A Novel Short-Term Photovoltaic Power Forecasting Approach based on Deep Convolutional Neural Network
A Novel Short-Term Photovoltaic Power Forecasting Approach based on Deep Convolutional Neural Network
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DOI：
10.1080/15435075.2021.1875474
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发表时间：
2021-01-31
影响因子：
3.3
通讯作者：
Yildiz, Ceyhun
中科院分区：
文献类型：
--
作者：
Korkmaz, Deniz;Acikgoz, Hakan;Yildiz, Ceyhun
在这项研究中，提出了一种新的光伏功率预测系统，该系统利用深度卷积神经网络（CNN）结构和输入信号分解算法。提出的CNN架构使用基于迁移学习的AlexNet提取深度特征来预测短期功率。选择历史功率、太阳辐射、风速和温度数据作为输入。利用经验模式分解（EMD）算法对历史功率信号进行分解。为了提取深度特征，所有输入参数都被转换为2D特征图并馈送到CNN的输入。实验是在位于土耳其的装机容量为1000 kW的并网光伏电站（PVPP）上实现的。在局部多云、阴雨天气、大雨天气和晴天四种天气条件下进行实验，验证了该方法的有效性。所得结果与基准回归算法进行了比较。当分析结果时，所提出的方法给出了最高的相关系数（R）和最低的均方根误差（RMSE），平均绝对误差（MAE），和SMAPE值在所有层位和天气条件下。对于1 ~ 5 h的提前量，该方法的平均R值分别为97.28%、95.77%、94.49%、93.61%和92.62%。提前1- 5 h的平均RMSE值分别为4.90%、6.30%、7.50%、8.00%和9.17%。实验结果表明，该方法优于传统的回归算法，并揭示了其竞争力的性能有效的结果。
In this study, a novel photovoltaic power forecasting system that utilizes a deep Convolutional Neural Network (CNN) structure and an input signal decomposition algorithm is proposed. The proposed CNN architecture extracts deep features to forecast short-term power using transfer learning-based AlexNet. The historical power, solar radiation, wind speed, and temperature data are selected as the input. The signal decomposition algorithm called Empirical Mode Decomposition (EMD) is utilized to decompose the historical power signal into sub-components. In order to extract deep features, all input parameters are converted to 2D feature maps and feed to the input of the CNN. The experiments are realized on a grid-tied Photovoltaic Power Plant (PVPP) that has 1000 kW installed capacity located in Turkey. The experiments are performed under four weather conditions as partial cloudy, cloudy-rainy, heavy-rainy, and sunny days to show the effectiveness of the proposed method. The obtained results are compared with the benchmark regression algorithms. When the results are analyzed, the proposed method gives the highest Correlation Coefficient (R) and the lowest Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and SMAPE values under all horizons and weather conditions. For 1-h to 5-h ahead, the average R values of the proposed method are obtained as 97.28%, 95.77%, 94.49%, 93.61%, and 92.62%, respectively. The average RMSE values are observed as 4.90%, 6.30%, 7.50%, 8.00%, and 9.17% for 1-h to 5-h ahead. The experimental results confirm that the proposed method outperforms the conventional regression algorithms and reveals effective results with its competitive performance.