Edge sensing data-imaging conversion scheme of load forecasting in smart grid

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 %.