Estimation of hourly global solar irradiation on tilted absorbers from horizontal one using Artificial Neural Network for case study of Mashhad

Shaddel, Mehdi; Javan, Dawood Seyed; Baghernia, Parisa

doi:10.1016/j.rser.2015.08.023

Cited by 48 publications

(15 citation statements)

References 16 publications

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“…Thereby, ML methodologies are taken into consideration due to their accurate outputs compared to classical methods. Moreover, most of the studies that are done by using ML methods, which are proposed in scientific journals, are for monthly and daily GSR [2]. However, for more precise GSR prediction in the short time interval that leads to better performance in transferring solar energy to electrical one and for more efficient solar systems design, hourly global solar radiation (HGSR) can be forecasted instead of daily and monthly.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the increase in the world's population with their demand for electricity can cause limitations in traditional energy production. Therefore, modern and more efficient methods had to be found to produce electricity instead of the, previously mentioned, conventional sources [2]. Thereby, renewable energy has started to take the lead in this field in the last decades.…”

Section: Introductionmentioning

confidence: 99%

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Hourly Global Solar Radiation Estimation Based on Machine Learning Methods in Eskisehir

Alsafadi¹,

Filik²

2020

Eskişehir Technical University Journal of Science and Technology a - Applied Sciences and Engineering

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Due to the increasing importance of knowing the amount of global solar radiation (GSR) that is incident on solar panels; short term data, such as hourly global solar radiation (HGSR), is essentially required to obtain more accurate and reliable power generation prediction. Nowadays, Machine Learning (ML) methods are becoming a huge trend for data forecasting. Therefore, in this paper, a comparison between Collares-Pereira & Rabl empirical model modified by Gueymard (CPRG) and ML methods for HGSR estimation in Eskişehir city in Turkey is conducted. Artificial Neural Network (ANN), Regression Tree (RT), and Support Vector Regression (SVR) are ML methods that are used to predict HGSR. Besides, hourly metrological and geographical parameters for the year 2014 are taken as inputs in the training models. The inputs are solar time, solar hour angle, Julian day number, daily GSR, longitude, latitude, hourly average humidity, hourly temperature, and hourly pressure. To demonstrate these techniques, a comparison is implemented using MATLAB software with the help of existing toolboxes. Finally, this study proves that ML methods outperform the CPRG model, not to mention they have far more accurate results.Although almost all ML models gave similar results, SVR was the best among them with a correlation coefficient of 0.979532 for the training set and 0.978244 for the testing set. In a nutshell, ML are very efficient methods in that should be taken into consideration to perfectly estimate HGSR.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hourly Global Solar Radiation Estimation Based on Machine Learning Methods in Eskisehir

Alsafadi¹,

Filik²

2020

Eskişehir Technical University Journal of Science and Technology a - Applied Sciences and Engineering

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“…Electric power is paramount today. With the growth of global economy and rising concerns for the environment since the 1990s, clean energy has become a world-wide focus (Wang, Wang, & Su, 2011;Shaddel, Javan, & Baghernia, 2016), thus increasing the renewable energy contribution to the world's energy matrix.…”

Section: Introductionmentioning

confidence: 99%

“…There has been a significant reduction in the cost of the main elements of electric power generation systems from renewable sources, which has made this type of energy attractive. According to (Shaddel et al, 2016), among the different types of renewable energy sources, solar energy is one of the best alternatives for fossil fuels because of its abundance and environmentally friendly characteristics. Solar energy lends itself to the installation of small and medium scale plants in residential and commercial buildings (Pereira et al, 2017) in urban areas, not requiring exclusive and dedicated areas typically away from large populated centers for their installation.…”

Section: Introductionmentioning

confidence: 99%

Performance of photovoltaic generators under superficial dust deposition on their modules derived from anthropogenic activities

et al. 2020

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Performance of photovoltaic generators is affected by, among other factors, surface dust deposition on photovoltaic modules, which causes uniform soft shading, reducing the solar radiation reaching photovoltaic cells. Several researches have demonstrated this effect through studies in specific localities. As deposition of particulate matter varies according to locality, these results cannot be generalized or used in other places, since these studies do not characterize their local dust. Considering these research opportunities, this paper evaluates the deposition effects on photovoltaic modules by different types of dust from anthropogenic activities recurrent in urban areas, a typical situation of distributed microgeneration. Particulate matter of iron ore, building construction, quarry, and mineral coal are characterized morphologically and dimensionally. Then, their effects on photovoltaic module performance are analyzed under different deposition densities according to typical urban deposition rates. The results show that the different dust materials studied attenuate the power generation capacity to different extents, and superficial dust density of the particulate matter also produces different effects. These results can be used in performance and economic feasibility analyzes of photovoltaic generators in different locations with similar pollution characteristics.

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“…Solar radiation forecasting with multiple parameters neural networks have been proposed in [22]. The authors of [23] have presented about estimation of hourly global solar irradiation on tilted absorbers from horizontal one using ANN have been proposed in [24]. However, all of the aforementioned only consider one station and do not consider the station its surround.…”

Section: Introductionmentioning

confidence: 99%

K-NN Decomposition Artificial Neural Network Models for Global Solar

Kartini¹,

Chen²

2017

(JCEE

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This paper proposes a novel methodology for forecasting of one hourly global solar irradiance (GSI). This methodology is a combination of k-NN decompotition method and artificial neural network (ANN) algorithm modelling. The k-NN Decomposition-ANN method is designed to forecast GSI for 60 min ahead based on meteorology data for the target PV station which position is surrounded by eight other adjacent PV stations. The novelty of this method is taking into account the meteorology data. A set of GSI measurement samples was available from the PV station in Taiwan which is used as test data. The first method implements k-NN Decomposition as a preprocessing technique prior to ANN method. The error statistical indicators of k-NN Decomposition-ANN model and the root-mean-square error (RMSE) is 20 W/m2. The models forecasts are then compared to measured data and simulation results indicate that the k-NN Decomposition-ANN-based model presented in this research can calculate hourly GSI with satisfactory accuracy.

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Estimation of hourly global solar irradiation on tilted absorbers from horizontal one using Artificial Neural Network for case study of Mashhad

Cited by 48 publications

References 16 publications

Hourly Global Solar Radiation Estimation Based on Machine Learning Methods in Eskisehir

Hourly Global Solar Radiation Estimation Based on Machine Learning Methods in Eskisehir

Performance of photovoltaic generators under superficial dust deposition on their modules derived from anthropogenic activities

K-NN Decomposition Artificial Neural Network Models for Global Solar

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