Estimation of monthly global solar radiation in the eastern Mediterranean region in Turkey by using artificial neural networks

Şahan, Muhittin; Yakut, Emre

doi:10.1051/epjconf/201612806001

Cited by 2 publications

(2 citation statements)

References 8 publications

(10 reference statements)

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“…ese, however, still face some practical difficulties such as lack of reliable aerosol information, which affects the accuracy of the estimations, especially under clear sky conditions [11,23]. Artificial neural networks (ANNs) have also been used to compute solar irradiance at the Earth's surface, for example, Sahan and Yakut [24] used a suit of meteorological and geographical parameters as inputs to an ANN model to estimate monthly average global solar radiation on a horizontal surface at five locations in the Mediterranean region, Ozgoren et al [25] developed an ANN model for estimating the monthly mean daily sum of global solar radiation using meteorological data of 31 stations across Turkey, Kumar and Kaur [26] used five parameters, i.e., temperature, pressure, relative humidity, precipitation, and clearness index as input parameters for a feed-forward neural network to estimate solar radiation in the Hamirpur region in India, while Hasni et al [27] were able to estimate the global solar radiation for Bechar city in the south western region of Algeria using only air temperature and relative humidity in addition to hour, day, and month of the year. As highlighted, these artificial neural networks also require meteorological data as input parameters, in addition to temporal and geographical parameters.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Clear Sky Global Horizontal Irradiance Models: Simple Models Adapted for Local Conditions

Kwarikunda

Chiguvare

2021

Journal of Renewable Energy

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Evaluation of the maximum solar energy potential of a given area for possible deployment of solar energy technologies requires assessment of clear sky solar irradiance for the region under consideration. Such localized assessment is critical for optimal sizing of the technology to be deployed in order to realize the anticipated output. As the measurements are not always available where they are needed, models may be used to estimate them. In this study, three different models were adapted for the geographical location of the area under study and used to estimate clear sky global horizontal irradiance (GHI) at three locations in the subtropical desert climate of Namibia. The three models, selected on the basis of input requirements, were used to compute clear sky GHI at Kokerboom, Arandis, and Auas. The models were validated and evaluated for performance using irradiance data measured at each of the sites for a period of three years by computing statistical parameters such as mean bias error (MBE), root mean square error (RMSE), and the coefficient of determination (R2), normalized MBE, and normalized RMSE. Comparative results between modelled and measured data showed that the models fit well the measured data, with normalized root mean square error values in the range 4–8%, while the R2 value was above 98% for the three models. The adapted models can thus be used to compute clear sky GHI at these study areas as well as in other regions with similar climatic conditions.

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Section: Introductionmentioning

confidence: 99%

Performance Analysis of Clear Sky Global Horizontal Irradiance Models: Simple Models Adapted for Local Conditions

Kwarikunda

Chiguvare

2021

Journal of Renewable Energy

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“…These values are calculated as in the Equations(2) and(3)given below. Here, the real values of show the estimated values of ̂ and the number of estimates[14]:…”

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confidence: 99%

Power transformer demand forecast with Box Jenkins ARIMA model

Kuvat

Adali

2020

International Journal of Energy Applications and Technologies

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Demand forecasting is based on the principle of trying to forecast the demand for the outputs of enterprises in the field of manufacturing or service for the next periods. It requires the estimation of various future scenarios, if necessary, taking measures and taking steps, and during the application phase, the technique that is most suitable for the characteristics of the examined data set is selected and used. As a result of a healthy analysis carried out in this way, detailed plans and strict measures can be taken for the unknown, negative scenarios of the future. This study analyzes the characteristics of a series of power transformers of a company operating in the electromechanical industry in the past years, and as a result of this analysis, the Box Jenkins Autoregressive Integrated Moving Average method (ARIMA), which best fits the results, is expected to occur for power transformers in the future. It was made to estimate the amount of demand. Within the scope of this study, firstly, the most suitable model was tried to be determined by taking into consideration the past 132 months data of PTS. It was decided that the best choice among the alternative models was the ARMA (4,4) x (0,1) 12 model. The model was found to be stable and it was decided that the root mean square error (RMSE), mean absolute percentage error (MAPE) and Theil inequality coefficient values determined in the performance measurements were appropriate.

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Estimation of monthly global solar radiation in the eastern Mediterranean region in Turkey by using artificial neural networks

Cited by 2 publications

References 8 publications

Performance Analysis of Clear Sky Global Horizontal Irradiance Models: Simple Models Adapted for Local Conditions

Performance Analysis of Clear Sky Global Horizontal Irradiance Models: Simple Models Adapted for Local Conditions

Power transformer demand forecast with Box Jenkins ARIMA model

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