Search

With the emergence of cyber-physical systems (CPSs) in utility systems like electricity, water, and gas networks, data collection has become more prevalent. While data collection in these systems has numerous advantages, it also raises concerns about privacy as it can potentially reveal sensitive information about users. To address this issue, we propose a Bayesian approach to control the adversarial inference and mitigate the physical-layer privacy problem in CPSs. Specifically, we develop a control strategy for the worst-case scenario where an adversary has perfect knowledge of the user’s control strategy. For finite state-space problems, we derive the fixed-point Bellman’s equation for an optimal stationary strategy and discuss a few practical approaches to solve it using optimization-based control design. Addressing the computational complexity, we propose a reinforcement learning approach based on the Actor-Critic architecture. To also support smart meter privacy research, we present a publicly accessible “Co-LivEn” dataset with comprehensive electrical measurements of appliances in a co-living household. Using this dataset, we benchmark the proposed reinforcement learning approach. The results demonstrate its effectiveness in reducing privacy leakage. Our work provides valuable insights and practical solutions for managing adversarial inference in cyber-physical systems, with a particular focus on enhancing privacy in smart meter applications., QC 20240226

Progress in the field of Non-Intrusive Load Monitoring (NILM) has been attributed to the rise in the application of artificial intelligence. Nevertheless, the ability of energy disaggregation algorithms to disaggregate different appliance signatures from aggregated smart grid data poses some privacy issues. This paper introduces a new notion of disclosure risk termed energy disaggregation risk. The performance of Sequence-to-Sequence (Seq2Seq) NILM deep learning algorithm along with three activation extraction methods are studied using two publicly available datasets. To understand the extent of disclosure, we study three inference attacks on aggregated data. The results show that Variance Sensitive Thresholding (VST) event detection method outperformed the other two methods in revealing households' lifestyles based on the signature of the appliances. To reduce energy disaggregation risk, we investigate the performance of two privacy-preserving mechanisms based on microaggregation and Discrete Fourier Transform (DFT). Empirically, for the first scenario of inference attack on UK-DALE, VST produces disaggregation risks of 99%, 100%, 89% and 99% for fridge, dish washer, microwave, and kettle respectively. For washing machine, Activation Time Extraction (ATE) method produces a disaggregation risk of 87%. We obtain similar results for other inference attack scenarios and the risk reduces using the two privacy-protection mechanisms.

Monitoring water flow helps to identify leaks and wastage, leading to better management of water resources and conservation of this precious resource. To address this challenge, there is a need for an efficient and sustainable water management system. This paper presents an Internet of Things (IoT) based solution that involves retrofitting existing analog water meters using readily available off-the-shelf electronic components. Real-time data collection and analysis are performed through edge computation, which locally processes water meter images captured by the camera and extracts water meter readings. These readings are transmitted to the cloud for storage and further analysis. Various strategies have been implemented to optimize supply-current usage, preserving charge-discharge cycles of solar-powered batteries even in adverse environmental conditions. To streamline the firmware update process for multiple connected devices, a broadcasting technique is employed, offering the benefits of reduced manual labor and time savings. To assess the reliability and performance of developed solution, field deployment is conducted over several months, enabling the characterization of water usage patterns across different locations. Integrating energy harvesting capabilities into system reduces maintenance costs and promotes eco-friendly energy practices. Overall, this solution offers an effective and comprehensive approach towards achieving efficient and sustainable water management., Validerad;2024;Nivå 2;2024-07-02 (joosat);Full text license: CC BY-NC-ND 4.0; Funder: Technology Innovation Hub- Information Technogy (TIH-IT) CHANAKYA Fellowship Program; TIH Foundation for Internet of Things (loT) and Internet of Everything (loE) (Grant Number: TIH loT/2023-03/HRD/CHANAKYA/SL/CFP-018): Ministry of Electronics and Information Technology (MEITY), Government of India (Grant Number: 3070665 (2020)

Bidirectional active energy flows are expected to increase in electrical distribution systems (DSs) worldwide as a result of future incentives to exchange energy within local energy communities. Distributed generation (e.g., solar rooftop photovoltaic systems) in combination with full-cycle pulsewidth regulated loads (e.g., thermal appliances or some energy diverters), battery storage systems, or regenerative loads will result in periodic changes in the energy flow direction. If the regulation periods are close to the aggregation time window of smart revenue meters (RMs), the deviation from the correct readings of export and import registers might be significant. Subsequently, economic transactions that rely on readings from RMs, especially in community grids, might fail. The article presents an overview of the active power and energy metrics that are either already being implemented in static RMs or derived from other applications of active power measurement. A parametric analysis and an experimental case-study demonstrated quantitatively that different metrics and different influencing factors/conditions can lead to large deviations in the readings of RMs in community grids, causing significant technical and financial consequences. Finally, a new testing procedure capable of verifying the susceptibility of RMs to these quickly changing bidirectional energy flows is proposed and experimentally demonstrated with the goal of including it in future updates of the relevant standards.

Knowledge of customer phase connection in low-voltage distribution networks is important for Distribution System Operators (DSOs). This paper presents a novel data-driven phase identification method based on Bayesian inference, which uses load consumption profiles as inputs. This method uses a non-linear function to establish the probability of a customer being connected to a given phase, based on variations in the customer’s consumption and those in the phase feeders. Owing to the Bayesian inference, the proposed method can provide up-to-date certainty about the phase connection of each customer. To improve the detection of those customers that are more difficult to identify, after obtaining the up-to-date certainty for all users, the consumption of those who have an up-to-date certainty above a certain percentile compared with the rest of the substation (those that are more likely to be correctly classified) is subtracted from the phase in which they are classified. The performance of the proposed method was evaluated using a real (non-synthetic) low-voltage distribution network. Favourable results (with accuracies higher than 97 %) were obtained in almost all cases, regardless of the percentage of Smart Meter penetration and the size of the substation. A comparison with other state-of-the-art methods showed that the proposed method outperforms (or equals) them. The proposed method does not necessarily require previously labelled data; however, it can handle them even if they contain errors. Having previous information (partial or complete) increases the performance of phase identification, making it possible to correct erroneous previous labelling.

In this paper, we consider the problem of privacy preservation in the average consensus problem when communication among nodes is quantized. More specifically, we consider a setting where nodes in the network can be curious, while certain nodes in the network want to ensure that their initial states cannot be inferred exactly by these curious nodes. Curious nodes are not malicious, i.e., they try to identify the initial states of other nodes based on the data they receive during their operation (and some of them might even collude) but do not interfere in the computation in any other way. Each node in the network (including curious nodes) can opt to execute a privacy-preserving algorithm (so as not to reveal the initial state it contributes to the average calculation) or its underlying (plain) average consensus algorithm (if privacy is not a concern). We propose two privacy-preserving event-triggered quantized average consensus algorithms. Under certain topological conditions, both proposed algorithms allow the nodes who adopt privacy-preserving protocols to preserve their privacy and at the same time to obtain, after a finite number of steps, the exact average of the initial states while processing and transmitting quantized information. We also present illustrative examples and comparisons of our algorithms against other algorithms in the existing literature, and discuss a motivating application in which smart meters in a smart grid collect real-time demands in a privacy-preserving manner., QC 20230508

Smart meters are often understood as central devices in the smart, flexible and sustainable electric grid, enabling consumers to adapt their electricity use to current supply. In this study we are interested in how different actors in a Swedish context understand the smart meters and fit them into their locally situated practices and contexts, and we analyze the smart meters with the concept of boundary objects as an analytical lens with the purpose of describing and explaining tensions between actors in intended and actual use of smart meters. The case study builds on empirical material from policy documents, web pages and user forums, and include the Swedish government, authorities, grid operators and electricity suppliers as well as consumers. The main conclusions are that consumers were not included in the design and roll-out of the smart meters, resulting in that the available information requires consumers to be active and resourceful experts.

Non-intrusive load monitoring (NILM) is a well-researched concept that aims to provide insights into individual appliance energy usage without the need for dedicated meters. This paper explores the possibility of applying the NILM concept to disaggregate energy data from a community level to a household level. By doing so, it addresses privacy concerns associated with real-time household data collection through smart meters. The proposed approach leverages sequence-to-point learning, a deep learning method, to perform the disaggregation. A method that showed promising results in the domain of NILM. The results show good performance on the evaluation metrics, but they also show that the model does a poor job of capturing the load signature., CSE3000 Research Project, Computer Science and Engineering

Smart grids have a growing role in today's society. Therefore, it is crucial to ensure their proper functioning at all time. While research already investigated the islanding of the electrical side of the smart grids, we look at this problem from a network security perspective, this network being very decentralized and highly communicant. To prevent the loss communication and a possible blackout of the sub-grid through islanding, we used a combination of the group authentication protocol and the lightweight verifiable secret sharing to propose an alternative authentication method. This method allows the different nodes to build trust as well as to identify and exclude malicious nodes trying to either join or prevent the group authentication. We also discuss the main security properties of our protocol and its limitations., info:eu-repo/semantics/published

The European directive on energy efficiency requires that all meters in multi-apartment buildings installed after 25 October 2020 shall be remotely readable devices where technically feasible and cost effective in terms of being proportionate in relation to the potential energy savings. In practise, this means that some manufacturers produce meters that monitor energy consumption in very short intervals, for example, less than two minutes; even though the directive expects to provide billing information to consumers only once a month starting from 2022. This paper reviews privacy and security risks stemming from the short readouts and provides recommendations for manufacturers and suppliers. The paper focuses on a Wireless M-Bus metering devices that we observed being sold and advertised as the solution to fulfill the European directive on energy efficiency requirements. Nevertheless, we believe that many recommendations and observations are applicable also to other protocols., Evropská směrnice o efektivním využívání energií předpokládá, že budou všechna měřidla v bytových domech instalovaná po 25. říjnu 2020 vzdáleně čitelná pokud je to technicky proveditelné a cenově výhodné v porovnání s potenciálními úsporami energie. V praxi to znamená, že někteří výrobci dodávají na trh měřidla, která monitorují spotřebu energie ve velmi krátkých intervalech, např. po 2 minutách, přestože směrnice očekává vyhotovení vyúčtování měsíčně od r. 2022. Tento článek se zabývá riziky z pohledu bezpečnosti a soukromí vyplývajících z takto častého čtení dat a dává doporučení výrobcům a dodavatelům měřidel. Článek se zaměřuje na protokol Wireless M-Bus a zařízení, která jsme koupili v běžné prodejní síti. Myslíme si však, že doporučení jsou aplikovatelná i na měřidla využívající jiné technologie.

The introduction of advanced metering infrastructure (AMI) smart meters has given rise to fine-grained electricity usage data at different levels of time granularity. AMI collects high-frequency daily energy consumption data that enables utility companies and data aggregators to perform a rich set of grid operations such as demand response, grid monitoring, load forecasting and many more. However, the privacy concerns associated with daily energy consumption data has been raised. Existing studies on data anonymization for smart grid data focused on the direct application of perturbation algorithms, such as microaggregation, to protect the privacy of consumers. In this paper, we empirically show that reliance on microaggregation alone is not sufficient to protect smart grid data. Therefore, we propose DFTMicroagg algorithm that provides a dual level of perturbation to improve privacy. The algorithm leverages the benefits of discrete Fourier transform (DFT) and microaggregation to provide additional layer of protection. We evaluated our algorithm on two publicly available smart grid datasets with millions of smart meters readings. Experimental results based on clustering analysis using k-Means, classification via k-nearest neighbor (kNN) algorithm and mean hourly energy consumption forecast using Seasonal Auto-Regressive Integrated Moving Average with eXogenous (SARIMAX) factors model further proved the applicability of the proposed method. Our approach provides utility companies with more flexibility to control the level of protection for their published energy data.

The Smart Grids concept is transforming the relationship of customers with the electricity in different ways. This paper provides a general overview of some potential applications to be developed under this conceptual framework, which have as a common denominator the use of non-intrusive load monitoring techniques. These techniques make it possible to disaggregate consumption based on specific measurements at certain locations in the electricity grid, without implement measurement points in each device to be monitored. Some of these new functionalities are particularly relevant for electricity grids in developing countries, which present complex challenges and need modernisation, while others are motivated by specific demands in developed countries. In all cases, the use of non-intrusive load monitoring techniques opens up new fields of applied research and technological development ranging from power grids to social issues., Las redes eléctricas inteligentes están transformando la relación de los usuarios con la electricidad de diferentes maneras. En este artículo se resumen algunas potenciales aplicaciones que se desarrollarán en los próximos años bajo este marco conceptual, las cuales tienen como denominador común el uso de técnicas de monitoreo no intrusivo de cargas. Estas técnicas permiten desagregar consumos a partir de mediciones puntuales en ciertos lugares de la red eléctrica, sin necesidad de monitorear dispositivos en forma individual. Algunas de estas nuevas funcionalidades tienen especial relevancia en las redes eléctricas de los países en vías de desarrollo, las cuales presentan retos complejos y necesitan una modernización, mientras que otras tienen su motivación en demandas específicas de países desarrollados. En todos los casos, el uso de técnicas de monitoreo no intrusivo de cargas abre nuevos campos de investigación y desarrollo tecnológico aplicado que van desde aspectos técnicos de las redes eléctricas hasta cuestiones de índole social.

Part of book: ISBN 978-91-89039-14-8QC 20220817, Stories from homes of the smart grid, Resistance and Power. On Smart Grids for the Many

We design optimal privacy-enhancing and cost-efficient energy management strategies for consumers that are equipped with a rechargeable energy storage. The Kullback-Leibler divergence rate is used as privacy measure and the expected cost-saving rate is used as utility measure. The corresponding energy management strategy is designed by optimizing a weighted sum of both privacy and cost measures over a finite time horizon, which is achieved by formulating our problem into a belief-state Markov decision process problem. A computationally efficient approximated Q-learning method is proposed as a generalization to high-dimensional problems over an infinite time horizon. At last, we explicitly characterize a stationary policy that achieves the steady belief state over an infinite time horizon, which greatly simplifies the design of the privacy-preserving energy management strategy. The performance of the practical design approaches are finally illustrated in numerical experiments., QC 20210113

The theory of this undertaking is that subjects, communicated through huge scope web-based media organizations, rough power usage occasions (e.g., utilizing high force utilization gadgets like a dryer) with high precision. Customarily, scientists have proposed the utilization of keen meters to demonstrate gadget specific power usage designs. In any case, these methods experience the ill effects of versatility and cost difficulties. To alleviate these difficulties, we propose an online media network-driven model that uses huge scope text based and geospatial information to estimated power usage designs, without the requirement for actual equipment frameworks (e.g., like keen meters), therefore giving a promptly versatile wellspring of information. The technique is approved by considering the issue of power use disaggregation, where energy utilization rates from a nine-month duration in San Diego, combined with 1.8 million tweets from a similar area and interval of time, are used to consequently decide exercises that require huge or modest quantities of power to achieve. The framework decides 200 points on which to identify power related occasions and finds 38 of these to be substantial descriptors of energy usage. At long last, the generalizability of our model is contrasted and a climate based model, given by the U.S. Branch of Energy.

In this paper a brief introduction is given about a smart grid and their associated communication technologies. This paper also gives details about different communication frameworks and their associated technologies. Comprehensive technologies related to communication issues are explained here related to smart grid. The given framework given can be useful for properly design a smart grid communication system. In this paper short description of three entities, communication oriented framework for the best communication network in the smart grid is explained.

The development of thorough probability models for highly volatile load profiles based on smart meter data is crucial to obtain accurate results when developing grid planning and operational frameworks. This paper proposes a new top-down modeling approach for residential load profiles (RLPs) based on multivariate elliptical copulas that can capture the complex correlation between time steps. This model can be used to generate individual and aggregated daily RLPs to simulate the operation of medium and low voltage distribution networks in flexible time horizons. Additionally, the proposed model can simulate RLPs conditioned to an annual energy consumption and daily weather profiles such as solar irradiance and temperature. The simulated daily profiles accurately capture the seasonal, weekends, and weekdays power consumption trends. Five databases with actual smart meter measurements at different time resolutions have been used for the model's validation. Results show that the proposed model can successfully replicate statistical properties such as autocorrelation of the time series, and load consumption probability densities for different seasons. The proposed model outperforms other multivariate state-of-the-art methods, such as Gaussian Mixture Models, by one order of magnitude in two different distance metrics for probability distributions.

The energy landscape for the Low-Voltage (LV) networks is undergoing rapid changes. These changes are driven by the increased penetration of distributed Low Carbon Technologies, both on the generation side (i.e. adoption of micro-renewables) and demand side (i.e. electric vehicle charging). The previously passive ‘fit-and-forget’ approach to LV network management is becoming increasing inefficient to ensure its effective operation. A more agile approach to operation and planning is needed, that includes pro-active prediction and mitigation of risks to local sub-networks (such as risk of voltage deviations out of legal limits). The mass rollout of smart meters (SMs) and advances in metering infrastructure holds the promise for smarter network management. However, many of the proposed methods require full observability, yet the expectation of being able to collect complete, error free data from every smart meter is unrealistic in operational reality. Furthermore, the smart meter (SM) roll-out has encountered significant issues, with the current voluntary nature of installation in the UK and in many other countries resulting in low-likelihood of full SM coverage for all LV networks. Even with a comprehensive SM roll-out privacy restrictions, constrain data availability from meters. To address these issues, this paper proposes the use of a Deep Learning Neural Network architecture to predict the voltage distribution with partial SM coverage on actual network operator LV circuits. The results show that SM measurements from key locations are sufficient for effective prediction of the voltage distribution, even without the use of the high granularity personal power demand data from individual customers., Algorithmics

Socioeconomic characteristics are influencing the temporal and spatial variability of water demand, which are the biggest source of uncertainties within water distribution system modeling. Improving current knowledge of these influences can be utilized to decrease demand uncertainties. This paper aims to link smart water meter data to socioeconomic user characteristics by applying a novel clustering algorithm that uses a dynamic time warping metric on daily demand patterns. The approach is tested on simulated and measured single-family home data sets. It is shown that the novel algorithm performs better compared with commonly used clustering methods, both in finding the right number of clusters as well as assigning patterns correctly. Additionally, the methodology can be used to identify outliers within clusters of demand patterns. Furthermore, this study investigates which socioeconomic characteristics (e.g., employment status and number of residents) are prevalent within single clusters and, consequently, can be linked to the shape of the cluster’s barycenters. In future, the proposed methods in combination with stochastic demand models can be used to fill data gaps in hydraulic models., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Water Resources, Sanitary Engineering

Assessing the impact of climate change on water demand is a challenging task. This paper proposes a novel methodology that quantifies this impact by establishing a link between water demand and weather based on climate change scenarios, via Coupled General Circulation Models. These models simulate the response of the global climate system to increasing greenhouse gas concentrations by reproducing atmospheric and ocean processes. In order to establish the link between water demand and weather, Random Forest models based on weather variables were used. This methodology was applied to a district metered area in Naples (Italy). Results demonstrate that the total district water demand may increase by 9–10% during the weeks with the highest temperatures. Furthermore, results show that the increase in water demand changes depending on the social characteristics of the users. The water demand of employed users with high education may increase by 13–15% when the highest temperatures occur. These increases can seriously affect the capacity and operation of existing water systems., Sanitary Engineering

Unidad de excelencia María de Maeztu CEX2019-000940-M, The present work aims to describe and analyze the results of the interventions carried out in the Barcelona pilot site of the EmpowerMed project. The overall objective of EmpowerMed is to tackle energy poverty and to help improve the health of people in coastal areas of Mediterranean countries, with a particular focus on women. The main support approach implemented in Barcelona is Collective Advisory Assemblies (CAA), in the framework of Alliance against Energy Poverty. CAA is an innovative, collaborative empowering engagement tool that offers an alternative to the more traditional one-off individual household advice and support approaches. CAAs take place together with a monitoring campaign where: electricity consumption is analyzed to optimize the supply contracts, and indoor environmental comfort to provide recommendations for wellbeing im-provement. Through the different approaches, a characterization of the people that have participated in the Barcelona pilot site was completed, from a socioeconomic, energy, thermal comfort and air quality perspective. Additionally, it was compared with a group of energy poverty non-affected households, which are involved in the monitoring campaign. Finally, the impact was quantified in terms of empowerment of energy poverty population and, potential economic savings.

Efforts to combat climate change through limiting our greenhouse gas emissions (GHG),is the main driver of the energy transition and entail strategies to decarbonize the energy sector. However, these strategies come with various social and technical challenges whichare important to address, especially regarding emerging consumer participation in the system as producers and consumers i.e., prosumers. The aim of this study is to investigate how smart technical solutions can be better diffused to improve participation of consumers in the energy transition through building knowledge and direct engagement as prosumers. Similarly, this study aims to investigate if there is potential for such smart technical solutions to help improve social acceptance of renewables and smart meter devices which also support “prosumerism”, through exploring Ferroamp, and its unique smart solution modular system as an example in the Swedish context. This study conducted three qualitative interviews, one with a solar seller who acts as a representative and interacts with a large consumer base as well as 2 customers of the Ferroamp system. These interviews led to conclusions regarding the opportunity of smart solutions to facilitate greater knowledge and participation in the Swedish Energy Transition; however, key challenges regarding technological awareness and economic knowledge of the consumer base remain, which has implications for actors trying to increase smart technology penetration in Sweden. Therefore, this study highlights the importance of increasing consumers’ technological awareness and better communicating economic benefits if we are to increase consumer acceptance, participation, and engagement in the energy transition moving forward.

With the widespread adoption of smart meters in buildings, an unprecedented amount of high-resolution energy data is released, which provides opportunities to understand building consumption patterns. Accordingly, research efforts have employed data analytics and machine learning methods to segment customers based on their load profiles, which help utilities and energy providers promote customized/ personalized targeting for energy programs. Existing energy consumption segmentation techniques use assumptions that could reduce clusters' quality in representing their members. Therefore, in this paper, we investigated a two-stage clustering method for capturing more representative load shape temporal patterns and peak demands through a cluster merging approach. In the first stage, load shapes are clustered (using classical clustering algorithms) by allowing a large number of clusters to accurately capture variations in energy use patterns, and cluster centroids are extracted by accounting for limited shape misalignment within the range of Demand Response (DR) timeframes. In the second stage, clusters with similar centroids and power magnitude ranges are merged using Complexity-Invariant Dynamic Time Warping. We used three datasets consisting of ~250 households (~15000 profiles) to demonstrate the efficacy of the framework, compared to baseline methods, and discuss the impact on energy management. The proposed investigated merging-based clustering also increased correlation between cluster centroids and the corresponding members by 3-9% for different datasets.

Este producto forma parte de una serie de infografías de divulgación científica que buscan reseñar algunas de las investigaciones más importantes en las que ha tenido participación la Universidad EAFIT, publicadas en las revistas especializadas más prestigiosas del mundo

The present work aims to describe and analyze the results of the interventions carried out in the Barcelona pilot site of the EmpowerMed project. The overall objective of EmpowerMed is to tackle energy poverty and to help improve the health of people in coastal areas of Mediterranean countries, with a particular focus on women. The main support approach implemented in Barcelona is Collective Advisory Assemblies (CAA), in the framework of Alliance against Energy Poverty. CAA is an innovative, collaborative empowering engagement tool that offers an alternative to the more traditional one-off individual household advice and support approaches. CAAs take place together with a monitoring campaign where: electricity consumption is analyzed to optimize the supply contracts, and indoor environmental comfort to provide recommendations for wellbeing improvement. Through the different approaches, a characterization of the people that have participated in the Barcelona pilot site was completed, from a socioeconomic, energy, thermal comfort and air quality perspective. Additionally, it was compared with a group of energy poverty non-affected households, which are involved in the monitoring campaign. Finally, the impact was quantified in terms of empowerment of energy poverty population and, potential economic savings., Peer Reviewed, Postprint (published version)

The work presents potential improvements in electricity metering from applying an event-driven metering, or EDM, approach, instead of traditional time-based or energy-based metering. The thesis contributes a method for automatically updating the event triggers on the basis of past measurements, thereby decreasing the number of measurements that each consumer property must send. Secondly, the author introduces a series of filters that can be used for further reducing the quantity of data sent and analyses the impact of such filters’ use in reconstruction of the original signal. Finally, the impact of communication errors on the reconstruction of the signal is examined. These improvements produce greater data compression without sacrificing the quality of the signal reconstruction. The results can serve as a foundation for more efficient deployment of advanced metering infrastructure., Tiivistelmä Tämä työ esittelee mahdollisia saavutettavia etuja, kun sähkön kulutuksen mittauksessa käytetään tapahtuma pohjaista mittausta (event-driven metering, EDM) verrattuna perinteiseen aikapohjaiseen tai energiapohjaiseen mittaamiseen. Väitöstyö tuottaa menetelmän, jossa automaattisesti päivitetään mittaustapahtumaliipaisut pohjautuen aiempiin mittauksiin, jolloin eri kulutuspisteiden (asiakkaiden) lähetettävien mittausten lukumäärää ja datan määrää voidaan merkittävästi vähentää. Lisäksi työssä esitetään useita suodatusmenetelmiä, joilla lähetettävän datan määrää voidaan edelleen vähentää sekä analysoidaan, kuinka hyvin alkuperäinen mittaussignaali voidaan rekonstruoida kyseisiä suodattimia hyödyntäen. Lisäksi väitöstyössä tutkitaan tiedonsiirtovirheiden vaikutusta alkuperäisen mittaussignaalin rekonstruktioon. Työssä osoitetaan, että ehdotetut parannukset tuottavat tehokkaan datanpakkausmenetelmän alkuperäisen mittaussignaalin laadun kärsimättä. Työn tuloksia voidaan hyödyntää entistä tehokkaampien älysähköverkkojen mittausinfrastruktuurien kehittämisessä.

Nowadays, smart meters are deployed in millions of residential households to gain significant insights from fine-grained electricity consumption data. The information extracted from smart meter data enables utilities to identify the socio-demographic characteristics of electricity consumers and then offer them diversified services. Traditionally, this task is implemented in a centralized manner with the assumption that utilities have access to all the smart meter data. However, smart meter data are measured and owned by different retailers in the retail market who may not be willing to share their data. To this end, a distributed electricity consumer characteristics identification method is proposed based on federated learning, which can preserve the privacy of retailers. Specifically, privacy-perseverance principal component analysis (PCA) is exploited to extract features from smart meter data. On this basis, an artificial neural network is trained in a federated manner with three weighted averaging strategies to bridge between smart meter data and the socio-demographic characteristics of consumers. Case studies on the Irish Commission for Energy Regulation (CER) dataset verify that the proposed federated method has comparable performance with the centralized model on both balanced and unbalanced datasets.

In future smart energy systems, consumers are expected to change their load patterns as they become a significant source of flexibility. To ensure reliable load profile forecasts for long-term energy planning, conventional classification approaches will not hold and more advanced solutions are required. In this article, we propose an automatic, data-driven clustering methodology that accounts for heterogeneity in electricity consumers’ load profiles using unsupervised learning. We consider hourly load measurements from 9412 smart-meters from the commercial and industrial sector in Denmark. A wavelet transform is applied to min-max scaled load data, and the extracted wavelet coefficients are used as input to the K-means clustering algorithm. Through cluster validation, eight clearly distinct load profiles are identified and compared to the industry classification of the cluster constituents. Finally, the flexibility potential is traced for each cluster.

Globally, smart meters measuring the water consumption with a high temporal resolution at consumers' households are deployed at an increasing rate. In addition to their use for billing or leak detection purposes, smart meters may provide detailed knowledge of the wastewater inflow to the sewer systems in space and time and open up new types of system analyses aimed at closing the urban water balance. In this study, we first validate the smart meter data against other, independent water distribution data. Subsequently, we use a detailed hydrodynamic sewer system model to link the smart meter data from almost 2,000 consumers with in-sewer flow observations in order to simulate the wastewater component of the dry weather flow (DWF) and to identify potential anomalies. Results show that it is feasible to use smart meter data as input to a distributed urban drainage model, as the temporal dynamics of the model results and in-sewer flow observations match well. Furthermore, the study suggests that in-sewer flow observations may be subject to unrecognised uncertainties, which make them unsuitable for advanced investigations of the DWF composition, and this underlines the necessity of collecting data from independent sources. The study also exemplifies that digital system integration in the water sector may be complicated. However, overcoming these obstacles may improve both offline and real-time urban drainage management.

Demand-response programs can help utilities manage rapidly evolving electric grids, but these programs are subject to the complexities of human behavior. This paper explores a novel method for uncovering heterogeneity in households. We use a machine-learning method known as a Conditional Inference Tree (c-tree) algorithm to categorize households based on their energy behavior characteristics collected via smart meters, and explore how this translates through into heterogeneity in their real-world response to a DR program. Using data from randomized controlled trial, we generate estimates of the changes in energy use caused by the program within each household group. Our results show that the c-tree approach differentiates households by their energy-use characteristics in a way that increases the spread in enrollment rates and critical peak reduction among household groups, compared with the spreads achieved via several conventional segmentation methods. Thus, the c-tree analysis enables the most tailored targeting of major potential energy savers and could provide the greatest increase in cost-effectiveness of household recruitment into DR programs. Our results also offer fresh insights into the relationships between household energy behavior characteristics – such as peak energy use and “structural winningness” (the ability to save money under a DR program without changing energy-use behaviors) – and household decisions about enrolling in DR programs and reducing energy use. Our research also demonstrates the potential of smart meter data, combined with machine learning and econometric methods, to provide significant value to utilities, program implementers, researchers, and other stakeholders.

The smart grid is a new revolution in the energy sector in which information and communication system is integrated within the conventional electric power generation and distribution system. A key component of this system is the Advanced Metering Infrastructure (AMI), which enables two-way communication between consumer and utility company via a smart meter. In the smart home energy management system (SHEMS), the smart meter is commissioned to monitor, control, trade, and log the energy consumption. Due to the diversification of the appliances in SHEMS, manufactured by different vendors with uncommon standards and deregulation of the communication protocol, then applications and devices in SHEMS become difficult to interoperate. With IoT invention, the smart meter should be able to address the heterogeneity issues. This paper propose an IoT smart meter architecture that addresses the heterogeneity problem in SHEMS by making use of middleware platform which solves interoperability problem in any IoT scenarios. A prototype system using Raspberry Pi and Kaa IoT middleware is reported., QC 20210223

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works, Big data analysis is becoming an increasing field of interest for research to analyse, identify and predict final user’s behaviour. For this reason, in the energy sector, smart metering is generally used to find new business opportunities and, theoretically, it is said that it could also help to fight energy poverty issues. Nonetheless, when tackling social injustice issues, the deployment of massive technology might also bring other side effects. This study analyses the capability of smart metering to mitigate energy poverty in Europe according to the current approaches of projects and the risks it might bring to the more vulnerable layers of our society., Peer Reviewed, Postprint (updated version)

The integration of advanced measuring technologies in distribution systems allows distribution system operators to have better observability of dynamic and transient events. In this work, the applications of distribution grid measurement technologies are explored in detail. The main contributions of this review are (a) a comparison of eight advanced measurement devices for distribution networks, based on their technical characteristics, including reporting periods, measuring data, precision, and sample rate; (b) a review of the most recent applications of micro-Phasor Measurement Units, Smart Meters, and Power Quality Monitoring devices used in distribution systems, considering different novel methods applied for data analysis; and (c) an input-output table that relates measured quantities from micro-Phasor Measurement Units and Smart Meters needed for each specific application found in this extensive review. This paper aims to serve as an important guide for researches and engineers studying smart grids., Peer Reviewed, Postprint (published version)

Water leakage detection at the household level is going to become one of the most challenging goals in the fields of water metering and smart sensors. While sudden leakage seems to be easier to be noted, the small one is giving researchers a hard time. The main topic of this paper is the digital processing of mechanical register images to provide water flow rate metrics, which can allow small leakage detection. The register images are automatically gathered by an electronic add-on device, which is also featured with a short range antenna to communicate with a gateway. The image processing techniques and experimental test results are finally presented and discussed. The authors' research is funded by the European Association of National Metrology Institutes (EURAMET) within the European Metrology Program for Innovation and Research (EMPIR). © 2020 IEEE.

The introduction of smart meters sparked concerns about privacy breach through real-time monitoring of electric power consumption. Valuable private information about occupancy, behaviour, health, religion and wealth can be extracted from the user’s power profile which urges measurements to protect the integrity of the user. One physical mitigation technique to assure privacy is explored using energy storage systems. Real energy storage technologies are limited in their energy capacities and power capabilities, which have to be appropriately sized to fulfil their role. This paper analyses and compares different energy storage technologies (li-ion, lead-acid, electric double layer capacitor and flywheel) for the protection of residential users by estimating the minimal required capacities and costs for both single and multiple user cases. The analysis is based on actual measured user data from the REDD data set. The results show that the integrity can be protected with reasonable capacities and investments ranging in the margin of market available products., QC 20191107

Increased sensing and data collection in electric power systems from utility to minigrid to individual household scale are resulting in an explosion of data collection about users and providers of electricity services. In the push to expand energy access for poor communities, the collection, use, and curation of these data have historically taken a back seat to the goal of expanding energy access but are increasingly being recognized as important issues. We review the nascent literature on this topic, characterize current data management practices, and examine how expanding access to data and data sharing are likely to provide value and pose risks to key stakeholders: end users of electricity, microutilities, macroutilities, governments, development institutions, and researchers. We identify the key opportunities and tensions and provide recommendations for the design and implementation of new data-sharing practices and platforms. Our review and analysis suggest that although a common and open platform for sharing technical data can mitigate risks and enable efficiency, fewer benefits are likely to be realized from sharing detailed financial data. We also recommend codesigning practices with each stakeholder group, increasing legal protections for end users of electricity and using deep qualitative data in addition to quantitative metrics.

The recent trend for monitoring and control of the distribution networks requires methods capable to calculate the network’s operating conditions. One way to monitor the distribution network is by state estimation. Although previously this method was established only for the transmission networks, its use has begun to also make sense for the distribution networks, due to the increased availability of smart meters. Typically, state estimation is a non-linear iterative procedure. Research aims to overcome this problem, by creating a non-iterative procedure. To achieve this, linearity of measurement function is a necessity. Although linearity is always desired, the measurement inputs or the procedure renders the state estimation problem difficult to formulate it in a linear fashion. The scope of this thesis is to create a linear state estimation algorithm for the distribution network. To achieve linearity, a reformulation of the states for the state estimation was used at this thesis. This reformulation created a linear optimization problem, which is easier and faster to be solved (Weighted Least Squared method). Since the measurements are not enough to provide full observability, based on these methods, additional assumptions were made. As an alternative to the primary method used for the state estimation, another optimization method was used, which is called Least Absolute Value. This method will be based on the same inputs as the previous method, but different optimization goal. The goal was to compare the two methods and find each one’s limitations. Measurements used for this thesis were provided tracked and provided by DEPsys S.A., by their main module, called GridEye. GridEye devices are synchronized SCADA-type devices, installed on cabinets of the distribution grid, based on the DSO needs. All measurements provided were from real networks within Switzerland. The smart meter device data were provided by collaborative projects of DEPsys S.A.. Smart meter devices w

The stringent requirement of the demand-supply equilibrium for delivering electricity has traditionally been dealt with a supply-side perspective, assuming that the demand is not alterable. With the promises of the smart grid, demand side management techniques are increasingly becoming more feasible. A demand-side management technique, called demand response (DR), aims at inducing changes in electricity load in response to financial incentives, some of which involve bidding as the underlying facilitator. It is well-established that the effectiveness of the DR is proportional to the number of participants. Yet, many of the DR programs, including those involving bidding, may suffer due to consumer privacy concerns. Within this context, in this paper, we propose a distributed and multi-unit privacy guaranteeing bidding mechanism as part of a DR program without relying on any third party, trusted or not, to protect the participants' bidding information, except obviously for the winning price and the winner exposed to the utility. To the best of our knowledge, this is the first such approach for the DR bidding programs. We provide a security analysis of our approach under the honest-but-curious and active adversary assumptions and prove the privacy assuring property.

Desde el punto de vista de la demanda de energía eléctrica, tanto los smart meters como la smart grid son convenientes, pero se alega que pueden afectar la salud o invadir la privacidad. Tanto la salud como la privacidad son bienes protegidos en la Constitución Nacional así como en normas infraconstitucionales en Argentina. Entre éstas, se pueden considerar la Ley de Protección de Datos, la de Acceso a la Información Pública, la de Marco Regulatorio Eléctrico, la de Argentina Digital, la de Lealtad Comercial, la de Defensa del Consumidor y ciertas normas de contenido penal. Esta legislación, tomada como muestra, parece hábil para encarar inicialmente la llegada tanto de los smart meters como de la smart grid, que son necesarios, en especial para beneficio de los usuarios de la red. Sin embargo, algunos interrogantes perduran, y, en su caso, se pueden adoptar respuestas remediativas o bien anticipatorias.

[ES]: En este trabajo se propone una recopilación de experiencias realizadas en la península ibérica desarrolladas con el objetivo de identificar áreas en riesgo de pobreza energética. Para ello se muestran algunos indicadores desarrollados en Portugal para la escala nacional en comparación con otros desarrollados en España para la escala de la edificación. Se analizan los métodos estableciendo las coincidencias y sus principales diferencias, con el objeto de analizar la posibilidad de establecer estrategias comunes. Entre las experiencias de los dos países se pasa de la escala nacional a la de la edificación, pasando por la regional, municipal y de barrio. En ese sentido, se presentan conclusiones sobre la forma de proceder para poner en relación todas las escalas de la eficiencia energética en el sector de la edificación y construir una futura metodología común de evaluación precisa de la pobreza energética que permita afrontar el problema con base en información., [EN]: This paper describes a compilation of studies aimed at identifying areas at risk of energy poverty in the Iberian Peninsula. Examples of indicators developed in Portugal at the national and regional scale are compared with indicators developed in Spain at the urban and building scale. The similarities and differences of both methods are analysed to feed into the design of common strategies. The scales employed in the two countries range from national to regional, municipal to, neighbourhood and neighbourhood to building. Conclusions therefore focus on how best to accommodate for all building sector energy efficiency scales and future common methodologies for the accurate assessment of energy poverty.

The paper deals with water leakage beyond the meter at household level. The automatic leakage detection is emerging as priority task for scientists and technicians. Each possible solution must include the use of smart meters for the continuous survey of domestic water consumption. Water smart meters aren't widespread yet, and the few either are prototypes or exhibit not adequate battery and communication performance. The authors, participants to the EMPIR 17IND13 Metrowamet project, propose an embedded system that can work with different water sensors, whose characteristics have been suitably designed for respecting stringent requirements of energy efficiency, computational burden and wireless transmission capability.

The increased complexity of electric distribution networks, entails higher requirements on the interoperability between the information and communication technology (ICT) infrastructure and the power system. In current literature, distribution network monitoring methods require reliable operation of the ICT infrastructure, and depend on load estimation accuracy in cases of unavailable measurements. In the low voltage (LV) distribution network, ICT infrastructure is sensible to noise and congestion, and load estimation is challenged by irrational consumer behaviour. Such limitation impede the implementation of currently proposed methods. The conditions are complicated further when considering the impact of emerging data privacy concerns that can prevent utilization of household consumption data. Therefore, this work proposes a novel method for voltage magnitude interval estimation by utilizing the information from existing data acquisition systems in LV distribution networks. The proposed method is based on knowledge of load connection, and estimates the entire radial feeder voltage magnitude conditions from measurements of voltage magnitudes from minimum one node. The proposed method can therefore be implemented on existing ICT infrastructure and effectively keeps the privacy of consumers consumption profiles. The ability of the proposed method to estimate the feeder voltage interval is illustrated by an example and evaluated using Monte Carlo simulations for different meter distribution and accuracy scenarios.

El cliente doméstico era el gran olvidado del sistema eléctrico. A pesar de su peso en el consumo total, hasta hace pocos años su poder de intervenir en el mercado era extremadamente limitado. La aparición de las nuevas tecnologías permiten, por un lado obtener datos de consumo horarios individuales y, por otro lado, analizarlos de manera agregada. En este trabajo, se han analizado los consumos agregados de clientes domésticos de tres grandes ciudades españolas. El objetivo es intentar caracterizar sus curvas de demanda y preguntarnos si sería posible un ahorro de costes sin necesidad de variar sus patrones de consumo. Los resultados mostrados confirman esta hipótesis, siendo tremendamente rentable para los consumidores el tratamiento de los datos masivos a los que se han tenido acceso gracias a las nuevas tecnologías.

Advances in technology continuously reshape habits, behaviours and interactions at individual, organisational and societal levels. Information and Communication technology (ICT) is pervasive, and is the bearer of crucial information, analysis and responses to the recipient. It assists in processes such as understanding phenomena and taking appropriate action. It also provides communication means, platforms and tools to interact with one another. In the United Kingdom’s (UK) Energy industry, smart meters are currently being deployed by energy suppliers to their customers. These are presented as a revolutionary device that supports a more efficient energy use at home and/or in the workplace, and helps customers save on their energy bill and reduce carbon footprint. Using interpretivist phenomenological Human-centred qualitative research, The thesis work explores the impact of using smart meter devices, as perceived by UK customers, in helping to monitor, use and manage energy consumption at home. Also, the study looks into alternative technology and customers’ expectations in the context of energy efficiency. And finally, the principles of a design is presented to respond to customers demands and desires. Passive participant observation, Future workshop and inspiration cards session are the methods and techniques applied in this research to collect data. The findings constitute good grounds for individual consumers to be aware of the opportunities made available by alternative technology. At organisational level it is a basis for energy suppliers to change their behaviour towards a changing demand and adapt by switching their core business to supplying Information and analysis as well as energy efficiency management solutions. At societal level government bodies and regulating agencies can tackle sustainability and green energy issues by controlling the profit driven character of privatised energy companies and responding to real market demand as perceived by consume

Los efectos del cambio climático son unos de los principales problemas ambientales que afectan al mundo en la actualidad, siendo las políticas y medidas de prevención y adaptación los grandes retos de la sociedad. Una de las múltiples consecuencias del cambio climático será la escasez de recursos hídricos disponibles y todo ello, teniendo en cuenta un incremento y concentración de población en las áreas urbanas. Por lo tanto, uno de los principales retos en los ámbitos urbanos será garantizar en cantidad y calidad suficiente el suministro de agua, especialmente en áreas con estrés hídrico. El trabajo que aquí se presenta tratará de analizar cómo la instalación de contadores de agua de telelectura (Smart Meters) en la ciudad de Alicante puede contribuir a la adaptación y mitigación del cambio climático. Como principal conclusión extraída cabe señalar que la adopción de esta tecnología ha permitido controlar el consumo y suministro de agua, hacer una gestión más sostenible del recurso (reducir el agua suministrada no registrada tanto por pérdidas como por fraude) y el descenso de emisiones de CO2 debido a la sustitución y disminución de los trayectos para leer el contador de agua de una forma manual por una lectura remota., The effects of Climate Change are one of the main environmental problems that will affect the world in the future, and the policies and measures of prevention and adaptation will be the great challenges of the society. One of the multiple consequences of Climate Change will be the scarcity of available water resources, all of this, taking into account an increase and concentration of population in urban areas. Therefore, one of the main challenges in urban areas will be to guarantee quantity and quality water supply, especially in water stress areas. The research presented here will analyze how the installation of meters with remote reading (Smart Meters) in the city of Alicante could contribute to the adaptation and mitigation of Climate Change. The main conclusion is that the adoption of this technology has made it possible to control the consumption and supply water and, therefore to make a more sustainable management of the resource (reducing the water supply, losses and fraud) and the reduction of CO2 emissions due to the replacement and reduction of the attempts to read the conventional meter by remote reading.

The aim of this paper is to analyze the economic and environmental aspects of installing PV facilities for residential electricity users. This paper explores, in a conservative approach, the installation of a PV capacity to compensate the consumption with the production for each moment, never feeding electricity into the utility network and without storage. The approach proposed is illustrated by applying different power PV capacities in alternative locations (Marseille, Madrid and Seville), using the hourly demand provided for the smart meters. Combining the load curve of each user, the irradiation and PV production of each location, the cost of equipments, the hourly emission in the whole market, the variable price of electricity for residential users and the energy needs to build a PV facility. The model calculates, for each individual the optimal PV power to install and the emissions avoided. The results show that, with the current cost of the PV facilities and variable prices of electricity, the PV are, from an economic and environmental point of view, profitable in all the locations analyzed. This initiative will be more profitable for private investors and, additionally, for the environment in the next three years. A massive installation of these facilities in Spain and France will contribute to achieving their Nationally Determined Contribution (NDC) of the Paris agreement (COP-21), fulfilling, in Spain, the current legal restrictions.