crossrefEnergy and AICited by 0

Kite-based airborne wind energy on Mars: A hybrid physics-analytical and machine learning feasibility framework with uncertainty quantification

Roystan V. Castelino, Panagiotis Kosmopoulos

Related papers

crossrefEnergy and AI

Machine Learning-based uncertainty quantification in Capacity Expansion Models

Yusuf Shaltout, Arijit Upadhyay, Alexandre Oudalov, Georgios Mavromatidis

crossrefEnergy and AI

A scalable machine learning framework for hybrid solar–wind site suitability screening

Chisom Jude Okeke, David E. Ebuara, John U. Edet, Richard E. Blanchard, Jonathan Wilson

crossrefEnergy and AI

Detecting intrusions in Internet of Energy (IoE) systems using an automated machine learning framework

Abdallah Moubayed

Also available via: Energy and AI

crossrefEnergy and AI

Integrated machine-learning framework for prediction of transient fuel flow dynamics in a gasoline direct injection (GDI) injector

Kyungwon Lee, Sungkwang Mun, Le Zhao, Jiwoong Park, Anqi Zhang, Yuanjiang Pei, et al.

crossrefEnergy and AI

Machine learning-based surrogate models trained with limited datasets for a thermoelectric energy harvester

Eugen Vambolt, Niklas Pöpel, Johannes Wieczorek, Mahla Moazamigoudarzi, Johannes Rothmayr, Jan Lohbreier

crossrefEnergy and AI

A unified benchmark of deep learning and classical machine learning models for weather-based solar power forecasting: Balancing complexity and skill

Dávid Markovics, Gergő Bendegúz Békési, Martin János Mayer