A State-of-Art-Review on Machine-Learning Based Methods for PV

Tina, Giuseppe Marco; Ventura, Cristina; Ferlito, Sergio; Vito, Saverio De

doi:10.3390/app11167550

Cited by 58 publications

(42 citation statements)

References 119 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some of the traditional load forecasting models include [16] where Tina, G.M et al presented a state of the art review on ML-based methods for P.V.s, anomalies, fault, and optimization detection and analysis. The review discussed several past studies of machine learning algorithms used explicitly in P.V.…”

Section: Traditional Modelsmentioning

confidence: 99%

Deterioration of Electrical Load Forecasting Models in a Smart Grid Environment

Azeem

Ismail

Jameel

et al. 2022

Sensors

View full text Add to dashboard Cite

Smart Grid (S.G.) is a digitally enabled power grid with an automatic capability to control electricity and information between utility and consumer. S.G. data streams are heterogenous and possess a dynamic environment, whereas the existing machine learning methods are static and stand obsolete in such environments. Since these models cannot handle variations posed by S.G. and utilities with different generation modalities (D.G.M.), a model with adaptive features must comply with the requirements and fulfill the demand for new data, features, and modality. In this study, we considered two open sources and one real-world dataset and observed the behavior of ARIMA, ANN, and LSTM concerning changes in input parameters. It was found that no model observed the change in input parameters until it was manually introduced. It was observed that considered models experienced performance degradation and deterioration from 5 to 15% in terms of accuracy relating to parameter change. Therefore, to improve the model accuracy and adapt the parametric variations, which are dynamic in nature and evident in S.G. and D.G.M. environments. The study has proposed a novel adaptive framework to overcome the existing limitations in electrical load forecasting models.

show abstract

Section: Traditional Modelsmentioning

confidence: 99%

Deterioration of Electrical Load Forecasting Models in a Smart Grid Environment

Azeem

Ismail

Jameel

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The presented dataset is useful for simulating its performance in various operating conditions. Recently, machine learning [1] , [2] , [3] , [4] , [5] and fuzzy logic-based [6] , [7] , [8] , [9] maximum power point tracking (MPPT) have been used to ensure the PV systems can operate at optimal efficiency. All the techniques require a dataset to test their algorithm's effectiveness before deploying it on an existing PV system.…”

Section: Value Of the Datamentioning

confidence: 99%

Solar panels simulation data generated using LTSpice under different operating conditions

Sood

Ruppert²,

Mahto³

2022

Data in Brief

View full text Add to dashboard Cite

“…Five performance metrics were adopted in this research paper to assess the deterioration prediction models and compare their prediction accuracies. The equations of mean absolute percentage error, mean absolute error, root mean square percentage error, root relative squared error, and relative absolute error can be mathematically expressed using Equations ( 16)- (20), respectively [57][58][59].…”

Section: Performance Assessmentmentioning

confidence: 99%

Hybrid Grey Wolf Optimization-Based Gaussian Process Regression Model for Simulating Deterioration Behavior of Highway Tunnel Components

et al. 2021

View full text Add to dashboard Cite

Highway tunnels are one of the paramount infrastructure systems that affect the welfare of communities. They are vulnerable to higher limits of deterioration, yet there are limited available funds for maintenance and rehabilitation. This state of circumstances entails the development of a deterioration model to forecast the performance condition behavior of critical tunnel elements. Accordingly, this research paper proposes an integrated deterioration prediction model for five highway tunnel elements, namely, cast-in-place tunnel liners, concrete interior walls, concrete portal, concrete ceiling slab, and concrete slab on grade. The developed deterioration model is envisioned in two fundamental components, which are model calibration and model assessment. In the first component, an integrated model of Gaussian process regression and a grey wolf optimization algorithm (GWO-GPR) is introduced for deterioration behavior prediction of highway tunnel elements. In this regard, the grey wolf optimizer is exploited to improve the prediction accuracies of the Gaussian process through optimal estimation of its hyper parameters and to automatically interpret the significant deterioration factors. The second component involves three tiers of performance evaluation comparison, statistical significance comparisons, and consolidated ranking to assess the prediction accuracies of the developed GWO-GPR model. In this regard, the developed model is validated against six widely acknowledged machine learning models, which are back-propagation artificial neural network, Elman neural network, cascade forward neural network, generalized regression neural network, support vector machines, and regression tree. Results demonstrate that the developed GWO-GPR model significantly outperformed other deterioration prediction models in the five tunnel elements. In cast-in-place tunnel liners it accomplished a mean absolute percentage error, mean absolute error, root mean square percentage error, root relative squared error, and relative absolute error of 1.65%, 0.018, 0.21%, 0.018, and 0.147, respectively. In this context, it was inferred that the developed GWO-GPR model managed to reduce the prediction errors of the back-propagation artificial neural network, Elman neural network, and support vector machines by 84.71%, 76.91%, and 69.6%, respectively. It can be concluded that the developed deterioration model can assist transportation agencies in creating timely and cost-efficient maintenance schedules of highway tunnels.

show abstract

A State-of-Art-Review on Machine-Learning Based Methods for PV

Cited by 58 publications

References 119 publications

Deterioration of Electrical Load Forecasting Models in a Smart Grid Environment

Deterioration of Electrical Load Forecasting Models in a Smart Grid Environment

Solar panels simulation data generated using LTSpice under different operating conditions

Hybrid Grey Wolf Optimization-Based Gaussian Process Regression Model for Simulating Deterioration Behavior of Highway Tunnel Components

Contact Info

Product

Resources

About