Energy Analytics - Opportunities for Energy Monitoring and Prediction with smart Meters

By 2019, Norway will complete the national rollout of advanced metering systems (AMS) for all customers. Beyond near-time monitoring of voltage quality and frictionless billing of customers, such a rollout opens a host of possibilities. However, a full-scale rollout is not without challenges. For instance, throughput limitations of radio-mesh networks, privacy considerations, and bounds on compute and Storage infrastructure limit the cardinality of metering data to levels below that of which established techniques(for example non-intrusive load disaggregation) require. Pilot projects are now exploring how to mitigate these challenges as well as seeking novel opportunities that open up through data fusion and recent advances in machine learning. In this contribution, we outline the capabilities of the Norwegian AMS system and describe established use-cases and non-intrusive load monitoring. We then discuss a pilot on detection of electric vehicles. Based on preliminary findings, we map the path forward.publishedVersio

Energy Analytics - Opportunities for Energy Monitoring and Prediction with smart Meters

By 2019, Norway will complete the national rollout of advanced metering systems (AMS) for all customers. Beyond near-time monitoring of voltage quality and frictionless billing of customers, such a rollout opens a host of possibilities. However, a full-scale rollout is not without challenges. For instance, throughput limitations of radio-mesh networks, privacy considerations, and bounds on compute and Storage infrastructure limit the cardinality of metering data to levels below that of which established techniques(for example non-intrusive load disaggregation) require. Pilot projects are now exploring how to mitigate these challenges as well as seeking novel opportunities that open up through data fusion and recent advances in machine learning. In this contribution, we outline the capabilities of the Norwegian AMS system and describe established use-cases and non-intrusive load monitoring. We then discuss a pilot on detection of electric vehicles. Based on preliminary findings, we map the path forward.publishedVersio

Charging load pattern extraction for residential electric vehicles: a training-free nonintrusive method

Extracting the charging load pattern of residential electric vehicle (REV) will help grid operators make informed decisions in terms of scheduling and demand-side response management. Due to the multistate and high-frequency characteristics of integrated residential appliances from the residential perspective, it is difficult to achieve accurate extraction of the charging load pattern. To deal with that, this article presents a novel charging load extraction method based on residential smart meter data to noninvasively extract REV charging load pattern. The proposed algorithm harnesses the low-frequency characteristics of the charging load pattern and applies a two-stage decomposition technique to extract the characteristics of the charging load. The two-stage decomposition technique mainly includes: the trend component of the charging load being decomposed by seasonal and trend decomposition using loess method, and the low-frequency approximate component being decomposed by discrete wavelet technology. Furthermore, based on the extracted characteristics, event monitoring, and dynamic time warping is applied to estimate the closest charging interval and amplitude. The key features of the proposed algorithm include 1) significant improvement in extraction accuracy; 2) strong noise immunity; 3) online implementation of extraction. Experiments based on ground truth data validate the superiority of the proposed method compared to the existing ones

Energy Data Analytics for Smart Meter Data

The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal

Low-frequency non-intrusive load monitoring of electric vehicles in houses with solar generation : generalisability and transferability

Electrification of transportation is gaining traction as a viable alternative to vehicles that use fossil-fuelled internal combustion engines, which are responsible for a major part of carbon dioxide emissions. This global turn towards electrification of transportation is leading to an exponential energy and power demand, especially during late-afternoon and early-evening hours, that can lead to great challenges that electricity grids need to face. Therefore, accurate estimation of Electric Vehicle (EV) charging loads and time of use is of utmost importance for different participants in the electricity markets. In this paper, a scalable methodology for detecting, from smart meter data, household EV charging events and their load consumption with robust evaluation, is proposed. This is achieved via a classifier based on Random Decision Forests (RF) with load reconstruction via novel post-processing and a regression approach based on sequence-to-subsequence Deep Neural Network (DNN) with conditional Generative Adversarial Network (GAN). Emphasis is placed on the generalisability of the approaches over similar houses and cross-domain transferability to different geographical regions and different EV charging profiles, as this is a requirement of any real-case scenario. Lastly, the effectiveness of different performance and generalisation loss metrics is discussed. Both the RF classifier with load reconstruction and the DNN, based on the sequence-to-subsequence model, can accurately estimate the energy consumption of EV charging events in unseen houses at scale solely from household aggregate smart meter measurements at 1–15 min resolutions

Situation Awareness for Smart Distribution Systems

In recent years, the global climate has become variable due to intensification of the greenhouse effect, and natural disasters are frequently occurring, which poses challenges to the situation awareness of intelligent distribution networks. Aside from the continuous grid connection of distributed generation, energy storage and new energy generation not only reduces the power supply pressure of distribution network to a certain extent but also brings new consumption pressure and load impact. Situation awareness is a technology based on the overall dynamic insight of environment and covering perception, understanding, and prediction. Such means have been widely used in security, intelligence, justice, intelligent transportation, and other fields and gradually become the research direction of digitization and informatization in the future. We hope this Special Issue represents a useful contribution. We present 10 interesting papers that cover a wide range of topics all focused on problems and solutions related to situation awareness for smart distribution systems. We sincerely hope the papers included in this Special Issue will inspire more researchers to further develop situation awareness for smart distribution systems. We strongly believe that there is a need for more work to be carried out, and we hope this issue provides a useful open-access platform for the dissemination of new ideas