Data Normalisation-Based Solar Irradiance Forecasting Using Artificial Neural Networks

Arora, Isha; Gambhir, Jaimala; Kaur, Tarlochan

doi:10.1007/s13369-020-05140-y

Cited by 20 publications

(2 citation statements)

References 39 publications

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“…SVR was found to be the best performing ML method in their experiments producing MSE of 0.000011548 and RMSE of 0.0034. Likewise, solar PV performs very well in hotter regions, as shown in [38] , [31] , [49] , which is not very surprising as the higher the temperature, the higher the voltage we get from our solar cells [70] . In hotter areas, the simulation data are more stable and do not vary much, which helps predict more accurate results.…”

Section: Resultssupporting

confidence: 56%

A systematic review on predicting PV system parameters using machine learning

Jobayer

Shaikat

Rashid

et al. 2023

Heliyon

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Section: Resultssupporting

confidence: 56%

A systematic review on predicting PV system parameters using machine learning

Jobayer

Shaikat

Rashid

et al. 2023

Heliyon

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“…This ensures that the differences in the values of different indicators can be ignored, and forward normalization was also performed. This method is called Min-max normalization, and the calculation method for the conversion function is as follows (Arora et al, 2021). The formula for calculating the positive directional indicator is shown as formula 5.…”

Section: Data Preparationmentioning

confidence: 99%

Quantitative analysis and comprehensive evaluation of coarse aggregate morphology for asphalt pavement

Zhang,

Cheng,

Kong

et al. 2024

Front. Mater.

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The morphological characteristics of coarse aggregate play a crucial role in the mechanical properties, durability, and construction performance of asphalt mixtures and pavements in highway engineering. This study aims to quantitatively analyze the morphological characteristics of coarse aggregate and establish comprehensive evaluation indicators for its quality and performance. Two-dimensional cross-sectional images of coarse aggregates are obtained using the self-developed laser scanning equipment, and edge features are extracted using the Canny operator and edge tracing algorithm. Six morphological characteristic indicators are selected, and factor analysis is employed to propose comprehensive shape and edge feature for coarse aggregates. The results show that the proposed evaluation indicators effectively capture the shape and edge feature of coarse aggregates, with the exception of roughness, which is deemed unsuitable for evaluation. Factor analysis of six indicator values in 2,700 two-dimensional images led to the proposal of Shape Factor (SF) and Edge Feature Factor (EFF) as significant factors affecting aggregate shape and edge characteristics, respectively. Following optimization, SF ranged from 0.041 to 1.793, while EFF ranged from 0.368 to 1.745. The spearman’s rank correlation coefficient revealed strong correlations between the evaluation of coarse aggregate shape and edge features using the line laser-based comprehensive evaluation index. Specifically, correlation coefficients of 0.864 and 0.805 exceeded those of commonly used methods such as AIMS and X-ray CT, indicating the rationality and accuracy of the self-developed laser scanning device in evaluating aggregate morphology. This research provides strong support for the construction of highway engineering and optimization of coarse aggregate production and supply.

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