Smart Meters Research Articles

Using smart meter data to analyse low carbon technology loads in Northern Ireland

Using smart meter data to analyse low carbon technology loads in Northern Ireland

Empowering consumers through smart meter technology

Empowering consumers through smart meter technology

Low voltage network balancing to enhance photovoltaic hosting capacity - operational environment experiment with low smart meters coverage

Low voltage network balancing to enhance photovoltaic hosting capacity - operational environment experiment with low smart meters coverage

Interdisciplinary role of statistics in energy consumption prediction within smart grid big data analytics

The study of energy consumption in buildings is crucial due to environmental and financial impacts. Smart metering is suggested to improve energy efficiency, but predicting electrical consumption accurately remains challenging, considering various factors. To tackle this, we propose a Multi-objective Golden Eagle Optimization (MGE) based Hybrid Deep Dynamic Convolutional Fuzzy Network (HDDCFN) method. This approach optimizes algorithm parameters and prediction models using multi-objective optimization, considering historical usage and weather conditions. The research is conducted using Matlab.

Multi-Step Clustering of Smart Meters Time Series: Application to Demand Flexibility Characterization of SME Customers

Multi-Step Clustering of Smart Meters Time Series: Application to Demand Flexibility Characterization of SME Customers

INTEGRATED DATA ACQUISITION PLATFORM FOR EXPLAINABLE CONTROL IN DISTRICT HEATING SYSTEMS

This paper presents a comprehensive data acquisition platform designed for the intelligent management of District Heating Systems (DHS), aiming to optimize energy efficiency, reduce environmental impact, and minimize heat loss. A DHS is a centralized network that transfers thermal energy to multiple buildings via an insulated pipeline infrastructure. Traditional DHS configurations rely on PLCs and SCADA for data collection and heating control, but integrating real-time monitoring and advanced decision-making capabilities can significantly enhance system efficiency. Our Data Acquisition Platform aggregates data from diverse sources, including IoT sensors, weather stations, and smart meters, into a unified database for time-series analysis. The platform supports automated data retrieval through cron job scheduling and integrates with SCADA systems for remote data collection and monitoring. It is integral part of an intelligent DHS control approach named XAI4HEAT. This approach leverages explainable artificial intelligence algorithms and model-based predictive control to dynamically adjust heat supply based on demand and weather forecasts. Key benefits of this approach include improved load balancing, optimized energy distribution, and the potential integration of alternative energy sources.

DYNAMIC GENERATIVE RESIDUAL GRAPH CONVOLUTIONAL NEURAL NETWORKS FOR ELECTRICITY THEFT DETECTION

Illegal electricity users pose a significant threat to the economic and security aspects of the power system by illicitly accessing or manipulating electrical resources. With the widespread adoption of Advanced Metering Infrastructure (AMI), researchers have turned to leveraging smart meter data for electricity theft detection. However, existing models rely on methods that model a single electricity load curve and cannot capture the temporal dependencies, periodicity, and underlying features between electricity consumption cycles. This study introduces a novel electricity theft detection method based on dynamic residual graph networks. Innovatively, it proposes a dynamic topological graph construction technique that allows for the real-time updating of adjacency matrices during the training process, thereby effectively capturing the complex relationships in electricity usage patterns. Utilizing the MixHop graph convolutional network, it delves into the temporal sequence dependencies, periodicity, and hidden characteristics within user electricity consumption data. Additionally, to address the issue of model instability caused by scarce theft data, we employ the SMOTE (Synthetic Minority Over-sampling Technique) oversampling technique and enhance overall classification performance by modifying class weights in the loss function. We trained this network architecture on the real SGCC (State Grid Corporation of China) dataset, and the results demonstrate its superiority over other mainstream existing models.

Experimental Comparison of Encryption Algorithms On Smart Devices

Now, the technological environments rely on a large number of users and devices interconnected in such a way as to be able to share information and exchange resources. For such reasons, security becomes of prime importance inside these networks. Especially, encryption algorithms widely used in smart metering systems form the very backbone of ensuring security. It is unique according to every single parameter: the level of security achieved, speed, operational complexity, length, and type of key used. A comparison of performance and throughput for the most used encryption algorithms, such as AES-128, AES-192, RC4, Blowfish, and ECDSA, is presented here. Such devices, for instance, smart meters, usually represent very resource-constrained computational capability, memory, and data transfer. For these reasons, the experiments investigate performance impacts of using different encryption algorithms on a smart meter environment in a scaled setup. Experiments were run on a laptop device and then downscaled according to the limitation characteristics of the smart meter target device. As for the scaling, it has been performed concerning key factors: CPU of the smart meter, RAM, and cache memory. Execution times of the encryption and decryption processes were measured, as well as the throughput of messages for various file sizes. Quantitative key results obtained included: RC4 with a throughput of 25.37 Kbytes/sec, whereas AES-128 has 4.87 Kbytes/sec, while in ECDSA-256 the performance is much lower: its verification throughput amounts to 0.0023 Kbytes/sec. The results showed significant variations in performance, speed, and throughput between these algorithms. Even considering small smart devices, the encryption and decryption processes yielded efficient throughputs. These findings underscore the importance of choosing the right encryption algorithm for smart meters, balancing both security requirements and resource limitations to ensure optimal performance.

Advanced integration of IoT and AI algorithms for comprehensive smart meter data analysis in smart grids

Abstract Smart grids utilize advanced communication, information, and control technologies to build a more friendly power supply system. However, the development of technology leads to the increasing complexity of smart meter (SM) data in the power grid, and analyzing SM data becomes difficult. Therefore, in order to realize the effective analysis and forecasting of users’ electricity consumption behavior, an SM data analysis technique integrating the Internet of Things and artificial intelligence is proposed, which contains three major techniques: meter reading systems, load clustering, and load forecasting, and at last the technique is validated. Finally, the technology is validated. The experimental results showed that the average value of the Davis-Balding index for the proposed method combining recursive graphs and convolutional autoencoders was 1.87, which was significantly lower than the average value of 2.35 for other methods. The average value of the Dunn index was 0.086, which was higher than the 0.065 for the comparison method. In the load forecasting model, the model that employs multi-task learning and a gated cycle unit demonstrates superior performance across all comparison schemes, with the lowest average absolute percentage error of 12.90%. This is significantly lower than the comparison model’s 15.35%, which highlights the enhanced efficacy of the proposed model. The study realizes the effective analysis and prediction of consumers’ electricity consumption behavior and provides new ideas and solutions for the optimization and management of smart grids.

Sustainability or instant gratification? A study on Chinese perspectives of ‘pay-as-you-use’ smart energy meters in budget hotels

In response to the growing demand for sustainable practices in the hospitality industry, many hotels have concentrated on operational measures to reduce energy consumption. However, the potential for promoting voluntary customer-initiated energy-saving behaviors has been largely overlooked. This study fills that gap by investigating how environmental values (egoistic, altruistic, and biospheric) among Chinese consumers shape their attitudes, personal norms, and behavioral intentions to book budget hotels equipped with ‘pay-as-you-use’ smart energy meters (SMEMs). Unlike previous research, which primarily focuses on consumers’ willingness to pay a premium for green hotels, this study emphasizes cost-saving, pro-environmental behaviors—particularly relevant for cost-conscious travelers. The central challenge lies in understanding how financial incentives and real-time energy usage feedback can effectively drive sustainable behavior in transient hotel settings. The study also examines the moderating role of instant gratification in these relationships. This study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS and multiple-group analysis (MGA) on data collected from 929 Chinese participants. The findings reveal that environmental values significantly influence attitudes, personal norms, and intentions to book hotels with SMEMs. Moreover, the relationship between environmental values and behavioral intentions is moderated by the level of instant gratification, underscoring the importance of immediate rewards in promoting sustainable consumer behavior. This study contributes to the literature by extending the Values-Attitude-Behavior theory and also provides practical insights for hotel managers seeking to align sustainability goals with consumers’ preferences for cost savings and instant gratification.

Resource Optimization for Grid-Connected Smart Green Townhouses Using Deep Hybrid Machine Learning

This paper examines Connected Smart Green Townhouses (CSGTs) as a modern residential building model in Burnaby, British Columbia (BC). This model incorporates a wide range of sustainable materials and smart components such as recycled insulation, Photovoltaic (PV) solar panels, smart meters, and high-efficiency systems. The CSGTs operate in grid-connected mode to balance on-site renewables with grid resources to improve efficiency, cost-effectiveness, and sustainability. Real datasets are used to optimize resource consumption, including electricity, gas, and water. Renewable Energy Sources (RESs), such as PV systems, are integrated with smart grid technology. This creates an effective framework for managing energy consumption. The accuracy, efficiency, emissions, and cost are metrics used to evaluate CSGT performance. CSGTs with one to four bedrooms are investigated considering water systems and party walls. A deep Machine Learning (ML) model combining Long Short-Term Memory (LSTM) and a Convolutional Neural Network (CNN) is proposed to improve the performance. In particular, the Mean Absolute Percentage Error (MAPE) is below 5%, the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are within acceptable levels, and R2 is consistently above 0.85. The proposed model outperforms other models such as Linear Regression (LR), CNN, LSTM, Random Forest (RF), and Gradient Boosting (GB) for all bedroom configurations.

Topology identification and parameters estimation of LV distribution networks using open GIS data

The topology of low-voltage distribution networks (LVDNs) is crucial for system analysis, e.g., distributed energy resources (DERs) integration, network hosting capacity analysis, state estimation, and electric vehicle charging management. However, it is frequently unavailable or incomplete. This paper develops a data-driven topology identification approach for LVDNs with a high proportion of underground cables. The proposed approach exploits the fact that underground cables usually follow the street pattern, thus relying on open street map (OSM) and smart meter (SM) data. Three stages compose the proposed approach: In the first stage, a hierarchical minimum spanning tree algorithm is proposed to generate the initial topology with an accurate number of sub-branches from the pre-processed OSM data and peak demand. In the second stage, based on the limited SM data, the location of breakpoints in mesh topology caused by circle roads is verified and reconstructed to guarantee the radial structure of LVDNs. Finally, given multiple incomplete SM datasets, three data-driven optimization models based on a state estimation model are constructed to mitigate the error of cable length induced by OSM data. The feasibility of the proposed topology identification approach is verified on three actual LVDNs in The Netherlands and multiple incomplete SM datasets. Furthermore, the minimal amount of SM data needed to minimize the error of cable length is analyzed.

Evaluation of water consumption at district scale by analyzing smart water meter data

Abstract In urbanization, understanding of water consumption is necessary for water supply infrastructure development. We have been studying water consumption at Iride-Chibata District in the development of water supply management in Kosai City, Shizuoka Prefecture, Japan. Here the water consumption has been managed by reading water meter at each household every two months. Meanwhile, with the provision of smart metering for electricity, the district has installed the smart meters, which can hourly monitor water consumption, at all of 1,871 households. This study analysed the smart water meter data of all households to evaluate the water consumption at district scale. The average daily water consumption per household was higher on weekends than on weekdays. The hourly consumption showed two peaks at the hours of 7:00 – 11:00, and 18:00 – 22:00, respectively corresponding to morning and evening activities in a household. Either morning or evening, the peaks of weekdays occurred one hour earlier than those of weekends. The evening peaks were higher than the morning ones, that might come from Japanese evening bathing culture. These findings may help for timeframe water supply planning.

A biometric and PUF-based authentication with Preserving anonymity in smart grid environment

The intelligent grid environment is an electricity transmission and distribution system utilizing information and communication technology with two main components: the Smart Meter (SM) and the Service Provider (SP). Unfortunately, wireless mode communication allows the attacker to get data from the public channels and then alter, delete the data, and perform a severe attack such as DoS, impersonation, and tracking attack. In addition, the smart meter is vulnerable to cloning attacks. This research proposes a new authentication protocol utilizing Biometrics and Physical Unclonable Function (PUF). Intuitive analysis DY model ensures our protocol successfully attains security properties and withstands well-known attacks; formal analysis using mathematical and programming models such as Mao-and-Boyd Logic, Real-or-Random Model, and Scyther tools prove that our protocol also satisfies security features and resists well-known attacks. Subsequently, computational complexity comparisons are carried out to prove that our proposal has a low computational burden.

Analysis of Qatar's electricity landscape: Insights from load profiling, clustering, and policy recommendations

Demand side management (DSM) is an important strategy for promoting sustainable consumption in resource-rich countries with high purchasing power and subsidized tariffs. Global energy and environmental resource consumption have increased rapidly due to advances in production and transportation, leading to inefficient and wasteful use of resources. DSM aims to address these issues by promoting efficiency. To implement appropriate DSM strategies, a greater understanding of consumer behavior is needed. To do so, load profiling and load clustering are two popular methods that can be used. This paper aims to i) summarize the most recent global load profiling and clustering works ii) use official smart meter data to understand key electricity consumption trends in Qatar, such as temperature-demand correlation, weekend vs. weekday, and public holiday consumption patterns, and iii) perform load clustering to propose policies that would help manage the electricity load in Qatar for its green growth. This study provides insights into the electricity consumption trends of various sectors in Qatar, including commercial, government, hospitality, and residential sectors. It was found that among all the sectors there were only two usage periods of the same time. There is naturally a strong correlation between temperature and electricity consumption throughout the sectors. Furthermore, it was observed that the consumption of the sectors is highly similar which leads to multiple sectors being present in the same cluster. Finally, policy changes are proposed based on the results to encourage demand response programs in Qatar.

Identification of Load Profiles for Rural and Urban Consumers in Bihor County, Romania

Abstract The purpose of this study is to identify the load profile of the residential rural and urban residential consumers in Bihor County. This profiling will be a first step in identifying the appropriate load profile forecasting method for the specific consumer profile. Also, this profiling could help develop policies on increasing consumer energy awareness, or adapting the implementation of smart meters in rural or urban areas and applying some specific hourly prices. This load profiling will be assessed versus the EU profiles and conclusions will be drawn on this comparison. This study was developed using a multi annual database including consumers from a few cities and tens of villages in Bihor County. Each database was filtered for noise and absurd values. The nature of power consumption is well known for its temporal variability and this paper will attempt to highlight this essential aspect of load profiling.

Implementing OM-BR digital certificates in smart meters in Brazil

Implementing OM-BR digital certificates in smart meters in Brazil

Smart metering and remote monitoring for biogas production and management: Small scale biogas plants as a case for the study.

Abstract Biogas potential assessments indicate that low-income countries have biogas resource potential to substantially contribute to the clean cooking transitioning and also reduce carbon emissions linked to the use of solid biomass as cooking fuels. However, the estimated biogas potentials have not been harnessed, and its contribution to the clean cooking targets remains off-targets. Literature often focuses on barriers to the technology adoption and diffusion. There is lack of mechanisms to monitor the gas production and management, specifically at small scale use. This study combines smart biogas metering and remote monitoring of biodigester conditions to ascertain user-end dynamics for a family-sized biodigesters use. Daily biodigesters’ health is remotely monitored using IoT device. Biogas production, usage, leakage, and venting are used to study the role of technology biogas production and management and its contribution to clean cooking pathways, as well as Green House Gas (GHG) emissions mitigations. Findings indicate that the success of the technology is not only hindered by barriers reported in literature. Rather, user practices affect the biogas production and management. Results emphasise the need for developing mechanisms to enhance energy demand side management, irrespective of the primary resource, resource-to-energy conversion technology, and scale of use.

Slashing the surplus – how prosumers with smart metering respond to regulatory restrictions on self-consumption in Croatia

With the diffusion of prosumerism, where households act both as producers and consumers of energy, policy makers must strike a balance between encouraging microgeneration and regulating this new prosumer segment on the energy market. However, effective policy implementation depends on prosumers’ behavioural reactions. This paper provides evidence on the interplay between digital real-time information and regulation of self-consumption for rooftop photovoltaics (PV) in Croatia. Croatian households that produce more annual electricity than they consume are automatically re-classified as renewable traders, which means additional administrative duties and less favorable tax treatment. This creates perverse incentives to reduce PV generation or increase energy consumption by year-end. We document the behavioural reactions to this policy design, indicating that energy production and consumption are highly elastic regarding regulatory incentives, but only if these incentives are made transparent and accessible with timely information. We collected two survey waves (n = 54 and n = 80) and smart meter data (n = 39), which illustrate the behavioural reaction before and after year-end. According to the survey wave before year-end, almost half of the participants considered curtailing their PV output. According to the smart meter data, a sizable share did indeed take action by shutting down PV production or by powering additional devices to reduce the surplus near year-end. In a second survey wave in the new year, prosumers provide ex-post insights on the specific measures taken to reduce surplus. We discuss research insights regarding the transparency and control offered by metering feedback, and how this can influence household behaviour within regulatory frameworks.

Fog Cloud Computing Based on Lightweight Scheme for Secured Smart Grids

The integration of fog and cloud computing in smart grids presents a promising approach to enhance the security and efficiency of energy management systems. This paper proposes a lightweight cryptographic scheme tailored for fogcloud architectures to secure smart grid communications. The scheme leverages the distributed nature of fog computing to reduce latency while maintaining robust security measures against cyber threats. By incorporating quantum-resistant cryptographic techniques, the proposed solution addresses the unique challenges of data confidentiality, integrity and privacy in smart grids. Our approach is designed to be resource-efficient, making it suitable for deployment in environments with limited computational power, such as smart meters and Internet of Things (IoT) devices. Comprehensive performance evaluations demonstrate that the proposed scheme not only enhances security but also improves the overall efficiency and reliability of smart grid operations. The findings suggest that this lightweight scheme is a viable solution for securing next-generation smart grids, providing a balance between security and operational demands. Performance evaluation demonstrates that the scheme provides secure and efficient authentication while minimising computational and communication overheads.