• Energy Research

Sensor validation plays a key role in automatic control and online monitoring of complex systems, ensuring high performance and guaranteeing safety. Even in cases of safe industrial processes, the unreliability of sensor measurements may lead to a degraded performance and a reduced system yield. It is therefore both economical and essential to validate the credibility of sensor measurements. Timely detection and accurate diagnosis of actual, or even impending, sensor failures enable appropriate remedial actions to be taken, such as rescheduling of maintenance, reconfiguration of corrupted control loops or initialization of emergency shutdown. This paper presents a sensor validation coprocessing element, which is being developed in a FPGA- based processing node. The goal of the processing node is to provide adaptive conditioning monitoring, for a sensor network operating within a wind farm.

The prevalence of the Internet of things (IoT) and smart meters devices in smart grids is providing key support for measuring and analyzing the power consumption patterns. This approach enables end-user to play the role of prosumers in the market and subsequently contributes to diminish the carbon footprint and the burden on utility grids. The coordination of trading surpluses of energy that is generated by house renewable energy resources (RERs) and the supply of shortages by external networks (main grid) is a necessity. This paper proposes a hierarchical architecture to manage energy in multiple smart buildings leveraging federated deep reinforcement learning (FDRL) with dynamic load in a distributed manner. Within the context of the developed FDRL-based framework, each agent that is hosted in local building energy management systems (BEMS) trains a local deep reinforcement learning (DRL) model and shares its experience in the form of model hyperparameters to the federation layer in the energy management system (EMS). Simulation studies are conducted using one EMS and up to twenty smart houses that are equipped with photovoltaic (PV) systems and batteries. This iterative training approach enables the proposed discretized soft actor-critic (SAC) agents to aggregate the collected knowledge to expedite the overall learning procedure and reduce costs and CO2 emissions, while the federation approach can mitigate privacy breaches. The numerical results confirm the performance of the proposed framework under different daytime periods, loads, and temperatures. 7 pages, 6 figures, accepted for publication at IEEE CAMAD 2022

Cloud-based radio access networks (C-RAN) are expected to face important challenges in the forthcoming fifth generation (5G) communication systems. For this reason, more flexible C-RAN architectures have recently been proposed in the literature, where the radio communication stack is partitioned and placed across different RAN nodes to tackle the 5G capacity and latency requirements. In this paper, we show that this functional split also supports energy efficiency, especially when it is combined with bandwidth adaptation. To this aim, we have built a dynamic hotspot prototype, where the hardware-accelerated physical-layer is placed in the remote radio head, and higher software-based layers are placed in a server (either directly connected or remotely accessible). This setup allowed us to experimentally evaluate the power consumption of key hardware modules when adapting the bandwidth and the modulation and coding scheme. The real-time operation of the testbed allows further experimentation with different 5G use cases and the evaluation of other key performance indicators. TRUE pub

The paper introduces an advanced Decentralized Energy Marketplace (DEM) integrating blockchain technology and artificial intelligence to manage energy exchanges among smart homes with energy storage systems. The proposed framework uses Non-Fungible Tokens (NFTs) to represent unique energy profiles in a transparent and secure trading environment. Leveraging Federated Deep Reinforcement Learning (FDRL), the system promotes collaborative and adaptive energy management strategies, maintaining user privacy. A notable innovation is the use of smart contracts, ensuring high efficiency and integrity in energy transactions. Extensive evaluations demonstrate the system's scalability and the effectiveness of the FDRL method in optimizing energy distribution. This research significantly contributes to developing sophisticated decentralized smart grid infrastructures. Our approach broadens potential blockchain and AI applications in sustainable energy systems and addresses incentive alignment and transparency challenges in traditional energy trading mechanisms. The implementation of this paper is publicly accessible at \url{https://github.com/RasoulNik/DEM}. 6 pages