A Comprehensive Review of the Artificial Neural Networks (ANN) Methodology Implementations for Analysing and Forecasting the Efficiency of Solar Water Heater Collector Under Various Tilt Angles
Keywords:
Tilt angle (TA), ANN models, RBF, MLFNN, ANFIS, ELM, Solar heater system, Solar energy (SE), solar collector (SC), solar radiation (SR)Abstract
This article examines tilt angle and reviews artificial neural network (ANN) models for tilt angle (TA) prediction and optimization in solar water heaters (SWH). The paper provides a summary of design simulations, parameters, applications, and mathematical approaches that are used in a variety of applications. The quantity of references analysing TA deployment in context of research publications are increasing. The number of countries involved in solar system operations has increased dramatically. Many models and test procedures for determining the optimal TA in various solar schemes has been created, each of which is identified by their mathematical models and tracking techniques, as evidenced by recent research. The 4 ANN models like Extreme learning machine (ELM), radial basic function (RBF), Multilayer Feed forward neural network (MLFNN), and Artificial Neuro-fuzzy inference System (ANFIS) are compared by estimating the root mean square error (RMSE) value of training and testing of models. The ELM performs better compared to other models. The variables of TA like slips, inclination, height, and width are also mentioned in this article. The analysis of SWH at various tilt angles and development of ANN model for optimization of the TA is also discussed in this article. Here 35 research paper related to development and analysis at various tilt angle has been reviewed and understand their impact of different ANN approaches on the performance of SWH.
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