Beyond site suitability: Investigating temporal variability for utility-scale solar-PV using soft computing techniques

Adedeji, Paul A.; Akinlabi, Stephen A.; Madushele, Nkosinathi; Olatunji, Obafemi O.

doi:10.1016/j.ref.2021.07.008

Cited by 6 publications

(1 citation statement)

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“…Recent fascinating advances in machine learning (ML) tools resulted in their applications in different academic and industrial areas, including nanotechnology 26 , solar energy utilization 27 , energy efficiency 28 , renewable energy forecasting 29 , biomass, biofuels, and environmental preservation 30 . On the grounds, different topologies of ML such as artificial neural networks (ANNs) 31 , adaptive neuro-fuzzy inference systems (ANFIS) 32 , the support vector regression (SVR), random forest (RF), and group method of data handling (GMDH) have been widely applied to the paradigm design, data mining, fault tracing, and algorithm detection.…”

Section: Introductionmentioning

confidence: 99%

Applying feature selection and machine learning techniques to estimate the biomass higher heating value

Abdollahi,

Ranjbar,

Razeghi Jahromi

2023

Sci Rep

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The biomass higher heating value (HHV) is an important thermal property that determines the amount of recoverable energy from agriculture byproducts. Precise laboratory measurement or accurate prediction of the HHV is essential for designing biomass conversion equipment. The current study combines feature selection scenarios and machine learning tools to establish a general model for estimating biomass HHV. Multiple linear regression and Pearson’s correlation coefficients justified that volatile matter, nitrogen, and oxygen content of biomass samples have a slight effect on the HHV and it is better to ignore them during the HHV modeling. Then, the prediction performance of random forest, multilayer and cascade feedforward neural networks, group method of data handling, and least-squares support vector regressor are compared to determine the intelligent estimator with the highest accuracy toward biomass HHV prediction. The ranking test shows that the multilayer perceptron neural network better predicts the HHV of 532 biomass samples than the other intelligent models. This model presents the outstanding absolute average relative error of 2.75% and 3.12% and regression coefficients of 0.9500 and 0.9418 in the learning and testing stages. The model performance is also superior to a recurrent neural network which was recently developed in the literature using the same databank.

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