The Use of the Artificial Neural Network (ANN) Method to Forecast the Performance of Solar Collector Systems
Keywords:
Artificial Neural Network, Learning Algorithm, Multi-layer perceptron Thermal performance, Solar energy collectorAbstract
Whatever wind and solar collection device designed to work mostly in low to mid-temperature area. Must include a solar collector at its core. As a result, an efficient solar collector system design with optimal performance is needed. Intelligent system design is a helpful method for optimizing the efficiency of such systems, even if many other strategies are used to improve system performance. Artificial Neural Network (ANN) is a kind of intelligence method that is utilized in system modeling, simulation, and control. In comparison to other traditional methods, the ANN tool solves difficult and nonlinear problems quicker and more accurately. The artificial neural network (ANN) method Economics, economics, art, military, trade, and technology are just a few of the sectors where it's applied. Our ANN tool's main task is model building, which will be done with the use of empirical observations. From solar energy systems, and this technique does not need separate programming like other traditional methods. The goal of this research is to look at how artificial intelligence (AI) may be used to forecast to assess the effectiveness of wind and solar collections and to review relevant requirements for the proposed study this same ANN approach is an excellent tool for forecasting solar panel function. Collector systems, as shown by the study reported in this article.
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References
. Amato F, López A, Peña-Méndez EM, Vaňhara P, Hampl A, Havel J. Artificial neural networks in medical diagnosis. Journal of Applied Biomedicine. 2013.
. Yegnanarayana B. Artificial neural networks for pattern recognition. Sadhana. 1994;
. Li H, Zhang Z, Liu Z. Application of artificial neural networks for catalysis: A review. Catalysts. 2017. [4]. Marugán AP, Márquez FPG, Perez JMP, Ruiz Hernández D. A survey of artificial neural network in wind energy systems. Applied Energy. 2018.
. Wu Y chen, Feng J wen. Development and Application of Artificial Neural Network. Wirel Pers Commun. 2018; [6]. Chen TCT, Liu CL, Lin HD. Advanced artificial neural networks. Algorithms. 2018.
. Mahesh A. Solar collectors and adsorption materials aspects of cooling system. Renewable and Sustainable Energy Reviews. 2017.
. Liang R, Zhang J, Ma L, Zhao L. Dynamic simulation on thermal performance of gas-liquid separated solar collector system with R245fa. Appl Therm Eng. 2014;
. Bejan A, Kearney DW, Kreith F. Second law analysis and synthesis of solar collector systems. J Sol Energy Eng Trans ASME. 1981;
. Alaydi JY. Modeling of a Parabolic Solar-Collector System for WaterDesalination. Global. 2013;