## 2 Role of AI and IoT in the Hyperconnected Intelligent World
### Energy Theft Detection  
IoET is an emerging archetype that is gaining more attention given the world energy crisis and its relation to climate change. In this context, IoET offers unprecedented leverage over controlling energy consumption (EC) and production, aiming to maximise the energy system efficiency and minimise energy wastage. For instance, IoET is proposed to control energy consumption and reduce energy demand peaks in [7]. The latter is a prime cause for energy wastage as it causes the system to temporarily overproduce outside peak periods. Other IoET applications in the context of smart buildings and smart homes include energy management of smart grids, battery storage systems, electric mobility, and renewable energy sources, as detailed in [8]. An optimal energy management system of smart grids that leverages IoET is proposed in [9]. The system employs private blockchain technology for secure data transactions and accounts for the high uncertainties of renewable energy sources and the charging demands of plug-in hybrid electric vehicles. This work is concerned with energy theft that harms energy providers in terms of revenue loss and system inefficiency. It significantly impacts EC costs and reduces the chances of new powerhouses due to induced fluctuation in demand and supply. The annual revenue loss by energy theft is estimated to be approximately USD 96 billion worldwide ([[https://www.prnewswire.com/news-releases/96-billion-is-lost-every-year-to-electricity-theft-300453411.html](https://www.prnewswire.com/news-releases/96-billion-is-lost-every-year-to-electricity-theft-300453411.html)]([https://www.prnewswire.com/news-releases/96-billion-is-lost-every-year-to-electricity-theft-300453411.html](https://www.prnewswire.com/news-releases/96-billion-is-lost-every-year-to-electricity-theft-300453411.html)), accessed on 28 December 2023) and is expected to increase. Conventional methods for ETD, including physical onsite inspections to detect and analyse the cause of energy losses, are time-consuming, costly, and labour-intensive. The rise of IoET and the spread of smart meters, together with the advances in AI, render access to and mining of EC data possible at the right time for early ETD. Deep learning methods using convolutional neural networks (CNNs) have been proven to successfully extract latent features in time series EC data for accurate representation and classification [10]. In the context of ETD, theft occurrences are less than 10% of EC data, a major challenge to data-driven CNN methods, which tend to replicate and even reinforce the bias in a skewed dataset. In this case, maximising ETD whilst reducing false alarms (i.e., honest EC samples misclassified as theft) are competing goals that necessitate an energy-aware model for accurate representation. The authors in [2] present a state-of-the-art ETD and demonstrate the superiority of the proposed model compared to previous works, where they reduce the revenue loss due to ETD by 30% by improving the ETD rate and reducing false alarms. However, they propose a centralised learning approach in which granular EC data need to be sent to the server at the risk of overloading the communication network and exposing the data to leakage and privacy breaches.