The penetration of renewable energies has increased during the last decades since it has become an effective solution to the world’s energy challenges. Among all renewable energy sources, photovoltaic (PV) technology is the most immediate way to convert solar radiation into electricity. Nevertheless, PV power output is affected by several factors, such as location, clouds, etc. As PV plants proliferate and represent significant contributors to grid electricity production, it becomes increasingly important to manage their inherent alterability. Therefore, solar PV forecasting is a pivotal factor to support reliable and cost-effective grid operation and control. In this paper, a stacked long short-term memory network, which is a significant component of the deep recurrent neural network, is considered for the prediction of PV power output for 1.5 h ahead. Historical data of PV power output from a PV plant in Nicosia, Cyprus, were used as input to the forecasting model. Once the model was defined and trained, the model performance was assessed qualitative (by graphical tools) and quantitative (by calculating the Root Mean Square Error (RMSE) and by applying the k-fold cross-validation method). The results showed that our model can predict well, since the RMSE gives a value of 0.11368, whereas when applying the k-fold cross-validation, the mean of the resulting RMSE values is 0.09394 with a standard deviation 0.01616.

中文翻译：

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使用LSTM网络的太阳能光伏发电功率预测

自从可再生能源已成为解决世界能源挑战的有效解决方案以来，可再生能源的渗透率在过去几十年中有所增加。在所有可再生能源中，光伏（PV）技术是将太阳辐射转化为电能的最直接方法。然而，光伏发电的输出受多个因素的影响，例如位置，云层等。随着光伏电站的激增并代表着电网发电的重要贡献，管理其固有的可变性变得越来越重要。因此，太阳能光伏预报是支持可靠且经济高效的电网运行和控制的关键因素。在本文中，作为深度递归神经网络的重要组成部分的堆叠式长期短期记忆网络被认为可预测1的PV功率输出。提前5小时。来自塞浦路斯尼科西亚的一家光伏电站的光伏发电输出的历史数据被用作预测模型的输入。一旦定义并训练了模型，就对模型的性能进行了定性（通过图形工具）和定量（通过计算均方根误差（RMSE）并应用k倍交叉验证方法）评估。结果表明，由于RMSE的值为0.11368，因此我们的模型可以很好地进行预测，而应用k倍交叉验证时，所得RMSE值的平均值为0.09394，标准偏差为0.01616。通过定性（通过图形工具）和定量（通过计算均方根误差（RMSE）并应用k倍交叉验证方法）评估模型性能。结果表明，由于RMSE的值为0.11368，因此我们的模型可以很好地进行预测，而应用k倍交叉验证时，所得RMSE值的平均值为0.09394，标准偏差为0.01616。通过定性（通过图形工具）和定量（通过计算均方根误差（RMSE）并应用k倍交叉验证方法）评估模型性能。结果表明，由于RMSE的值为0.11368，因此我们的模型可以很好地进行预测，而应用k倍交叉验证时，所得RMSE值的平均值为0.09394，标准偏差为0.01616。