Nowadays, in contemporary building and energy management systems (BEMSs), the predominant approach involves rule-based methodologies, typically employing supervised or unsupervised learning, to deliver energy-saving recommendations to building occupants. However, these BEMSs often suffer from a critical limitation—they are primarily trained on building energy data alone, disregarding crucial elements such as occupant comfort and preferences. This inherent lack of adaptability to occupants significantly hampers the effectiveness of energy-saving solutions. Moreover, the prevalent cloud-based nature of these systems introduces elevated cybersecurity risks and substantial data transmission overheads. In response to these challenges, this article introduces a cutting-edge edge computing architecture grounded in virtual organizations, federated learning, and deep reinforcement learning algorithms, tailored to optimize energy consumption within buildings/homes and facilitate demand response. By integrating energy efficiency measures within virtual organizations, which dynamically learn from real-time inhabitant data while prioritizing comfort, our approach effectively optimizes inhabitant consumption patterns, ushering in a new era of energy efficiency in the built environment.

Noise modeling in power line communications has recently drawn the attention of researchers. However, when characterizing the noise process in narrowband communications, previous works have only focused on small-scale phenomena involving fine-grained details. Nevertheless, the communication link's reliability is also affected by long-term noise phenomena that might affect transfer rates at higher layers as well. This paper addresses the problem of long-term noise characterization for narrowband power line communications and provides a statistical analysis of the long-term trends affecting the noise levels. We present a statistical description of the noise process in the time and frequency domains based on real field measurements in the FCC band (10 kHz–490 kHz). The collected data comprises more than 1.8 billion samples taken from three different locations over a time period of approximately ten days. The noise samples have been statistically analyzed by considering stationarity, autocorrelation, and independence. Although our results—being unprecedented—are interesting per se, they improve the noise pattern knowledge, thus paving the way for the design and implementation of more robust PLC protocols.

Thermal monitoring during charging increases the safety and efficiency of Electric Vehicles (EVs) batteries. Up to now, no thermal sensing solutions are able to perform temperature sensing for each battery cell in the EV due to the cost, deployment complexity, and/or safety reasons. In this paper, a complete wireless thermal monitoring system for EV battery charging surveillance is presented. The designed sensing device is wireless, fully passive, with small feature size, and low-cost thus can be deployed on a large scale for cell-level battery temperature sensing. The maximum reading distance of the device is >5 m in the ambient, with a sensing precision of ±1.5 °C (3σ) from −20 to 80 °C. The whole system is validated in-house using a 18650-36V-4.4A Li-ion battery cell to track its temperature change when being charged at different C-rates.