Deployment Of Smart Meters Research Articles

What is the effect of weather on household electricity consumption? Empirical evidence from Ireland

We explore the links between weather variables and residential electricity consumption using high-resolution smart metering data. While weather factors have been used for grid-level electricity demand estimations, the impact of different weather conditions on individual households has not been fully addressed. The deployment of smart meters enables us to analyse weather effects in different periods of the day using hourly panel datasets, which would previously have been impossible. To conduct the analysis, fixed-effects models are employed on half-hourly electricity consumption data from 3827 Irish household meters. We demonstrate that temperature has robust and relatively flat effects on electricity demand across all periods, whereas rain and sunshine duration show greater potential to affect individual behaviour and daily routines. The models show that the most sensitive periods differ for each weather variable. We also test the responses to weather factors for weekends and workdays. Weather sensitivities vary with the day of the week, which might be caused by different household patterns over the course of the week. The methodology employed in this study could be instructive for improving understanding behavioural response in household energy consumption. By using only weather indicators, this approach can be quicker and simpler than traditional methods – such as surveys or questionnaires – in identifying the periods when households are more responsive.

Analysis of regulatory process for the implementation of smart metering in Brazil

The expansion of smart metering technology worldwide has represented a change in the energy sector, as it incorporates new technologies for energy consumption. In Brazil, this topic has been contributing to the formation of a regulatory framework regarding new technologies and to the technological training of the national industry. However, the country still needs to overcome some challenges in this sector regarding, for example, energy loss and the lack of interoperability among power metering systems and equipment. In Europe, some countries are in an advanced process of smart metering implementation since they have created all the regulatory and technological foundation needed to support this advancement. This article brings as novelty the analysis of different generations of smart meters in order to classify the existing technologies in Brazil. Given the advanced position of some countries in Europe in the deployment of smart meters, the study also aims to analyze the Brazilian regulatory process aimed at smart metering from the various European Commission measures and directives, as well as some initiatives practiced in specific countries, such as Italy and the United Kingdom. In addition, the study aims to compare the minimum functionalities of the European Union’s measurement system with the Distributed Measurement System (DMS) used in Brazil. These analyses aim to identify the level of smart metering in Brazil and present the best practices adopted in Europe to contribute to the formation of public policies aimed at the dissemination of smart metering in the country.

Resource Efficient Advanced Metering Infrastructure Model

Advanced Metering Infrastructure (AMI) enables two-way communication between smart devices and utility control centers. This involves remote monitoring and control of energy consumption as well as other parameters in the electrical power network in real time. However, increasing technologies in AMI due to huge deployment of smart meters, integration of devices and application of sensors, demand a strong architectural model with the best network topology to guarantee efficient usage of network resources with minimal latency. In this work, a resource efficient multi-hop network architecture is proposed using hybrid media access protocols. The architecture combines queuing and random-access protocol to achieve optimal network performance. Numerical results show that the probability of delay incurred by an arbitrary smart meter depends on the mean and distribution of the queue switch over a period. It is also observed that for a single queued system, the throughput performance is equal to the existing hybrid method. As the number of smart meters increases to 500, the throughput of the proposed method improves by 10% compared to the existing method. Likewise, as the number of smart meters increases to 500, the delay reduced by 15% compared to the existing method.
 Keywords: Advanced Metering Infrastructure; hybrid media access protocols; Smart Meter; Smart Grid; Power Network.

Characterization of Uncertainties in Smart City Planning: A Case Study of the Smart Metering Deployment

Making cities smart represents a major potential for sustainable development, where both the quality of life and the economy improve. Implementing new and efficient solutions in a smart city involves a large spectrum of uncertainties related to the size and project complexity. Characterization and assessment of the variables uncertainty in planning methodology seem necessary to reach the best decision about the best approach to achieve favorable realization outcomes for planned projects. By including uncertainties in the planning, assessment makes it possible to calculate result uncertainties for all expectations, and project cost-effectiveness. In this way, planning can be improved, if the most important parameters of result uncertainties are identified, better defined, and controlled. This study describes a parameter uncertainty characterization methodology applied on the cost-benefit analysis of smart city development with a case study, focused on smart metering infrastructure. Parameter uncertainty characterization is performed based on its variable nature (epistemic and aleatory), time-dependency, and the available information. Cost-benefit analysis results are given as both point value and as uncertainties. Uncertainty is considered for 25 variables of investment and operating costs, and benefits estimation. The presented methodology in smart city planning provides a way to better identify the critical parameters for achieving the defined objectives.

Analysis of Residential Consumers’ Attitudes Toward Electricity Tariff and Preferences for Time-of-Use Tariff in Korea

In recent years, a Time-of-Use (TOU) tariff for residential consumers has received much more attention with the increasing deployment of smart meters for residential consumers in Korea. The introduction of the TOU tariff for residential consumers is expected to allow residential consumers to have a choice of electricity rate plans in addition to load management. An analysis of residential consumers’ preferences for the TOU tariff is needed to identify TOU attributes’ levels. This paper analyzes Korean consumers’ attitudes toward current residential electricity tariff and preferences for TOU tariff using demographic characteristics collected by a face-to-face survey. Consumers’ preferences on key attributes of TOU tariff are analyzed using the conjoint analysis, and attitudes toward current residential electricity tariff are estimated by the multiple indicators multiple causes (MIMIC) model. The analysis results of this study show that based on their attitude toward electricity tariff, consumers’ attitudes can be divided into three latent variables, and preferences for TOU tariff are discrete by group rather than continuous by individual consumer.

Closed-Loop Aggregated Baseline Load Estimation Using Contextual Bandit With Policy Gradient

Demand response (DR) is an important technique to explore the demand-side flexibility. The wide deployment of smart meters makes it possible to quantify the baseline load. As an intermediate agent, demand response aggregator needs to obtain the aggregated baseline load (ABL) for the DR event. Previous studies about the household level estimation focus on the estimation method. However, for ABL estimation, customer division is an important issue. A major limitation is the mismatch between the objectives of segmentation and estimation. Therefore, this paper proposes a new closed-loop method for estimating the ABL, which utilizes the contextual bandit with policy gradient to link the segmentation with the estimation. As such, the ABL estimation accuracy can guide the segmentation to divide the customers. The segmentation and estimation optimize collaboratively to improve the ABL estimation accuracy. An ensemble method for combining network’s weights during the training process is proposed. Moreover, a pre- and post-event adjustment method is developed to further improve the estimation accuracy. Comprehensive comparisons demonstrate the proposed method can achieve the best estimation performance with regard to the MAPE and RMSE. It improves the estimation accuracy by 7% in terms of MAPE, and 11% in terms of RMSE.

Design and Implementation of a Smart Energy Meter Using a LoRa Network in Real Time

Nowadays, the development, implementation and deployment of smart meters (SMs) is increasing in importance, and its expansion is exponential. The use of SMs in electrical engineering covers a multitude of applications ranging from real-time monitoring to the study of load profiles in homes. The use of wireless technologies has helped this development. Various problems arise in the implementation of SMs, such as coverage, locations without Internet access, etc. LoRa (long range) technology has great coverage and equipment with low power consumption that allows the installation of SMs in all types of locations, including those without Internet access. The objective of this research is to create an SM network under the LoRa specification that solves the problems presented by other wireless networks. For this purpose, a gateway for residential electricity metering networks using LoRa (GREMNL) and an electrical variable measuring device for households using LoRa (EVMDHL) have been created, which allow the development of SM networks with large coverage and low consumption.

Three-Phase State Estimation of a Low-Voltage Distribution Network with Kalman Filter

The state estimation of distribution networks has long been considered a challenging task for the reduced availability of real-time measures with respect to the transmission network case. This issue is expected to be improved by the deployment of modern smart meters that can be polled at relatively short time intervals. On the other hand, the management of the information coming from many heterogeneous meters still poses major issues. If low-voltage distribution systems are of interest, a three-phase formulation should be employed for the state estimation due to the typical load imbalance. Moreover, smart meter data may not be perfectly synchronized. This paper presents the implementation of a three-phase state estimation algorithm of a real portion of a low-voltage distribution network with distributed generation equipped with smart meters. The paper compares the typical state estimation algorithm that implements the weighted least squares method with an algorithm based on an iterated Kalman filter. The influence of nonsynchronicity of measurements and of delays in communication and processing is analyzed for both approaches.

Compression Techniques of Electrical Energy Data for Load Monitoring: A Review

In recent years, the electric grid has experienced increasing deployment, use, and integration of smart meters and energy monitors. These devices transmit big time-series load data representing consumed electrical energy for load monitoring. However, load monitoring presents reactive issues concerning efficient processing, transmission, and storage. To promote improved efficiency and sustainability of the smart grid, one approach to manage this challenge is applying data-compression techniques. The subject of compressing electrical energy data (EED) has received quite an active interest in the past decade to date. However, a quick grasp of the range of appropriate compression techniques remains somewhat a bottleneck to researchers and developers starting in this domain. In this context, this paper reviews the compression techniques and methods (lossy and lossless) adopted for load monitoring. Selected top-performing compression techniques metrics were discussed, such as compression efficiency, low reconstruction error, and encoding-decoding speed. Additionally reviewed is the relation between electrical energy, data, and sound compression. This review will motivate further interest in developing standard codecs for the compression of electrical energy data that matches that of other domains.

A non-intrusive load monitoring algorithm based on multiple features and decision fusion

With the large-scale deployment of smart meters and wide application of various machine learning algorithms, non-intrusive load monitoring (NILM) has attracted the attention of academia and industry. However, machine learning algorithms often suffer from high variability in load identification performance due to different features. In view of the poor generalization ability and low accuracy of load identification using an individual feature or classifier model, this paper proposes a novel NILM method that accomplishes deep feature fusion and classifier model fusion by an improved Dempster–Shafer (D–S) evidence theory. Firstly, we extract the power features, current harmonic features, and voltage–current (V–I) trajectory features from the input signals. Then, K-nearest neighbor (KNN), random forest (RF), and convolutional neural networks (CNN) are employed to identify load appliances using three individual features. Finally, the probability estimates of each classifier are transmitted to the aggregator for aggregation to obtain the final identification results by the improved D–S evidence theory. The experimental results on the PLAID dataset show that the proposed method can significantly improve identification performances.

A comparative analysis of patterns of electricity use and flexibility potential of domestic and non-domestic building archetypes through data mining techniques

The large-scale deployment of smart meters has led to significant amount of electricity demand data available, driving it into the realm of Big Data. It is a major challenge to exploit this Big Data in order to characterise electricity use patterns and to support demand response policies. In this paper, we perform a featured-based cluster analysis on nine building archetypes (hospitals, schools, offices, hotels, flats, houses etc.) to identify electricity use patterns. Then, four metrics are developed, which are entropy, load curviness, peak intensity and index of hourly ramp rates, to measure these archetypes’ suitability to be involved in demand response schemes. A significant difference in electricity use patterns between the archetypes is found, as well as among the seasons and days of the week. We present a number of metrics for each archetype to establish which type of archetype should be prioritised for demand response programmes in terms of peak management, ramp rates as well as demand flexibility. A key finding of our study is that households offer more demand flexibility than the non-domestic sector and should therefore be incentivized to participate in dynamic electricity tariffs.

Systematic Review of Electricity Demand Forecast Using ANN-Based Machine Learning Algorithms.

The forecast of electricity demand has been a recurrent research topic for decades, due to its economical and strategic relevance. Several Machine Learning (ML) techniques have evolved in parallel with the complexity of the electric grid. This paper reviews a wide selection of approaches that have used Artificial Neural Networks (ANN) to forecast electricity demand, aiming to help newcomers and experienced researchers to appraise the common practices and to detect areas where there is room for improvement in the face of the current widespread deployment of smart meters and sensors, which yields an unprecedented amount of data to work with. The review looks at the specific problems tackled by each one of the selected papers, the results attained by their algorithms, and the strategies followed to validate and compare the results. This way, it is possible to highlight some peculiarities and algorithm configurations that seem to consistently outperform others in specific settings.

Neural Network Based Home Energy Management for Modelling and Controlling Home Appliances under Demand Response

Nowadays, the consumption of homes is around 40% of the total world consumption. Furthermore, 21% of the total greenhouse gas emissions are produced by homes. The emergence of smart grids has presented new opportunities for home energy management (HEM) systems for the purpose of reducing energy in the residential sector. Demand response (DR) tool that curtails and shifts demand to enhance the consumption of energy at home. It usually creates optimal schedules for energy consumption by considering load profiles, the cost of energy, level of comfort people, and environmental concerns. The deployment of smart meters, it is possible to control the load by using HEM system with demand response (DR) enabled appliances. Without a proper system, it is difficult to efficiently control the energy in houses. In this work, a Neural Network technique as a controller to control the energy in the building with DR strategy is developed to control and reduce peak demand load. Reduce the electricity cost and power consumption for the appliances while maintaining customer comfort is the motivation of this work. The electrical appliance such as air conditioning (AC), electric water heater (WH), washing machine (WM), and refrigerator (RF) were modeled using the Matlab program. The designed model can make an accurate decision in scheduling and shifting the operation of the electrical appliance at the peak time by scheduling the s domestic household at a specific time with no affecting the customer’s preference.

Three-Phase State Estimation for Distribution-Grid Analytics

Power-distribution grids consist of assets such as transformers, cables, and switches, of which the proper utilization is essential for the provision of a secure and reliable power supply to end customers. Distribution-system operators (DSOs) are responsible for the operation and maintenance of these assets. Due to the increased use of renewable sources such as wind and solar, grid assets are prone to operation conditions outside safe boundaries, such as overloading, large voltage unbalance, and a rise in voltage. At present, distribution grids are poorly monitored by DSOs, and the above-mentioned problems may thereby go unnoticed until the failure of a critical asset occurs. The deployment of smart meters in distribution grids has enabled measurements of grid variables such as power, current, and voltage. However, their measurements are used only for billing purposes, and not for monitoring and improving the operating condition of distribution grids. In this paper, a state-estimation algorithm is proposed that utilizes smart-meter data for offline analysis, and estimates the loading of grid assets and power losses. Single- and three-phase state-estimation algorithms are compared through simulation studies on a real-life low-voltage distribution grid using measured smart-meter data. The three-phase state-estimation algorithm based on the nonlinear weighted least-squares method was found to be more accurate in estimating cable loading and line power losses. The proposed method is useful for DSOs to analyze power flows in their distribution grids and take necessary actions such as grid upgrades or the rerouting of power flows.

A methodology for energy key performance indicators analysis

With the advent of the smart grid era, the electrical grid is becoming a complex network in which different technologies coexist to bring benefits to both customers and operators. This paper presents a methodology for analyzing Key Performance Indicators (KPIs), providing knowledge about the performance and efficiency of energy systems, focusing on the demand side. In the first stage of the methodology, the baseline KPIs are calculated. In the second stage, all KPIs are updated to be compared with the baseline ones. In fact, due to the dynamic nature of players in a smart grid, this methodology plays a crucial role in the performance assessment. Moreover, the proper definition and selection of KPIs is usually a challenging task since KPIs can be applied to evaluate diverse areas within a smart grid. Such areas include building energy efficiency, home communications, and smart metering deployment, just to mention a few. In the proposed methodology, the information obtained from a KPI can be driven to distinct aspects such as efficiency, environment, reliability, power quality, safety, security, or cost reduction. Through a case study from a real implementation of an energy system, we show how to assess energy consumption efficiency, thus improving energy management.

Roadmap for Smart Metering Deployment in Cameroon

This article is a contribution to the elaboration of a roadmap that will help move from classical metering systems to smart metering in Cameroon. The aim is to eliminate errors in index reading and billing, frauds that lead to outages for non-payment. The meeting with staff, the annual report of the energy distribution company, the implementation in other countries and the documentations of development strategy in Cameroon, allow us to elaborate a roadmap for smart metering in Cameroon. This roadmap is divided into two phases resumed as follows: the first phase is to put in place a national monitoring committee, its functional organization chart, and its missions; the second phase is the deployment of equipment. We will start in large cities, followed by medium cities and end with smaller cities. This roadmap could serve as a guide to deploy smart meters in the Central Africa sub-region and any other country in the need.

Validating Clustering Frameworks for Electric Load Demand Profiles

Large-scale deployment of smart meters has made it possible to collect sufficient and high-resolution data of residential electric demand profiles. Clustering analysis of these profiles is important to further analyze and comment on electricity consumption patterns. Although many clustering techniques have been proposed in the literature over the years, it is often noticed that different techniques fit best for different datasets. To identify the most suitable technique, standard clustering validity indices are often used. These indices focus primarily on the intrinsic characteristics of the clustering results. Moreover, different indices often give conflicting recommendations, which can only be clarified with heuristics about the dataset and/or the expected cluster structures-information that is rarely available in practical situations. This article presents a novel scheme to validate and compare the clustering results objectively. Additionally, the proposed scheme considers all the steps prior to the clustering algorithm, including the preprocessing and dimensionality reduction steps, in order to provide recommendations over the complete framework. Accordingly, the proposed strategy is shown to provide better, unbiased, and uniform recommendations as compared to the standard clustering validity indices.

Solar Prosumers in the German Energy Transition: A Multi-Level Perspective Analysis of the German ‘Mieterstrom’ Model

The expansion of photovoltaics in German cities has so far fallen short of expectations. The concept of ‘tenant electricity’ (‘Mieterstrom’ in German), in which tenants of a building are supplied with solar power produced on site, offers great potential here. A study on behalf of the German Federal Ministry for Economic Affairs and Energy estimated the number of tenant households with good conditions for solar tenant electricity at 3.8 million. At the same time, the federal tenant electricity promotion scheme has been in place since 2017, but only about 1% of the annual budget has been claimed. The aim of this study is to identify the barriers for and drivers of diffusion of the tenant electricity model. To this end, a qualitative document analysis and a range of semi-structured expert interviews have been conducted. The theoretical framework used to guide the analysis is the multi-level perspective. The main barrier found for tenant electricity diffusion is the legal framework on the regime level, which also leads to high transaction costs of implementing tenant electricity. A social barrier is the inertia of some residents to actively concern themselves with their electricity supply and switch to a tenant electricity contract. Among its drivers are long-term trends such as the increasing electricity demand in urban areas, technical developments like blockchain technology and the increasing deployment of smart meters, and the EU Renewable Energy Directive. As long as the restrictive legal framework prevails, the further diffusion of tenant electricity will remain limited.

Capturing high-resolution water demand data in commercial buildings

Abstract Water demand measurements have historically been conducted manually, from meter readings less than once per month. Leading water service providers have begun to deploy smart meters to collect high-resolution data. A low-cost flush counter was developed and connected to a real-time monitoring platform for 119 ultra-low flush toilets in 7 buildings on a university campus to explore how building users influence water demand. Toilet use followed a typical weekly pattern in which weekday use was 92% ± 4 higher than weekend use. Toilet demand was higher during term time and showed a strong, positive relationship with the number of building occupants. Mixed-use buildings tended to have greater variation in toilet use between term time and holidays than office-use buildings. The findings suggest that the flush sensor methodology is a reliable method for further consideration. Supplementary data from the study's datasets will enable practitioners to use captured data for (i) forecast models to inform water resource plans; (ii) alarm systems to automate maintenance scheduling; (iii) dynamic cleaning schedules; (iv) monitoring of building usage rates; (v) design of smart rainwater harvesting to meet demand from real-time data; and (vi) exploring dynamic water pricing models, to incentivise optimal on-site water storage strategies.

Data falsification attacks in advanced metering infrastructure

Smart grids are the cutting-edge electric power systems that make use of the latest digital communication technologies to supply end-user electricity, but with more effective control and can completely fill end user supply and demand. Advanced Metering Infrastructure (AMI), the backbone of smart grids, can be used to provide a range of power applications and services based on AMI data. The increased deployment of smart meters and AMI have attracted attackers to exploit smart grid vulnerabilities and try to take advantage of the AMI and smart meter’s weakness. One of the possible major attacks in the AMI environment is False Data Injection Attack (FDIA). FDIA will try to manipulate the user’s electric consumption by falsified the data supplied by the smart meter value in a smart grid system using additive and deductive attack methods to cause loss to both customers and utility providers. This paper will explore two possible attacks, the additive and deductive data falsification attack and illustrate the taxonomy of attack behaviors that results in additive and deductive attacks. This paper contributes to real smart meter datasets in order to come up with a financial impact to both energy provider and end-user.