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Edge sensing data-imaging conversion scheme of load forecasting in smart grid
Sustainable Cities and Society ( IF 11.7 ) Pub Date : 2020-06-30 , DOI: 10.1016/j.scs.2020.102363
Xiaozhu Liu , Zhiyang Xiao , Rongbo Zhu , Jun Wang , Lu Liu , Maode Ma

Edge sensing data in smart grid provides vast valuable information, which promotes further innovated smart power applications in Internet of things (IoT) oriented smart cities and society. While in power load prediction, the potential relationships between the time series of power load data and the characteristics of temperature, weather and date, have not been explored comprehensively, which degrades the accuracy of load prediction in smart grid. In order to extract the generalized features and latent relationships in power load related edge sensing data, a power load prediction scheme based on edge sensing data-imaging conversion (DIC) is proposed to improve the forecasting accuracy in smart cites and society. DIC employs empirical mode decomposition (EMD) for power load time series data and combines it with characteristic time series including temperature, weather and date to form an image-like structure. And a DIC-based convolutional neural network (DI-CNN) is presented to implement convolution. Experimental results show that, compared with long short-term memory (LSTM), support vector machines (SVM), and CNN, the proposed DIC scheme improves the training speed by 61.7 %, reduces root mean square error (RMSE) by 32.9 % at least, and enhances the prediction accuracy by 1.4 %.



中文翻译:

智能电网负荷预测的边缘传感数据成像转换方案

智能电网中的边缘感测数据可提供大量有价值的信息,从而可在面向物联网(IoT)的智能城市和社会中促进进一步创新的智能电源应用。在电力负荷预测中,尚未全面探索电力负荷数据的时间序列与温度,天气和日期的特征之间的潜在关系,这降低了智能电网负荷预测的准确性。为了提取与电力负荷有关的边缘传感数据的广义特征和潜在关系,提出了一种基于边缘传感数据-影像转换(DIC)的电力负荷预测方案,以提高智能城市和社会的预测精度。DIC对功率负载时间序列数据采用了经验模式分解(EMD),并将其与包括温度,天气和日期在内的特征时间序列相结合,形成了类似图像的结构。提出了一种基于DIC的卷积神经网络(DI-CNN)来实现卷积。实验结果表明,与长短期记忆(LSTM),支持向量机(SVM)和CNN相比,拟议的DIC方案将训练速度提高了61.7%,将均方根误差(RMSE)降低了32.9%。最小化,并将预测准确性提高了1.4%。

更新日期:2020-06-30
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