Development of Feed-Forward Back-Propagation Neural Model to Predict the Energy and Exergy Analysis of Solar Air Heater

Ghritlahre, Harish Kumar

doi:10.17737/tre.2018.4.2.0078

Cited by 17 publications

(9 citation statements)

References 38 publications

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“…e basic principle behind every NN is artificial neurons. It constitutes the process element, which receives the input signal and generates the output for the neighboring process after performing with activation function and associated weights [32]. Figure 1 illustrates the architecture of a simple artificial neuron.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Performance Analysis of FFBP-LM-ANN Based Hourly GHI Prediction Using Environmental Variables: A Case Study in Chennai

Sushmi

Subbulekshmi

2022

Mathematical Problems in Engineering

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Sun is the sustainable and abundantly available alternative resource on the planet Earth. The uncertain nature of the source caused due to various environmental factors increases the need to quantify the irradiation potential at the targeted location, especially for power smoothing processes, solar-fed electrical applications, and the utility grid. The irradiation measuring devices lead to appropriate maintenance and more expenses and are ineffective under dynamically varying irradiation conditions. Hence, a robust ML-based forecasting technique is sufficient to predict GHI under dynamically varying irradiation profiles caused by various environmental factors. Therefore, this study focuses on short-term (hourly) GHI forecasting using the FFBP-LM-ANN approach. In most of the related articles, the selection of independent variables and designing the network is being a challenge and obstacle in attaining fast and efficient prediction. Such a scenario increases the overall computation complexity and understanding. Hence, this study was intended to use a simple process to crucially select 5 environmental factors and to optimally choose the number of neurons for designing the network for better irradiation prediction. Thus, the network is designed with an input layer of 5 selected environmental variables and one hidden layer with 20 neurons, and hourly GHI in the output layer is performed. The approach is trained using the Levenberg-Marquardt BP algorithm in MATLAB toolbox, with the help of a 4-year dataset received for the rooftop panels of VIT University, Chennai, from NREL. The performance of the model during training and testing was validated and analyzed using 9 performance matrices. As a result, FFBP-LM-ANN satisfactorily predicts hourly GHI for the targeted location based on rRMSE of 7.21%, MAE of 0.042, MBE of 0.000492, R of 0.96, MAPE of 44.4%, MRE of 9.5%, and NSE of 94% obtained under testing process. Moreover, the model has performed much better when compared with 9 related models that exist in literature based on the input variables used, network design, epoch, and RMSE. Subsequently, such a predictor will be adaptable and more suitable for the explicit prediction of hourly GHI for different regions across the world having varying climatic conditions, since the study model is designed for locations facing robust climatic nature. More importantly, the designed model is superior with only environmental variables, which are rarely found in the article, rather than geographical variables, which are predominantly used in most of the related literature.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Performance Analysis of FFBP-LM-ANN Based Hourly GHI Prediction Using Environmental Variables: A Case Study in Chennai

Sushmi

Subbulekshmi

2022

Mathematical Problems in Engineering

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“…Basic steps flow chart of ANN simulation technique [71] The basic steps of ANN simulation technique are shown in flow chart Figure 6. These important steps are followed in ANN prediction [5,65]. 1.…”

Section: Sum Of Square Errormentioning

confidence: 99%

“…They also concluded that the LM based ANN model was best model. [65] developed feed forward neural network model to predict the energy and exergy efficiency of transverse wire rib roughened solar air heater. To achieve this aim, they collected 50 sets of experimental data and calculated values of energy and exergy efficiencies.…”

Section: Performance Prediction Of Solar Water/ Air Heating Systems Umentioning

confidence: 99%

Implementation of ANN technique for performance prediction of solar thermal systems: A Comprehensive Review

Ahmad¹,

Ghritlahre²,

Chandrakar³

2020

Tr Ren Energy

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“…Sahu and Prasad [25] found that a relative roughness height e/Dh = 0.0422, a relative roughness pitch P/e = 10, and a relative angle of attack /90 = 0.3333 yielded the maximum exergy efficiency for solar air heaters with arcshaped wires. In particular, Ghritlahre et al, [31,33,34] and Ghritlahre [32] recommended the use of the artificial neural network (ANN) technique to predict the energy and exergy performance of a SAH due to its high accuracy.…”

Section: Exergy Efficiencymentioning

confidence: 99%

A Review of Energy and Exergy Analyses of a Roughened Solar Air Heater

Phu

Luân

2020

ARFMTS

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In this paper, eleven roughness elements in solar air heater duct analysed both energy and exergy were reviewed. Various roughness geometries such as ribs, twisted tap, baffles, and metal waste were surveyed about heat transfer and friction when the air flow is passing absorber plate. Evaluation criteria of roughness on the absorber plate including thermohydraulic performance parameter, thermal efficiency, effective efficiency, and exergy efficiency were presented and compared. Results showed that protruded ribs in arc shape indicated the largest Nusselt number. The ribs exhibited the highest thermohydraulic performance parameter at a Reynolds number greater than 5000. Jet impingement with arc-shaped ribs and roughness elements of metal waste were found the smallest exergy efficiencies. The biggest effective and exergy efficiencies were obtained to be 70% and 1.9%, respectively. The thermohydraulic performance parameter varied from 0.5 to 2.0. The review paper aims to provide information about roughness geometries investigated both first and second laws of thermodynamics and figure of merits to overview artificial roughness in a solar air heater.

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Development of Feed-Forward Back-Propagation Neural Model to Predict the Energy and Exergy Analysis of Solar Air Heater

Cited by 17 publications

References 38 publications

Performance Analysis of FFBP-LM-ANN Based Hourly GHI Prediction Using Environmental Variables: A Case Study in Chennai

Performance Analysis of FFBP-LM-ANN Based Hourly GHI Prediction Using Environmental Variables: A Case Study in Chennai

Implementation of ANN technique for performance prediction of solar thermal systems: A Comprehensive Review

A Review of Energy and Exergy Analyses of a Roughened Solar Air Heater

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