Solar Energy Potential Forecasting and Optimization Using Artificial Neural Network: South Africa Case Study

Leholo, Sempe Thom; Owolawi, Pius A.; Akindeji, Kayode Timothy

doi:10.1109/aicai.2019.8701372

Cited by 14 publications

(7 citation statements)

References 11 publications

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“…For example, Cui et al [7] proposed a novel maximum power point tracking (MPPT) method which is based on Rprop neural network. Leholo et al [22] showed that the train_LM gives better performance than train_SCG (Scaled Conjugate Gradient), train_BFGS (Quasi-Newton) and train_RP. Kliment et al [21] showed that the Rprop algorithm can be consider for the most robust because of its iteration process is based on the sign of the gradient, not its magnitude.…”

Section: Resilient Backpropagation (Rp)mentioning

confidence: 99%

Effect of Hidden Neuron Size on Different Training Algorithm in Neural Network

Kumar¹,

Sodhi²

2022

Comm. Math. Appl.

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We use different types of training algorithms in the neural network. But, we cannot say which kind of training algorithm is fast for a given problem. So, in this survey paper, we are trying to find which types of training algorithm are better for categorization problems. For this purpose, we used ten types of training algorithms in the pattern network in MATLAB. We used Levenberg-Marquardt (LM), Bayesian regularization backpropagation (BR), BFGS Quasi-Newton (BFG), Resilient Backpropagation (RP), Scaled Conjugate gradient backpropagation (SCG), Conjugate Gradient with Powell/Beale Restarts (CGB), Fletcher-Powell Conjugate Gradient (CGF), Polak-Ribiere Conjugate Gradient (GDM), One Step Secant (OSS), and Variable Learning Rate Backpropagation (GD) algorithm. In this survey paper, we also check, affects of these algorithms on neural network when we applied different types of hidden neuron size. During this survey we found some new facts. We found that RP, SCG, CGB, CGF and OSS are fastest algorithms. BFG takes more time with respect to hidden neuron size. GDM and GD take more epochs. BR algorithm is not acceptable for image categorization.

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Section: Resilient Backpropagation (Rp)mentioning

confidence: 99%

Effect of Hidden Neuron Size on Different Training Algorithm in Neural Network

Kumar¹,

Sodhi²

2022

Comm. Math. Appl.

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“…Sempe Leholo et al 2019 [41] performed hourly average solar irradiance forecasting by an artificial neural network with various training functions. Remark: the results revealed that the Levenberg Marquardt (LM) training function associated with ANN achieves good results compared with other training functions.…”

Section: Literature Reviewmentioning

confidence: 99%

Novel Cooperative Multi-Input Multilayer Perceptron Neural Network Performance Analysis with Application of Solar Irradiance Forecasting

Madhiarasan

Louzazni

Roy

2021

International Journal of Photoenergy

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To forecast solar irradiance with higher accuracy and generalization capability is challenging in the photovoltaic (PV) energy system. Meteorological parameters are highly influential in solar irradiance, leading to intermittent and randomicity. Forecasting using a single neural network model does not have sufficient generalization ability to achieve the optimal forecasting of solar irradiance. This paper proposes a novel cooperative multi-input multilayer perceptron neural network (CMMLPNN) to mitigate the issues related to generalization and meteorological effects. Authors develop a proposed forecasting neural network model based on the amalgamation of two inputs, three inputs, four inputs, five inputs, and six inputs associated multilayer perceptron neural network. In the proposed forecasting model (CMMLPNN), the authors overcome the variance based on the meteorological parameters. The amalgamation of five multi-input multilayer perceptron neural networks leads to better generalization ability. Some individual multilayer perceptron neural network-based forecasting models outperform in some situations, but cannot assure generalization ability and suffer from the meteorological weather condition. The proposed CMMLPNN (cooperative multi-input multilayer perceptron neural network) achieves better forecasting accuracy with the generalization ability. Therefore, the proposed forecasting model is superior to other neural network-based forecasting models and existing models.

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“…Machine-learning models and deep-learning techniques, including support-vector machines [64][65][66][67][68][69][70]] and artificial neural networks (ANN) [71][72][73][74][75] have been booming data-driven prediction models. Deep learning includes CNN [76], deep neural network (DNN) [77][78][79], long short-term memory [64,[80][81][82][83][84][85], and the other hybrid models used in multistep predictions of solar energy. A method for predicting solar radiation sequences was introduced by using multiscale decomposition techniques, such as empirical mode decomposition (EMD), integrated empirical mode decomposition (EEMD), and wavelet decomposition, to investigate several clear sky index data [86], and based on linear, the method performs an autoregressive process (AR) and a nonlinear method.…”

Section: The Status Of Machine-learning Technology Used In Renewable-...mentioning

confidence: 99%

A Survey of Machine Learning Models in Renewable Energy Predictions

et al. 2020

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The use of renewable energy to reduce the effects of climate change and global warming has become an increasing trend. In order to improve the prediction ability of renewable energy, various prediction techniques have been developed. The aims of this review are illustrated as follows. First, this survey attempts to provide a review and analysis of machine-learning models in renewable-energy predictions. Secondly, this study depicts procedures, including data pre-processing techniques, parameter selection algorithms, and prediction performance measurements, used in machine-learning models for renewable-energy predictions. Thirdly, the analysis of sources of renewable energy, values of the mean absolute percentage error, and values of the coefficient of determination were conducted. Finally, some possible potential opportunities for future work were provided at end of this survey.

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Solar Energy Potential Forecasting and Optimization Using Artificial Neural Network: South Africa Case Study

Cited by 14 publications

References 11 publications

Effect of Hidden Neuron Size on Different Training Algorithm in Neural Network

Effect of Hidden Neuron Size on Different Training Algorithm in Neural Network

Novel Cooperative Multi-Input Multilayer Perceptron Neural Network Performance Analysis with Application of Solar Irradiance Forecasting

A Survey of Machine Learning Models in Renewable Energy Predictions

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