A method to predict solar photovoltaic soiling using artificial neural networks and multiple linear regression models

Chiteka, Kudzanayi; Arora, Rajesh; Sridhara, S. N.

doi:10.1007/s12667-019-00348-w

Cited by 22 publications

(8 citation statements)

References 43 publications

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“…Also, artificial intelligence (AI) techniques can play an important role in soiling prediction. These techniques use artificial neural network (ANN) models to predict the daily and the cumulative PV soiling loss based on the current environmental data [166][167][168]. ANN models might also be used to model dust deposition affecting transmittance of PV modules [169], forecasting output power [170], studying the effect of soiling on energy production [171], and estimating the soiling losses based on the density of dust and particle size composition under artificial soiling conditions [172,173].…”

Section: Antistatic Coating With Mechanical Vibrationsmentioning

confidence: 99%

A review of dust accumulation on PV panels in the MENA and the Far East regions

2022

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This paper presents a comprehensive review regarding the published work related to the effect of dust on the performance of photovoltaic panels in the Middle East and North Africa region as well as the Far East region. The review thoroughly discusses the problem of dust accumulation on the surface of photovoltaic panels and the severity of the problem. Moreover, a survey of the most advanced cleaning techniques is presented, and their applicability is evaluated. There are plenty of techniques that have been used to remove the dust accumulated on the surface of PV panels, and these include manual and self-cleaning methods. However, it is concluded from the presented review that there is a strong need for developing new cleaning methods especially for the Middle East and North Africa region, which do not consume water and have low capital and operational costs with less human intervention, especially for hot, arid, and dusty regions.

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Section: Antistatic Coating With Mechanical Vibrationsmentioning

confidence: 99%

A review of dust accumulation on PV panels in the MENA and the Far East regions

2022

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“…A Boruta variable selection method was employed to reduce the number of metrics used in model fitting. The Boruta method for feature selection is popularly used with machine learning applications in genetics, health sciences, and other disciplines where the number of predictor metrics is often much larger than the number of observations [52][53][54]. This method copies each metric and randomly permutes the data creating a set of noise metrics.…”

Section: Area-based Modellingmentioning

confidence: 99%

Use of Multi-Temporal LiDAR to Quantify Fertilization Effects on Stand Volume and Biomass in Late-Rotation Coastal Douglas-Fir Forests

Kelley

Trofymow

Filipescu

et al. 2021

Forests

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Forest fertilization is common in coastal British Columbia as a means to increase wood production and potentially enhance carbon sequestration. Generally, the effects of fertilization are determined by measuring sample plots pre- and post-treatment, resulting in fertilization effects being determined for a limited portion of the treatment area. Applications of remote sensing-based enhanced forest inventories have allowed for estimations to expand to the wider forested area. However, these applications have not focused on monitoring the effects of silvicultural treatments. The objective of this research was to examine if a multi-temporal application of the LiDAR area-based method can be used to detect the fertilization effects on volume, biomass, and height in a second-growth Douglas-fir (Pseudotsuga menziesii) stand. The study area on Vancouver Island was fertilized in January 2007, and sample plots were established in 2011. LiDAR acquisitions were made in 2004, prior to fertilization, and in 2008, 2011, and 2016, covering both treated and untreated areas. A total of 29 paired LiDAR blocks, comprised of four 20 m resolution raster cells, were selected on either side of the fertilization boundary for analysis of the effects across several different stand types differing in the percentage of Douglas-fir, site index, and age. Random forest (RF) plot-level models were developed to estimate total stem volume and total stem biomass for each year of LiDAR acquisition using an area-based approach. Plot level results showed an increase in stem volume by 13% fertilized over control from 2005 to 2011, which was similar to a 14% increase in above-ground carbon stocks estimated using a tree-ring stand reconstruction approach. Plot-level RF models showed R2 values of 0.86 (volume) and 0.92 (biomass) with relative cross-validated root mean square errors of 12.5% (volume) and 11.9% (biomass). For both the sample plots and LiDAR blocks, statistical results indicated no significant differences in volume or biomass between treatments. However, significant differences in height increments were detected between treatments in LiDAR blocks. The results from this research highlight the promising potential for the use of enhanced forest inventory methods to rapidly expand the assessment of treatment effects beyond sample plots to the stand, block, or landscape level.

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“…A more complex multivariate linear equation is used to check whether there is a statistically noticeable association among the sets of variables [7][8].…”

Section: Multivariate Regressionmentioning

confidence: 99%

Automatic solution for solar cell photo-current prediction using machine learning

et al. 2021

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In this paper, we discuss the prediction of future solar cell photo-current generated by the machine learning algorithm. For the selection of prediction methods, we compared and explored different prediction methods. Precision, MSE and MAE were used as models due to its adaptable and probabilistic methodology on model selection. This study uses machine learning algorithms as a research method that develops models for predicting solar cell photo-current. We create an electric current prediction model. In view of the models of machine learning algorithms for example, linear regression, Lasso regression, K Nearest Neighbors, decision tree and random forest, watch their order precision execution. In this point, we recommend a solar cell photocurrent prediction model for better information based on resistance assessment. These reviews show that the linear regression algorithm, given the precision, reliably outperforms alternative models in performing the solar cell photo-current prediction Iph

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A method to predict solar photovoltaic soiling using artificial neural networks and multiple linear regression models

Cited by 22 publications

References 43 publications

A review of dust accumulation on PV panels in the MENA and the Far East regions

A review of dust accumulation on PV panels in the MENA and the Far East regions

Use of Multi-Temporal LiDAR to Quantify Fertilization Effects on Stand Volume and Biomass in Late-Rotation Coastal Douglas-Fir Forests

Automatic solution for solar cell photo-current prediction using machine learning

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