An Improved Prediction Approach on Solar Irradiance of Photovoltaic Power Station

Dong, Haiying; Yang, Lei; Zhang, Shengrui; Li, Yuan

doi:10.11591/telkomnika.v12i3.4017

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…Some of the ML techniques reported by researchers are support vector machines regression procedures, [25][26][27] various types of neural networks, 28,29 Gaussian Processes, 30 and hybrid methodologies, Mix of these and alternate procedures. [31][32][33] It has been observed in almost all the cases that ML techniques showed terrific results in these solar radiation estimation scenarios. A comparative study of extreme gradient boosting, support vector machine (SVM), and some empirical models to predict daily GSR as a function of rainfall for the humid subtropical climate of China was performed by Fan et al 14 They observed that including rainfall in GSR estimation improves the performance of ML models up to 11.5%.…”

Section: Literature Reviewmentioning

confidence: 95%

“…They used hybridization of linear regression and HS models with a temperature difference as input. Recently, numerous ML models were developed to predict GSR. Some of the ML techniques reported by researchers are support vector machines regression procedures, 25–27 various types of neural networks, 28,29 Gaussian Processes, 30 and hybrid methodologies, Mix of these and alternate procedures 31–33 . It has been observed in almost all the cases that ML techniques showed terrific results in these solar radiation estimation scenarios.…”

Section: Literature Reviewmentioning

confidence: 99%

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Development of machine learning models based on air temperature for estimation of global solar radiation in India

Husain

Khan

2021

Env Prog and Sustain Energy

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The designing of solar thermal systems need accurate information on global solar radiation (GSR). In the present study, six machine learning models, for example, random forest, k‐nearest neighbors, Gaussian process regression, support vector machine, multilayer perception, and XGBoost, are developed for GSR prediction with only air temperature as input for different climatic zones of India. The performance of machine learning models is also compared with some well‐known empirical models. The results show that generally, the performance of the machine learning models is better than empirical models, though, for a few climatic zones, empirical models give a better prediction. The top‐performing models are k‐nearest neighbors and XGBoost. Thus, we highly recommend temperature‐based models to predict GSR in the regions of India where only air temperature data are available. The accurate information of future GSR can be easily obtained by combining future air temperature forecasts with KNN/XGBoost models. These models can be extremely helpful in designing solar thermal systems in those regions where solar radiation facility is not available.

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Section: Literature Reviewmentioning

confidence: 95%

Section: Literature Reviewmentioning

confidence: 99%

Development of machine learning models based on air temperature for estimation of global solar radiation in India

Husain

Khan

2021

Env Prog and Sustain Energy

View full text Add to dashboard Cite

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“…Several studies have been proposed in the last decades and added to more conventional ones for predicting solar irradiation values [4][5][6][7][8]. Since each technique has different theoretical basis, the obtained results are also usually different.…”

Section: Introductionmentioning

confidence: 99%

Estimation of daily vertical solar irradiation by the use of meteorological data

Benziane

Ayad

Saoudi

et al. 2019

IJAPE

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Energy has been recognized as one of the most inputs for social and economic improvement and a clean energy is a challenge in the future. Therefore, renewable energy has become one of the most popular sources of energy. The solar energy, is one of this renewable energy, has the most propriety which is the durability. Due to its geographical location, Algeria has one of the world’s most important gas reserves, and has in addition one of the highest solar reservoirs in the world. Solar irradiation is an essential parameter for many applications such as the design and performance of renewable energy systems, but this is not always available, especially in remote locations. The prediction of solar irradiation values is often the only practical way to acquire these data. The objective of this work is to develop a model for the prediction of the Vertical Solar Irradiation (VSI) based on real meteorological data. This model is based on the Artificial Neural Networks (ANN).

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Performance of Two Variable Machine Learning Models to Forecast Monthly Mean Diffuse Solar Radiation across India under Various Climate Zones

et al. 2022

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For the various climatic zones of India, machine learning (ML) models are created in the current work to forecast monthly-average diffuse solar radiation (DSR). The long-term solar radiation data are taken from Indian Meteorological Department (IMD), Pune, provided for 21 cities that span all of India’s climatic zones. The diffusion coefficient and diffuse fraction are the two groups of ML models with dual input parameters (sunshine ratio and clearness index) that are built and compared (each category has seven models). To create ML models, two well-known ML techniques, random forest (RF) and k-nearest neighbours (KNN), are used. The proposed ML models are compared with well-known models that are found in the literature. The ML models are ranked according to their overall and within predictive power using the Global Performance Indicator (GPI). It is discovered that KNN models generally outperform RF models. The results reveal that in diffusion coefficient models perform well than diffuse fraction models. Moreover, functional form 2 is the best followed by form 6. The ML models created here can be effectively used to accurately forecast DSR in various climates.

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An Improved Prediction Approach on Solar Irradiance of Photovoltaic Power Station

Cited by 3 publications

References 14 publications

Development of machine learning models based on air temperature for estimation of global solar radiation in India

Development of machine learning models based on air temperature for estimation of global solar radiation in India

Estimation of daily vertical solar irradiation by the use of meteorological data

Performance of Two Variable Machine Learning Models to Forecast Monthly Mean Diffuse Solar Radiation across India under Various Climate Zones

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