motion into electrical signals, triboelectric nanogenerators provide promising interfaces toward passive, flexible, distributed and intelligent sensing for next‐generation automotive cockpits to support three core functional dimensions: driver state monitoring, vehicle state monitoring, and human ...
Haiqiu Tan   +9 more
wiley   +1 more source

Intraday Functional PCA Forecasting of Cryptocurrency Returns

open access: yesJournal of Forecasting, EarlyView.
ABSTRACT We study the functional PCA (FPCA) forecasting method in application to functions of intraday returns on Bitcoin. We show that improved interval forecasts of future return functions are obtained when the conditional heteroscedasticity of return functions is taken into account.
Joann Jasiak, Cheng Zhong
wiley   +1 more source

Predicting EU Emissions Allowance Prices Using Macroeconomic Indicators and Hybrid AI Models

open access: yesJournal of Forecasting, EarlyView.
ABSTRACT Predicting carbon allowance prices has grown more crucial in relation to carbon market regulation, financial strategy, and environmental policy development. This study examines a hybrid forecasting system that combines deep learning with ensemble machine learning models to forecast the price fluctuations of EU Emissions Allowance (EUAs) within
Saptarshi Ganguly   +2 more
wiley   +1 more source

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