In this paper, an edge-side model training study is conducted on a resource-limited smart meter. The motivation of grid-edge intelligence and the concept of on-device training are introduced. Then, the technical preparation steps for on-device training are described. A case study on the task of photovoltaic power forecasting is presented, where two representative machine learning models are investigated: a gradient boosting tree model and a recurrent neural network model. To adapt to the resource-limited situation in the smart meter, "mixed"- and "reduced"-precision training schemes are also devised. Experiment results demonstrate the feasibility of economically achieving grid-edge intelligence via the existing advanced metering infrastructures.

--With the increasing integration of smart meters in electrical grids worldwide, detecting energy theft has become a critical and ongoing challenge. Artificial intelligence (AI)-based models have demonstrated strong performance in identifying fraudulent consumption patterns; however, previous works exploring the use of machine learning solutions for this problem demand high computational and energy costs, limiting their practicality - particularly in low-theft scenarios where continuous inference can result in unnecessary energy usage. This paper proposes a lightweight detection unit, or watchdog mechanism, designed to act as a pre-filter that determines when to activate a long short-term memory (LSTM) model. This mechanism reduces the volume of input fed to the LSTM model, limiting it to instances that are more likely to involve energy theft thereby preserving detection accuracy while substantially reducing energy consumption associated with continuous model execution. The proposed system was evaluated through simulations across six scenarios with varying theft severity and number of active thieves. Results indicate a power consumption reduction exceeding 64%, with minimal loss in detection accuracy and consistently high recall. These findings support the feasibility of a more energy-efficient and scalable approach to energy theft detection in smart grids. In contrast to prior work that increases model complexity to achieve marginal accuracy gains, this study emphasizes practical deployment considerations such as inference efficiency and system scalability. The smart grid is an increasingly integral component of modern energy infrastructure across the globe.

Advanced Metering Infrastructure (AMI) data from smart electric and gas meters enables valuable insights for utilities and consumers, but also raises significant privacy concerns. In California, regulatory decisions (CPUC D.11-07-056 and D.11-08-045) mandate strict privacy protections for customer energy usage data, guided by the Fair Information Practice Principles (FIPPs). We comprehensively explore solutions drawn from data anonymization, privacy-preserving machine learning (differential privacy and federated learning), synthetic data generation, and cryptographic techniques (secure multiparty computation, homomorphic encryption). This allows advanced analytics, including machine learning models, statistical and econometric analysis on energy consumption data, to be performed without compromising individual privacy. We evaluate each technique's theoretical foundations, effectiveness, and trade-offs in the context of utility data analytics, and we propose an integrated architecture that combines these methods to meet real-world needs. The proposed hybrid architecture is designed to ensure compliance with California's privacy rules and FIPPs while enabling useful analytics, from forecasting and personalized insights to academic research and econometrics, while strictly protecting individual privacy. Mathematical definitions and derivations are provided where appropriate to demonstrate privacy guarantees and utility implications rigorously. We include comparative evaluations of the techniques, an architecture diagram, and flowcharts to illustrate how they work together in practice. The result is a blueprint for utility data scientists and engineers to implement privacy-by-design in AMI data handling, supporting both data-driven innovation and strict regulatory compliance.

--The widespread integration of new technologies in low-voltage distribution networks on the consumer side creates the need for distribution system operators to perform advanced real-time calculations to estimate network conditions. In recent years, data-driven models based on machine learning and big data analysis have emerged for calculation purposes, leveraging the information available in large datasets obtained from smart meters and other advanced measurement infrastructure. However, existing data-driven algorithms do not take into account the quality of data collected from smart meters. They lack built-in anomaly detection mechanisms and fail to differentiate anomalies based on whether the value or context of anomalous data instances deviates from the norm. This paper focuses on methods for detecting and mitigating the impact of anomalies on the consumption of active and reactive power datasets. It proposes an anomaly detection framework based on the Isolation Forest machine learning algorithm and Fast Fourier Transform filtering that works in both the time and frequency domain and is unaffected by point anomalies or contextual anomalies of the power consumption data. The importance of integrating anomaly detection methods is demonstrated in the analysis important for distribution networks with a high share of smart meters. Index T erms --anomaly detection; machine learning; Isolation forest; Fourier transform; smart meters I.

Abstract: The Industrial Internet of Things (IIoT) has revolutionized industries by enabling automation, real-time data exchange, and smart decision-making. However, its increased connectivity introduces cybersecurity threats, particularly in smart metering networks, which play a crucial role in monitoring and optimizing energy consumption. This paper explores the challenges associated with securing IIoTbased smart metering networks and proposes a Machine Learning (ML)-based Intrusion Detection and Prevention System (IDPS) for safeguarding edge devices. The findings suggest that integrating ML-driven IDPS in IIoT smart metering environments enhances security, efficiency, and resilience against evolving cyber threats. Keywords: IIoT, Smart Metering, Intrusion Detection System (IDS), Intrusion Prevention System (IPS), Machine Learning, Cybersecurity, Anomaly Detection, Edge Computing, Network Security, Smart Grid. 1. Introduction Everything globally, from body sensors to contemporary cloud computing, is included in the Internet of Things (IoT). It creates a sophisticated distributed system by connecting humans, machines, and networks everywhere; it improves the quality of human life by enabling reliable machine-to-machine and machineto-human connections [1]. The integration of conventional Internet of Things (IoT) principles in manufacturing industries and applications is referred to as the Industrial Internet of Things (IIoT) [2].

The majority of modern consumer-level energy is generated by real-time smart metering systems. These frequently contain anomalies, which prevent reliable estimates of the series' evolution. This work introduces a hybrid modeling approach combining statistics and a Convolutional Autoencoder with a dynamic threshold. The threshold is determined based on Mahalanobis distance and moving averages. It has been tested using real-life energy consumption data collected from smart metering systems. The solution includes a real-time, meter-level anomaly detection system that connects to an advanced monitoring system. This makes a substantial contribution by detecting unusual data movements and delivering an early warning. Early detection and subsequent troubleshooting can financially benefit organizations and consumers and prevent disasters from occurring.

Federated Learning (FL) is a distributed learning scheme that enables deep learning to be applied to sensitive data streams and applications in a privacy-preserving manner. This paper focuses on the use of FL for analyzing smart energy meter data with the aim to achieve comparable accuracy to state-of-the-art methods for load forecasting while ensuring the privacy of individual meter data. We show that with a lightweight fully connected deep neural network, we are able to achieve forecasting accuracy comparable to existing schemes, both at each meter source and at the aggregator, by utilising the FL framework. The use of lightweight models further reduces the energy and resource consumption caused by complex deep-learning models, making this approach ideally suited for deployment across resource-constrained smart meter systems. With our proposed lightweight model, we are able to achieve an overall average load forecasting RMSE of 0.17, with the model having a negligible energy overhead of 50 mWh when performing training and inference on an Arduino Uno platform.

This study explores the crucial interplay between aggregators and building occupants in activating flexibility through Demand Response (DR) programs, with a keen focus on achieving robust decarbonization and fortifying the resilience of the energy system amidst the uncertainties presented by Renewable Energy Sources (RES). Firstly, it introduces a methodology of optimizing aggregated flexibility provision strategies in environments with limited data, utilizing Discrete Fourier Transformation (DFT) and clustering techniques to identify building occupant's activity patterns. Secondly, the study assesses the disaggregated flexibility provision of Heating Ventilation and Air Conditioning (HVAC) systems during DR events, employing machine learning and optimization techniques for precise, device-level analysis. The first approach offers a non-intrusive pathway for aggregators to provide flexibility services in environments of a single smart meter for the whole building's consumption, while the second approach carefully considers building occupants' thermal comfort profiles, while maximizing flexibility in case of existence of dedicated smart meters to the HVAC systems. Through the application of data-driven techniques and encompassing case studies from both industrial and residential buildings, this paper not only unveils pivotal opportunities for aggregators in the balancing and emerging flexibility markets but also successfully develops end-to-end practical tools for aggregators. Furthermore, the efficacy of this tool is validated through detailed case studies, substantiating its operational capability and contributing to the evolution of a resilient and efficient energy system.

This paper proposes a cyber-physical architecture for the secured social operation of isolated hybrid microgrids (HMGs). On the physical side of the proposed architecture, an optimal scheduling scheme considering various renewable energy sources (RESs) and fossil fuel-based distributed generation units (DGs) is proposed. Regarding the cyber layer of MGs, a wireless architecture based on low range wide area (LORA) technology is introduced for advanced metering infrastructure (AMI) in smart electricity grids. In the proposed architecture, the LORA data frame is described in detail and designed for the application of smart meters considering DGs and ac-dc converters. Additionally, since the cyber layer of smart grids is highly vulnerable to cyber-attacks, t1his paper proposes a deep-learning-based cyber-attack detection model (CADM) based on bidirectional long short-term memory (BLSTM) and sequential hypothesis testing (SHT) to detect false data injection attacks (FDIA) on the smart meters within AMI. The performance of the proposed energy management architecture is evaluated using the IEEE 33-bus test system. In order to investigate the effect of FDIA on the isolated HMGs and highlight the interactions between the cyber layer and physical layer, an FDIA is launched against the test system. The results showed that a successful attack can highly damage the system and cause widespread load shedding. Also, the performance of the proposed CADM is examined using a real-world dataset. Results prove the effectiveness of the proposed CADM in detecting the attacks using only two samples.

The present study proposes clustering techniques for designing demand response (DR) programs for commercial and residential prosumers. The goal is to alter the consumption behavior of the prosumers within a distributed energy community in Italy. This aggregation aims to: a) minimize the reverse power flow at the primary substation, occuring when generation from solar panels in the local grid exceeds consumption, and b) shift the system wide peak demand, that typically occurs during late afternoon. Regarding the clustering stage, we consider daily prosumer load profiles and divide them across the extracted clusters. Three popular machine learning algorithms are employed, namely k-means, k-medoids and agglomerative clustering. We evaluate the methods using multiple metrics including a novel metric proposed within this study, namely peak performance score (PPS). The k-means algorithm with dynamic time warping distance considering 14 clusters exhibits the highest performance with a PPS of 0.689. Subsequently, we analyze each extracted cluster with respect to load shape, entropy, and load types. These characteristics are used to distinguish the clusters that have the potential to serve the optimization objectives by matching them to proper DR schemes including time of use, critical peak pricing, and real-time pricing. Our results confirm the effectiveness of the proposed clustering algorithm in generating meaningful flexibility clusters, while the derived DR pricing policy encourages consumption during off-peak hours. The developed methodology is robust to the low availability and quality of training datasets and can be used by aggregator companies for segmenting energy communities and developing personalized DR policies.