Thermal Efficiency Prediction of a Solar Low Enthalpy Steam Generating Plant Employing Artificial Neural Networks

Tzuc, O. May; Bassam, A.; Flota-Bañuelos, Manuel; López, Esteban Egea; Ricalde-Cab, Lifter; Quijano, Ramiro; Pasos, Alan E. Vega

doi:10.1007/978-3-319-30447-2_5

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…These characteristics have led many countries to pay attention on the design policies, strategies, and technology for its use [1][2][3][4]. In this context, photovoltaic (PV) systems are one of the most mature solar technologies, and it is constantly increasing its competitiveness and decreasing its production costs [5].…”

Section: Introductionmentioning

confidence: 99%

Temperature Estimation for Photovoltaic Array Using an Adaptive Neuro Fuzzy Inference System

et al. 2017

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Abstract:Module temperature is an important parameter of photovoltaic energy systems since their performance is affected by its variation. Several cooling controllers require a precise estimation of module temperature to reduce excessive heating and power losses. In this work, an adaptive neuro fuzzy inference system technique is developed for temperature estimation of photovoltaic systems. For the learning process, experimental measurements comprising six environmental variables (temperature, irradiance, wind velocity, wind direction, relative humidity, and atmospheric pressure) and one operational variable (photovoltaic power output) were used as training parameters. The proposed predictive model comprises a zero-order Sugeno neuro fuzzy system with two generalized bell-shaped membership functions per input and 128 fuzzy rules. The model is validated with experimental information from an instrumented photovoltaic system with a fitness correlation parameter of R = 95%. The obtained results indicate that the proposed methodology provides a reliable tool for estimation of modules temperature based on environmental variables. The developed algorithm can be implemented as part of a cooling control system of photovoltaic modules to reduce the efficiency losses.

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Section: Introductionmentioning

confidence: 99%

Temperature Estimation for Photovoltaic Array Using an Adaptive Neuro Fuzzy Inference System

et al. 2017

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Modeling and optimization of a solar parabolic trough concentrator system using inverse artificial neural network

Tzuc

Bassam

Soberanis

et al. 2017

Journal of Renewable and Sustainable Energy

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In this paper, an artificial neural network inverse (ANNi) model is applied to optimize the thermal performance (η) of parabolic trough concentrators. A feedforward neural network architecture is trained using an experimental database from parabolic trough concentrators operations. Rim angle (φr), inlet (Tin) and outlet (Tout) fluid temperatures, ambient temperature (Ta), water flow (Fw), direct solar radiation (Gb), and the wind velocity (Vw) were used as main input variables within the neural network model to estimate the thermal performance with a correlation coefficient of R2 = 0.9996 between experimental and simulated values. The sensitivity analysis is carried out to verify the effect of all input variables. The optimal operation conditions of parabolic trough concentrators are established using artificial neural network inverse modeling (ANNi) to achieve optimal operation conditions of parabolic trough concentrators. The results indicated that ANNi is a feasible tool for Parabolic Trough Concentrators optimization.

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Estimation of the operating temperature of photovoltaic modules using artificial intelligence techniques and global sensitivity analysis: A comparative approach

Tzuc

Bassam

Méndez-Monroy

et al. 2018

Journal of Renewable and Sustainable Energy

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In this work, four artificial intelligence (AI) techniques, based on Artificial Neural Networks, Support Vector Machine (SVM), and Regression Tree Ensembles, were used to estimate the operating temperature of photovoltaic (PV) modules (TPV). The models' input parameters correspond to experimental measurements of environmental (solar radiation, ambient temperature, relative humidity, wind speed, and wind direction) and operational (power output and tracking system) variables. Several AI models were trained and statistically compared with the measured data using a computational methodology that determines the performance and accuracy of the AI technique. Finally, a global sensitivity analysis was conducted to identify the ability of each technique to reflect the physical coherence of the phenomenon that is under study. It is reported that the four techniques can provide an estimate having a precision of about 93%. On the other hand, the sensitivity analysis demonstrates that all the models cannot correctly interpret the physical interaction of the input parameters with respect to TPV, where the SVM is reported to be the most appropriate. The results indicate that the proposed methodology is a viable alternative for the estimation of TPV by AI techniques. This methodology can be implemented as an alternative tool in the development of smart PV module cooling systems to improve its performance and to reduce its operating costs.

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Thermal Efficiency Prediction of a Solar Low Enthalpy Steam Generating Plant Employing Artificial Neural Networks

Cited by 4 publications

References 19 publications

Temperature Estimation for Photovoltaic Array Using an Adaptive Neuro Fuzzy Inference System

Temperature Estimation for Photovoltaic Array Using an Adaptive Neuro Fuzzy Inference System

Modeling and optimization of a solar parabolic trough concentrator system using inverse artificial neural network

Estimation of the operating temperature of photovoltaic modules using artificial intelligence techniques and global sensitivity analysis: A comparative approach

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