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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.

End-users are transiting towards more active, integrating new low-carbon (LC) technologies and bringing unpredictability to low-voltage (LV) distribution networks. Although smart meters have a great potential in increasing the observability, they are mostly being employed only for billing purposes, leaving many other possibilities unexploited, further complicating the many analyses required for effective operational planning and real-time (RT) operation. Detection of phase consumption of end-users is significantly difficult, due to the nonlinear relationships between obtained phase voltage measurements and aggregated end-user consumption. Machine learning (ML) is increasingly used for these and similar problems, and therefore, in this paper, a neural network (NN) – based model is developed to detect end-user consumption in an LV distribution network from available voltage measurements and aggregated end-user consumption. Furthermore, the influence of topology on the output values of the model is investigated and a graph neural network (GNN) – based model is created that considers both the structure and data of the distribution network elements. Both models are tested on the real-world LV distribution network with more than 150 end- users. The results showed the effectiveness of both models in determining the distribution of end-user consumption, with the GNN-based model showing significantly better results. Such a model can help the energy utilities to overcome this time-consuming problem and lay a good foundation for further analyzes required to enable operation and planning of distribution networks.

There are 28 million homes in the UK, with a requirement for 53 million Smart: gas and, electricity meters. Out of these, 20 million homes will rely on Telefonica’s mobile network, to connect them to energy supply companies, raising concerns over their connectivity., These ‘connected’ gas and electric meters are more commonly and collectively known, as ‘smart meters’ and are typically connected to a Wide Area Network via a, Communication Hub. It is vitally important that wide scale ubiquitous connectivity is, provided to deliver service to 99% of the UK population, while avoiding any non-essential, investment., The radio prediction models used by Mobile Network Operators typically use a statistical, approach, and therefore to increase model accuracy at a particular point location you, simply increase the prediction error margin of the link budget. However, this reduces the, coverage area by the inverse square of the range reduction, meaning that improving the, accuracy of the model will result in a significant reduction in the predicted coverage, footprint, resulting in large numbers of additional base stations being required. Making it, difficult to meet contractual obligations in a financially viable way, and significantly, delaying rollout., Based on the signal strength measurements conducted at the Building Research, Establishment determining building penetration losses in both 900 and 2,100 MHz band, this thesis presents building penetration loss models using these measurements that are, practical and cost effective when compared to traditional statistical propagation loss, models. Building on this model, the thesis then provides an alternative method of, calculating reliable mobile network coverage for wide area networks serving smart meters, without requiring a significant prediction ‘error margin’., The aim of the research documented in this thesis is to develop a prediction system that, accurately predicts in-building mobile coverage, whilst ensuring that the predicted range of, the cell was not reduced. The provided framework then provides a highly accurate, estimation of coverage, enabling a binary decision to install a Smart Meter or not. This, thesis also provides alternative solutions for hard-to-reach locations such as enhancing the, coverage range of antennas with a mesh radio network technology. Extensive, measurements are made in Newbury in both suburban and open environments for, validation and delivery of a simple statistical model for the 868 MHz band in United, Kingdom conurbations., Results presented in this thesis will analyse the effectiveness of a Mesh radio network's, ability to provide an effective coverage extension method at 868 MHz. As it is expected, that Mesh radio will contribute to a significant increase in the number of connected smart, meters. Finally, a propagation model derived from the measurement campaign data will, then provide a method to estimate the likely coverage extension range of a mesh radio, gateway.

This paper presents a novel supervised machine learning-based electric theft detection approach using the feature engineered-CatBoost algorithm in conjunction with the SMOTETomek algorithm. Contrary to the previous literature, where the missing observations in data are either ignored or imputed with average values, this work utilizes k-Nearest neighbor technique for missing data imputation; thus, an accurate and realistic estimation of the missing data is achieved. To mitigate the biasness to the majority data class, the proposed model utilizes the SMOTETomek algorithm, which neutralizes the mentioned effect by managing a proper balance between over-sampling and under-sampling techniques. Feature Extraction and Scalable Hypothesis (FRESH) algorithm is utilized at the later stage of the proposed NTL detection framework to extract and select the most relevant data features from the provided dataset. Afterward, the model is trained using the CatBoost algorithm to classify the consumers into two distinct categories, i.e., genuine and theft. Finally, to interpret the model’s decision for the corresponding predictions, the tree-SHAP algorithm is utilized. To validate the efficacy of the proposed ML based theft detection approach, its performance is compared with that of the traditional gradient boosting ML algorithms such as XGBoost, lightGBM, Ensemble bagging, boosting ML models, and other conventional ML models using five of the most widely used performance metrics, i.e., precision, accuracy, F1score Kappa and MCC. The proposed technique achieved an accuracy of 93% and a detection rate of 92%, which is significantly higher than all the considered competing algorithms under identical dataset and hyperparameters.

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

The rapidly growing power data in smart grids have created difficulties in security management. The processing of large-scale power data with the use of artificial intelligence methods has become a hotspot research topic. Considering the early warning detection problem of smart meters, this paper proposes an abnormal data detection network based on Deep Reinforcement Learning, which includes a main network and a target network composed of deep learning networks. This work uses the greedy policy algorithm to find the action of the maximum value of Q based on the Q-learning method to obtain the optimal calculation policy. It also uses the reward value and discount factor to optimize the target value. In particular, this study uses the fuzzy c-means method to predict the future state information value, which improves the computational accuracy of the Deep Reinforcement Learning model. The experimental results show that compared with the traditional smart meter data anomaly detection method, the proposed model improves the accuracy of meter data anomaly detection.

Taking on the challenge of motivating users to drink water regularly, we designed a smart water bottle that can track water intake behavior and inform users about this behavior through ambient feedback. We then conducted two studies to explore the bottle's feedback design from the perspective of users and designers. First, we conducted semi-structured interviews with 10 prospective users and found that they would like to receive personalized, precise, gamified and reminding feedback. Second, we conducted a design workshop with 13 professional designers to explore the range of visualizations that can be used to give feedback. Analyzing these visualizations, we identified three reminder types (augmenting, restoring and balancing) and six visualization styles grouped according to three dimensions of ambient displays (representation fidelity, notification level, aesthetic emphasis). In this paper, we first explain our water bottle concept along with existing solutions. Then, we report the results of these studies. Finally, we discuss the potential implications of the results for our own work as well as for designing ambient displays aimed at supporting users' water intake tracking practices., NA

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.

The impact of new technologies within and across industries is only felt through their widespread diffusion, yet studies of technology diffusion are scarce compared to other aspects of the innovation process. The electric power industry is one industry that is currently undergoing substantial change as a result of both technological and institutional innovations. In this dissertation I examine the economic rationale for the adoption of smart meters by electric power utilities and the relationship between smart meters and the evolving electric power industry. I contribute to empirical research on technology diffusion by studying the early diffusion of smart meters in the US electric power industry.Using a panel dataset and econometric models, I analyze the determinants of both the interfirm and intrafirm diffusion of smart meters in the United States. The empirical findings suggest multiple drivers of smart meter diffusion. Policy and regulatory support have had a significant, positive impact on adoption but have not been the only relevant determinants. The findings also suggest that utility characteristics and some combination of learning, cost reductions, and technology standards have been important determinants affecting smart meter diffusion. I also explore the policy implications resulting from this analysis for enhancing the diffusion of smart meters. The costs and benefits of adopting smart meters have been more uncertain than initially thought, suggesting that some policy support for adoption was premature. The coordination of policies is also necessary to achieve the full benefits of using smart meters.

With rapid growth of urban populations and limited water resources, achieving an appropriate balance between water supply capacity and residential water demand poses a significant challenge to water supplying agencies. With the recent emergence of smart metering technology, where water use can be monitored and recorded at high resolution (e.g., observations of water use every 5 seconds), most existing research has been aimed at providing water managers with detailed information about the water use behavior of their consumers and the performance of water using fixtures. However, replacing existing meters with smart meters is expensive, and effectively using data produced by smart meters can be a roadblock for water utilities that lack sophisticated information technology expertise. The research in this dissertation presents low cost, open source cyberinfrastructure aimed at addressing these challenges. Components developed include an open source algorithm for identifying and classifying water end use events from smart meter data, a low cost datalogging and computational device that enables existing water meters to collect high resolution data and compute end use information, and a detailed water demand model that uses end use event information to simulate residential water use at a municipality level. Using this cyberinfrastructure, we conducted a case study application in the cities of Logan and Providence, Utah. We tested the applicability of the disaggregation algorithm in quantifying water end uses for different meter sizes and types. We tested the datalogging computational device at a residential household and demonstrated collection, disaggregation, and transfer of high resolution flow data and classified events into a secure server. Finally, we demonstrated a water demand model that simulates the detailed water end uses of Logan’s residents using a combination of a set of representative water end use events and monthly billing data. Using the data we collected and the outputs from the model, we demonstrated opportunities for conserving water through improving the efficiency of water using fixtures and promoting behavior changes.

Power distribution system operation has become increasingly complex with the large-scale integration of Distributed Energy Resources (DERs). This has led to a paradigm shift in the operation and planning of the power distribution network. Following the integration of DERs, situational awareness has become very important in distribution systems, including the primary and secondary distribution networks. The major aim of situational awareness is to provide operational visibility of the network, enabling the key information for a stable power distribution network. The foremost step for better situational awareness is integrating high-performance monitoring devices capable of monitoring the distribution network in real-time. Existing time synchronised measurement devices such as Phasor Measurement Units (PMUs) were primarily designed for transmission network monitoring. For economic reasons, these are not viable for monitoring Medium Voltage (MV) and Low Voltage (LV) distribution networks with vast numbers of nodes. Therefore, the fi rst part of this thesis proposes a monitoring device integrating smart meters using the IEEE-1588 protocol for time synchronisation purposes. This addresses the need for improved monitoring and situational awareness in MV and LV distribution networks. Test results indicate the proposed technique is expected to reduce the overall cost of the distribution network monitoring implementation compared to the system based on high-cost Phasor Measurement Unit (PMUs). These results are possible with the help of utilising the already existing smart meter's sensors to collect measurements and synchronise them using the IEEE-1588 time synchronisation technique. The next part of the thesis focuses on proposing a uniform time synchronisation technique for different distribution network applications. These applications utilise a uniform homogenous time synchronisation technique with different time criticality. A test bench is established to analyse the IEEE 1588 protocol for different time stampings showing seamless and cost-effective deployment in the distribution network. The third part of the thesis proposes a robust Distribution System State Estimation (DSSE) considering the measurements available at the LV level of the network. Computational complexity is considered to be an important factor in state estimation performance at the MV and LV levels. Therefore, this thesis proposes a linear state estimation technique for low voltage networks that solves the challenge of iterative approaches that are generally sensitive to erroneous measurement and are not computationally efficient. The effectiveness of the proposed algorithm is evaluated using a novel modi ed IEEE 906 European low voltage network, and the outputs of the state estimation studies are compared with the already existing literature. The proposed algorithm provides a two-layer state estimation technique for a realistic, unbalanced MV/LV distribution network which solves the convergence problem in the radial LV network caused by high R/X ratios. The utilisation of the LV state estimation states as the MV level measurements increases the state estimation accuracy while also reducing the need to install high-cost MV meters for monitoring purposes.

The task of determining the phase connection of customers, known as phase identification, is crucial to obtain accurate distribution system models. This paper starts with a thorough literature review of the existing phase identification methods, which are broadly divided into three categories: hardware-based, real power-based, and voltage-based methods. This is followed by multiple case studies assessing the accuracy of six real power- and voltage-based phase identification algorithms on four realistic distribution test systems. Synthetic load profiles along with network models are used to quantify accuracy of each method for different scenarios: varying advanced metering infrastructure (AMI) coverage, number of initially mislabeled customer phases, number of available samples, and measurement noise. A case study using a real AMI data set, including field verification, is also provided. Finally, several aspects key to accurate phase identification are discussed in detail.

This paper investigates the effect of data integrity attacks on the central control of the microgrids (MGs), which can lead to severe blackouts and load shedding. It assesses this cyber attack from the steady state and optimal scheduling point of view. In order to stop the cyber hacking, a new deep learning-based framework has been developed based on the generative adversarial networks (GANs). In this framework, two networks compete with each other, wherein the first network generates fake data, and the second one is responsible for the data classification. In order to get into the most optimal features, a new optimization method based on a modified teaching-learning based optimization (TLBO) algorithm is also devised to reinforce the GAN model and help a better matching training process. In addition, a new modification is introduced for TLBO to avoid premature convergence and provide high population diversity. To show the effectiveness of the proposed framework, a real dataset of several smart metering devices in a MG has been tested. Results illustrate the high performance of the proposed framework, comparing to the well-known conventional detection frameworks with hit rate of 93.11%, miss rate of 6.89%, false alarm rate of 7.76% and correct reject rate of 92.24%.

Internet of Things (IoT) technologies are opening new opportunities and services for different smart grid applications such as advanced meter infrastructures (AMI), distributed energy resources, and electric vehicles. Among these applications, AMIs represent the first step toward future smart grid implementation by enabling the reading and recording of electric power consumption on request or a schedule. In this study, the main focus is designing an IoT-based architecture to support AMIs’ daily reporting and billing in a residential grid. First, the network modeling for AMI is discussed. Long range (LoRa) technology, as one of the promising candidates for long range low power wide area networks (LPWANs), has been studied and discussed. A comprehensive analysis of the LoRa-based architecture is given for a case study of an actual residential grid, Puerto Montt, Chile. The results are analyzed and discussed for packet delivery ratio, energy consumption, throughput, number of collisions, and frequency distribution.

The implementation of the Measuring, Monitoring and Control System (MMCS) in Chile was the key element of the most important regulatory and political crisis which has affected the Chilean electric sector. In the news this period was named as “smart-meters crisis”. This crisis was also one of the elements that contributed to the social outburst in October 2019. This paper presents both the technical and regulatory background that explain the failure of a public policy intended to modernize the electric distribution grid. It is described the arguments that promoted the investment of the companies on the MMCS, the antecedents that lead the Government to eliminate the obligations opting for the voluntariness in relation to the change of smart meters during the implementation process and the dilemma generated with regards to the pilot projects developed by some electrical distribution companies. Finally, a solution is proposed for the smart-meter’s property dilemma.

Simple analysis of energy consumption patterns recorded by smart meters can be used to deduce household occupancy. With access to higher-resolution smart-meter readings, we can infer more detailed information about the household including the use of individual electric appliances through non-intrusive load monitoring techniques. The extent of privacy concerns caused by smart meters has proved to an obstacle in the roll-out of smart meters in some countries. This highlights the need for investigating smart-meter privacy. Mechanisms for ensuring smart-meter privacy fall in broad categories of data manipulation, demand shaping, and load scheduling. In smart-meter data manipulation, the smart meter collects real, potentially high-resolution data about the energy consumption within the house. This data is then manipulated before communication with to utility providers and retailers. The manipulation could be non-stochastic, such as aggregation, binning, and down-sampling, or stochastic, such as additive noise. In demand shaping and load scheduling, smart-meter readings are communicated without any interference but the consumption is manipulated by renewable energy sources, batteries, or shifting loads to render non-intrusive load monitoring ineffective. In this study, the author reviews these approaches and presents several methods relying on homomorphic encryption, differential privacy, information theory, and statistics for ensuring privacy.

Advanced metering infrastructure (AMI) plays a key role in power systems. Since smart meters and meter collectors are synchronised to the time synchronisation devices (TSDs) in the head end system (HES) of AMI, they are vulnerable to global positioning system (GPS) spoofing-based time synchronisation attack (TSA). Impacts of GPS spoofing-based TSA on AMI are investigated in this study. It is uncovered that, since AMI is a distributed networked system and metering data and control commands transmitted in AMI could be of large latency, data and commands with large latency over the specific threshold are considered to be invalid according to validity verification mechanism of average distributed system. Therefore, the disorder in time synchronisation induced by GPS spoofing-based TSA could disable functions of HES of AMI, such as meter reading and remote control. A time jitter detection-based approach is developed to identify and prevent from GPS spoofing-based TSA. A high-precision oven-controlled crystal oscillator with cumulative error compensation is utilised to identify time jitter of the satellite clock and help ride through sustained GPS spoofing-based TSA. Simulation on FPGA demonstrates the effectiveness of the proposed approach.

This paper develops a novel approach for distribution system monitoring and state estimation, where time synchronisation of smart-meter measurements is carried out via the Precision Time Protocol (PTP). The approach is based on the concept of a Modified Smart Meter (MSM), a distribution system monitoring instrument that enables accurate time synchronisation of smart meter data. The design, application, communication technique and protocols of the MSM are described in detail. The proposed MSM device features PTP-based time synchronisation of smart meter measurements, and the concept of unbundling is applied to collect measurements utilising the existing smart meter sensors. This is expected to reduce the overall implementation cost of an MSM-based distribution network monitoring system compared to a system based on Phasor Measurement Units (PMUs). The problem of requiring open sky access for GPS links can potentially be solved by means of PTP synchronisation. Three-phase state estimation simulations using the IEEE-13 and 123 bus unbalanced test networks are employed to demonstrate the applicability of the MSM, and its performance is compared to standard PMU devices. The results indicate that the MSM may represent a workable monitoring solution for MV and LV distribution networks, with an acceptable trade-off between cost and performance.

The development of a smart grid electricity distribution network with advanced technology in smart metering will produce a massive amount of data. However, the limitation in communication network bandwidth makes it hard to transmit these data to the control center. Data compression is one of the best solutions to overcome this limitation. This study presents the use of multiresolution matrix factorisation (MMF) as a data compression technique for a smart distribution system. MMF will compress a data matrix into a core matrix with lower dimension via a series of orthogonal transformations. Experimental results gained from this study show that MMF is applicable in compressing large size data into lower dimension matrix with low error rates and high in speed. The MMF compression technique is able to reduce the volume of data to be transmitted through the communication network and thus save the bandwidth. Besides, MMF compression performs faster than the singular value decomposition method, especially with large size matrix. Findings from this study prove that MMF can serve as an alternative data compression technique for the smart distribution system, with a potential for an online application due to the high speed and high accuracy of the algorithm.

Made available in DSpace on 2020-12-11T19:25:56Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-06-01 Recently, the Brazilian Electricity Regulatory Agency (ANEEL) regularized the electricity prepayment system and opened a general discussion about the technologies, resources, and implementations for the prepayment metering system. In several countries, the consumer charges the value equivalent to the kilowatt-hours that he wishes to buy for use. However, some consumers purchase credit with the expectation that the charge remains for a specific time, but they may be surprised with the credit end before the expected time. For this reason, this paper proposes automatic consumption management for prepaid meters that analyzes the consumer appliance behavior, performs the temporal analysis of consumption by appliances, defines the cost management per load with high energy consumption and sends sound signals to the consumer ends the appliance usage. The objective of this management system is to ensure that the credit will be used for the consumer expected time. Univ Estadual Campinas, Campinas, SP, Brazil Univ Fed Sao Joao del Rei, Ouro Branco, MG, Brazil Univ Estadual Paulista, Campus Sorocaba, Sao Paulo, Brazil Univ Estadual Paulista, Campus Sorocaba, Sao Paulo, Brazil

This paper proposes an architecture of locality electricity market (LEM) for peer-to-peer (P2P) energy trading among a group of residential prosumers (consumers and producers) with renewable energy resources, smart meters, information and communication technologies, and home energy management systems in a smart residential locality. Prosumers may sell(buy) their excess generation(demand) in LEM at a profitable prices compared to the utility prices in P2P fashion. In order to manage the trading in LEM, a common portal named as locality electricity trading system (LETS) is introduced. The purpose of LETS is to prepare a trading agreement between the participants by fixing a price for every deal based on the quoted price and day-ahead power trading schedule given by the participants. An enhanced intelligent residential energy management system (EIREMS) is proposed at the prosumers' premises to enable their participation in the day-ahead energy trading process and in real-time scheduling of schedulable loads and battery for reducing the electricity bill with due consideration to the operational constraints and LETS agreement. The performances of proposed LETS and EIREMS are validated through a few case studies on a locality with ten prosumers. The proposed methodology endorses marginal economic benefit for all the participants.

Advances in distributed generation and increased contribution of renewable energy source (RES) require development of smart grid technologies. Smart metering systems, as a part of smart grid technologies, in cooperation with modern buildings equipped with building management system allows for improvement of energy efficiency. It is possible to partially cover the power demand of a building from the local RESs. However, in order to ensure maximum added value, energy management system (EMS) is essential. This article presents the project and practical implementation of an EMS implemented in smart-meter. The designed system is based on an original algorithm using fuzzy logic. The rule base was created in FCL language and the implementation was carried out in C++ with the object-oriented programming (OOP). For the efficiency rating indicator, peak-to-average ratio (PAR) was selected. This ratio depending on the daily load profile decreased within a range from 15 to 54%, and the average value was 30%. The proposed energy management algorithm helps to reduce energy consumption at peak demand by 34%, with the total reduction of energy consumption during the day of 7%. The described solution demonstrates a potential for real implementation and was tested in hardware.

To improve the energy efficiencies of building cooling systems, manufacturers are increasingly utilizing variable speed drive (VSD) motors in system components, e.g. compressors and condensers. While these technologies can provide significant energy savings, these benefits are only realized if these components operate as intended and under proper control. Undetected faults can foil efficiency gains. As such, it's imperative to monitor cooling system performance to both identify faulty conditions and to properly inform building or multi-building models used for predictive control and energy management. This paper presents nonintrusive load monitoring (NILM) based “mapping” techniques for tracking the performance of a building's central air conditioning from smart electrical meter or energy monitor data. Using a multivariate linear model, a first mapping disaggregates the air conditioner's power draw from that of the total building by exploiting the correlations between the building's line-current harmonics and the power consumption of the air conditioner's VSD motors. A second mapping then estimates the air conditioner's heat rejection performance using as inputs the estimated power draw of the first mapping, the building's zonal temperature, and the outside environmental temperature. The usefulness of these mapping techniques are demonstrated using data collected from a research facility building on the Masdar City Campus of Khalifa University. The mapping techniques combine to provide accurate estimates of the building's air conditioning performance when operating under normal conditions. These estimates could thus be used as feedback in building energy management controllers and can provide a performance baseline for detection of air conditioner underperformance.

Electric load data are essential for data-driven approaches (including deep learning) in smart grid, and advanced smart meter technologies provide fine-grained data with reliable communications. Despite the recent development of smart metering devices, however, missing data still arise due to unexpected device power off, communication failure, measuring error, or other unknown reasons. In this paper, we investigate a deep learning framework for missing imputation of smart meter data by leveraging a denoising autoencoder (DAE). Then, we compare the performance of the proposed DAE with traditional methods as well as other recently developed generative models, e.g., variational autoencoder and Wasserstein autoencoder. The proposed DAE based imputation shows significantly better results compared to other methods in terms of root mean square error (RMSE) by up to 28.9% for point-wise error, and by up to 56% for daily-accumulated error.

This study proposes a modular water monitoring IoT system that enables quantitative and qualitative measuring of water in terms of an upgraded version of the water infrastructure to sustain operational reliability. The proposed method could be used in urban and rural areas for consumption and quality monitoring, or eventually scaled up to a contemporary water infrastructure enabling water providers and/or decision makers (i.e., governmental authorities, global water organization, etc.) to supervise and drive optimal decisions in challenging times. The inherent resilience and agility that the proposed system presents, along with the maturity of IoT communications and infrastructure, can lay the foundation for a robust smart water metering solution. Introducing a modular system can also allow for optimal consumer profiling while alleviating the upfront adoption cost by providers, environmental stewardship and an optimal response to emergencies. The provided system addresses the urbanization and technological gap in the smart water metering domain by presenting a modular IoT architecture with consumption and quality meters, along with machine learning capabilities to facilitate smart billing and user profiling.

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.

Industry 4.0 and its technologies allow advancements in communications, production and management efficiency across several segments. In smart grids, essential parts of smart cities, smart meters act as IoT devices that can gather data and help the management of the sustainable energy matrix, a challenge that is faced worldwide. This work aims to use smart meter data and household features data to seek the most appropriate methods of energy consumption prediction. Using the Cross-Industry Standard Process for Data Mining (CRISP-DM) method, Python Platform, and several prediction methods, prediction experiments were performed with household feature data and past consumption data of over 470 smart meters that gathered data for three years. Support vector machines, random forest regression, and neural networks were the best prediction methods among the ones tested in the sample. The results help utilities (companies that maintain the infrastructure for public services) to offer better contracts to new households and to manage their smart grid infrastructure based on the forecasted demand.

The integration of the internet of things (IoT) concept into the traditional electricity grid introduces several critical vulnerabilities. Intrusion detection systems (IDSs) can be effective countermeasures against cyberattacks, however, they require considerable computational and storage resources. As IoT-enabled metering devices have limited resources, IDSs cannot efficiently ensure security. To this end, trust evaluation schemes have emerged as promising solutions toward protecting resource-constrained metering devices. In this work, we proposed a trust evaluation scheme for the smart grid, that is based on direct trust evaluation and recommendation. The proposed hierarchical scheme is able to evaluate the trustiness of each metering device without requiring any significant modifications to the already deployed infrastructure. Additionally, the proposed scheme features is dynamic, meaning that it is robust against non-adversarial events that negatively impact the device’s trustiness. To validate the performance of the proposed scheme, we carry out network-level simulations and investigate how the various network parameters impact the trust evaluation performance.

The smart grid paradigm and development of smart meters has led to availability of large volumes of data. This data is expected to assist in power system planning/operation and the transition from passive to active electricity users. With recent advances in machine learning, this data can be used to learn system dynamics. This paper explores two model-free reinforcement learning (RL) techniques - policy iteration (PI) and fitted Q-iteration (FQI) for scheduling the operation of flexibility providers - battery and heat pump in a residential microgrid. The proposed algorithms are data-driven and can be easily generalized to fit the control of any flexibility provider without requiring expert knowledge to build a detailed model of the flexibility provider and/or microgrid. The algorithms are tested in multi-agent collaborative and single agent stochastic microgrid settings - with the uncertainty due to lack of knowledge on future electricity consumption patterns and photovoltaic production. Simulation results show that PI outperforms FQI with a 7.2% increase in photovoltaic self-consumption in the multi-agent setting and a 3.7% increase in the single agent setting. Both RL algorithms perform better than a rule-based controller, and compete with a model-based optimal controller, and are thus, a valuable alternative to model- and rule- based controllers. ispartof: IET Smart Grid vol:3 issue:1 pages:1-11 status: published

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 nonrepudiation 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!), light-weight, 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.

This paper focuses on obtaining the optimal communication topology in the smart grid architecture, i.e., what is the optimal communication setup of smart meters in a smart building. The fact that smart meters also consume energy, more often than not, gets ignored by researchers and engineers. In this paper, we will show that smart meter networks can consume significantly less energy with optimal scheduling. Numerical results show that the overall energy consumption can be reduced by implementing the optimal communication architecture and transmission rate setup, rather than implementing a straightforward communication architecture with uniform channel bandwidth.

Topology verification supports effective management, loss reduction, and energy conservation strategies in low-voltage distribution networks across China. Of particular interest is the users-transformer connectivity relationship. Existing research shows that voltage profile similarity based on voltage data from the Advanced Metering Infrastructure (AMI) can determine the users-transformer connectivity relationship in a neighbourhood. This article analyses the electrical principles of the users-transformer connectivity relationship based on the similarity of voltage profiles and voltage data from the AMI and derives appropriate formulas that express those relationships. The results demonstrate that voltage profile similarity is not only related to electrical distance but also related to user load profiles. The validity of the derived formulas is validated in two scenarios: a correct relationship between a user and a transformer and a partial misjudgement between a user and a transformer. This article presents a theoretical foundation for analysing the connectivity relationship in low-voltage distribution networks using voltage data from AMI.

This study presents a smart power strategy coordination for optimal electricity supply. It aims to coordinate the energy flow on the electrical system for a residential application in the southern area of the African continent, in conjunction with an intelligent demand management control strategy. It has been observed that there is a potential for renewable energy resources that can enhance the energy and development of the African continent if used efficiently. In addition, the continent is suffering from several energy crises, which could be resolved by appropriate energy control approaches. It has been observed that incorporating an autonomous smart strategy that can coordinate a hybrid energy supply for a residential energy demand could improve the power grid performance of the Southern African region. In this paper, a smart strategy that develops a dynamic real-time energy structure control is proposed. This approach uses the ability of smart metering to create a flexible communication control strategy (FCCS), which will manage the demand and the supply of energy. It ensures the consumer's optimal power supply while ensuring stability and good performance on the utility side.

To detect energy theft attacks in advanced metering infrastructure (AMI), we propose a detection method based on principal component analysis (PCA) approximation. PCA approximation is introduced by dimensionality reduction of high dimensional AMI data and the authors extract the underlying consumption trends of a consumer that repeat on a daily or weekly basis. AMI data is reconstructed using principal components and used for computing relative entropy. In the proposed method, relative entropy is used to measure the similarity between two probability distributions derived from reconstructed consumption dataset. When energy theft attacks are injected into AMI, the probability distribution of energy consumption will deviate from the historical consumption, so leading to a larger relative entropy. The proposed detection method is tested under different attack scenarios using real-smart-meter data. Test results show that the proposed method can detect theft attacks with high detection percentage.

Wireless power transmission is a promising technique to provide continuous power supply for the limited capacitance of smart meters (SMs) with short distance communication. In this study, the authors propose a remote-relay-nodes (RRNs)-aided wireless SM system, where several geographically separated RRNs are connected to a concentrator or an acquire terminal via radio-over-fibre. A RRN selection scheme is employed in the wireless power transfer phase. With the energy harvested, the SM can report special or urgent events in the wireless information transfer phase. The asymptotic reporting probability and reliability of smart metering are derived in terms of close form over fading channels. Numerical results are provided to verify the theoretical analysis. The optimal deployment of RRNs can be also efficiently obtained by averaging the locations of a SM in the network.

In this paper, a pricing alternative based on the scheme of Time of Use Pricing (TOUP) is proposed for customers connected to the Cooperativa Eléctrica San Pedro de Atacama (CESPA) microgrid, located in the far north of Chile. With this proposal, the promotion of Demand-Side Management, DSM, aims at optimizing both the use of electric energy and the available infrastructure. The pricing proposal replaces the current tariff scheme based on sections of energy consumption and does not give an incentive for customers to efficiently manage their energy consumption. The proposal considers the creation of time bands and their corresponding tariff formulas, to obtain economic benefits both for customers and the electric company. Study cases consider the operation of a photovoltaic plant of 2 MWp in the electric system of CESPA, which is currently underway. The obtained results report benefits for all parts of the electric market. For customers, favorable pricing up to a 19.1% monthly reduction in electric bills is shown, whereas the company presents an increment in their average monthly income of about 7.7%.

The introduction of a new pricing mechanism, the increasing-block tariff (IBT), will not only affect electricity bills for residents, but also lead to a change in residential electricity consumption behaviours. Understanding these consumption patterns will help create more accurate load forecasting and increase the efficiency of the IBT. This study proposes an innovative clustering-based approach for short-term load forecasting under the IBT in China. The new approach initially partitions households into homogeneous groups each of which has distinctive consumption patterns under the IBT, each consumer segment can then select the most appropriate model for load forecasting, and the predicted load demands of different clusters are aggregated to derive the total usage. In particular, the IBT-related attributes are newly introduced into the clustering analysis. The utility and effectiveness of the proposed model is confirmed through a realistic dataset that contains the daily household-level consumption data of 533 households from April 2014 to February 2015. Consequently, the households are classified into five clusters with distinctive consumption patterns, including low-demand and insensitivity to high temperature ( C l u s t e r 1 ), ordinary users and sensitivity to high temperature ( C l u s t e r 2 ), ordinary users and sensitivity to the IBT ( C l u s t e r 3 ), high-demand consumers and sensitivity to high temperature ( C l u s t e r 4 ), and luxury consumers ( C l u s t e r 5 ). In addition, the obtained experimental results demonstrate that the proposed approach can not only achieve better prediction accuracy (e.g., the mean absolute percentage error (MAPE) improves from 3.82% to 2.28% by using autoregressive integrated moving average (ARIMA)), but also provide better flexibility for hybrid modelling. From the practical implication point of view, the proposed forecasting model can help power companies to provide a reliable and high-quality electricity supply as well as to establish appropriate schedules of operations and maintenance within a certain area. Moreover, the identified consumption behaviours can be analysed and used to improve the design and promote awareness/acceptance of the IBT.

This paper proposes a framework to investigate the value of sharing privacy-protected smart meter data between domestic consumers and load serving entities. The framework consists of a discounted differential privacy model to ensure individuals cannot be identified from aggregated data, a ANN-based short-term load forecasting to quantify the impact of data availability and privacy protection on the forecasting error and an optimal procurement problem in day-ahead and balancing markets to assess the market value of the privacy-utility trade-off. The framework demonstrates that when the load profile of a consumer group differs from the system average, which is quantified using the Kullback-Leibler divergence, there is significant value in sharing smart meter data while retaining individual consumer privacy., Comment: 5 pages, 4 figures, submitted to the 2021 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT NA)

In the era of information technology and big data, the extraction, commodification, and control of personal information are redefining how people relate and interact. In Scandinavian countries, it is intrinsic to the culture to share similar values in order to achieve social trust. Consequently, these countries have the highest social trust rate worldwide (Beilman, Lilleoja 2015). However, even the most trustable societies can be challenged by the conflicts of interest that are induced through the use of big data. In this chapter, the focus is to find the connections of Scandinavian culture of trust, in particular, when it comes to the collection of big data generated through smart meters. To identify these connections we decided to take a systematic approach that includes: 1. an analysis of what can be found on the Internet. We adopt this approach because this is where a significant amount of public discussion takes place. 2. A systematic literature review of scientific articles indexed in the Scopus platform for a period of 10 years (2008 until 2018). We synthesize the results with a focus on demographic characteristics, the subject area, the concept and main claims they do, as well as the results they provide. State of the art, advances and gaps were identified with focus on the concepts of surveillance capitalism, big data, data ethics and Scandinavian trust in the context of smart grids. In the era of information technology and big data, the extraction, commodification, and control of personal information is redefining how people relate and interact. However, the challenges that big data collection and analytics can introduce in trust-based societies, like those of Scandinavia, are not yet understood. For instance, in the energy sector, data generated through smart appliances, like smart metering devices, can have collateral implications for the end-users. In this paper, we present a systematic review of scientific articles indexed in Scopus to identify possible relationships between the practices of collecting, processing, analysing, and using people's data and people's responses to such practices. We contextualise this by looking at research about Scandinavian societies and link this to the academic literature on big data and trust, big data and smart meters, data ethics and the energy sector, surveillance capitalism, and subsequently performing a reflexive thematic analysis. We broadly situate our understanding of culture in this context on the interactions between cognitive norms, material culture, and energy practices. Our analysis identified a number of articles discussing problems and solutions to do with the practices of surveillance capitalism. We also found that research addresses these challenges in different ways. While some research focuses on technological amendments to address users’ privacy protection, only few examine the fundamental ethical questions that discuss how big data practices may change societies and increase their vulnerability. The literature suggests that even in highly trusting societies, like the ones found in Scandinavian countries, trust can be undermined and weakened.

Summarization: Smart meters, one of the crucial enablers of the smart-grid concept and cornerstones in smart planning for cities, offer the opportunity for consumers to address their energy consumption effectively through timely and accurate data on their energy usage. However, previous studies have shown that smart meters may not lead to the desired energy savings unless actively used by households. To this end, the research presented in this paper investigates the penetration of smart meters at community level and explores how such a metering system can help people to understand and manage their energy use better. It examines the awareness about smart meters, looks into their presence in current accommodation and focuses on the views people have about smart meters. For this purpose, a questionnaire was prepared and distributed to a group of individuals residing in the wide area of Ancona province in Italy. Although the deployment of modern second-generation smart meters started in 2017 replacing the outdated smart meters massively installed in the 2000s, the results show low-to-moderate levels of awareness of modern smart meters among the respondents and a low presence of second-generation metering devices in their current accommodation. However, the general view expressed by the participants about smart meters is positive. The findings demonstrate that respondents are in need not only of a gauge that measures energy consumption but also of a tool that assists them to manage effectively their energy use. Παρουσιάστηκε στο: 3rd International conference on Smart and Sustainable Planning for Cities and Regions

Smart meters have the potential for improving the accuracy of demand forecasts and the energy efficiency, also allowing for the reduction in energy consumption. Mostly the smart meter data is used for the measurement of energy usage at the consumer side that is then sent to the utility providers for billing purposes and demand planning. The Internet of Things (IoT) is used to collect the data from smart meters, and that data is used for the calculations and visualization for smart grid maintenance and future decisions. The number of smart meters is constantly increasing and so is the data from those meters. Gathering and performing analytics on such data is a hard computational task. In this study, we have highlighted the role of IoT, edge computing, and big data and analytics focusing on how to speed up the information retrieval from the smart grid data and how that information can be used for multiple purposes, not only for billing purposes. A framework for more efficiently analyzing data obtained from smart meters is presented, which utilizes edge computing and big data and analytics to process raw data to useful information. Post-print / Final draft

Smart meters can help citizens in optimizing energy consumption patterns. However, mixed evidence exists on their effectiveness in reducing energy demand and especially in levelling off the daily peaks of electricity load curves. Here, we evaluate the impact of providing real-time feedback on electricity consumption from a field trial in Italy. We combine standard regressions with machine learning techniques on high-frequency data to quantify impacts on both levels and patterns of electricity use. Results indicate that real-time feedback can moderately decrease electricity consumption (between 0.5 and 1.9% depending on model specification), but that it does not promote load shifting throughout the day by itself. Machine learning reveals evidence of significant household heterogeneity in the behavioral response.

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 objective of this study was the definition and determination of sustainable water consumption indicators for activity categories, as well as the evolution of water consumption in commercial buildings. These indicators were determined through statistical analyses using Shewhart charts. Within a broader scope, the research proposed a methodology to automate sustainable management of water consumption in building operation using BIM–IoT–FM integration. The scientific rigor of the methodology was based on the precepts of design science research. The methods proposed for the construction of functionalities and the application of the reference indicators provided an optimized analysis of water consumption and the detection of excess consumption and leaks. The methodology, implemented in an online prototype, AquaBIM, could deliver a significant advance for building management. A conceptual test of AquaBIM evaluated the consumption indicators and validated our methodology through its application in a commercial building. The building consumption analyses showed a potential for approximately 15% savings. In addition, five requirements of the international sustainability certification AQUAHQE were met. The results of our research provide an innovative approach for the automation of sustainable building management and could be expanded to monitor and report on the consumption of other critical resources such as electricity and gas.

© 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.

This paper presents the importance and applications of smart cities in view of taxonomy in urbanization particularly in Asia and Africa economies. It describes the characteristics and architecture of smart cites and reviews on the recent technological developments. The paper analyses the social impacts due to up-gradation of existing cities. The implementation goals like policies and standards are still in progressive state. The international organizations like IEEE, ISO, IEC etc are focused in this emerging area and prepared road map for successful deployment of technologies in cities. In this way of development, there are some interesting challenges like visualization, integration, privacy etc, need to be addressed with specific and innovative solutions. The paper highlights the opportunities in developing and governance of smart cities.

Electricity load forecasting plays an important role in planning and a vital role in the operational management of an electric power system based on smart grids. In this work, several data-driven approaches are used to forecast the individual electricity demand for the subsequent hour of three Italian households in a nanogrid context. For each user, the tested prediction models exploit only the historic series of total electricity consumption. The results show similar performances in all models implemented. Despite a widespread delay, the predictions follow the measurement trend well, while also highlighting the particular difficulty of predicting peak values.

© 2019 Elsevier Ltd Smart meters have been widely deployed in power networks since the last decade. This trend has resulted in an enormous volume of data being collected from the electricity customers. To gain benefits for various stakeholders in power systems, proper data mining techniques, such as clustering, need to be employed to extract the underlying patterns from energy consumptions. In this paper, a comparative study of different techniques for load pattern clustering is carried out. Different parameters of the methods that affect the clustering results are evaluated and the clustering algorithms are compared for two data sets. In addition, the two suitable and commonly used data size reduction techniques and feature definition/extraction methods for load pattern clustering are analysed. Furthermore, the existing studies on clustering of electricity customers are reviewed and the main results are highlighted. Finally, the future trends and major applications of clustering consumption patterns are outlined to inform industry practitioners and academic researchers to optimize smart meter operational use and effectiveness.