论文标题
使用ANN（深度学习）的PV面板的能源预测需求和响应系统
Energy prediction of pv panels for demand and response system using ANN (deep learning)
论文作者
论文摘要
可再生能源的来源是未来，这是由于不可再生来源引起的环境问题而产生能源。可再生能源的最大问题是，PV太阳能电池板等设备产生的功率取决于许多不确定的因素。这些因素包括太阳照射，风速，温度，每天的阳光小时以及太阳能电池板的表面温度。行业和当局可以通过ML使用这种预测的电力来控制功耗。 Power Treecast具有多种应用，可在将来促进绿色能源的使用。本文还将有助于确定PV功率产生对各种天气/环境因素的依赖性。对于本文，我们使用了回归和ANN模型来预测功率。最后，将使用回归以及ANN模型的功率预测结果与实际功率输出进行了比较。总体而言，ANN与其他机器学习模型相比表现出色，因为其先进的功能选择技术。
Renewable sources of energy are the future due to the environmental problems caused by non-renewable sources to produce energy. The biggest issue with renewable energy sources is that the power produced by devices such as PV solar panels depend on many uncertain factors. These factors include Solar irradiation, wind speed, temperature, hours of sunlight per day, and surface temperature of solar panels. Industries and authorities can use this predicted power through ML to control power consumption. Power forecast has multiple applications that promote the usage of green energy in the future. This paper will also help to determine the dependence of PV power production on various weather/environmental factors. For this paper, we have used regressions and ANN models to predict power. In the end, the results of power prediction using regression as well as the ANN model are compared with the actual power output. Overall, ANN performs excellently compared to the other machine learning models because of its advanced feature selection techniques.
