Smart Meters

The introduction of advanced meters has simplified the overall payment system over the industrial use of power performed using electronic meters. Opposed to smart meters, traditional power meters have enormous drawbacks. There is a massive increase in load supply. The issue is that these needs are not fully achieved. Although the requirements were to be met, various problems emerge concerning the loss of energy, which then, in turn, has a negative impact on the country's economy. Offenses like misuse of electricity are on the rise every day. Not only has this, but the new post-payment system situation still seemed to be collapsing. The Smart Meter, therefore, solves such issues. In addition to monitoring electricity, Smart Meter will also share information on energy usage between utilities and customers. The smart meter is quite an essential component of the smart grid and is intended to provide numerous investors with budget-effective, ecological and social benefits. Smart metering utilizing Smart Grid provides tracking, security and regulation of the large area. The paper gives a comprehensive overview of the role of Smart Meters in Smart Grid, its Architecture, Advantages, and Disadvantages and so on.

High penetration of variable and non-programmable distributed generation has brought new challenges to the power system operation and is highlighting the need of a smarter grid. One of the key requirements in this regard is developing and deploying smart metering systems in distribution networks. In this paper we present the actual situation in the Italian distribution networks and we discuss the opportunities and challenges of applying new metering systems and introducing a flexible, multi-utility, multi-service metering architecture. Some off-the-shelf or prototype smart meters, selected to be tested in an ongoing European project, named FLEXMETER, are presented.

""In the endeavour to reduce the energy consumption of households, Home Energy Management systems (HEMS) have been receiving increasing attention as a potential solution. HEMS are devices that give eco-feedback on household’s energy consumption. In recent years several studies have reported on the energy savings that HEMS can achieve. These figures seem impressive, often ranging from 5 to 15% reductions in electricity or gas consumption. However, researchers tend to focus on the resulting direct energy reductions within the home and often over a limited period of time. They have not taken the overall life cycle impact of the HEMS itself into account. As there is evidence that the achieved savings decrease over time and that HEMS become obsolete before their technical lifespan has ended, a more holistic approach is necessary to assess whether or not HEMS can effectively contribute to household energy savings.
A trade-off needs to be made between the energy needed for production, use and disposal of the device versus the amount of energy saved by using it. Therefore a life cycle assessment and cumulative energy demand (CED) method were used to analyse three distinct types of HEMS. Based on literature, several scenarios were developed in order to find the break-even point, where e invested = e saved. The results show that the overall impact is dependent on the type of HEMS, and that if the duration of use is short or the achieved savings are small, the benefits do not always outweigh the (environmental) costs.
In light of the uncertain long-term effects of HEMS it can be argued that these devices should not be developed as stand-alone products, but should be integrated in existing products instead. Care should be taken that HEMS are not developed with unnecessarily elaborate parts or functionalities and that their own electricity consumption is minimized.""

In the early phase of household technology, delivery of electricity is completely depended on traditional energy meters. These meters play a key role in measuring the consumption of electrical energy in individual households. The usage of these meters has been slowly declining with the advancement in technology as rapid changes has been made to encounter the problems occurred by the traditional meters. The major
problem arises when habitants are unaware of their daily behavior. Monthly feedback given to the consumers is not sufficient as the consumers will not have knowledge on how much energy does the individual appliances consume. To overcome the problems of traditional electricity meters, Smart Meters have been upgraded and developed.
With the use of Smart Meter data, energy alerts (signals) will be provided to the consumers based on hourly utilization of energy. The primary objective of the Smart Meters is to reduce the energy consumption in the households being installed in. This work utilizes what was obtained from Eko Electricity Distribution Company of Nigeria. The real time Smart Meter data sets are obtained from Eko ElectricityDistribution Company of Nigeria?

Studies have shown that householders’ perceptions of their water use are often not well matched with their actual water use. There has been less research however, investigating whether this bias is related to specific categories of end use and/or specific types of socio-demographic and socio-psychological household profiles. A high resolution smart metering study producing a detailed end use event registry as well as psycho-social and socio-demographic surveys, stock inventory audits and self-reported water diaries was completed for 252 households located in South-east Queensland, Australia. The study examined the contributions of end uses to total water use for each group that self-identified as “low”, “medium” or “high” water users. A series of univariate tests (i.e. analysis of variance) were conducted to examine a range of variables that characterise each self-identified water usage group including age, income, percentage of water efficient stock (e.g. low-flow taps), family size and composition and water conservation intentions and attitudes. The level of information consumers receive on their water bill as well as the diurnal end use patterns were also examined. The paper concludes with a discussion of the general characteristics (i.e. income, age, gender and family composition) of groups that tended to overestimate or underestimate their water use and how this knowledge can be used to inform demand management policy such as targeted community education programmes and community-based social marketing. Further, the potential for positive economic and sustainable development outcomes from this research is also discussed.

In this work, we address the problem of providing fast and on-line households appliance load detection in a non-intrusive way from aggregate electric energy consumption data. Enabling on-line load detection is a relevant research problem as it can unlock new grid services such as demand-side management and raises interactivity in energy awareness possibly leading to more green behaviours. To this purpose, we propose an On-line-NILM (Non-Intrusive Load Monitoring) machine learning algorithm combining two methodologies: i) Unsupervised event-based profiling and ii) Markov chain appliance load modelling. The event-based part performs event detection through contiguous and transient data segments, events clustering and matching. The resulting features are used to build household-specific appliance models from generic appliance models. Disaggregation is then performed on-line using an Additive Factorial Hidden Markov Model from the generated appliance model parameters. Our solution is implemented on the cloud and tested with public benchmark datasets. Accuracy results are presented and compared with literature solutions, showing that the proposed solution achieves on-line detection with comparable detection performance with respect to non on-line approaches.

The aim of the research presented in this thesis was to infer design-related insights and guidelines to improve the use and effectiveness of home energy management systems (HEMS). This was done through an empirical evaluation of the longitudinal effectiveness of these devices and an exploration of factors that influence their use and effectiveness. Three case studies executed with three different HEMS in households, a life cycle assessment (LCA) on those three HEMS, as well as a reflection on the challenges of both researching and implementing HEMS in existing housing gave a comprehensive picture of the opportunities and barriers for HEMS and their potential for achieving behavior change and energy reductions. The research revealed five typical use patterns that emerged amongst households. It also revealed average energy savings of 7.8%, which however decreased in the follow-up that was conducted, and factors that may influence the use and effectiveness of HEMS. Nonetheless, the LCA calculations divulged that the HEMS can achieve net energy savings when taking their embedded energy into account. Through a combination of these findings, design-related insights and guidelines where formulated concerning HEMS.

Among the Sustainable Development Goals, ‘Green Issues’ have attracted significant research on sustainability transitions and regional diversification. The introduction of green environmental technologies within the frame of the Fourth Industrial Revolution is crucial for the diversification of local, sustainable activities to protect the environment against negative climate changes. The present paper provides evidence of the positive correlation among green activities if, and only if, green culture and capabilities are robust and exist. Close international coordination is needed. We point out that smart energy-designed systems are a real revolution in the post-industrial society dominated by the service sectors. Therefore, promoting ‘intelligent’ meters is a robust policy action in world energy-based economies. We investigate the policy effects for smart meter rollout in European countries by testing this green policy tool on different economic literature strands. A theoretical model is introduced, showing that a sustainable and efficient policy instrument will reinforce and develop local green culture. The spatial unit of investigation is the EU-28, and it verifies the effectiveness of smart meters as a valid post-industrial design tool toward more sustainable environmental policies.

Both the FCC and WHO have ignored the increased adverse effects that 5G will have worldwide on HEALTH and on the ENVIRONMENT. With respect to HEALTH EFFECTS, over the past two decades research on the effects of microwaves has clearly shown that as frequencies increase, and as wavelengths shorten and become more complex, and as it does with 5G, that harmful biological effects increase as well. Important replicated effects, observed in many different laboratories at very low levels of RF exposure, includes neurological damage in animals such as leakage of the blood brain barrier. 

In February of 2020 Blue Cross Blue Shield released a report that provides strong supporting evidence of an unprecedented increase in early onset Dementia, confirming an increase in Early Onset Dementia or in Alzheimer’s in humans AS PREDICTED from this research.  According to this report, in the 4 years between 2013 and 2017, early-onset dementia and Alzheimer’s disease combined diagnosis rates TRIPLED for adults aged 30 to 64, going up from 4.2/10,000 adults to 12.6/10,000 adults.  Autism in children shows a similar unprecedented increase.  "According to National Health data released last week, autism incidence among Irish children is now at 4.3%, an 82% rise in five years.

With respect to ENVIRONMENTAL Effects, the impact that digital technologies have on climate change through their  carbon footprint - almost entirely unreported by the media - includes the expected doubling of our human carbon footprint - from 4% to 8%, 2,400 million tons of CO2 by 2025 - should 5G replace what we currently have.  A prediction made BEFORE the acceleration in use of digital technologies because of pandemic restrictions worldwide.

The  Traditional  Power  utilities  are  gradually  moving
towards  the  Smart  Grids.  These  Grids  deploy  a  very
large  number  of  smart  meters  at  the  consumers’  sites
using bi-directional communication networks based on
Internet  protocols.  With  the  reliance  on  these
protocols,  the  Smart  Grids  become  vulnerable  to
various  cyber-attacks.  Smart  meters  collect
consumption  data  and  allow  customers  other  useful
functions.  Consumers  are  worried  about  the  privacy,
integrity,  availability,  and  confidentiality  when
managing  their  future  power  consumption.  In  an
attempt  to  contribute  to  the  protection  of  these  smart
meters  from  attacks,  two  approaches  based  on
cryptographic  protocols  are  proposed  for  securing  the
direct connection of smart meters to collectors.

The FCC fails to take into account the adverse effects that 5G will have on HEALTH and CLIMATE CHANGE. This second issue, the impact that digital technologies have on climate change through its carbon footprint - almost entirely unreported by the media - includes the expected doubling of our human carbon footprint - from 4% to 8%, 2,400 million tons of CO2 by 2025 - should 5G replace what we currently have. 

With respect to HEALTH EFFECTS, over the past two decades research on the effects of microwaves has clearly shown that as frequencies increase, and as wavelengths shorten and become more complex, and as it does with 5G, that harmful biological effects increase as well. Important replicated effects, observed in many different laboratories at very low levels of RF exposure, includes neurological damage in animals such as leakage of the blood brain barrier.  In February of 2020 Blue Cross Blue Shield released a report that provides strong supporting evidence of an unprecedented increase in early onset Dementia, confirming an increase in Early Onset Dementia or in Alzheimer’s in humans AS PREDICTED from this research.  According to this report, in the 4 years between 2013 and 2017, early-onset dementia and Alzheimer’s disease combined diagnosis rates TRIPLED for adults aged 30 to 64, going up from 4.2/10,000 adults to 12.6/10,000 adults. 

Autism in children shows a similar unprecedented increase.  "According to National Health data released last week, autism incidence among Irish children is now at 4.3%, an 82% rise in five years. One in 16 boys is affected."  And given the current Covid-19 pandemic, it seems important to emphasize that having a leaky blood-brain barrier - at the same time as having a viral infection - does NOT seem a good thing, and may well explain the brain damage and inflammation reported in some Covid-19 cases.  And as Panagopoulos has shown in his 2019 review, the extreme and unpredictable variability of real-life cell phone signals - which 5G will markedly increase - results in ever more complex and biologically harmful signals to living cells/organisms.

The electrical grid in most of the developing countries has inefficiencies in different areas such as transmission and distribution, power quality, grid reliability and system protection. These inadequacies in grid operations and asset protection, along with revenue leakage constitute an overall troubled energy profile. The fixes usually proposed in this regard are directed at reducing distribution network losses and improving residential and commercial demand side management. Lessons from the residential and commercial implementation of smart meters can be extended to industrial consumers to help relieve grid congestion and achieve better efficiency goals. This paper reviews smart meter technology and applications across residential, commercial and industrial sectors. We point out the areas for power quality and energy efficiency improvement within industries and propose ways for achieving them through smart meters, specifically in the context of Pakistan. We have incorporated empirical evidence from experimental setup at our university grid as proof of concept. We have also elaborated on the implementation methodology to avoid the possible pitfalls in the proposed solution.

When pressed, even industry scientists will admit that there exists a great deal of scientific research on both sides of the issue of whether low-level RF radiation causes harm—with industry funded scientists mostly on one side, and industry independent scientists on the other, but with all research done by credentialed, respected scientists.

In most situations like this, where legitimate scientific controversy exists over a potential environmental hazard, the precautionary principle would have come into play many years ago. It hasn't. Instead of the government requiring industry to demonstrate the safety of smart meters through peer reviewed research studies, customers suffering ill effects with scant resources and no scientific expertise find themselves forced to go to court to try to prove that smart meters can cause harm.

The fact that low level microwave exposures can clearly cause DNA breakage and cancer in animals does not actually seem the most important point.  Instead, one must understand that the simple fact that low levels of microwave exposures have biological effects opens the proverbial can of worms, with respect to all the safety studies the microwave industry has NOT done for RF radiation potentially causing OTHER diseases than cancer, while exponentially increasing human exposure to microwaves in a wide variety of new and untested frequencies and devices year after year.

Individual exposure from smart meter radio frequency microwaves (RF) basically depends on three factors: 1. The power of the RF emitter that determines the maximum strength of the signal; 2. The % duty cycle (the percentage of time a smart meter transmits); and 3. Distance from the smart meter. The first factor can vary from model to model, the second two vary widely even for a single meter, depending on a variety of circumstances, including location and time of day.

Although omitted from California Council on Science and Technology’s 2011 report, applying the inverse-square law to CCST’s smart meter distance data, one can calculate the relative strength of the RF from a cell phone held to the ear, to that of a smart meter (50% duty cycle) “held to the ear,” one-half inch away. At that distance a smart meter also sends out about 20 times the RF of a cell phone on maximum power, and 100 times the RF of a cell phone on minimum power, 100,000 µW/cm2 for the smart meter, compared to 5,000 and 1,000 µW/cm2 for a cell phone.

Increasing the efficiency of electricity transmission is nearing the top of the agenda in many countries around the world. Turkey, the world’s most newly industrialized country, is no different. Modernizing the current transmission grids to smart grids (SG) and the national rollout of smart meters (SM), are some of the measures taken by the government to meet the growing demand for electricity. Consumer acceptance and engagement are among the most important elements for the success of SG and SM, however, there have not been much studies done among Turkish electricity consumers. This purpose of this study is to fill this void, by detailing the attitudes, awareness and expectations among Turkish citizens regarding SM and listing recommendations for energy companies based on the findings. Through an online questionnaire, responses from 504 social media users were collected and analyzed. Results show that the consumers are open towards the acceptance of SM, but there is a need to raise ...

Trials and evaluations of energy and water consumption feedback are premised on understandings of consumption as a rational and individual decision-making process. This paper draws on two alternative conceptualisations of consumption to understand the role and effectiveness of consumption feedback delivered through an in-home display (IHD). The first considers how consumption is mediated by socio-technical systems of energy and water provision, and the second views it as part of social practices such as laundering, eating and heating. The paper draws on these conceptualisations to analyse a qualitative dataset of interviews and tours with 28 Australian households participating in three separate IHD feedback programs. The paper finds that IHDs are an important visualisation tool that illuminate otherwise invisible systems of energy and water provision. However, they have the potential to legitimise particular practices and to overlook those considered non-negotiable. The paper concludes that IHDs can play a role in making socio-technical systems of energy and water provision more relevant to householders' everyday lives, and in questioning and debating non-negotiable practices. This will necessitate repositioning and blurring the roles and responsibilities of resource providers and consumers. As a mediating device with the ability to extend both inside and outside the home, the IHD provides a unique platform for reorienting its role towards these ends.

real-time mode, the ability to monitor the physiological state of the patient in a different interval of time. Devices such as blood-pressure monitors, glucose meters, heart moni- toring implants, Electroencephalography (EEG), Electrocardiogram (ECG), and Electromyo- graphy (EMG) wearable devices allow health providers to collect the patient health in- formation locally and make a real-time decision based on the Patient Health Data (PHD). Hospitals have been adopting the IoT for many years and now they have healthcare IoT devices in patients’ rooms and their bodies. However, the medical agencies, hospitals, and companies do not consider the security risk of healthcare IoT devices connected to a Local Area Network (LAN) or Wide Area Network (WAN). The IoT devices can be easily hacked and may lead to several potentially life-threatening risks due to poor authenti- cation and encryption practices. Existing machine learning algorithms and blockchain ap- proach working in the cloud computing environment are unable to meet the Quality of Service (QoS) like reliability, authentication, identification, and security requirements of healthcare IoT devices. Most of the traditional machine learning algorithms and techniques for healthcare IoT lacks the real-world implementation for secure data transmission. There- fore, blockchain is introduced for secure and reliable transaction in healthcare IoT. Whereas Fog Computing (FC) is introduced to extend the services of the cloud at the edge of net- works. Integration of FC with blockchain can overcome the issue of healthcare IoT device identification, authentication, and verification for scalable frequent data transmission in a decentralized environment. Hence, a novel solution for the abovementioned problem is proposed using FC and blockchain. It includes an FC-based three-tier architecture, an an- alytical model, a mathematical framework, and an Advanced Signature-Based Encryption (ASE) algorithm for healthcare IoT device identification, verification, and Patient Health Data (PHD) authentication. The aim is to extend secure data transmission for healthcare IoT and end-users availing the real-time services. The proposed model and algorithm will be able to provide services for transactions and transmission near the edge in a secure manner. By analyzing the generated results from the proposed novel ASE algorithm for throughput, packet error, reliability, and malicious node detection accuracy; it is observed that the ASE algorithm in the FC environment easily outperforms the cloud and the other existing state of the art techniques such as FogBus, Femto cloud, Blockchain Fog-based Ar- chitecture Network (BFAN), and BeeKeeper. The malicious node detection accuracy of the ASE algorithm in the FC environment is 91% and in the cloud is 83%. Whereas the relia- bility percentage of the ASE algorithm in FC is 95% and in the cloud is 87%. The proposed approach is tested on simulators iFogSim (Net-Beans) and SimBlock.

Synopsis:
First, from  testimony given by PacifiCorp Project Manager under oath, none of Aclara I-210+C AMI Meter units now installed in millions of homes throughout Oregon have ever undergone testing to assure that their RF emissions comply with the FCC standard. 

Second, according to electrical engineer William Bathgate, TESCO, the company that PacifiCorp hired to test sample Aclara I-210+C AMI Meters under unrealistic laboratory conditions does not have the necessary ANAB or NVLAP accreditation required by the FCC to substantiate compliance, which renders all of their tests invalid.

This means that the PacifiCorp roll-out of Aclara I-210+C AMI Meters across Oregon did not comply with the FCC regulations for the testing of RF emitting devices, and in fact occurred unlawfully, during which, the Oregon PUC failed in their duty to protect the public.

Third, in point of fact the PUC did worse than that, as they held no public meetings, solicited only the input of “stakeholders” such as PacifiCorp, and then in order to ensure compliance initially set onerous fees that required that anyone who chose to opt-out would have to pay a $137 fee for NOT putting one in, as well as an additional new $36 per month reading fee - a total fee of $569 for the first year alone, for exactly the same service that customers had previously. These opt-out fees - extraordinarily high compared to any other state in the country - in effect forced low income families who could not afford to pay these fees, to allow the installation of smart meters on their homes and businesses, even if they would have opted out for health, safety, or privacy reasons had the costs of doing so not seemed so high.

Con la inminente llegada de la cuarta revolución industrial, la red eléctrica se está moviendo hacia una red eléctrica inteligente, lo que le permite un sin fin de aplicaciones nuevas como lo son los sistemas de medición inteligente. Estas nuevas aplicaciones generan una cantidad inmensa de datos de diversas fuentes y para brindar la "inteligencia" necesaria se necesita del análisis y procesamiento de los datos. Para ello, es necesario de contar con repositorios de datos adecuados que permitan el almacenamiento, procesamiento y aplicación de los datos. Este trabajo presenta una arquitectura escalable de lagos de datos para redes eléctricas inteligentes utilizando un
enfoque de cómputo borde-niebla-nube. Los resultados parciales muestran que
es factible su implementación y que puede ser trasladada a otros contextos de
sistemas ciber físicos o de IoT.

The problem of Non-Instrusive Load Monitoring (NILM) is usually formulated as a single-channel blind source separation task, whose successful solution enable fast and convenient load identification and energy disaggregation. When applied at test time, NILM algorithms aim to identify the operating characteristics of individual appliances from an aggregate power measurement of the entire house. Recent advances in deep learning gave rise to many methods that mostly focus on learning a direct mapping from aggregate measurement to individual appliance power. However, these methods are not only computationally expensive, but they often suffer from overfitting and don't generalize very well. In this paper, we propose a novel NILM method that leverages advances in statistical learning that haven't been properly applied in this domain before. The proposed method consists of three stages: a) a Bayesian non-parametric learning-based approach for appliance state extraction; b) synthetic minority oversampling technique (SMOTE) for data augmentation and mitigating the heavy imbalance in switching events; and c) appliance-specific lightweight long short-term memory (LSTM) networks for status classification for each appliance. We adopt a “differential” input (the difference before and after the switching event) to reduce the complexity of network training and make the proposed method robust to multi-appliance switching events. Experiments are conducted to demonstrate the effectiveness of the proposed method, achieving superior performance when compared to recent methods. An ablation study is conducted to demonstrate the effectiveness of each module of our method. Finally, we investigate the quality of generated synthetic samples.

El abaratamiento de las energías renovables aunado al amplio uso de tecnologías de información y comunicaciones en la red eléctrica, entre otros factores han hecho que los mercados eléctricos principalmente en el sector minorista entre los usuarios finales estén cambiando de forma muy dinámica. En este trabajo se presenta un estudio de las diversas alternativas de mercados eléctricos minoristas transactivos y se propone un modelo mercado eléctrico simple con estas características adaptado para el entorno de los usuarios finales mexicanos.

ABSTRACT The following analysis and presentation summarizes the implementation in Matlab / Simulink environment of a prototype model digital energy circuit for measurement of the consumption of electric energy of an electrification network in the installations of customer of electric energy that can be applied as part of any consumption smart meter.

KEY WORDS: energy measurement, smart meter, energy circuit, digital to frequency, voltage to frequency, energy pulses, digital signal processing.

The accurate monitoring of distribution grids is essential to enable the intelligent management and control of future Smart Grids. Several challenges prevent an easy development of the state estimation tools needed to assess the operating conditions of distribution networks. The lack of a suitable measurement infrastructure is one of the most challenging aspects to face. However, in last years, several utilities started a massive deployment of smart meters in their networks. The proper use of these measurements is key to enhance the performance of distribution system state estimators. This paper presents a two-level approach conceived to efficiently include smart meter measurements in low voltage grid state estimation. The proposed solution relies on a cloud-based smart metering architecture, which allows scalability and interoperability among different off-the-shelf meters. Moreover, a suitable design of the estimation algorithm, using the uncertainty propagation theory, is proposed in order to maximize the accuracy of the estimation results. Tests performed on a sample low voltage network show the performance and the main features of the proposed state estimation solution.

Purpose. We analyze a set of smart meters implementation projects and provide insights and recommendations to facilitate smart metering deployment strategies.
Design/methodology/approach. Several significant projects are analyzed on different fronts: scale, technology, economics, and regulation using a common methodology to unfold patterns that constitute key components of successful smart meters diffusion.
Findings. Key elements and controllable enabling patterns from Europe-wide SM implementation projects are identified together with drivers and barriers for patterns replication.
Practical implications. We provide a framework considering different stakeholders that will help distribution system operators to accelerate and extend smart meters’ penetration.
Originality/value. Based on the Meter-ON project (supported by the 7th Framework Program of the European Commission) we put valuable information on the same basis for comparison purposes to facilitate the large-scale deployment of smart meters in Europe

This paper describes the design, implementation and testing of a distributed wireless system conceived for residential consumers which allows remote monitoring of energy consumption and power quality events from multiple electrical appliances. The system can be operated by the user from a central computer, which receives the collected data from a wireless sensor network based on the IEEE 802.15.4 and ZigBee standards. The development of the system involved the design and implementation of the hardware of the wireless sensor nodes; the software embedded in the network nodes, which manages the acquisition and transmission of data from the sensor nodes to the computer; as well as the user interface software in the central computer. Performance tests show that the developed prototype is able to provide measurements with very good accuracy and precision, for both voltage and current.

Smart grid utility provider collects consumers' power consumption data for three main reasons: billing, analysis, and operation. Billing needs coarse-grained data where there are no, or minimal, privacy concerns. While analysis and operation needs fine-grained data which can highly explore consumers' privacy. Hence, consumers might be reluctant to allow for operational metering to protect their privacy.This paper presents detail description of a reliable DNA-based privacy-preserving (DNAPP) scheme in smart grid. DNAPP assures robust authentication, confidentiality, message integrity, and non-repudiation across the smart grid as well as assuring high consumers' privacy. The scheme demonstrates many good security features, such as: high complexity of O(n!), lightweight , scalable, minimum overhead, no cryptography key exchange between the communicating parties as each of them can determine the key locally and independently. This scheme does not require any level of modifications to the existing smart grid infrastructure or smart meter. It only requires some software modifications.

Identification of electrical appliance usage(s) from the meter panel power reading has become an area of study in its own right. Many approaches over the years have used signal processing approaches at a high sampling rate (1 second typically) to evaluate the appliance load signature and subsequently used pattern recognition techniques for identification from (a) previously trained classifier(s). The proposed approach tries to identify the usage of high power consuming appliance(s) by using the aggregate power consumption at 10 minutes interval from the meter panel. The novelty of the approach lies in using a time series windowing approach which gives addition information about an aggregate power state. The usage of hour of the day as input to the systems also takes into account the temporal behavior of residential users. The usage of Multi-label classification approach for identification is also new for this domain. The model is tested over the IRISE data set and the results are encouraging. Due to its low sampling rate with time stamped aggregate power at 10 minutes scale as the only input from the user, the proposed approach is both practical and affordable.

One of the most relevant applications today in the smart grid is the use of artificial intelligence and analysis techniques over the data generated, allowing both end-users and utility companies new applications. Energy fraud is considered one of the main non-technical losses for utilities that translate into economic losses, so being able to detect and minimize fraud becomes a vital necessity for utilities. This paper presents a machine learning model that allows the detection of abnormal behaviors in readings of consumption and/or production of electrical energy that can be classified as energy fraud. The proposed model is based on regression and classification techniques over an edge-fog computing architecture, and the results obtained show that its implementation in smart metering systems is adequate.

In recent years, there have been many progressions in how the energy supply network operates due to smart meter technology installation. Due to its implementation, there has been an extensive collection of energy data usage. The vast array of usage data has provided a challenge to the energy sector industry. This data can provide valuable insights to improve energy suppliers’ energy regulation in load devising, examining trends, and forecasting areas such as time of use, demand, loss, and supply. In this paper, we will have a comparative study and examination of the methodology to create the Characterization profiles based on household usage, which can help achieve this goal. Based on these results, a series of Profile Classes is substantiated and represents common energy usage patterns. As a result, consumers and the way they consume energy can get characterized by individual consumer traits. With this, we will be able to identify the main characteristics that define a cluster. Due to this, future trends and significant applications of clustering consumption patterns can be utilized to acquaint industry practitioners and academic researchers to optimize smart meter operational use, furthermore, its effectiveness. It could help further promote messages such as people’s awareness of how much energy they use, therefore possibly reduce overall usage.

Technological advancements facilitate the transition to a decentralised and smart distributed generation electricity system where active customers will have a key role. Such a transition can contribute to making the electricity systems cleaner, more secure, more efficient and less expensive. Nevertheless, the promotion of distributed generation requires reforms to the applicable legislation, so that it fits the new reality. Accordingly, the Commission has put forward a proposal for a new Directive on the common rules for the internal electricity market. This proposal shows the Commission’s support in distributed generation and focuses on the promotion of self-consumption, instead of net metering, and on the empowerment of electricity customers through smart meter technologies and secure data management and data protection regimes. This proposal might originate the development of a supranational legislative framework fitting technological innovation in the field of electricity, but i...