Weather is one of the main problems in implementing forecasts for photovoltaic panel systems. Since it is the main generator of disturbances and interruptions in electrical energy. It is necessary to choose a reliable forecasting model for better energy use. A measurement prototype was constructed in this work, which collects in-situ voltage and current measurements and the environmental factors of radiation, temperature, and humidity. Subsequently, a correlation analysis of the variables and the implementation of artificial neural networks were performed to perform the system forecast. The best estimate was the one made with three variables (lighting, temperature, and humidity), obtaining an error of 0.255. These results show that it is possible to make a good estimate for a photovoltaic panel system.

翻译：天气是光伏板系统实施预测的主要问题之一，因其是电能产生扰动和中断的主要来源。为优化能源利用，必须选择可靠的预测模型。本研究构建了一个测量原型系统，可原位采集电压、电流测量值以及辐射、温度、湿度等环境因子。随后进行了变量相关性分析并实施人工神经网络以实现系统预测。最佳估算采用三个变量（光照度、温度与湿度），其误差为0.255。研究结果表明，对光伏板系统进行良好估算是可行的。