The ongoing digitalisation of the district heating sector, particularly the installation of smart heat meters (SHMs), is generating data with unprecedented extent and temporal resolution. This data ...offers potential insights into heat energy use at a large scale, supporting policymakers and district heating utility companies in transforming the building sector. Clustering is crucial for representing this wealth of data in human-understandable groups, necessitating consideration of seasonality.
Advancing current research in clustering SHM data, this work applies an established co-clustering approach, FunLBM, considering seasonal variation without fixed season definitions. Furthermore, to enhance the understanding of differentiating factors between clusters, the possibility to understand cluster memberships based on 26 building characteristics was analysed using classification and variable selection methods.
Applying FunLBM on a large-scale hourly dataset from single-family houses revealed six well-separated energy use clusters each distributed over six-temporal clusters, which are correlated with the exterior temperature, yet not following fixed seasons. Variable selection and classification showed that building characteristics describing the building with a high level of detail are insufficient to explain cluster membership (Matthew’s correlation coefficient (MCC) ≈0.3).
By merging the energy use clusters based on profile and magnitude similarities, classification performance significantly improved (MCC ≈0.5). In both cases, simple and readily available building characteristics yield similar insights to detailed ones, emphasising their cost-effectiveness and practicality.
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•Co-clustering of smart heat meter data to establish season-independent clusters.•Analysis of energy use clusters based on 26 building characteristics.•Classification and variable selection to identify the minimum information needed.•Statistical data leads to the same insight as detailed building data.•Prediction of energy use clusters with building characteristics has a low accuracy.
With the rise of user energy consumption data as a significant data asset, data privacy has emerged as a critical concern. To address users' diverse attitudes towards data sharing and their varied ...usage requirements, this paper introduces an adaptive privacy protection framework for user energy data based on dictionary learning and watermarking techniques. Central to this framework is an innovative digital watermark anonymization method designed to meet the dual objectives of encryption and anonymous data sharing. This method employs sparse dictionary decomposition to embed confidential user information within sparse coefficients, significantly enhancing computational efficiency while minimally impacting the integrity of the original data. Additionally, through sparse data representation, the framework achieves effective data compression, addressing the challenges of extensive storage requirements inherent in maintaining original energy consumption data, encryption results, and the data sharing process. Security analysis and case studies demonstrate the proposed method's robustness against eavesdropping, unauthorized access, and other security threats.
•A hierarchical privacy-preserving scheme considering diverse use of smart meter data.•An efficient compression and secure sharing method based on cloud storage.•Flexible anonymous control for energy consumption data based on blind watermark.•The adaptive blind watermark method has lower distortion and controllable capacity.•The scheme robustness against eavesdropping and unauthorized access.
With increasing renewable generation and demand response, the load profiles of distribution feeders become more fluctuating and uncertain, requiring real-time load estimation (RTLE) with high ...temporal granularity. Smart meters (SM) provide new data sources that have the potential to enable RTLE. However, it is cost prohibitive to communicate and process real-time high-resolution data from a massive number of SMs. To address the challenge, this paper proposes a novel solution to RTLE using High-Reporting-Rate SMs (HRRSMs) installed at a sparsely selected subset of customers in the feeder. The first step is to select customers for installing HRRSMs based on clustering, such that load profiles can best represent those of the others and the whole feeder. Then, a state-of-the-art Deep Learning (DL) model is trained to capture the relation between the historical load profiles of the selected customers and that of the feeder. Finally, real-time HRRSM data from the selective customers is fed to the trained model to perform RTLE with high resolution. The method is also robustified to address anomalies in real-time HRRSM data streams. The proposed method is validated on a large real-world SM dataset. Simulation results show that even with a small number of HRRSM installation, the proposed method can track feeder loads with much improved accuracy and temporal granularity compared with conventional methods based on historical data of regular SMs, providing a cost-effective solution to the monitoring of distribution feeder loads.
•A novel high-resolution Real-Time Load-Estimation (RTLE) method is proposed to estimate unmeasured feeder or subfeeder load.•A state-of-the-art deep-learning model is utilized for RTLE.•A HRRSM placement strategy is proposed for effective monitoring of feeder- or subfeeder-level load.•The proposed RTLE method is designed to be robust against anomalies in real-time HRRSM data.
After deploying a large number of smart meters, utilities are challenged with managing a massive set of interval energy consumption data and decoding the information into meaningful measures that can ...help them. These new tasks need a more detailed understanding of customers than was previously sought because customers vary widely in their usage, needs, and suitability for different programs and pricing packages. Defining and describing different customer segments will equip decision makers with information to advance not only in pricing and program marketing but also resource allocation and program development. Therefore, among many applications, customer lifestyle segmentation can unlock potential energy-savings and can help utilities understand operating requirements and better coordinate energy resources for grid management. This paper focuses on how to segment customers' lifestyles based on their electricity consumption data and provides suggestions on which lifestyle groups can be good candidates for certain energy programs based on the segmentation result.
In the building sector, remodeling of existing buildings that have low energy efficiency is a required strategy to achieve carbon neutrality. To establish a cost-effective remodeling strategy for ...existing buildings, accurate measurement of the thermal insulation performance of existing building envelopes is necessary. In general, to evaluate the insulation performance of a building envelope, the U-value is calculated based on the heat flux measured using a heat flow meter, which is an expensive physical sensor. This study developed an in-situ virtual heat flow meter (vHFM) model that can replace existing heat flow meter sensors. The in-situ vHFM model satisfied ASHRAE Guideline 14 with an R2 of 0.989, coefficient of variation root mean square error of 15.24%, and normalized mean bias error of −0.06% compared to the data measured using a traditional sensor. In addition, the validation through two case studies demonstrated that the field applicability of the in-situ vHFM model was very high.
•An in-situ virtual heat flow meter (vHFM) model was developed and validated.•The in-situ vHFM model can accurately predict the heat flux in existing building walls using indoor air temperature, outdoor air temperature, indoor wall surface temperature, and temperature difference between indoor and outdoor air.•An artificial neural network (ANN) model performed best of four regression models.•The ANN-based in-situ vHFM model satisfied ASHRAE Guideline 14.•The developed in-situ vHFM model was evaluated using two case studies.
The widespread use of smart meters in households paves the way for retailers to understand household patterns through electricity usage data. This insight helps them offer personalized services and ...create better demand response strategies. However, smart meter data is highly heterogeneous since it is collected by different retailers using various data sampling methods, over different time periods, and from households with distinct characteristics. Additionally, the labels of household characteristics are obtained by questionnaires, which is labor-intensive and time-consuming, leaving much data unlabeled while privacy concerns prevent data sharing among retailers. To address these challenges, we propose a novel Semi-Supervised Federated Analytics approach for Heterogeneous Smart Meter Data (SF-Heter). This method keeps raw data local and exchanges analytics outputs, called prototypes, between retailers and a central server, thus dealing with heterogeneous data and protecting privacy. SF-Heter utilizes a new model structure named MODlinear, which enhances feature extraction through contrastive learning and multi-kernel time-series analysis. Meanwhile, SF-Heter efficiently utilizes unlabeled data by generating high-quality pseudo-labels and prototypes using MODlinear and integrated with a quality-controlled semi-supervised loss mechanism. Extensive tests on the Irish dataset show that SF-Heter effectively handles data heterogeneity and optimizes the use of unlabeled data.
Power-line communications (PLC) have proven to be susceptible to the next-generation power transmission/distribution systems with end-to-end communication capability, considered as a revolutionary ...and evolutionary regime of existing power grids. More importantly, with recent advancements in PLC technological regulation, standardization and certification have spurred a lot of interest in the field of advanced communication and control technologies for heterogeneous networks. These advancements are expected to greatly enhance efficiency and reliability of future power systems with renewable energy resources, as well as distributed intelligence and demand response (DR) programs. As various national and international organizations have started to draw PLC regulations, standards and technologies for countries, these standards intend to determine important criteria such as bandwidth, modulation types, channel coding schemes, operating frequency and electromagnetic capability limits from fixed indoor/outdoor applications to smart grid (SG) applications. Depending on the worldwide PLC regulation, standardization and technological developments, security schemes built around them can become interoperable from a standard point of view, but still have incompatible configurations or different maturity levels, or include non-standardized PLC functions.
Moreover, the PLC based systems/solutions for renewable energy integration, are also surveyed in terms of distributed-power system (DPS) and distributed energy resources (DERs) units monitoring/controlling and management purposes. Thus, a particular section is dedicated to PLC based systems/solutions utilization of renewable energy sources (RESs) in SG covering all aspects of a monotype and hybrid energy plants. Finally, the survey is carried on by reviewing the most recent and comprehensive articles to highlight the importance of PLC in a logical way in the smart grid for readers.
Nowadays the rationalization of electrical energy consumption is a serious concern worldwide. Energy consumption reduction and energy efficiency appear to be the two paths to addressing this target. ...To achieve this goal, many different techniques are promoted, among them, the integration of (artificial) intelligence in the energy workflow is gaining importance. All these approaches have a common need: data. Data that should be collected and provided in a reliable, accurate, secure, and efficient way. For this purpose, sensing technologies that enable ubiquitous data acquisition and the new communication infrastructure that ensure low latency and high density are the key. This article presents a sensing solution devoted to the precise gathering of energy parameters such as voltage, current, active power, and power factor for server farms and datacenters, computing infrastructures that are growing meaningfully to meet the demand for network applications. The designed system enables disaggregated acquisition of energy data from a large number of devices and characterization of their consumption behavior, both in real time. In this work, the creation of a complete multiport power meter system is detailed. The study reports all the steps needed to create the prototype, from the analysis of electronic components, the selection of sensors, the design of the Printed Circuit Board (PCB), the configuration and calibration of the hardware and embedded system, and the implementation of the software layer. The power meter application is geared toward data centers and server farms and has been tested by connecting it to a laboratory server rack, although its designs can be easily adapted to other scenarios where gathering the energy consumption information was needed. The novelty of the system is based on high scalability built upon two factors. Firstly, the one-on-one approach followed to acquire the data from each power source, even if they belong to the same physical equipment, so the system can correlate extremely well the execution of processes with the energy data. Thus, the potential of data to develop tailored solutions rises. Second, the use of temporal multiplexing to keep the real-time data delivery even for a very high number of sources. All these ensure compatibility with standard IoT networks and applications, as the data markup language is used (enabling database storage and computing system processing) and the interconnection is done by well-known protocols.
Water meter under-registration results in apparent losses and lost revenue for municipalities. Municipalities should scientifically determine the optimal replacement periods for the meters in their ...particular municipality, as this would result in the formulation and implementation of appropriate meter replacement strategies and a reduction of apparent losses due to water meter inaccuracies. A water meter management database was analysed using the relative meter error method to determine if a relationship exists between domestic water meter age, total registered volume and accuracy, as well as the volume of apparent water losses caused by inaccuracies due to domestic water meter age and total registered volume. The net present value chain (NPVCn) method was used to determine the optimal domestic water meter replacement period. This study found no relation between water meter age and total registered volume. A relation was found between water meter age and accuracy and well as between total registered volume and accuracy. The median relative meter error was found to decrease with increasing water meter age and to increase from under-registration to over-registration as the total registered volume increased. The study also determined the volume of apparent water losses caused by domestic water meter inaccuracies due to age and total registered volume for this particular municipality to be 1.814 kL∙meter−1∙month−1, which translated to 2.81% of the municipality's total system input volume. The optimal water meter replacement period of the municipality was determined using the NPVCn method to be at water meter ages of 9, 12 and 16 years and total registered volumes of 3 971, 5 162 and 6 750 kL at discount rates of 10%, 8% and 6%, respectively. This means that the municipality can now proactively replace its water meters so as to minimize the impact of meter inaccuracies on non-revenue water.
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