Abstract
In this article, we introduced an artificial neural network (ANN) based computational model to predict the output power of three types of photovoltaic cells, mono-crystalline (mono-), multi-crystalline (multi-), and amorphous (amor-) crystalline. The prediction results are very close to the experimental data, and were also influenced by numbers of hidden neurons. The order of the solar generation power output influenced by the external conditions from smallest to biggest is: multi-, mono-, and amor- crystalline silicon cells. In addition, the dependences of power prediction on the number of hidden neurons were studied. For multi- and amorphous crystalline cell, three or four hidden layer units resulted in the high correlation coefficient and low MSEs. For mono-crystalline cell, the best results were achieved at the hidden layer unit of 8.
Highlights
The renewable energy sources, such as photovoltaic (PV) cell power generation [1], will become important in the future [2], as it has a great potential to solve the current energy crisis and is environment-friendly to solve the current environmental crisis [3]
For mono-crystalline cell, the best results were achieved at the hidden layer unit of 8
The prediction results are very close to the experimental data, and are influenced by numbers of hidden neurons, especially for crystalline cells at lower light intensity and temperature
Summary
The renewable energy sources, such as photovoltaic (PV) cell power generation [1], will become important in the future [2], as it has a great potential to solve the current energy crisis and is environment-friendly to solve the current environmental crisis [3]. The output power of PV cells depends on the solar radiation intensity, device material and device temperature [4] and so on. Mono-crystalline, multi-crystalline, and amorphous crystalline silicon solar PV cells exhibit different characteristics in the external work conditions. The artificial neural network (ANN) method has received a considerable amount of attention for power prediction [10]. This is because the ANN methods are used to model complex nonlinear dynamic systems with great success. The system dynamics can be emulated by feeding a measured
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
