Search Results (93)

Understanding the characteristics of residential hot-water consumption can be useful for developing effective water-conservation strategies in response to increasing pressure on natural resources. This study systematically investigates residential hot-water consumption through direct monitoring of over 40 domestic fixtures (belonging to six different end-use categories) in five Italian households, recorded over a period ranging from approximately two weeks to nearly four months, and using smart meters with 5 min resolution. A multi-step analysis is applied—at both household and end-use levels, explicitly differentiating tap uses by purpose and location—to (i) quantify daily per capita hot-water consumption, (ii) calculate hot-water ratios, and (iii) assess daily profiles. The results show an average total water consumption of 106.7 L/person/day, with at least 26.1% attributed to hot water. In addition, daily profiles reveal distinct patterns across end uses: hot- and cold-water consumption at kitchen sinks are not aligned over time (with cold water peaking before meals and hot water used predominantly afterward), while bathroom taps show more synchronized use and a marked evening peak in hot-water consumption. Study findings—along with the related open-access dataset—provide a valuable benchmark based on field measurements to support in the process of water demand modeling and the development of targeted demand-management strategies. Full article

Distribution network operators face the complex challenge of maintaining stable electricity access for diverse consumers while balancing economic constraints, user comfort, and the impact of stochastic events, particularly the increasing integration of renewable energy sources and electric vehicles. To address these challenges, this paper introduces a novel decision-making system for energy management within smart energy meters, leveraging a specifically designed fuzzy inference system. This fuzzy inference system autonomously interprets real-time energy consumption patterns and responds to control commands from distribution network operators, optimizing energy flow at the consumer level. Unlike generic energy management approaches, this study provides a detailed mathematical model of the proposed low-cost fuzzy inference system-based system, explicitly outlining its rule base and inference mechanisms. Simulation studies conducted under varying load conditions and renewable generation profiles demonstrate the system’s effectiveness in achieving a balanced response to grid demands and user needs, yielding a quantifiable reduction in peak demand during simulated stress scenarios. Furthermore, experimental validation on resource-constrained embedded platforms confirms the practical feasibility and real-time performance of the proposed system on low-cost smart energy meter hardware. The differential contribution of this work lies in its provision of a computationally efficient and readily implementable fuzzy logic-based solution tailored for the limitations of low-cost smart energy meters, offering a viable alternative to more complex artificial intelligence algorithms. The findings underscore the necessity and justification for optimizing algorithm code for resource-constrained smart energy meter deployments to facilitate widespread adoption of advanced energy management functionalities. Full article

Power consumption data play a crucial role in demand management and abnormality detection in smart grids. Despite its management benefits, analyzing power consumption data leads to profiling consumers and opens privacy issues. To demonstrate this, we present a power profiling model for smart grid consumers based on real-time load data acquired from smart meters. It profiles consumers’ power consumption behavior by applying the daily load factor and the dynamic time warping (DTW) clustering algorithm. Due to the invariability of signal warping of this algorithm, time-disordered load data can be profiled and consumption features can be extracted. By this model, two load types are defined and the related load patterns are extracted for classifying consumption behavior by DTW. The classification methodology is discussed in detail. To evaluate the performance of the proposed model for profiling, we analyze the time-series load data measured by a smart meter in a real case. The results demonstrate the effectiveness of the proposed profiling method, achieving an F-score of $0.8372$ for load type clustering in the best case and an overall accuracy of $77.17\%$ for power profiling. Full article

This article highlights the development of a platform for monitoring three-phase energy consumption within a university campus. The core of this platform is low-cost IoT energy sensors, which are designed to transmit real-time data to the data center’s server through different IoT communication technologies, enhancing the preexisting electrical measurement network. The newly recommended measurement structure enables the electrical consumption data collection required for analyzing patterns and proposing forecast models to optimize electricity usage. The major contribution of this work is the design and implementation of smart three-phase energy meters based on the selection of various energy sensors and wireless communication technologies, and then the set up of a global IoT architecture that offers real-time data acquisition, storage, download, and visualization, capitalizing on the campus’s diverse energy profiles for detailed characterization. The proposed platform is considered the cornerstone toward the implementation of a collaborative smart microgrid, allowing forecasting and electrical consumption optimization, enabling research into potential opportunities for energy efficiency in our campus, and enhancing the performance of existing electrical infrastructure. Full article

Efficient energy management relies on uncovering meaningful consumption patterns from large-scale electricity load demand profiles. With the widespread adoption of sensor technologies such as smart meters and IoT-based monitoring systems, granular and real-time electricity usage data have become available, enabling deeper insights into consumption behaviors. Clustering is a widely used technique for this purpose, but previous studies have primarily focused on a limited set of algorithms, often treating clustering as a black-box approach without addressing interpretability. This study explores a wide number of clustering algorithms by comparing hard clustering algorithms (K-Means, K-Medoids) versus soft clustering techniques (Fuzzy C-Means, Gaussian Mixture Models) in segmenting electricity consumption profiles. The clustering performance is evaluated using five different clustering validation indices (CVIs), assessing intra-cluster cohesion and inter-cluster separation. The results show that soft clustering methods effectively capture inter-cluster characteristics, leading to better cluster separation, whereas intra-cluster characteristics exhibit similar behavior across all clustering approaches. This study assesses which CVIs provide reliable evaluations independent of clustering algorithm sensitivity. It provides a comprehensive analysis of the different CVIs’ responses to changes in data characteristics, highlighting which indices remain robust and which are more susceptible to variations in cluster structures. Beyond evaluating clustering effectiveness, this study enhances interpretability by introducing two decision tree models, axis-aligned and sparse oblique decision trees, to generate human-readable rules for cluster assignments. While the axis-aligned tree provides a complete explanation of all clusters, the sparse oblique tree offers simpler, more interpretable rules, emphasizing a trade-off between full interpretability and rule complexity. This structured evaluation provides a framework for balancing transparency and complexity in clustering explanations, offering valuable insights for utility providers, policymakers, and researchers aiming to optimize both clustering performance and explainability in sensor-driven energy demand analysis. Full article

Unmanned Aerial Vehicles (UAVs) are integral to the development of smart city infrastructures, enabling essential services such as real-time surveillance, urban traffic regulation, and cooperative environmental monitoring. UAV-to-UAV communication networks, despite their adaptability, have significant limits stemming from onboard battery constraints, inclement weather, and variable flight trajectories. This work presents a thorough examination of energy and spectral efficiency in UAV-to-UAV communication over four frequency bands: 2.4 GHz, 5.8 GHz, 28 GHz, and 60 GHz. Our MATLAB R2023a simulations include classical free-space path loss, Rayleigh/Rician fading, and real-time mobility profiles, accommodating varied heights (up to 500 m), flight velocities (reaching 15 m/s), and fluctuations in the path loss exponent. Low-frequency bands (e.g., 2.4 GHz) exhibit up to 50% reduced path loss compared to higher mmWave bands for distances exceeding several hundred meters. Energy efficiency (${\eta }_e$) is evaluated by contrasting throughput with total power consumption, indicating that 2.4 GHz initiates at around 0.15 bits/Joule (decreasing to 0.02 bits/Joule after 10 s), whereas 28 GHz and 60 GHz demonstrate markedly worse ${\eta }_e$ (as low as ${10}^{-3}$–${10}^{-4}\mathrm{bits}/\mathrm{Joule}$), resulting from increased path loss and oxygen absorption. Similarly, sub-6 GHz spectral efficiency can attain $4\times {10}^{-12}\mathrm{bps}/\mathrm{Hz}$ in near-line-of-sight scenarios, whereas 60 GHz lines encounter significant attenuation at distances above 200–300 m without sophisticated beamforming techniques. Polynomial-fitting methods indicate that the projected ${\eta }_e$ diverges from actual performance by less than 5% after 10 s of flight, highlighting the feasibility of machine-learning-based techniques for real-time power regulation, beam steering, or multi-band switching. While mmWave UAV communication can provide significant capacity enhancements (100–500 MHz bandwidth), energy efficiency deteriorates markedly without meticulous flight planning or adaptive protocols. We thus advocate using multi-band radios, adaptive modulation, and trajectory optimisation to equilibrate power consumption, ensure connection stability, and meet high data-rate requirements in densely populated, dynamic urban settings. Full article

With the growth of the nations, the commercial and public services sectors have recently seen an increase in their electricity usage. This demonstrates how crucial it is to understand a building’s behavior in order to lower its usage. This requires on-site data collection by qualified professionals and specialized equipment, which represents high costs. However, multiple studies have demonstrated that it is possible to find electricity-saving strategies from the study of electricity usage, recorded in an hourly period or less, captured by smart meters. In this context, the present study applies a methodology to determine useful information on the operation and characteristics of public buildings on the Ecuadorian coast based on the data gathered over a period of five consecutive months from smart meters. The methodology consists of four steps: (1) data cleaning and filling, (2) time-series decomposition, (3) the generation of consumption profile and (4) the identification of the temperature influence. According to the results, the pre-cooling of spaces accounts for 5% of all electricity used in the commercial buildings, while prolonged shutdown uses 10%. Approximately USD 1100 per month would be spent on the main building and USD 78 on the agency as a result. Full article

In this paper, we propose a novel methodology for estimating missing data in energy consumption datasets. Conventional data imputation methods are not suitable for these datasets, because they are time series with special characteristics and because, for some applications, it is quite important to preserve the shape of the daily energy profile. Our answer to this need is the use of autoencoders. First, we split the problem into two subproblems: how to estimate the total amount of daily energy, and how to estimate the shape of the daily energy profile. We encode the shape as a new feature that can be modeled and predicted using autoencoders. In this way, the problem of imputation of profile data are reduced to two relatively simple problems on which conventional methods can be applied. However, the two predictions are related, so special care should be taken when reconstructing the profile. We show that, as a result, our data imputation methodology produces plausible profiles where other methods fail. We tested it on a highly corrupted dataset, outperforming conventional methods by a factor of 3.7. Full article

Behind the meter (BTM) distributed energy resources (DERs), such as photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicle (EV) charging infrastructures, have experienced significant growth in residential locations. Accurate load forecasting is crucial for the efficient operation and management of these resources. This paper presents a comprehensive survey of the state-of-the-art technologies and models employed in the load forecasting process of BTM DERs in recent years. The review covers a wide range of models, from traditional approaches to machine learning (ML) algorithms, discussing their applicability. A rigorous validation process is essential to ensure the model’s precision and reliability. Cross-validation techniques can be utilized to reduce overfitting risks, while using multiple evaluation metrics offers a comprehensive assessment of the model’s predictive capabilities. Comparing the model’s predictions with real-world data helps identify areas for improvement and further refinement. Additionally, the U.S. Energy Information Administration (EIA) has recently announced its plan to collect electricity consumption data from identified U.S.-based crypto mining companies, which can exhibit abnormal energy consumption patterns due to rapid fluctuations. Hence, some real-world case studies have been presented that focus on irregular energy consumption patterns in residential buildings equipped with BTM DERs. These abnormal activities underscore the importance of implementing robust anomaly detection techniques to identify and address such deviations from typical energy usage profiles. Thus, our proposed framework, presented in residential buildings equipped with BTM DERs, considering smart meters (SMs). Finally, a thorough exploration of potential challenges and emerging models based on artificial intelligence (AI) and large language models (LLMs) is suggested as a promising approach. Full article

Renewable Energy Communities (REC) can play a crucial role in enhancing citizen participation in the energy transition. Current European Union legislation enshrines energy communities and mandates Member States to encourage these organizations, promoting adequate conditions for their establishment. Nevertheless, uptake has been slow, and more research is needed to optimize the associated energy sharing. Using a Portuguese case study (REC Telheiras, Lisbon), this research aims to match local generation through four photovoltaic systems (totalizing 156.5 kWp of installed capacity) with household electricity consumption while cross evaluating the Portuguese legislation for energy sharing. The latter aim compares two scenarios: (a) current legislation (generated energy must be locally self-consumed before shared) and (b) equal share for members with a fixed coefficient. The evaluation is performed according to two indexes of self-consumption (SCI) and self-sufficiency (SSI), related to the simulation of four photovoltaic systems in public buildings, their associated consumption profiles, and an average household consumption profile of community members. The results show that, while maximizing self-consumption for the same values of generation and consumption, the number of participants is considerably lower for Scenario A (SCI = 100% is achieved with at least 491 residential members in Scenario A and 583 in Scenario B), implying that legislative changes enabling energy communities to better tailor sharing schemes may be necessary for them to become more attractive. The methods and results of this research can also be applied to other types of facilities, e.g., industrial and commercial consumers, if they are members of a REC and have smart meters in their installations. Full article

Zero-carbon shipping is the prime goal of the seaborne trade industry at this moment. The utilization of ammonia and liquid hydrogen propulsion in a carbon-free propulsion system is a promising option to achieve net-zero emission in the maritime supply chain. Meanwhile, optimal ship voyage planning is a candidate to reduce carbon emissions immediately without new buildings and retrofits of the alternative fuel-based propulsion system. Due to the voyage options, the precise prediction of fuel consumption and carbon emission via voyage operation profile optimization is a prerequisite for carbon emission reduction. This paper proposes a novel fuel consumption and carbon emission quantity prediction method which is based on the onboard measurement data of a smart ship. The prediction performance of the proposed method was investigated and compared to machine learning and LSTM-model-based fuel consumption and gas emission prediction methods. The results had an accuracy of 81.5% in diesel mode and 91.2% in gas mode. The SHAP (Shapley additive explanations) model, an XAI (Explainable Artificial Intelligence), and a CO₂ consumption model were employed to identify the major factors used in the predictions. The accuracy of the fuel consumption calculated using flow meter data, as opposed to power load data, improved by approximately 21.0%. The operational and flow meter data collected by smart ships significantly contribute to predicting the fuel consumption and carbon emissions of vessels. Full article

In the context of increasingly necessary energy transition, the precise modeling of profiles for low-voltage (LV) network consumers is crucial to enhance hosting capacity. Typically, load curves for these consumers are estimated through measurement campaigns conducted by Distribution System Operators (DSOs) for a representative subset of customers or through the aggregation of load curves from household appliances within a residence. With the instrumentation of smart meters becoming more common, a new approach to modeling profiles for residential customers is proposed to make the most of the measurements from these meters. The disaggregation model estimates the load profile of customers on a low-voltage network by disaggregating the load curve measured at the secondary substation level. By utilizing only the maximum power measured by Linky smart meters, along with the load curve of the secondary substation, this model can estimate the daily profile of customers. For 48 secondary substations in our dataset, the model obtained an average symmetric mean average percentage error (SMAPE) error of 4.91% in reconstructing the load curve of the secondary substation from the curves disaggregated by the model. This methodology can allow for an estimation of the daily consumption behaviors of the low-voltage customers. In this way, we can safely envision solutions that enhance the grid hosting capacity. Full article

The advancement of technology has significantly improved energy measurement systems. Recent investment in smart meters has enabled companies and researchers to access data with the highest possible temporal disaggregation, on a minute-by-minute basis. This research aimed to obtain data with the highest possible temporal and spatial disaggregation. This was achieved through a process of energy consumption measurements for six devices within seven houses, located in different communes (counties) of the Metropolitan Region of Chile. From this process, a data panel of energy consumption of six devices was constructed for each household, observed in two temporal windows: one quarterly (750,000+ observations) and another semi-annual (1,500,000+ observations). By applying a panel data econometric model with fixed effects, calendar-temporal patterns that help explain energy consumption in each of these two windows have been studied, obtaining explanations of over 80% in some cases, and very low in others. Sensitivity analyses show that the results are robust in a short-term temporal horizon and provide a practical methodology for analyzing energy consumption determinants and load profiles with panel data. Moreover, to the authors’ knowledge, these are the first results obtained with data from Chile. Therefore, the findings provide key information for the planning of production, design of energy market mechanisms, tariff regulation, and other relevant energy policies, both at local and global levels. Full article

Uncertainties related to the energy produced and consumed in smart grids, especially in microgrids, are among the major issues for both their design and optimal management. In that context, it is essential to have representative probabilistic scenarios of these environmental uncertainties. The intensive development and massive installation of smart meters will help to better characterize local energy consumption and production in the following years. However, models representing these variables over large timescales are essential for microgrid design. In this paper, we explore a simple method based on Markov chains capable of generating a large number of probabilistic production or consumption profiles from available historical measurements. We show that the developed approach can capture the main characteristics and statistical variability of real data on both short-term and long-term scales. Moreover, the correlation between both production and demand is conserved in generated profiles with respect to historical measurements. Full article

Broadband powerline communication is a technology developed mainly with consumer applications and bulk data transmission in mind. Typical use cases include file download, streaming, or last-mile internet access for residential buildings. Applications gaining momentum are smart metering and grid automation, where response time requirements are relatively moderate compared to industrial (real-time) control. This work investigates to which extent G.hn technology, with existing, commercial off-the-shelf components, can be used for real-time control applications. Maximum packet rate and latency statistics are investigated for different G.hn profiles and MAC algorithms. An elevator control system serves as an example application to define the latency and throughput requirements. The results show that G.hn is a feasible technology candidate for industrial IoT-type applications if certain boundary conditions can be ensured. Full article