| Data are key for providing added value in the Industry 4.0 paradigm, benefiting differentiation and innovation. However, high quality data, i.e., reliable and accurate data from sensors, are required. Nowadays, energy meters are being installed in many industries to achieve holistic submetering systems. In these systems, data can be lost due to meter faults, maintenance, power failure or communication drops, affecting negatively the data quality crucial for monitoring and decision making. Therefore, missing data should be filled.In this paper, we propose a method (GRU-AE) based on a denoising autoencoder (AE) with gated recurrent unit (GRU) layers in order to reconstruct electricity profiles that contain missing samples in submetering systems. GRU-AE is able to capture temporal and meter relations, filling gaps in the electricity profiles. Two implementations are presented: multi-head GRU-AE and multi-feature GRU-AE.The proposed method has proved to be more effective reconstructing electricity profiles in submetering systems than a similar approach that models each meter independently. GRU-AE could be useful even when more than one meter provide incomplete or no data at the same time. Both GRU-AE implementations provide similar reconstruction errors. However, a multi-feature GRU-AE could be more efficient in large submetering systems. |
*** Title, author list and abstract as seen in the Camera-Ready version of the paper that was provided to Conference Committee. Small changes that may have occurred during processing by Springer may not appear in this window.