ANFIS and ANN models to predict heliostat tracking errors

Sarr, Marie Pascaline; Thiam, Ababacar; Dieng, Biram

doi:10.1016/j.heliyon.2023.e12804

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…In order to better understand the reflection effect of sunlight on the mirror surface of a heliostat, this article establishes an interaction model [7] between sunlight and the heliostat, which can more intuitively observe the interaction between the two and determine a heliostat size and material that maximizes the reflection of sunlight. Due to the fact that sunlight is a conical light with a certain cone angle, this article uses the formula…”

Section: Modeling the Reflection Characteristics Of Heliostatsmentioning

confidence: 99%

Research on Optimization Design Based on Heliostat Field

2023

IJFET

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Heliostat field solar thermal power generation is an environmentally friendly technology that harnesses solar energy. It offers a clean and cost-effective method of generating power. Unlike some other solar technologies, it can provide uninterrupted energy day and night through efficient thermal storage systems, ensuring a stable and continuous power supply. This feature significantly enhances solar energy's effective utilization. The advantages of solar thermal power generation make it highly competitive in the commercial energy market. To optimize its design, this article calculates parameters such as annual average optical efficiency, output thermal power, and output thermal power per unit mirror area. It also considers constraints like heliostat size, layout, and collector placement to achieve a targeted annual average thermal power output of 60MW.By carefully adjusting the position coordinates of the absorption tower, heliostat size, installation height, the number of heliostats, and their positioning, an optimal solution is derived. The design strives to maximize the annual average output thermal power per unit mirror area while meeting specified criteria, ultimately improving the field's overall performance. This optimization aims to fully leverage solar energy resources, achieve maximum energy utilization, and economic benefits. In doing so, it contributes to sustainable development and green energy goals.

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Section: Modeling the Reflection Characteristics Of Heliostatsmentioning

confidence: 99%

Research on Optimization Design Based on Heliostat Field

2023

IJFET

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“…Computer-based soft computing methods, such as support vector machine (SVM), particle swarm optimization (PSO), fuzzy logic (FL), fuzzy decision tree (FDT), artificial neural networks ( ), wavelet neural network (WNN), genetic algorithm ( ), adaptive neuro-fuzzy inference system (ANFIS), co-active neuro-fuzzy inference system (CANFIS), convolutional neural network (CNN), imperialist competitive algorithm ( ) and, recurrent neural network (RNN) have recently been advanced in research areas of scientific, engineering, technological, and industrial courses [ [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] ]. These state-of-the-art mathematical modeling tools can capture high dimensional complex data, recognize inherent highly complex links from input-output data, find optimum patterns, and forecast target parameters [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of gross calorific value from coal analysis using decision tree-based bagging and boosting techniques

Munshi,

Jahan,

Howladar

et al. 2024

Heliyon

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“…The main goal of this method is to improve tracking accuracy and offer a more dependable metric for evaluating calibration results. The open-loop heliostat calibration, as typically executed in the majority of solar towers [3,4,5,6,7,8], constitutes a dataset-driven optimization targeting the primary function space orientation and aim point. Notably, this optimization is independent to the employed methodology and algorithmic approach.…”

Section: Introductionmentioning

confidence: 99%

Data set sampling and its implications on the heliostat calibration

Pargmann,

Leibauer,

Maldonado Quinto

et al. 2023

Advances in Solar Energy: Heliostat Systems Design, Implementation, and Operation

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The ongoing global energy transition towards sustainable and climate-neutral power generation has led to the increasing adoption of concentrated solar tower power plants, relying on heliostats for precise solar tracking. Heliostat calibration, vital for maintaining accurate alignment, traditionally assumes a decrease in accuracy over time due to various factors. However, the impact of data set sampling on reported tracking accuracy has been overlooked. This paper utilizes a kNN (k-Nearest Neighbors) data set sampling approach to investigate data set distribution's impact on model accuracy. Results indicate that conventional time-dependent sampling can lead to an overestimation of reported accuracies. In contrast, the kNN sampling approach demonstrates a strong correlation between model performance and the proximity of test data to training data. Simulations reveal that reported accuracy scores are influenced by the similarity between training and test data sets. The study highlights the critical importance of considering data set distribution when interpreting accuracy scores. The proposed method improves tracking accuracy and offers a dependable metric for evaluating calibration results. It provides valuable insights to enhance heliostat calibration models, advancing precise solar tracking in concentrated solar tower power plants and supporting the global transition towards sustainable energy solutions.

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ANFIS and ANN models to predict heliostat tracking errors

Cited by 7 publications

References 26 publications

Research on Optimization Design Based on Heliostat Field

Research on Optimization Design Based on Heliostat Field

Prediction of gross calorific value from coal analysis using decision tree-based bagging and boosting techniques

Data set sampling and its implications on the heliostat calibration

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