Analysis of transfer functions and normalizations in an ANN model that predicts the transport of energy in a parabolic trough solar collector

Reyes-Téllez, E.D.; Parrales, A.; Ramírez-Ramos, G.E.; Hernández, J.A.; Urquiza, G.; Heredia, Miguel; Sierra, Fernando

doi:10.5004/dwt.2020.26063

Cited by 23 publications

(7 citation statements)

References 34 publications

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“…The impact of adopting different transfer functions on the model performance is reported by Aidan et al, 2020 [97]. The influence of normalisation techniques and transfer function (LOG-SIG and TANSIG) on the ANN model for energy transportation is reported by Reyes-Tellez et al, 2020 [98], which is analysed using two assessment techniques. From Fig 9A and 9B, it is noted the highest RMSE value is noted for the normalisation technique 2 for the sub-set 7.…”

Section: Influence Of Normalisation Techniques Transfer Functions And...mentioning

confidence: 99%

Artificial neural network, machine learning modelling of compressive strength of recycled coarse aggregate based self-compacting concrete

Jagadesh,

Khan,

Priya

et al. 2024

PLoS ONE

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This research study aims to understand the application of Artificial Neural Networks (ANNs) to forecast the Self-Compacting Recycled Coarse Aggregate Concrete (SCRCAC) compressive strength. From different literature, 602 available data sets from SCRCAC mix designs are collected, and the data are rearranged, reconstructed, trained and tested for the ANN model development. The models were established using seven input variables: the mass of cementitious content, water, natural coarse aggregate content, natural fine aggregate content, recycled coarse aggregate content, chemical admixture and mineral admixture used in the SCRCAC mix designs. Two normalization techniques are used for data normalization to visualize the data distribution. For each normalization technique, three transfer functions are used for modelling. In total, six different types of models were run in MATLAB and used to estimate the 28th day SCRCAC compressive strength. Normalization technique 2 performs better than 1 and TANSING is the best transfer function. The best k-fold cross-validation fold is k = 7. The coefficient of determination for predicted and actual compressive strength is 0.78 for training and 0.86 for testing. The impact of the number of neurons and layers on the model was performed. Inputs from standards are used to forecast the 28th day compressive strength. Apart from ANN, Machine Learning (ML) techniques like random forest, extra trees, extreme boosting and light gradient boosting techniques are adopted to predict the 28th day compressive strength of SCRCAC. Compared to ML, ANN prediction shows better results in terms of sensitive analysis. The study also extended to determine 28th day compressive strength from experimental work and compared it with 28th day compressive strength from ANN best model. Standard and ANN mix designs have similar fresh and hardened properties. The average compressive strength from ANN model and experimental results are 39.067 and 38.36 MPa, respectively with correlation coefficient is 1. It appears that ANN can validly predict the compressive strength of concrete.

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Section: Influence Of Normalisation Techniques Transfer Functions And...mentioning

confidence: 99%

Artificial neural network, machine learning modelling of compressive strength of recycled coarse aggregate based self-compacting concrete

Jagadesh,

Khan,

Priya

et al. 2024

PLoS ONE

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“…The network calculates the product of the weights and their respective inputs (P), and this is summed for each neuron by adding an offset value (b), also known as bias. The simplified expression for this process is represented as Equation (3) (Reyes-Téllez et al, 2020):…”

Section: Data Calculationsmentioning

confidence: 99%

Evaluating the performance of the Anwaralardh photovoltaic power generation plant in Jordan: Comparative analysis using artificial neural networks and multiple linear regression modeling

Alma'asfa,

Fraige,

Abdul Aziz

et al. 2024

Int. J. Renew. Energy Dev.

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The global energy demand is rising, driven by population growth, economic development, and industrialization. Shifting towards renewable energy, like solar energy, is gaining momentum worldwide because of ecological concerns and resource depletion. This paper aims to utilize Artificial Neural Networks (ANNs) and multiple linear regression (MLR) modeling techniques to evaluate the productivity of 11 MW photovoltaic (PV) solar power plant currently operational in Jordan. The case study reveals that both models can be used to predict the daily, monthly, and yearly average power produced and system efficiency with reasonable accuracy. The ANN model exhibited promising results, where the best value for the coefficient of determination (R2) and mean absolute percentage error (MAPE) for training were 95.85% and 0.59%, respectively. However, R2 was 93.7%, and MAPE was 1.27% for validation tests. All these results were achieved using a 7-6-1 model, with a sample ratio of 1:1 for the data allocated in training and validation. When using multiple linear regression, the R2 and standard error values were 93.42% and 0.17%. On the other hand, the results showed that the yearly output power for actual and predicted by both models over the year was 24,399 MWh, 24,538 MWh, and 24,401 MWh, respectively. This research showed valuable results in the monthly output power for solar cells at the Anwaralardh PV power system project, contributing to a better understanding of solar energy generation in arid desert climates and emphasizing the potential of solar power plants to play a crucial role in achieving SDG 7 objectives.

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“…In order to perform the training, testing and validation, the experimental set of 165 experimental measurements was randomly divided into 3 parts -70% for training (115), 15% for testing (25) and 15% for validation (25). The type of the ANN is feedforward ANN, which was trained by Levenberg-Marquardt back propagation algorithm [5,6] and Matlab software. The chosen structure of the two-layer feedforward ANN with one hidden layer and one output layer is presented in figure 2.…”

Section: Ar-n 7520 Resist Profile Simulationmentioning

confidence: 99%

“…The chosen structure of the two-layer feedforward ANN with one hidden layer and one output layer is presented in figure 2. The transfer function is hyperbolic tangent sigmoid for the hidden layer neurons and linear transfer function for output layer neurons [6]. Different ANN structures are trained, tested and compared by the values of the training, validation and testing mean squared errors (MSE), as well as the regression multiple correlation coefficients R:…”

Section: Ar-n 7520 Resist Profile Simulationmentioning

confidence: 99%

Optimisation criteria for the process electron beam lithography of negative AR-N7520 resists

Koleva

Kostič

Andok

et al. 2023

J. Phys.: Conf. Ser.

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Experimental investigation of negative electron resist AR-N 7520 profiles using an electron beam lithography system ZBA23 (Raith) is performed at variation of the exposure doses and the exposure patterns. The form of the obtained after the exposure resist profiles is investigated and optimized. Artificial neural networks for the dependence of the overall geometry of the obtained resist profiles on process parameters are trained, tested and validated. Several overall geometry quality criteria for the shape of the developed resist profile cross-sections are defined. An approach, based on the defined overall quality characteristics and multicriterial parameter optimization, is proposed and implemented for fulfillment of the technological requirements for the produced resist profile dimensions.

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Analysis of transfer functions and normalizations in an ANN model that predicts the transport of energy in a parabolic trough solar collector

Cited by 23 publications

References 34 publications

Artificial neural network, machine learning modelling of compressive strength of recycled coarse aggregate based self-compacting concrete

Artificial neural network, machine learning modelling of compressive strength of recycled coarse aggregate based self-compacting concrete

Evaluating the performance of the Anwaralardh photovoltaic power generation plant in Jordan: Comparative analysis using artificial neural networks and multiple linear regression modeling

Optimisation criteria for the process electron beam lithography of negative AR-N7520 resists

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