Abstract
Recent advances in artificial intelligent techniques embedded into a Field Programmable Gate Array (FPGA) allowed the application of such technologies in real engineering problems (robotic, image and signal processing, control, power electronics, etc.), however, the application of such technologies in the solar energy field is very limited. The embedded intelligent algorithm into FPGA can play a very important role in energy and renewable energy systems for control, monitoring, supervision, etc. In this paper, the software as well as the implementation of intelligent predictors for solar irradiation on reconfigurable FPGA is described. FPGA technology was employed due to its development, flexibility and low cost. An experimental dataset of air temperature, solar irradiation, relative humidity and sunshine duration in a specific area is used; this database has been collected from 1998 to 2002 at Al-Madinah (Saudi Arabia). Initially, a MultiLayer Perceptron (MLP) is trained by using a set of 1460 patterns and then a set of 365 patterns are used for testing and validating the MLP-predictor. Six MLP-predictors (configurations) are proposed and developed by varying the MLP inputs data, while the output is always the global solar irradiation for different configurations [ G = f ˜ ( t , T , S , RH ) , G = f ˜ ( t , T , S ) , G = f ˜ ( t , T , RH ) , G = f ˜ ( t , S , RH ) G = f ˜ ( t , T ) and G = f ˜ ( t , S ) ] . Subsequently, the different MLP-predictors developed are written and simulated under the Very High Speed Integrated Circuit Hardware Description Language (VHDL) and ModelSim®. The best designed architecture for different MLP-predictors is then implemented under the Xilinx® Virtex-II FPGA (XC2v1000). The developed hardware devices permit the prediction of global solar irradiation using available air temperature, relative humidity and sunshine duration; therefore, the designed configurations are very suitable especially in areas, where there are no instruments for measuring the solar irradiation data.
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