Abstract 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 Matlab®, 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.

中文翻译：

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使用人工神经网络预测安装在其他位置的光伏太阳能系统的性能

摘要 光伏太阳能已遍布世界各地，而在巴西，这一能源在过去几年获得了相当大的空间，主要受能源危机的推动。然而，当安装在太阳辐射发生率较低的地区时，该技术会降低能源产生的效率。作为这种考虑的替代方案，可以在安装之前进行功率预测研究，根据直接影响发电的当地气候信息，验证系统实施的可行性并避免无回报的投资。因此，这项工作的目的是在人工神经网络的帮助下预测在不同地方安装 3kWp 光伏系统的可行性。因此，前馈网络用于训练，在 Matlab® 的支持下进行训练和验证，并插入温度和太阳辐射样本作为输入变量。通过性能方法，结果对本应用有利，验证了 RMSE% 在 13-20% 范围内，R 不小于 0.93。预测显示 RMSE% 约为 19-25%，平均功率接近光伏系统产生的实际值。