Smart Meters Research Articles

Long-term load forecasting for smart grid

Abstract The load forecasting problem is a complicated non-linear problem connected with the weather, economy, and other complex factors. For electrical power systems, long-term load forecasting provides valuable information for scheduling maintenance, evaluating adequacy, and managing limited energy supplies. A future generating, transmission, and distribution facility's development and planning process begins with long-term demand forecasting. The development of advanced metering infrastructure (AMI) has greatly expanded the amount of real-time data collection on large-scale electricity consumption. The load forecasting techniques have changed significantly as a result of the real-time utilization of this vast amount of smart meter data. This study suggests numerous approaches for long-term load forecasting using smart-metered data from an actual distribution system on the NIT Patna campus. Data pre-processing is the process of converting unprocessed data into a suitable format by eliminating possible errors caused by lost or interrupted communications, the presence of noise or outliers, duplicate or incorrect data, etc. The load forecasting model is trained using historical load data and significant climatic variables discovered through correlation analysis. With a minimum MAPE and RMSE for every testing scenario, the proposed artificial neural network model yields the greatest forecasting performance for the used system data. The efficacy of the proposed technique has been through a comparison of the acquired results with various alternative load forecasting methods.

Analyzing how the timing and magnitude of electricity consumption drive variations in household electricity-associated emissions on a high-VRE grid

Abstract Electrifying the residential sector is critical for national climate change adaptation and mitigation strategies, but increases in electricity demand could drive-up emissions from the power sector. However, the emissions associated with electricity consumption can vary depending on the timing of the demand, especially on grids with high penetrations of variable renewable energy. In this study, we analyze smart meter data from 2019 for over 100 000 homes in Southern California and use hourly average emissions factors from the California Independent System Operator, a high-solar grid, to analyze household CO2 emissions across spatial, temporal, and demographic variables. We calculate two metrics, the annual household electricity-associated emissions (annual-HEE), and the household average emissions factor (HAEF). These metrics help to identify appropriate strategies to reduce electricity-associated emissions (i.e. reducing demand vs leveraging demand-side flexibility) which requires consideration of the magnitude and timing of demand. We also isolate the portion of emissions caused by AC, a flexible load, to illustrate how a load with significant variation between customers results in a large range of emissions outcomes. We then evaluate the distribution of annual-HEE and HAEF across households and census tracts and use a multi-variable regression analysis to identify the characteristics of users and patterns of consumption that cause disproportionate annual-HEE. We find that in 2019 the top 20% of households, ranked by annual-HEE, were responsible for more emissions than the bottom 60%. We also find the most emissions-intense households have an HAEF that is 1.7 times higher than the least emissions-intense households, and that this spread increases for the AC load. In this analysis, we focus on Southern California, a demographically and climatically diverse region, but as smart meter records become more accessible, the methods and frameworks can be applied to other regions and grids to better understand the emissions associated with residential electricity consumption.

Promoting real-time electricity tariffs for more demand response from German households: a review of four policy options

Abstract Background Demand response is an important option for accommodating growing shares of renewable electricity, and therefore, crucial for the success of the energy transition in Germany and elsewhere. In conjunction with smart meters, real-time (or ‘dynamic’) electricity tariffs can facilitate the flexibilization of power consumption and reduce energy bills. Whilst such tariffs are already quite common in several EU member states, Germany lags behind in this respect. The country makes for an interesting case study because of the sheer volume of additional flexibility that its energy transition necessitates. Main text This paper discusses how German policymakers can make real-time tariffs more attractive for households and thus entice them to better adapt their consumption to current market conditions. Following an analysis of the current impediments to the adoption of such tariffs, we discuss four policy options: (1) a more ambitious legal definition of real-time tariffs that can promote market transparency and leverage potential savings for consumers, (2) a shift in energy taxation that encourages the uptake of renewable power and increases price spreads, (3) a new model of dynamic network charges which combines grid-serving and market-serving incentives, and (4) a subsidy for users of real-time tariffs that helps internalise the benefits they provide to all electricity consumers. Given the similar regulatory framework, our suggestions should generally also apply to other countries in Europe and beyond. Conclusions Overall, we argue that there is considerable scope for policymakers to better exploit market forces to ensure security of electricity supply at lower social cost. Our call for stricter regulation in order to allow the markets to better guide consumer behaviour may seem like a paradox—but it is one well worth embracing.

Determine the Profiles of Power Consumption in Commercial Buildings in a Very Hot Humid Climate Using a Temporary Series

With the growth of the nations, the commercial and public services sectors have recently seen an increase in their electricity usage. This demonstrates how crucial it is to understand a building’s behavior in order to lower its usage. This requires on-site data collection by qualified professionals and specialized equipment, which represents high costs. However, multiple studies have demonstrated that it is possible to find electricity-saving strategies from the study of electricity usage, recorded in an hourly period or less, captured by smart meters. In this context, the present study applies a methodology to determine useful information on the operation and characteristics of public buildings on the Ecuadorian coast based on the data gathered over a period of five consecutive months from smart meters. The methodology consists of four steps: (1) data cleaning and filling, (2) time-series decomposition, (3) the generation of consumption profile and (4) the identification of the temperature influence. According to the results, the pre-cooling of spaces accounts for 5% of all electricity used in the commercial buildings, while prolonged shutdown uses 10%. Approximately USD 1100 per month would be spent on the main building and USD 78 on the agency as a result.

Smart Energy Metering for Cost Comparative Analysis in Energy Management

Smart Energy Metering for Cost Comparative Analysis in Energy Management

A study on black screen fault detection of single-phase smart energy meter based on random forest binary classifier

A study on black screen fault detection of single-phase smart energy meter based on random forest binary classifier

A measurement error prediction framework for smart meters in typical regions

A measurement error prediction framework for smart meters in typical regions

Overload Alarm Prediction in Power Distribution Transformers

The growing demand for electricity puts more strain on the grid, requiring automated and proactive strategies such as overload prediction to improve grid maintenance. However, the intermittent nature of power distribution loads makes the prediction more challenging. This paper proposes a novel framework for overload alarm prediction in distribution transformers, aimed at enhancing the reliability and efficiency of grid operations. Leveraging real-world smart meter data and machine learning techniques, the proposed system develops a classification model to predict overloads for distribution transformers. Due to resource constraints, a new strategy is adopted to assess the significance of alarms based on expert observations. Subsequently, a new approach is developed to imitate the experts, leading to an automated decision-making process using random forest. Ultimately, the transfer learning strategy is utilized to predict overload alarms for distribution transformers facing data scarcity in real-world applications. The proposed system demonstrates high accuracy of overload alarm predictions, paving the way for developing more proactive grid maintenance strategies.

Integrating active demand into the distribution system using metaheuristic techniques

AbstractIntegrating non‐conventional renewable energy sources into distribution systems, alongside data science and enabling technological infrastructures, presents significant challenges, particularly in managing active demand. The rapid evolution of the electric energy system and increasing electricity demand highlight the need for reliable tracking and predictive methods to manage Distributed Energy Resources and digital infrastructure. These methods are essential for advancing carbon neutrality, democratizing environmental sustainability, and improving energy efficiency. Effective active demand monitoring requires understanding the transactional system concept, including digital infrastructure and decentralized demand. Although metaheuristic techniques are increasingly important in demand response integration, much research focuses on specific techniques rather than providing a comprehensive view of dynamic transaction integration for active demand. Technological advancements, like smart meters and communication systems, are shifting from basic consumption measurement to active customer participation. This article reviews key concepts in electrical distribution systems, such as active demand, DERs, and transactive systems. It examines prevalent metaheuristic techniques, emphasizing their role in integrating and predicting active demand and DER behaviors. Additionally, the study presents a methodology serving as a roadmap for efficient DER integration and the transition to active demand and transactive electricity systems, addressing gaps in the current literature.

Bi-Directional Charging with V2L Integration for Optimal Energy Management in Electric Vehicles

Electric vehicles (EVs) are becoming increasingly popular as an efficient transportation solution but they also present unique challenges for energy management. Bi-directional charging (BDC) is a solution that allows EVs to not only consume energy from the grid but also supply energy back to the grid. This facilitates vehicle-to-load (V2L) integration, where EVs can act as mobile power sources for homes, buildings, and the grid. V2L enables better energy management by utilizing EVs as a flexible resource to balance grid demand and supply in the proposed system. This is achieved through intelligent coordination between the EVs, charging stations, and the grid, using smart meters and communication networks. Integration of BDC and V2L also enables EVs to provide backup power during grid outages, reduce the need for costly grid infrastructure, and support renewable energy integration. BDC with V2L integration is a promising approach for optimal energy management in EVs and can play a significant role in the future of sustainable transportation and energy systems. The proposed model reached 95.13% charging efficiency, 95.03% energy management, 95.69% power rating, 96.28% voltage support and 87.99% temperature management.

IMPLEMENTATION OF INNOVATIVE INDUSTRY 4.0 TECHNOLOGIES FOR THE RECONSTRUCTION OF THE ENERGY SECTOR OF UKRAINE

Abstract. The article explores the implementation of innovative Industry 4.0 technologies in the energy sector of Ukraine. It investigates key issues in the energy sector, including the reduction of electricity production capacities due to destruction, capture, and damage to property, low levels of innovation activity in energy enterprises, high energy intensity of production, moral and physical aging of equipment, and a low influx of foreign direct investments into Ukraine's energy sphere. The majority of the analyzed period indicates a relatively low level of interest from foreign investors in the examined sector of the domestic economy. The study identifies the Industry 4.0 toolkit applicable for the restoration of damaged and modernization of outdated infrastructure, enhancing the efficiency of Ukraine's energy sector. It is noted that among the tools, special attention should be paid to monitoring and forecasting systems, the Internet of Things (IoT), big data, integrated digital platforms, smart financial technologies, smart meters, remote monitoring systems, and artificial intelligence. The author emphasizes the peculiarities of applying this toolkit to solve the diagnosed problems in the energy sector of Ukraine. It is noted that the use of Industry 4.0 tools, namely the Internet of Things and big data analytics, can significantly improve the monitoring and management of energy processes. The problem of low innovation activity of Ukrainian energy companies can be solved by creating integrated digital platforms and developing partnerships between companies to share innovations. The use of smart financial technologies can make Ukraine's energy sector more attractive to investors. The high level of electricity losses during generation and distribution can be addressed by introducing smart meters and remote monitoring systems.

A secure and efficient data aggregation scheme for cloud–edge collaborative smart meters

Smart meters are part of the Advanced Measurement Infrastructure (AMI) system in the smart grid. It facilitates data transfer between consumers and electricity suppliers (ES). However, the mass deployment of smart meters (SM) brings heavy overhead to grid operation and poses serious privacy threats. To this end, this paper proposes a secure and efficient data aggregation scheme of cloud–edge collaboration smart meters. At first, we standardize the users’ historical electricity load features and use the improved K-Means clustering algorithm to calculate the Euclidean distance between feature vectors to obtain the classification results of users’ load features. On this basis, ES generates relevant parameters to encrypt meter data and protect users’ data privacy based on classification results. The aggregator (Ag) performs the data aggregation, generates the overall signature using the Schnorr aggregation signature method, and sends it to the cloud server (CS). The ES queries the CS to obtain data and parses it to realize the customer billing service. Meanwhile, this paper executes a series of experiments, and the results show that the proposed scheme exhibits significant advantages in privacy protection and system operation efficiency.

Application Research of Power Measurement Data Exchange Based on Uninterruptible Power Supply

With the maturity of the smart meter embedded system integration mode, the power consumption information can be fed back to the management center by the intelligent terminal through the power Internet of Things. The smart terminal adopts the narrowband data communication mode. However, the narrowband channel interval is hindered by interference signals, resulting in serious data packet loss. Therefore, a stable and efficient acquisition scheme is needed to ensure its smooth interaction. Based on the application of smart meter data communication, we analyze the power consumption optimization and carrier transmission optimization of the power Internet of Things and propose a data analysis method based on self-learning and external characteristics, which is used to calculate the offline execution strategy of smart meters when they encounter factors such as power outage or intrusion. A production task-driving rule is designed. The external characteristics of deep learning data of the power Internet of Things are collected. The accuracy of data classification is judged according to the parallel Internet of Things of a convolutional neural network and a long-term and short-term memory deep neural network. When dealing with high-dimensional original data, the data network is graded to reduce the work of feature selection and the difficulty of training. Moreover, we improve the autonomous execution ability of an embedded terminal device in an offline state. We also strengthen the ability of abnormal data detection and judgment learning to meet the requirements of efficient and stable operation of the power grid. Simulation results verify that compared with the current mainstream schemes, the performance of the proposed scheme is improved by more than 17%. Besides, the fault tolerance rate is enhanced by 29%, and the power consumption after optimization is reduced by 26.3% and 42.8%. These effects highlight the role of data processing in embedded systems. It solves the problem of abnormal data and energy loss affecting the accuracy of error estimation of electric energy meters, opens up a new way for data acquisition and processing of large-scale smart meters, and further improves the service level of information management of power systems.

An Echo State Network-Based Light Framework for Online Anomaly Detection: An Approach to Using AI at the Edge

Production efficiency is used to determine the best conditions for manufacturing goods at the lowest possible unit cost. When achieved, production efficiency leads to increased revenues for the manufacturer, enhanced employee safety, and a satisfied customer base. Production efficiency not only measures the amount of resources that are needed for production but also considers the productivity levels and the state of the production lines. In this context, online anomaly detection (AD) is an important tool for maintaining the reliability of the production ecosystem. With advancements in artificial intelligence and the growing significance of identifying and mitigating anomalies across different fields, approaches based on artificial neural networks facilitate the recognition of intricate types of anomalies by taking into account both temporal and contextual attributes. In this paper, a lightweight framework based on the Echo State Network (ESN) model running at the edge is introduced for online AD. Compared to other AD methods, such as Long Short-Term Memory (LSTM), it achieves superior precision, accuracy, and recall metrics while reducing training time, CO2 emissions, and the need for high computational resources. The preliminary evaluation of the proposed solution was conducted using a low-resource computing device at the edge of the real production machine through an Industrial Internet of Things (IIoT) smart meter module. The machine used to test the proposed solution was provided by the Italian company SIFIM Srl, which manufactures filter mats for industrial kitchens. Experimental results demonstrate the feasibility of developing an AD method that achieves high accuracy, with the ESN-based framework reaching 85% compared to 80.88% for the LSTM-based model. Furthermore, this method requires minimal hardware resources, with a training time of 9.5 s compared to 2.100 s for the other model.

Introducing edge intelligence to smart meters via federated split learning

The ubiquitous smart meters are expected to be a central feature of future smart grids because they enable the collection of massive amounts of fine-grained consumption data to support demand-side flexibility. However, current smart meters are not smart enough. They can only perform basic data collection and communication functions and cannot carry out on-device intelligent data analytics due to hardware constraints in terms of memory, computation, and communication capacity. Moreover, privacy concerns have hindered the utilization of data from distributed smart meters. Here, we present an end-edge-cloud federated split learning framework to enable collaborative model training on resource-constrained smart meters with the assistance of edge and cloud servers in a resource-efficient and privacy-enhancing manner. The proposed method is validated on a hardware platform to conduct building and household load forecasting on smart meters that only have 192 KB of static random-access memory (SRAM). We show that the proposed method can reduce the memory footprint by 95.5%, the training time by 94.8%, and the communication burden by 50% under the distributed learning framework and can achieve comparable or superior forecasting accuracy to that of conventional methods trained on high-capacity servers.

A data aggregation protocol based on TFHE

Torus Fully Homomorphic Encryption (TFHE) is a probabilistic cryptosytem over the real torus which allows one to operate directly on encrypted data without first decrypting them. We present an aggregation protocol based on a variant of TFHE for computing the sum of sensitive data, working only with the corresponding ciphertexts. Our scheme is an ideal choice for a system of smart meters – electronic devices for measuring energy consumption – that demands consumers' privacy. In contrast to some other solutions, our proposal does not require any communication among smart meters and it is quantum-safe.

Distortion corrected kernel density estimator on Riemannian manifolds

Manifold learning obtains a low-dimensional representation of an underlying Riemannian manifold supporting high-dimensional data. Kernel density estimates of the low-dimensional embedding with a fixed bandwidth fail to account for the way manifold learning algorithms distort the geometry of the Riemannian manifold. We propose a novel distortion-corrected kernel density estimator (DC-KDE) for any manifold learning embedding, with a bandwidth that depends on the estimated Riemannian metric at each data point. Exploiting the geometric information of the manifold leads to more accurate density estimation, which subsequently could be used for anomaly detection. To compare our proposed estimator with a fixed-bandwidth kernel density estimator, we run two simulations including one with data lying in a 100 dimensional ambient space. We demonstrate that the proposed DC-KDE improves the density estimates as long as the manifold learning embedding is of sufficient quality, and has higher rank correlations with the true manifold density. Further simulation results are provided via a supplementary R shiny app. The proposed method is applied to density estimation in statistical manifolds of electricity usage with the Irish smart meter data.

An Ensemble Learning Method for the Fault Multi-classification of Smart Meters

An Ensemble Learning Method for the Fault Multi-classification of Smart Meters

Investigation of the Transition to Environmental Remote Sensing and Factors Influencing Effective Decision-Making on Soil Preparation and Sowing Timing: A Case Study

The advancement of smart metering technology is progressing steadily and inevitably across various key economic sectors. The utilizatio.n of remote sensors in agriculture presents unique characteristics and specific challenges. In this study, an on-site experiment was carried out on a Slovakian production farm to analyze the transition from traditional measurement methods to smart meters, focusing on timing decisions related to soil preparation and sowing and their relation to scientifically justified dates. Consequently, a clear distinction was observed in terms of the timing decisions made regarding agricultural activities during traditional, combined, and scientifically based approaches in meteorological data readings. This study contrasts these three scenarios and deliberates on the factors that need to be carefully evaluated before incorporating remote sensors into agricultural processes. This study serves as a valuable resource for individuals involved in the adoption of smart metering practices in the Eastern European agricultural sector and promotes an improved understanding of the interactions within smart-sensing, scientific developments, and land management that contribute to the goal of land-system sustainability.

Three-dimensional convolutional neural network for leak detection and localization in smart water distribution systems

Smart meters such as advanced metering infrastructure (AMI) can significantly improve identifying realistic sized leaks in water distribution networks (WDNs). However, to date, detection/localization methods for AMI systems are extremely limited. In this study, to examine the benefits of using AMIs for leak detection within distribution network, a three-dimensional (3D) convolutional neural network (CNN) deep learning (DL) model is proposed that can account for temporally and spatially distributed information of pressures. The 3D CNN is tested for a real WDN in Austin using the realistic sized leaks (e.g., 3 L/s for 150-mm pipes) that are generated from hydraulic simulations. The model's performance is evaluated using detection probability, false alarm rate, and localization pipe distance metrics. In addition, the strength of using DL for leak identification is examined by comparing the CNN results with those from an optimization-based model. The 3D CNN performed better than the optimization model indicating that DL has advantages over conventional tools such as optimization methods. However, its adaptability may limit its use in some cases. Since DL can be significantly impacted by hydraulic simulation model, a way to handle modelling error must be determined. In addition, as network changes occur, retraining is required that may be time consuming and have difficulty with the number of failure conditions.