Review of application of AI techniques to Solar Tower Systems

Milidonis, Kypros; Blanco, Manuel; Grigoriev, Victor; Panagiotou, Constantinos F.; Bonanos, Aristides M.; Constantinou, Marios; Pye, John; Asselineau, Charles-Alexis

doi:10.1016/j.solener.2021.06.009

Cited by 33 publications

(10 citation statements)

References 117 publications

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“…The CRPSS improvement is above 2.7% in some cases, indicating the enhanced performance of forecasting models. Furthermore, an optimized model shows a skill score improvement of 21% compared to the previous Cloud Motion Vector (CMV) model [59].…”

Section: Confidence Intervals and Ranges In Solar Forecasting Studiesmentioning

confidence: 92%

A Review of Solar Forecasting Techniques and the Role of Artificial Intelligence

Barhmi,

Heynen,

Golroodbari

et al. 2024

Solar

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Solar energy forecasting is essential for the effective integration of solar power into electricity grids and the optimal management of renewable energy resources. Distinguishing itself from the existing literature, this review study provides a nuanced contribution by centering on advancements in forecasting techniques. While preceding reviews have examined factors such as meteorological input parameters, time horizons, the preprocessing methodology, optimization, and sample size, our study uniquely delves into a diverse spectrum of time horizons, spanning ultrashort intervals (1 min to 1 h) to more extended durations (up to 24 h). This temporal diversity equips decision makers in the renewable energy sector with tools for enhanced resource allocation and refined operational planning. Our investigation highlights the prominence of Artificial Intelligence (AI) techniques, specifically focusing on Neural Networks in solar energy forecasting, and we review supervised learning, regression, ensembles, and physics-based methods. This showcases a multifaceted approach to address the intricate challenges associated with solar energy predictions. The integration of Satellite Imagery, weather predictions, and historical data further augments precision in forecasting. In assessing forecasting models, our study describes various error metrics. While the existing literature discusses the importance of metrics, our emphasis lies on the significance of standardized datasets and benchmark methods to ensure accurate evaluations and facilitate meaningful comparisons with naive forecasts. This study stands as a significant advancement in the field, fostering the development of accurate models crucial for effective renewable energy planning and emphasizing the imperative for standardization, thus addressing key gaps in the existing research landscape.

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Section: Confidence Intervals and Ranges In Solar Forecasting Studiesmentioning

confidence: 92%

A Review of Solar Forecasting Techniques and the Role of Artificial Intelligence

Barhmi,

Heynen,

Golroodbari

et al. 2024

Solar

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“…Large scale application of concentrated solar energy focuses on solar tower technology with a heliostat field concentrator, 180 , 181 , 182 , 183 , 184 , 185 because the parabolic dish concentrator configuration is limited in size considering mechanical constraints caused by wind and weight loads. 53 There is no commercial parabolic dish whose diameter is over 10 m in the current market.…”

Section: Concentrating Solar Energy To Thermal Energymentioning

confidence: 99%

Solar-driven thermochemical conversion of H2O and CO2 into sustainable fuels

Wei,

Pan,

Shi

et al. 2023

iScience

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“…Significant progress has been made in underwater vehicle technologies along with the booming development in big data [ 1 ], data mining [ 2 ], computer simulation [ 3 ], intelligent control [ 4 ], intelligent optimization [ 5 ], virtual reality [ 6 ], and artificial intelligence [ 7 ]. Underwater vehicles are playing an increasingly important part in workplaces where equipment and divers face access difficulties such as detection and salvage [ 8 ], dam detection [ 9 ], pipeline maintenance [ 10 ], cable servicing and laying [ 11 , 12 ], and marine resource development [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Design and Verification of Deep Submergence Rescue Vehicle Motion Control System

Jiang,

Zhang,

Wan

et al. 2023

Sensors

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A six degree-of-freedom (DOF) motion control system for docking with a deep submergence rescue vehicle (DSRV) test platform was the focus of this study. The existing control methods can meet the general requirements of underwater operations, but the complex structures or multiple parameters of some methods have prevented them from widespread use. The majority of the existing methods assume the heeling effect to be negligible and ignore it, achieving motion control in only four or five DOFs. In view of the demanding requirements regarding positions and inclinations in six DOFs during the docking process, the software and hardware architectures of the DSRV platform were constructed, and then sparse filtering technology was introduced for data smoothing. Based on the adaptive control strategy and with a consideration of residual static loads, an improved S-plane control method was developed. By converting the force (moment) calculated by the controller to the body coordinate system, the complexity of thrust allocation was effectively reduced, and the challenge of thrust allocation in the case of a high inclination during dynamic positioning was solved accordingly. The automatic control of the trimming angle and heeling angle was realized with the linkage system of the ballast tank and pump valve. A PID method based on an intelligent integral was proposed, which not only dealt with the integral “saturation” problem, but also reduced the steady-state error and overshooting. Water pool experiments and sea trials were carried out in the presence of water currents for six-DOF motion control. The responsiveness and precision of the control system were verified by the pool experiment and sea trial results and could meet the control requirements in engineering practice. The reliability and operational stability of the proposed control system were also verified in a long-distance cruise.

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Review of application of AI techniques to Solar Tower Systems

Cited by 33 publications

References 117 publications

A Review of Solar Forecasting Techniques and the Role of Artificial Intelligence

A Review of Solar Forecasting Techniques and the Role of Artificial Intelligence

Solar-driven thermochemical conversion of H2O and CO2 into sustainable fuels

Design and Verification of Deep Submergence Rescue Vehicle Motion Control System

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