A Comparison of Two Solar Radiation Models Using Artificial Neural Networks and Remote Sensing in Turkey

Yıldız, B. Yiğit; Şahin, Mehmet; Şenkal, Ozan; Peştemalci, V.; Emrahoğlu, Nuri

doi:10.1080/15567036.2011.650276

Cited by 13 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study's overall purpose is to omit input variables from the analysis so that very few variables are sufficient for predicting solar radiation. Yıldız et al [76] compare two ANN models used for solar radiation estimation. The first is based on input variables (i) latitude, (ii) longitude, (iii) altitude, (iv) month and metrological land surface temperature.…”

Section: Years (2010-2019)mentioning

confidence: 99%

Estimation of Solar Radiation with Consideration of Terrestrial Losses at a Selected Location—A Review

et al. 2023

View full text Add to dashboard Cite

The United Nations has set an ambitious goal to achieve net zero carbon emissions by 2050. This objective requires shifting towards green and renewable energy sources instead of conventional fossil fuels to address the global energy crisis without emitting greenhouse gases. While the energy radiated by the sun is one of the most abundant sources of energy available, its efficient and optimal use remains a significant challenge. To facilitate solar-energy-based applications, estimating the amount of solar energy available is crucial. Empirical and soft computing is the most-used method to estimate solar energy. This paper aims to analyze the existing techniques used in various models for estimating and predicting the quantity and quality of solar radiation using readily available data. Additionally, the study aims to identify the most appropriate techniques for developing prediction models using available explanatory variables. To fully harness the potential of solar energy, it is necessary to limit the terrestrial loss of solar radiation by minimizing the harmful effects of anthropogenic factors that reduce the quantity and quality of solar radiation in the area. This paper provides valuable insights to identify opportunities to maximize the potential of solar energy in different locations.

show abstract

Section: Years (2010-2019)mentioning

confidence: 99%

Estimation of Solar Radiation with Consideration of Terrestrial Losses at a Selected Location—A Review

et al. 2023

View full text Add to dashboard Cite

show abstract

“…This success can be attributed to the fact that these models are able to successfully learn from data and make predictions. For instance, Alawi et al [12], Mohandas et al [13], Reddy et al [14], and Yildiz et al [15] employed artificial neural networks to predict SR in Oman, Saudi Arabia, India, and Turkey, respectively. Deep learning is a subfield of ML which, in comparison to artificial neural networks and other types of machine learning, has a more robust learning capacity and calls for an increased amount of data in order to attain improved prediction accuracy [16,17].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence Model Solar Radiation Prediction for Renewable Energy Systems

2023

View full text Add to dashboard Cite

Solar power is an excellent alternative power source that can significantly cut our dependency on nonrenewable and destructive fossil fuels. Solar radiation (SR) can be predicted with great precision, and it may be possible to drastically minimize the impact cost associated with the development of solar energy. To successfully implement solar power, all projects using solar energy must have access to reliable sun radiation data. However, the deployment, administration, and performance of photovoltaic or thermal systems may be severely impacted by the lack of access to and the ambiguity of this data. Methods for estimating and predicting solar radiation can help solve these problems. Prediction techniques can be put to use in the real world to, for example, keep the power grid functioning smoothly and ensure that the supply of electricity exactly matches the demand at all times. Recently developed forecasting methods include the deep learning convolutional neural networks combined with long short-term memory (CNN-LSTM) model. This study provides a comprehensive examination of meteorological data, along with the CNN-LSTM methods, in order to design and train the most accurate SR forecasting artificial neural network model possible. Weather data was collected from a NASA meteorological station that included details such as the current temperature, the relative humidity, and the speed of the wind. This research revealed that SR is highly correlated with both temperature and radiation. Furthermore, the findings demonstrated that the CNN-LSTM algorithm outperformed the other algorithm-trained models, as evidenced by the performance score of the respective models, with a maximum coefficient determination (R²) > 95% and a minimum mean square error (MSE) of 0.000987 at the testing step. In comparison with different existing artificial intelligence models, the CNN-LSTM model outperformed the other models. These scenarios demonstrated that a basic implementation of CNN-LSTM can be used to supplement conventional methods for predicting SR, provide possibilities to monitor radiation at a low cost, and encourage the adoption of data-driven management.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

A Comparison of Two Solar Radiation Models Using Artificial Neural Networks and Remote Sensing in Turkey

Cited by 13 publications

References 14 publications

Estimation of Solar Radiation with Consideration of Terrestrial Losses at a Selected Location—A Review

Estimation of Solar Radiation with Consideration of Terrestrial Losses at a Selected Location—A Review

Application of Artificial Intelligence Model Solar Radiation Prediction for Renewable Energy Systems

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

Contact Info

Product

Resources

About