High accuracy data-driven heliostat calibration and state prediction with pretrained deep neural networks

Author:

Pargmann Max,Maldonado Quinto Daniel,Schwarzbözl Peter,Pitz-Paal Robert

Publisher

Elsevier BV

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Multi-Objective Particle Swarm Optimization-Based Optimization Model for Heliostat Field Design;2024 7th International Conference on Advanced Algorithms and Control Engineering (ICAACE);2024-03-01

2. Enhancing heliostat calibration on low data by fusing robotic rigid body kinematics with neural networks;Solar Energy;2023-11

3. Data set sampling and its implications on the heliostat calibration;Advances in Solar Energy: Heliostat Systems Design, Implementation, and Operation;2023-10-04

4. ANFIS and ANN models to predict heliostat tracking errors;Heliyon;2023-01

5. Towards a neural network based flux density prediction – Using generative models to enhance CSP raytracing;THE INTERNATIONAL CONFERENCE ON BATTERY FOR RENEWABLE ENERGY AND ELECTRIC VEHICLES (ICB-REV) 2022;2023

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