Search Results (27)

The increased usage of smart sensors has introduced both opportunities and complexities in managing residential energy consumption. Despite advancements in sensor data analytics and machine learning (ML), existing energy management systems (EMS) remain limited in interpretability, adaptability, and user engagement. This paper presents EnergiQ, an intelligent, end-to-end platform that leverages sensors and Large Language Models (LLMs) to bridge the gap between technical energy analytics and user comprehension. EnergiQ integrates smart plug-based IoT sensing, time-series ML for device profiling and anomaly detection, and an LLM reasoning layer to deliver personalized, natural language feedback. The system employs statistical feature-based XGBoost classifiers for appliance identification and hybrid CNN-LSTM autoencoders for anomaly detection. Through dynamic user feedback loops and instruction-tuned LLMs, EnergiQ generates context-aware, actionable recommendations that enhance energy efficiency and device management. Evaluations demonstrate high appliance classification accuracy (94%) using statistical feature-based XGBoost and effective anomaly detection across varied devices via a CNN-LSTM autoencoder. The LLM layer, instruction-tuned on a domain-specific dataset, achieved over 91% agreement with expert-written energy-saving recommendations in simulated feedback scenarios. By translating complex consumption data into intuitive insights, EnergiQ empowers consumers to engage with energy use more proactively, fostering sustainability and smarter home practices. Full article

The building sector, responsible for 40% of global energy consumption, faces increasing demands for sustainability and energy efficiency. Accurate energy consumption forecasting is essential to optimise performance and reduce environmental impact. This study introduces a hybrid machine learning framework grounded in Sparse, Interpretable, and Transparent (SIT) modelling to enhance building energy management. Leveraging the REFIT Smart Home Dataset, the framework integrates occupancy pattern analysis, appliance-level energy prediction, and probabilistic uncertainty quantification. The framework clusters occupancy-driven energy usage patterns using K-means and Gaussian Mixture Models, identifying three distinct household profiles: high-energy frequent occupancy, moderate-energy variable occupancy, and low-energy irregular occupancy. A Random Forest classifier is employed to pinpoint key appliances influencing occupancy, with a drop-in accuracy analysis verifying their predictive power. Uncertainty analysis quantifies classification confidence, revealing ambiguous periods linked to irregular appliance usage patterns. Additionally, time-series decomposition and appliance-level predictions are contextualised with seasonal and occupancy dynamics, enhancing interpretability. Comparative evaluations demonstrate the framework’s superior predictive accuracy and transparency over traditional single machine learning models, including Support Vector Machines (SVM) and XGBoost in Matlab 2024b and Python 3.10. By capturing occupancy-driven energy behaviours and accounting for inherent uncertainties, this research provides actionable insights for adaptive energy management. The proposed SIT hybrid model can contribute to sustainable and resilient smart energy systems, paving the way for efficient building energy management strategies. Full article

This research investigates the optimization potential of Renewable Energy Communities (RECs) through advanced demand-side management strategies. The study simulates a real distribution network and analyzes load profile optimization in a residential REC configuration, comparing two distinct approaches: Demand-Side Engagement (DSE) and Optimized Demand-Side Management (Opt-DSM). The methodology encompasses load-shifting strategies at the appliance level, progressing from spontaneous behavior patterns to algorithmic optimization. Starting from a baseline scenario of conventional consumption patterns, the research evaluates the effectiveness of both user-driven load shifting (DSE) and automated redistribution through genetic algorithms (Opt-DSM). The analysis framework addresses three key dimensions: economic efficiency through incentive optimization, social cohesion via collaborative engagement, and environmental sustainability through the optimal utilization of locally generated energy. Results demonstrate that enhanced generation-consumption synchronization through Opt-DSM yields superior outcomes for both distribution network performance and participant economics compared to DSE. However, successful implementation requires substantial technological infrastructure investment at individual and community levels, alongside significant modifications to established consumption patterns. This research contributes to the understanding of RECs as innovative socio-technical systems and provides figures to support the analysis related to the balance between technological optimization and user engagement in maximizing shared energy potential. Full article

This research focuses on optimizing renewable energy systems to achieve Nearly Zero-Energy Building (nZEB) Level 1 status, defined as reducing energy consumption by 87.5% to 100%. The major objectives are to explore the impact factors in the optimization of energy storage systems (ESSs), solar PV and ESS capacities, as well as energy consumption patterns. This study is based on monitoring data from an office building in Thailand with a 120 kW peak load and a 40 kW average load, equipped with a 160 kW photovoltaic (PV) system and 45 kWh from ESS. This study is based on comparing a simulation of a renewable energy system, particularly from unutilized solar energy, with building load demand to optimize the best system suitability for achieving nZEB Level 1 status. The results indicate that a 200 kW PV system combined with a 275 kWh ESS and a 250 kW PV system with an ESS capacity of 175 kWh can adequately supply the required clean energy demand. These findings provide insights on optimizing factors of renewable energy systems for buildings aiming to achieve sustainability targets. This work has summarized a framework including optimization impact factors with financial aspects which can be applied to similar cases. In addition, an analysis of working-day load profiles and appliance usage patterns has been performed to provide broader consumption insights. This approach identifies trends in HVAC, lighting, and electronics consumption, enabling the optimization scheme to be adapted to buildings with varying load patterns. Additionally, this study examines the effects of building operation hours on energy consumption. By adjusting operational schedules based on these insights, different renewable energy system capacities can be re-estimated to ensure achievement of the desired nZEB Level. Full article

As energy demand and electricity costs continue to rise, consumers are increasingly adopting energy-efficient practices and appliances, underscoring the need for detailed metering options like appliance-level load monitoring. Non-intrusive load monitoring (NILM) is particularly favored for its minimal hardware requirements and enhanced customer experience, especially in residential settings. However, commercial power systems present significant challenges due to greater load diversity and imbalance. To address these challenges, we introduce a novel neural network architecture that combines sequence-to-sequence, WaveNet, and ensembling techniques to identify and classify single-phase and three-phase loads using appliance power signatures in commercial power systems. Our approach, validated over four months, achieved an overall accuracy exceeding 93% for nine devices, including six single-phase and four three-phase loads. The study also highlights the importance of incorporating nonlinear loads, such as two different inverter-type air conditioners, within NILM frameworks to ensure accurate energy monitoring. Additionally, we developed a web-based NILM energy dashboard application that enables users to monitor and evaluate load performance, recognize usage patterns, and receive real-time alerts for potential faults. Our findings demonstrate the significant potential of our approach to enhance energy management and conservation efforts in commercial buildings with diverse and complex load profiles, contributing to more efficient energy use and addressing climate change challenges. Full article

Understanding household energy-consumption patterns is essential for developing effective energy-conservation strategies. This study aims to identify ‘out-profiled’ consumers—households that exhibit atypical energy-usage behaviors—by applying four distinct feature-selection methodologies. Specifically, we utilized the chi-square independence test to assess feature independence, recursive feature elimination with multinomial logistic regression (RFE-MLR) to identify optimal feature subsets, random forest (RF) to determine feature importance, and a combined fuzzy rough feature selection with fuzzy rough nearest neighbors (FRFS-FRNN) for handling uncertainty and imprecision in data. These methods were applied to a dataset based on a survey of 383 households in Brazil, capturing various factors such as household size, income levels, geographical location, and appliance usage. Our analysis revealed that key features such as the number of people in the household, heating and air conditioning usage, and income levels significantly influence energy consumption. The novelty of our work lies in the comprehensive application of these advanced feature-selection techniques to identify atypical consumption patterns in a specific regional context. The results showed that households without heating and air conditioning equipment in medium- or high-consumption profiles, and those with lower- or medium-income levels in medium- or high-consumption profiles, were considered out-profiled. These findings provide actionable insights for energy providers and policymakers, enabling the design of targeted energy-conservation strategies. This study demonstrates the importance of tailored approaches in promoting sustainable energy consumption and highlights notable deviations in energy-use patterns, offering a foundation for future research and policy development. Full article

This manuscript explores the relationship between the economic awareness (as a part of energy awareness) of Polish households and their sustainable energy consumption practices. Sustainable consumption is measured by the frequency of behaviors such as turning off electrical devices when not in use, removing mobile device chargers from sockets, switching off lights when leaving a room, preferring showers over baths, using washing machines and dishwashers only when full, and purchasing energy-efficient appliances and light bulbs. Economic awareness is gauged through variables such as knowledge of electricity tariffs, understanding of electric bill components, awareness of electricity prices, exact knowledge of electricity expenses, electricity usage in kWh, knowledge of effective energy-saving methods, and familiarity with the energy efficiency classes of appliances and light bulbs. This study presents profiles of households with high and low economic awareness regarding their electricity expenditures and examines how these profiles differ in their sustainable energy consumption behaviors. This research is based on a survey of 1407 Polish households conducted online in 2023. Data collected from the survey were subjected to statistical analysis and are presented in tables and graphs. The findings are discussed in the context of the existing literature in the field, highlighting the implications of economic awareness on sustainable energy consumption practices. This research contributes to understanding how economic knowledge influences energy-saving behaviors among Polish households, providing insights for policymakers and energy conservation initiatives. One of the key findings of this paper is the significant association between economic awareness, energy-saving knowledge, and the adoption of sustainable energy consumption behaviors among Polish households. This study reveals that households with higher levels of economic awareness demonstrate a notably higher frequency of practices related to sustainable energy consumption compared to those with lower economic awareness. Similarly, households equipped with greater knowledge about energy-saving techniques exhibit a higher propensity to adopt energy-efficient behaviors. This underscores important roles of economic literacy and education in fostering behavioral changes towards more sustainable energy practices, highlighting the importance of targeted interventions and educational campaigns aimed at enhancing economic awareness and promoting energy-saving knowledge among consumers. 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

Load-shifting is a demand-side management (DSM) strategy to support the efficiency of the electricity grid during hours of peak demand. Load-shifting at the appliance level is an interesting topic to review, since appliance usage is one of the main inputs of the load-profile analysis. More literature reviews on load-shifting at the appliance level are required, as this is a specific issue in the body of literature on load-profile research, though only a limited number of studies are available at this time. It is also essential to focus on appliance usage patterns to improve our understanding of the impacts and characteristics of different appliances. Existing studies on load-shifting have used commonly structured literature reviews; our work addresses the transparency of each stage and substage in the selection of the final list of studies. The findings show that efficiency has been achieved in installed-capacity reductions; costs, including those of emission reductions; and peak consumption reductions. The most frequently used method in load-shifting at the appliance level is to develop load-shifting optimization algorithms. This work contributes by providing a transparent process of drawing up a systematicity literature review as a source of knowledge and grounded theory. It also contributes to specific research on load-shifting at the appliance level by highlighting and discussing the key findings for the reader. In particular, it contributes to improving energy efficiency by describing load-shifting methods at the appliance level and identifying both controllable and uncontrollable appliances. This detailed literature review at the appliance level can make valuable contributions in support of decision- and policymaking by illuminating new dynamic systems specifically in load-shifting and in demand-side management in general for energy efficiency purposes. Full article

As the smart grid involves more new technologies such as electric vehicles (EVs) and distributed energy resources (DERs), more attention is needed in research to general energy storage (GES) based energy management systems (EMS) that account for all possible load shifting and control strategies, specifically with major appliances that are projected to continue electrification such as the electric water heater (EWH). In this work, a methodology for a modified single-node model of a resistive EWH is proposed with improved internal tank temperature for user comfort modeling and capabilities for conservation voltage reduction (CVR) simulations as well as Energy Star and Consumer Technology Association communications protocol (CTA-2045) compliant controls, including energy storage calculations for “energy take”. Daily and weekly simulations are performed on a representative IEEE test feeder distribution system with experimental load and hot water draw (HWD) profiles to consider user comfort. Sequential controls are developed to reduce power spikes from controls and lead to peak shavings. It is found that EWHs are suitable for virtual power plant (VPP) operation with sustainable tank temperatures, i.e., average water temperature is maintained at set-point or above at the end of the control period while shifting up to 78% of EWH energy out of shed windows per day and 75% over a week, which amounts to up to 23% of the total load shifted on the example power system. While CVR simulations reduced the peak power of individual EWHs, the aggregation effect at the distribution level negates this reduction in power for the community. The EWH is shown as an energy constant load without consistent benefit from CVR across the example community with low energy reductions of less than 0.1% and, in some cases, increased daily energy by 0.18%. Full article

These days, users consume more electricity during peak hours, and electricity prices are typically higher between 3:00 p.m. and 11:00 p.m. If electric vehicle (EV) charging occurs during the same hours, the impact on residential distribution networks increases. Thus, home energy management systems (HEMS) have been introduced to manage the energy demand among households and EVs in residential distribution networks, such as a smart micro-grid (MG). Moreover, HEMS can efficiently manage renewable energy sources, such as solar photovoltaic (PV) panels, wind turbines, and vehicle energy storage. Until now, no HEMS has intelligently coordinated the uncertainty of smart MG elements. This paper investigated the impact of PV solar power, MG storage, and EVs on the maximum solar radiation hours. Several deep learning (DL) algorithms were utilized to account for the uncertainties. A reinforcement learning home centralized photovoltaic (RL-HCPV) scheduling algorithm was developed to manage the energy demand between the smart MG elements. The RL-HCPV system was modelled according to several constraints to meet household electricity demands in sunny and cloudy weather. Additionally, simulations demonstrated how the proposed RL-HCPV system could incorporate uncertainty, and efficiently handle the demand response and how vehicle-to-home (V2H) can help to level the appliance load profile and reduce power consumption costs with sustainable power production. The results demonstrated the advantages of utilizing RL and V2H technology as potential smart building storage technology. Full article

The monitoring of power consumption and the forecasting of load profiles for residential appliances are essential aspects of the control of energy savings/exchanges at multiple hierarchical levels: house, house cluster, neighborhood, and city. External environmental factors (weather conditions) and inhabitants’ behavior influence power consumption, and their usage as part of forecasting activity may lead to added value in the estimation of daily-load profiles. This paper proposes a distributed sensing infrastructure for supporting the following tasks: the monitoring of appliances’ power consumption, the monitoring of environmental parameters, the generation of records for a database that can be used for both identifying load models and testing load-scheduling algorithms, and the real-time acquisition of consumption data. The hardware/software codesign of an integrated architecture that can combine the typical distributed sensing and control networks present in modern buildings (targeting user comfort) with energy-monitoring and management systems is presented. Methods for generating simplified piecewise linear (PWL) representations of the load profiles based on these records are introduced and their benefits compared with classic averaged representations are demonstrated for the case of peak-shaving strategies. The proposed approach is validated through implementing and testing a smart-meter node with wireless communication and other wired/wireless embedded modules, enabling the tight integration of the energy-monitoring system into smart-home/building-automation systems. The ability of this node to process power measurements with a programable granularity level (seconds/minutes/hours) at the edge level and stream the processed measurement results at the selected granularity to the cloud is identified as a valuable feature for a large range of applications (model identification, power saving, prediction). Full article

With widely deployed smart meters, non-intrusive energy measurements have become feasible, which may benefit people by furnishing a better understanding of appliance-level energy consumption. This work is a step forward in using graph signal processing for non-intrusive load monitoring (NILM) by proposing two novel techniques: the spectral cluster mean (SC-M) and spectral cluster eigenvector (SC-EV) methods. These methods use spectral clustering for extracting individual appliance energy usage from the aggregate energy profile of the building. After clustering the data, different strategies are employed to identify each cluster and thus the state of each device. The SC-M method identifies the cluster by comparing its mean with the devices’ pre-defined profiles. The SC-EV method employs an eigenvector resultant to locate the event and then recognize the device using its profile. An ideal dataset and a real-world REFIT dataset are used to test the performance of these two techniques. The f-measure score and disaggregation accuracy of the proposed techniques demonstrate that these two techniques are competitive and viable, with advantages of low complexity, high accuracy, no training data requirement, and fast processing time. Therefore, the proposed techniques are suitable candidates for NILM. Full article

Power distribution networks at the distribution level are becoming more complex in their behavior and more heavily stressed due to the growth of decentralized energy sources. Demand response (DR) programs can increase the level of flexibility on the demand side by discriminating the consumption patterns of end-users from their typical profiles in response to market signals. The exploitation of artificial intelligence (AI) methods in demand response applications has attracted increasing interest in recent years. Particle swarm optimization (PSO) is a computational intelligence (CI) method that belongs to the field of AI and is widely used for resource scheduling, mainly due to its relatively low complexity and computational requirements and its ability to identify near-optimal solutions in a reasonable timeframe. The aim of this work is to evaluate different PSO methods in the scheduling and control of different residential energy resources, such as smart appliances, electric vehicles (EVs), heating/cooling devices, and energy storage. This review contributes to a more holistic understanding of residential demand-side management when considering various methods, models, and applications. This work also aims to identify future research areas and possible solutions so that PSO can be widely deployed for scheduling and control of distributed energy resources in real-life DR applications. Full article

This study examines various low voltage levels applied to a direct current residential nanogrid (DC-RNG) with respect to the efficiency and component cost of the system. Due to the significant increase in DC-compatible loads, on-site Photovoltaic (PV) generation, and local battery storage, DC distribution has gained considerable attention in buildings. To provide an accurate evaluation of the DC-RNG’s efficiency and component cost, a one-year load profile of a conventional AC-powered house is considered, and AC appliances’ load profiles are scaled to their equivalent available DC appliances. Based on the modified load profiles, proper wiring schemes, converters, and protection devices are chosen to construct a DC-RNG. The constructed DC-RNG is modeled in MATLAB software and simulations are completed to evaluate the efficiency of each LVDC level. Four LVDC levels—24 V, 48 V, 60 V, and 120 V—are chosen to evaluate the DC-RNG’s efficiency and component cost. Additionally, impacts of adding a battery energy storage unit on the DC-RNG’s efficiency are studied. The results indicate that 60 V battery-less DC-RNG is the most efficient one; however, when batteries are added to the DC-RNG, the 48 V DC distribution becomes the most efficient and cost-effective option. Full article