Conference
2021
Investigation of the Effect of Annealing Temperature of Perovskite Layer on Performance of Perovskite Solar Cells
2021-09
World Renewable Energy Congress – 2021, Lisbon, Portugal
The world energy demand is continuously increasing. Due to environment issues, global warming, and other issues, there is a need for clean sources of energy, sustainable energy, i.e. renewable energy technologies. Photovoltaic (PV) technology employing solar energy is regarded as rather most promising and efficient technology among all the renewable energy industries. The further improvement in the efficiency and reliability and also reduction in cost are in great demand, for the healthy development of global economy. Among the third generation solar cells, perovskite cells have attracted a lot of attention due to their high efficiency and low cost construction methods. In this research, low-cost perovskite solar cell (PSC) was prepared, and its photovoltaic performance was investigated. In this project, we tried to get rid of some structures or materials which are harm to the environment. i.e. car batteries. By using lead sheets (from recycled car battery). However, by reusing car batteries we will avoid the disposal of toxic battery materials and provide an alternative, readily-available lead source for fabricating PSC. Perovskite solar cell was prepared by two-steps spin coating solution technique on glass substrates at various thermal annealing deposition temperature.
2020
Using Artificial Neural Networks to Predict Solar Radiation for Duhok City, Iraq.
2020-07
International Conference on Computer Science and Software Engineering (CSASE).. Publisher: IEEE
The amount of solar radiation received at the Earth's surface is influenced by local weather conditions. This paper investigates the effects of meteorological parameters on daily average solar radiation (DASR) in Duhok city, Iraq. Artificial Neural Networks (ANNs) based on multilayer preceptor feed-forward (MLP-FF) techniques are used to predict daily average solar radiation (DASR). The input variables used are a daily average of the relative humidity (RH), minimum temperature (T min ), maximum temperature (T max ), wind speed (WS), cloud layer (CL), atmospheric pressure (AP) and ultraviolet (UV) levels to estimate DASR. To identify and evaluate the effects of various input parameters on solar radiation, eight ANN-based models have been developed. To obtain the best estimation results, the number of neurons in the hidden layer has been varied. The best values of the Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and correlation coefficient (R) have been calculated. For some models, the results obtained show good and better predictive accuracy than others. The present study indicates that various of the meteorological parameters can have a significant effect on the forecasting of solar radiation
Back