The Estimation of Solar Radiation for Different Time Periods

Şenkal, Ozan; Şahin, Mehmet; Peştemalci, V.

doi:10.1080/15567030902967850

Cited by 21 publications

(4 citation statements)

References 23 publications

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“…The MLP based ANN works using back propagation algorithm. However, the application of MLP based artificial neural network (ANN) for predicting and forecasting of multifaceted hydrological and climatic phenomena and has gained popularity across the globe as it provides reliable results [72][73][74][75][76][77][78][79][80][81] . The novelty of the present study is that the application of MLP-ANN is not concentrated on the one or more states or overall India rather the present study considers predicting and forecasting rainfall for thirty-four meteorological sub-divisions of India.…”

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confidence: 99%

Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches

Praveen

Talukdar

Shahfahad

et al. 2020

Sci Rep

292

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this study analyzes and forecasts the long-term Spatio-temporal changes in rainfall using the data from 1901 to 2015 across India at meteorological divisional level. The Pettitt test was employed to detect the abrupt change point in time frame, while the Mann-Kendall (MK) test and Sen's innovative trend analysis were performed to analyze the rainfall trend. The Artificial Neural Network-Multilayer Perceptron (ANN-MLP) was employed to forecast the upcoming 15 years rainfall across India. We mapped the rainfall trend pattern for whole country by using the geo-statistical technique like Kriging in ArcGIS environment. Results show that the most of the meteorological divisions exhibited significant negative trend of rainfall in annual and seasonal scales, except seven divisions during. Out of 17 divisions, 11 divisions recorded noteworthy rainfall declining trend for the monsoon season at 0.05% significance level, while the insignificant negative trend of rainfall was detected for the winter and premonsoon seasons. Furthermore, the significant negative trend (−8.5) was recorded for overall annual rainfall. Based on the findings of change detection, the most probable year of change detection was occurred primarily after 1960 for most of the meteorological stations. The increasing rainfall trend had observed during the period 1901-1950, while a significant decline rainfall was detected after 1951. The rainfall forecast for upcoming 15 years for all the meteorological divisions' also exhibit a significant decline in the rainfall. The results derived from ECMWF ERA5 reanalysis data exhibit that increasing/ decreasing precipitation convective rate, elevated low cloud cover and inadequate vertically integrated moisture divergence might have influenced on change of rainfall in India. Findings of the study have some implications in water resources management considering the limited availability of water resources and increase in the future water demand. Rainfall is a key part of hydrological cycle and alteration of its pattern directly affect the water resources 1. The changing pattern of rainfall in consequence of climate change is now concerning issues to water resource managers and hydrologists 2. Srivastava et al. 3 and Islam et al. 4 reported that the changes of rainfall quantities and frequencies directly changing the stream flow pattern and its demand, spatiotemporal allocation of runoff , ground water reserves and soil moisture. Consequently, these changes showed the widespread consequences on the water resource, environment, terrestrial ecosystem, ocean, biodiversity , agricultural and food security. The drought and flood like hazardous events can be occurred frequently because of the extreme changes of rainfall trend 5. Gupta et al. 6 documented that the amount of soil moisture for crop production is totally determined by the amount of rainfall. The monsoon rainfall plays a vital role for agriculture in India. 68% of cultivated land to the total cultivated land of India is occupying by the rain fed agriculture which...

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confidence: 99%

Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches

Praveen

Talukdar

Shahfahad

et al. 2020

Sci Rep

292

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“…These can be classified in three main categories: statistical, physical, and artificial intelligence techniques . However, it has been observed that efficient results have been obtained in satellite data‐based SR estimation when artificial intelligence techniques such as artificial neural network (ANN) have been used . Although ANN‐based approaches show better performance compared with the aforementioned techniques, its most important disadvantage is the fact that learning speed is highly low in training stage especially when working with large data sets as in this study.…”

Section: Introductionmentioning

confidence: 86%

Application of extreme learning machine for estimating solar radiation from satellite data

Şahin

Kaya

Uyar

et al. 2013

Int. J. Energy Res.

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SUMMARYIn this paper, a simple and fast method based on extreme learning machine (ELM) for the estimation of solar radiation in Turkey was presented. To design the ELM model, satellite data of the National Oceanic and Atmospheric Administration advanced very high-resolution radiometer from 20 locations spread over Turkey were used. The satellite-based land surface temperature, altitude, latitude, longitude, month, and city were applied as input to the ELM, and the output variable is the solar radiation. To show the applicability of the ELM model, a performance comparison in terms of the estimation capability and the learning speed was made between the ELM model and conventional artificial neural network (ANN) model with backpropagation. The comparison results showed that the ELM model gave better estimation than the ANN model for the overall test locations. Moreover, the ELM model was about 23.5 times faster than the ANN model. The method could be used by researchers or scientists to design high-efficiency solar devices such as solar power plant and photovoltaic cell.

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“…Artificial neural networks (ANNs) based solar radiation prediction models have been developed by various researchers for different regions in Turkey [14][15][16][17][18][19][20][21][22][23][24]. Previous studies have shown that ANN prediction models estimate solar radiation more accurately than other linear, nonlinear and fuzzy logic models [25].…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Global Solar Radiation with the Artificial Neural Networks for the City of Sivas

Gürlek¹,

Şahin²

2018

European Mechanical Science

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In this study, global solar radiation in the city of Sivas was estimated by artificial neural networks (ANNs) using meteorological and geographical data obtained from four different measurement stations. Mean bias error (MBE), root mean square error (RMSE) and R 2 ranged from-1.264 MJ/m 2 to 0.938 MJ/m 2 , 0.710 MJ/ m 2 to 1.598 MJ/m 2 and 0.984 to 0.994, respectively. It is believed that ANN models could be used to predict global solar radiation for locations where only the temperature and sunshine duration data are available in the city of Sivas.

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The Estimation of Solar Radiation for Different Time Periods

Cited by 21 publications

References 23 publications

Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches

Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches

Application of extreme learning machine for estimating solar radiation from satellite data

Estimation of the Global Solar Radiation with the Artificial Neural Networks for the City of Sivas

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