Deep RNN-Based Photovoltaic Power Short-Term Forecast Using Power IoT Sensors
Deep RNN-Based Photovoltaic Power Short-Term Forecast Using Power IoT Sensors
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DOI：
10.3390/en14020436
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发表时间：
2021-01-01
期刊：
影响因子：
3.2
通讯作者：
Park, Neungsoo
中科院分区：
文献类型：
--
作者：
Ahn, Hyung Keun;Park, Neungsoo
由天气变化引起的光伏（PV）功率波动可能导致电力需求和供应的短期不匹配。因此，为了高效可靠地运行电网，需要针对这些波动进行短期光伏发电预测。在本文中，我们提出了一种基于深度RNN的光伏发电短期预测。为了反映天气变化的影响，该模型利用了实时收集的现场天气物联网数据集和电力数据。我们研究了所提出的基于RNN的深度预测模型的各个参数以及天气参数的组合，以找到准确的预测模型。实验结果表明，基于归一化RMSE，使用3层12个时间步长的RNN预测5和15 min光伏发电量的准确率分别为98.0%和96.6%。他们的R-2得分分别为0.988和0.949。在实验中提前1和3小时的光伏发电预测，其准确率分别为94.8%和92.9%。此外，他们的R-2得分分别为0.963和0.927。这些实验结果表明，提出的基于深度RNN的短期预测算法具有更高的预测精度。
Photovoltaic (PV) power fluctuations caused by weather changes can lead to short-term mismatches in power demand and supply. Therefore, to operate the power grid efficiently and reliably, short-term PV power forecasts are required against these fluctuations. In this paper, we propose a deep RNN-based PV power short-term forecast. To reflect the impact of weather changes, the proposed model utilizes the on-site weather IoT dataset and power data, collected in real-time. We investigated various parameters of the proposed deep RNN-based forecast model and the combination of weather parameters to find an accurate prediction model. Experimental results showed that accuracies of 5 and 15 min ahead PV power generation forecast, using 3 RNN layers with 12 time-step, were 98.0% and 96.6% based on the normalized RMSE, respectively. Their R-2-scores were 0.988 and 0.949. In experiments for 1 and 3 h ahead of PV power generation forecasts, their accuracies were 94.8% and 92.9%, respectively. Also, their R-2-scores were 0.963 and 0.927. These experimental results showed that the proposed deep RNN-based short-term forecast algorithm achieved higher prediction accuracy.