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Forecasting the Performance of a Photovoltaic Solar System Installed in other Locations using Artificial Neural Networks

DOI:10.1080/15325008.2020.1736211 期刊:Electric Power Components and Systems 出版年份:2020 更新时间:2025-09-19 17:13:59
摘要: Photovoltaic solar energy has been spread all over the world, and in Brazil this energy source has been getting considerable space in the last years, being driven mainly by the energy crises. However, when installed in regions with low incidence of solar irradiation, this technology presents a loss of efficiency in the generation of energy. As an alternative to this consideration, a power prediction study could be conducted prior to its installation, based on local climate information that directly influences power generation, verifying the feasibility of system implementation and avoiding unrewarded investment. Therefore, the objective of this work is to predict the viability of the installation of a photovoltaic system of 3kWp in different places, with the assist of an Artificial Neural Network. Thus, the feedforward network was used for the training, being trained and validated with the support of MatlabVR , and inserting samples of temperature and solar irradiation as input variables. Through the performance methods, the results are favorable for this application, presenting validations with RMSE% in the range of 13-20% and R of not less than 0.93. The predictions presented RMSE% around 19-25% and average powers close to the real values generated by the PV system.
作者: Amanda Suianny Fernandes Rocha,Fabiana Karla de O. M. V. Guerra,Marcelo Roberto Bastos Guerra Vale
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to predict the viability of the installation of a photovoltaic system of 3kWp in different places, with the assist of an Artificial Neural Network.

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.

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.

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