Energy Meter Data Research Articles

A data-driven ensemble technique for the detection of false data injection attacks in the smart grid framework

The major component of the smart grid (SG) is the advanced metering infrastructure (AMI), which monitors and controls the existing power system and provides interactive services for invoicing and electricity usage management with the utility. Including a cyber-layer in the metering system allows two-way communication but creates a new opportunity for energy theft, resulting in significant monetary loss. This article proposes an approach to detecting abnormal consumption patterns using energy metering data based on the ensemble technique AdaBoost, a boosting algorithm. Different statistical and descriptive features are retrieved from metering data samples, which account for extreme conditions. The model is trained for malicious and non-malicious data for five different attack scenarios, which are analyzed on the Irish Social Science Data Archive (ISSDA) smart meter dataset. In contrast to prior supervised techniques, it works well even with unbalanced data. The efficacy of the proposed theft detection method has been evaluated by comparing the accuracy, precision, recall, and F1 score with the other well-known approaches in the literature.

Research on the Electric Energy Metering Data Sharing Method in Smart Grid Based on Blockchain

Research on the Electric Energy Metering Data Sharing Method in Smart Grid Based on Blockchain

Energy Metering Data Estimation and Validation in Railways

The opening up of the railway to competition in Europe has been accompanied by many technical challenges never before seen in the railway field. One of these challenges concerns precise energy metering. As European railways have been a monopoly until now, the current systems can only measure the energy consumption of a single operator. In accordance with European law, the installation of an energy meter has been mandatory in new or refurbished rolling stocks since 2014. Nevertheless, these energy meters are still not fully reliable, with misreadings and missing readings being common occurrences. To address the problem of energy metering data reliability, this paper presents a method for validating the reading measurement based on a physical model and validates it in two different case studies. For the issue of energy metering data availability, different estimators are developed, and their performance are shown with the aim of calculating the missing consumptions for both case studies. Finally, the paper suggests how these methods may be implemented in other types of trains.

Recovery Algorithm of Power Metering Data Based on Collaborative Fitting

Electric energy metering plays a crucial role in ensuring fair and equitable transactions between grid companies and power users. With the implementation of the State Grid Corporation’s energy Internet strategy, higher requirements have been put forward for power grid companies to reduce costs and increase efficiency and user service capabilities. Meanwhile, the accuracy and real-time requirements for electric energy measurements have also increased. Electricity information collection systems are mainly used to collect the user-side energy metering data for the power users. Attributed to communication errors, communication delays, equipment failures and other reasons, some of the collected data is missed or confused, which seriously affects the refined management and service quality of power grid companies. How to deal with such data has been one of the important issues in the fields of machine learning and data mining. This paper proposes a collaborative fitting algorithm to solve the problem of missing collected data based on latent semantics. Firstly, a tree structure of user history data is established, and then the user groups adjacent to the user with missing data are obtained from this. Finally, the missing data are recovered using the alternating least-squares matrix factorization algorithm. Through numerical verification, this method has high reliability and accuracy in recoverying the missing data.

A Method of Integrated Energy Metering Simulation Data Generation Algorithm Based on Variational Autoencoder WGAN

With the development of integrated energy systems, the penetration rate of all types of distributed power sources continues to increase. In a grid oriented to integrated energy metering, the metering business of different energy sources needs to fully consider the dynamic characteristics of different user loads. This paper aims at the generation of large-scale integrated energy metering data in large-scale integrated energy digital simulation technology, starting from supporting the construction of an integrated energy metering simulation system, combining various business applications and artificial intelligence technology under the background of the energy Internet. For the requirements of energy metering simulation, a method of integrated energy metering simulation data generation based on variational autoencoder WGAN is proposed, and the effect of the method proposed in this paper is verified through actual metering scene data.

Research on Enterprise Energy Measurement Based on Sustainable Development

In the energy metering of enterprises, the purpose of energy saving and consumption reduction is achieved through the establishment of the information management system of energy metering instruments, the application of advanced metering and testing technology and the application of energy metering data.

An Energy Metering Chip With a Flexible Computing Engine

This paper presents an energy metering chip with a flexible computing engine. The flexible computing engine includes the metering program and a dedicated DSP. Besides providing the generally required energy metering data, the chip can also provide different energy metering and measurement data through the control registers accessible by a host MCU via the UART or SPI interface. The flexible computing engine adjusts the computing tasks of the DSP according to applications, reducing the amount of redundant computing and, therefore, reducing the power consumption. The chip can be used in general energy metering, fundamental energy metering, direct current (DC) energy metering, and various measurement and monitoring of the electricity signals. The energy metering chip is fabricated in a 0.11- $\mu$ m CMOS process. The chip meets the metering accuracy requirements by the international standard IEC-62053-21/22/23/24. The grid monitoring of the chip meets the international standard IEC-61000-4-30 Class S. The total power consumption when performing the energy metering is only 6.6 mW. The chip is currently in production.

Longitudinal analysis of energy metering data from non-domestic buildings

To identify, understand and reduce energy wastage in buildings, a significant indicator is time patterns of consumption linked to building occupancy. Advanced metering can log energy data at short half-hourly intervals or less. However, analysis of these data may still often follow traditional monitoring and targeting techniques developed previously for daily or weekly energy data. To explore the potential for advanced metering more fully, and to understand the energy consumption patterns and energy wastage in non-domestic buildings, a longitudinal study was made of energy data collected from approximately 300 buildings in Leicester, UK, between 2001 and 2008. This was the first such study of its kind. Evidence was gathered from gas, electricity and water meters, with water consumption being used as a proxy for building occupancy. Algorithms for cleaning the data are described. Four principal building failure modes for gas space heating were identified that cause excessive and wasteful energy consumption. In 2004, 34% of buildings were heated during unoccupied periods at weekends, although this reduced to 22% by 2008. Longitudinal analysis of night-time electricity baseloads showed an average annual rate of increase of around 8%, although wide variations exist between buildings. The method of identifying building control phenomena may be applied to any metered energy data set with a comparable sampling period. Les schémas temporels de consommation liés à l'occupation des bâtiments constituent un indicateur important pour identifier, comprendre et réduire le gaspillage énergétique dans les bâtiments. Les compteurs évolués sont capables d'enregistrer des données énergétiques à de brefs intervalles d'une demi-heure ou moins. Néanmoins, il est encore souvent possible que l'analyse de ces données suive des techniques traditionnelles de contrôle et de ciblage précédemment développées pour des données énergétiques quotidiennes ou hebdomadaires. Afin d'examiner plus complètement les possibilités offertes par les compteurs évolués et de comprendre les schémas de consommation d'énergie et le gaspillage énergétique dans les bâtiments non résidentiels, il a été réalisé une étude longitudinale des données énergétiques recueillies dans environ 300 bâtiments de Leicester, au Royaume-Uni, entre 2001 et 2008. Il s'agissait de la première étude de ce genre. Des données probantes provenant des compteurs de gaz, d'électricité et d'eau ont été rassemblées, la consommation d'eau étant utilisée comme une preuve indirecte d'occupation des bâtiments. Des algorithmes de nettoyage des données sont décrits. Concernant le chauffage au gaz des espaces, il a été identifié quatre modes principaux de défaillance dans les bâtiments, qui entraînent une consommation d'énergie excessive et sont source de gaspillage. En 2004, 34% des bâtiments étaient chauffés lors des périodes d'inoccupation les week-ends, cette proportion étant néanmoins ramenée à 22% en 2008. Une analyse longitudinale des charges de base électriques nocturnes a indiqué un taux annuel moyen d'augmentation d'environ 8%, bien que de grandes variations existent entre les bâtiments. Cette méthode d'identification des phénomènes de contrôle des bâtiments peut être appliquée à tout ensemble de données énergétiques mesurées par compteur couvrant une période d'échantillonnage comparable. Mots clés: compteurs évolués, performances des bâtiments, services au bâtiment, charge de base électrique, consommation d'énergie, gaspillage énergétique, gestion globale des bâtiments, chauffage des espaces, consommation d'eau

New approaches to gathering, analysis and interpretation of half hourly energy metering data from buildings

Brown, a Senior Research Fellow, under the supervision of Wright, is lead author. This work helped underpin the recently successful 4M (EPSRC) funding bid, regarding building monitoring.