Edge sensing data-imaging conversion scheme of load forecasting in smart grid. (November 2020)

• Record Type: Journal Article
• Authors: Liu, Xiaozhu; Xiao, Zhiyang; Zhu, Rongbo; Wang, Jun; Liu, Lu; Ma, Maode
• Is Part Of: Sustainable cities and society. Volume 62(2020)
• Publication Date: 2020-11
• Subjects: CNN convolutional neural network -- DIC data-imaging conversion scheme -- DI-CNN DIC prediction model -- EMD empirical modal decomposition -- IMFs intrinsic mode functions -- IoT Internet of things -- LSTM long short-term memory -- NN neural network -- RMSE root mean square error -- RNN recurrent neural network -- SVM support vector machines; Empirical mode decomposition -- Data-image conversion -- Load forecasting -- Smart grid -- Edge intelligence; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2020.102363
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: A data-imaging conversion scheme (DIC) is proposed in smart grid. Temperature, weather and date feature time series are considered. Empirical modal decomposition is adopted to form an image-like structure. A DIC-based convolutional neural network (CNN) prediction model is presented. Abstract: 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 %.