Title: A survey of intelligent load monitoring in IoT-enabled distributed smart grids

Authors: Jixiang Gan; Lei Zeng; Qi Liu; Xiaodong Liu

Addresses: School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China ' School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China ' School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China ' School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK

Abstract: Power load monitoring has been a research hotspot since a few years ago. With development of artificial intelligence, construction of smart grid has become the most important part of power load monitoring. At the same time, task scheduling mechanism combined with the distributed internet of things (IoT) improves efficiency of smart grid. In this paper, applications of cloud/edge platform in the data acquisition, processing and scheduling of the IoT is introduced step by step, as well as applications and differences of artificial intelligence algorithm in each step, including data acquisition, load disaggregation, load forecasting and so on. Finally, combined with various optimisation methods, future research directions are prospected, including data and network security issues, and challenges faced by cloud/edge architecture, adaptive fine-grained load disaggregation, and load forecasting.

Keywords: internet of things; IoT; smart grids; artificial intelligence; load disaggregation; load forecasting.

DOI: 10.1504/IJAHUC.2023.127781

International Journal of Ad Hoc and Ubiquitous Computing, 2023 Vol.42 No.1, pp.12 - 29

Received: 01 Jun 2022
Received in revised form: 19 Aug 2022
Accepted: 28 Sep 2022
Published online: 16 Dec 2022 *