研究目的
to predict the viability of the installation of a photovoltaic system of 3kWp in different places, with the assist of an Artificial Neural Network.
研究成果
The study demonstrated that Artificial Neural Networks can effectively predict the performance of photovoltaic systems in different locations, with RMSE% in the range of 13-25% and correlation coefficients of 0.86-0.99. This method provides a viable approach to assessing the feasibility of photovoltaic system installations in various regions.
研究不足
The study did not account for all possible climatic variations and their impacts on photovoltaic system performance. The ANN's predictions were based on historical data, which may not fully capture future variations.
1:Experimental Design and Method Selection:
The study used Artificial Neural Networks (ANNs) to predict the power generated by a photovoltaic system based on solar irradiance and temperature data.
2:Sample Selection and Data Sources:
Data were collected from a photovoltaic system installed in Mossoró/RN, Brazil, and from the Wunder Ground site for other locations.
3:List of Experimental Equipment and Materials:
A photovoltaic system of 3kW nominal power and MatlabVR software were used.
4:Experimental Procedures and Operational Workflow:
The ANN was trained with data from October 1, 2017, to March 31, 2018, and validated for different periods and locations.
5:Data Analysis Methods:
Performance was evaluated using RMSE% and regression methods.
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