Optimized Random Forest for Solar Radiation Prediction Using Sunshine Hours

Villegas-Mier, César G.; Rodríguez‐Reséndiz, Juvenal; Álvarez-Alvarado, José Manuel; Hernandez, H.; Odry, Ákos

doi:10.3390/mi13091406

Cited by 29 publications

(8 citation statements)

References 44 publications

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“…The root mean square error (RMSE) value is a common way to determine how accurate a model is. There are also algorithms like decision trees, random forests, support vector regression, and neural networks that are not linear and can be used to solve regression problems [24][25][26][27].…”

Section: Regressionmentioning

confidence: 99%

Advancements and Challenges in Machine Learning: A Comprehensive Review of Models, Libraries, Applications, and Algorithms

et al. 2023

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In the current world of the Internet of Things, cyberspace, mobile devices, businesses, social media platforms, healthcare systems, etc., there is a lot of data online today. Machine learning (ML) is something we need to understand to do smart analyses of these data and make smart, automated applications that use them. There are many different kinds of machine learning algorithms. The most well-known ones are supervised, unsupervised, semi-supervised, and reinforcement learning. This article goes over all the different kinds of machine-learning problems and the machine-learning algorithms that are used to solve them. The main thing this study adds is a better understanding of the theory behind many machine learning methods and how they can be used in the real world, such as in energy, healthcare, finance, autonomous driving, e-commerce, and many more fields. This article is meant to be a go-to resource for academic researchers, data scientists, and machine learning engineers when it comes to making decisions about a wide range of data and methods to start extracting information from the data and figuring out what kind of machine learning algorithm will work best for their problem and what results they can expect. Additionally, this article presents the major challenges in building machine learning models and explores the research gaps in this area. In this article, we also provided a brief overview of data protection laws and their provisions in different countries.

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Section: Regressionmentioning

confidence: 99%

Advancements and Challenges in Machine Learning: A Comprehensive Review of Models, Libraries, Applications, and Algorithms

et al. 2023

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“…They are less affected by noise in the data, robust to outliers, and stable. Villegas-Mier et al (2022) found that RFs gave a robust performance with similar results in two different scenarios. RFs delivered accurate and precise results when mapping SI at high latitudes (Babar et al, 2020).…”

Section: Related Literaturementioning

confidence: 68%

An evaluation of variable selection methods using Southern Africa solar irradiation data

Maposa,

Masache,

Mdlongwa

et al. 2024

J. energy South. Afr.

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Dimensionality poses a challenge in developing quality predictive models. Often when modelling solar irradiance (SI), many covariates are considered. Training such data has several disadvantages. This study sought to identify the best variable embedded selection method for different location and time horizon combinations from Southern Africa solar irradiance data. It introduced new variable selection methods into solar irradiation studies, namely penalised quantile regression (PQR), regularised random forests (RRF), and quantile regression forest (QRF). Stability analysis, performance and accuracy metric evaluations were used to compare them with the common lasso, elastic and ridge regression methods. The QRF model performed best in all locations followed by the shrinkage methods on hourly data. However, it was found that QRF is not sensitive to associations through correlations, thereby ignoring the relevance of variables while focusing on importance. Among the shrinkage methods, the lasso performed best in only one location. On the 24-hour horizon, elastic net dominated the performances among the shrinkage methods, but QRF was best in three locations of the six considered. Results confirmed that variable selection methods performed differently on different situational data sets. Depending on the strengths of the methods, results were combined to identify the most paramount variables. Day, total rainfall, and wind direction were superfluous features in all situations. The study concluded that shrinkage methods are best in cases of extreme multicollinearity, while QRF is best on data sets with outliers or/and heavy tails.

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“…RF algorithms perform very well from the start without leveraging hyperparameters or custom data. It shows an accuracy of 89.30% 19 .…”

Section: Machine Learning Model For Pv Panel Cleaning Frequencymentioning

confidence: 94%

An Artificial Intelligence and Machine Learning Model to Estimate the Cleaning Periodicity for Dusty Solar Photovoltaic (PV) Modules in A Composite Environment

Pimpalkar,

Sahu,

Patil

2023

jmmf

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Solar energy is harnessed on a considerable scale nowadays. By 2030, the solar power output is expected to increase to 2500 GW marginally. High cell temperatures and soiling significantly affect the performance of solar photovoltaic systems. This study clarifies the effect of dust deposition on the transmission and output power of photovoltaic modules. The analytical and machine-learning models were developed to analyze the effects of soil deposition on the photovoltaic panels. The field data were used to train and test the algorithm for developing the machine-learning model. An optimum cleaning and maintenance schedule is then proposed based on the site's environmental conditions. The novelty of the research was to gather environmental parameters in real-time conditions that affect the soiling rate of photovoltaic panels, further affecting the conversion efficiency of photovoltaic panels. Based on the theoretical model developed, the cleaning frequency of the module was observed to be 18 days, considering 5% power loss and dust density accumulation of 2g/m2. A random forest model was developed considering ambient temperature, solar irradiance, relative humidity, wind speed, dust concentration, and energy generated. The predicted cleaning frequency is observed to be 25 days using the random forest model.

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Optimized Random Forest for Solar Radiation Prediction Using Sunshine Hours

Cited by 29 publications

References 44 publications

Advancements and Challenges in Machine Learning: A Comprehensive Review of Models, Libraries, Applications, and Algorithms

Advancements and Challenges in Machine Learning: A Comprehensive Review of Models, Libraries, Applications, and Algorithms

An evaluation of variable selection methods using Southern Africa solar irradiation data

An Artificial Intelligence and Machine Learning Model to Estimate the Cleaning Periodicity for Dusty Solar Photovoltaic (PV) Modules in A Composite Environment

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