Search Results (64)

Demand Side Management (DSM) plays a crucial role in modern energy systems, enabling more efficient use of energy resources and contributing to the sustainability of the power grid. This study examines DSM strategies within a multi-environment context encompassing residential, commercial, and industrial sectors, with a focus on diverse appliance types that exhibit distinct operational characteristics and user preferences. Initially, a single-objective optimization approach using Genetic Algorithms (GAs) is employed to minimize the total energy cost under a real Time-of-Use (ToU) pricing scheme. This heuristic method allows for the effective scheduling of appliance operations while factoring in their unique characteristics such as power consumption, usage duration, and user-defined operational flexibility. This study extends the optimization problem to a multi-objective framework that incorporates the minimization of CO₂ emissions under a real annual energy mix while also accounting for user discomfort. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is utilized for this purpose, providing a Pareto-optimal set of solutions that balances these competing objectives. The inclusion of multiple objectives ensures a comprehensive assessment of DSM strategies, aiming to reduce environmental impact and enhance user satisfaction. Additionally, this study monitors the Peak-to-Average Ratio (PAR) to evaluate the impact of DSM strategies on load balancing and grid stability. It also analyzes the impact of considering different periods of the year with the associated ToU hourly schedule and CO₂ emissions hourly profile. A key innovation of this research is the integration of detailed, category-specific metrics that enable the disaggregation of costs, emissions, and user discomfort across residential, commercial, and industrial appliances. This granularity enables stakeholders to implement tailored strategies that align with specific operational goals and regulatory compliance. Also, the emphasis on a user discomfort indicator allows us to explore the flexibility available in such DSM mechanisms. The results demonstrate the effectiveness of the proposed multi-objective optimization approach in achieving significant cost savings that may reach 20% for industrial applications, while the order of magnitude of the trade-offs involved in terms of emissions reduction, improvement in discomfort, and PAR reduction is quantified for different frameworks. The outcomes not only underscore the efficacy of applying advanced optimization frameworks to real-world problems but also point to pathways for future research in smart energy management. This comprehensive analysis highlights the potential of advanced DSM techniques to enhance the sustainability and resilience of energy systems while also offering valuable policy implications. Full article

Load disaggregation has emerged as an effective tool for enabling smarter energy management in residential and commercial buildings. By providing appliance-level energy consumption estimation from aggregate data, it supports energy efficiency initiatives, demand-side management, and user awareness. However, several challenges remain in improving the accuracy of energy disaggregation methods. For instance, the amount of noise in energy consumption datasets can heavily impact the accuracy of disaggregation algorithms, especially for low-power consumption appliances. While disaggregation performance depends on hyperparameter tuning, the influence of data characteristics, such as noise, on hyperparameter selection remains underexplored. This work investigates the hypothesis that appliance-specific noise information can guide the selection of algorithm hyperparameters, like the input sequence length, to maximize disaggregation accuracy. The appliance-to-noise ratio metric is used to quantify the noise level relative to each appliance’s energy consumption. Then, the selection of the input sequence length hyperparameter is investigated for each case by inspecting disaggregation performance. The results indicate that the noise metric provides valuable guidance for selecting the input sequence length, particularly for user-dependent appliances with more unpredictable usage patterns, such as washing machines and electric kettles. Full article

Climate change and global warming are causing growing environmental concerns, prompting many countries to increase investments in renewable energies. The high growth rate of renewables in the energy systems brings significant intermittency challenges. Demand-side flexibility is presented as a viable solution to address this phenomenon. In this framework, this research study proposes a novel methodology to evaluate the flexibility potential that residential consumers can offer to the Distribution System Operator (DSO). Moreover, it pretends to provide guidelines and design of standardized parameters to disaggregate the aggregated energy consumption data of end-users. This step is essential to identify and characterize the primary energy consumption processes in the residential sector, laying the groundwork for future flexibility evaluation. Furthermore, the modeling of the energy consumption curves will enhance residential sector demand-side flexibility enabling end-users to modify their usual consumption patterns. The implemented methodology has been applied to real consumer data provided by the DSO of a Spanish municipality of about 29,000 habitants in the Alicante Province (Spain). Results achieved allowed the validation of the proposed methodology enabling the disaggregation of residential energy profiles and facilitating the subsequent dynamic assessment of residential end-user’s demand flexibility. Moreover, this work will provide valuable guidelines to carry out short-term energy resource planning and solve operational problems of the energy systems. Full article

The widespread deployment of electric vehicles (EVs) has introduced substantial challenges to electricity pricing, grid stability, and renewable energy integration. This paper presents the first real-time quantum-enhanced electricity pricing framework for large-scale EV charging networks, marking a significant departure from existing approaches based on mixed-integer programming (MILP) and deep reinforcement learning (DRL). The proposed framework incorporates renewable intermittency, demand elasticity, and infrastructure constraints within a high-dimensional optimization model. The objective is to dynamically determine spatiotemporal electricity prices that reduce system peak load, improve renewable utilization, and minimize user charging costs. A rigorous mathematical formulation is developed, integrating over 40 system-level constraints, including power balance, transmission limits, renewable curtailment, carbon targets, voltage regulation, demand-side flexibility, social participation, and cyber-resilience. Real-time electricity prices are treated as dynamic decision variables influenced by station utilization, elasticity response curves, and the marginal cost of renewable and grid electricity. The model is solved across 96 time intervals using a quantum-classical hybrid method, with benchmark comparisons against MILP and DRL baselines. A comprehensive case study is conducted on a 500-station EV network serving 10,000 vehicles, coupled with a modified IEEE 118-bus grid and 800 MW of variable renewable energy. Historical charging data with ±12% stochastic demand variation and real-world solar/wind profiles are used to simulate realistic conditions. Results show that the proposed framework achieves a 23.4% average peak load reduction per station, a 17.9% gain in renewable utilization, and up to 30% user cost savings compared to flat-rate pricing. Network congestion is mitigated at over 90% of high-traffic stations. Pricing trajectories align low-price windows with high-renewable periods and off-peak hours, enabling synchronized load shifting and enhanced flexibility. Visual analytics using 3D surface plots and disaggregated bar charts confirm structured demand-price interactions and smooth, stable price evolution. These findings validate the potential of quantum-enhanced optimization for scalable, clean, and adaptive EV charging coordination in renewable-rich grid environments. Full article

As the world transitions towards a low-carbon economy, understanding the employment implications of renewable energy growth is crucial, particularly in emerging economies like the BRICS nations, where energy demand and employment pressures are rapidly evolving. The justification for this study lies in the critical need to understand the employment effects of renewable energy growth in emerging economies, particularly in the BRICS nations, which account for a significant share of global energy demand and are poised to drive the next wave of renewable energy adoption. As these countries navigate the challenges of energy transition, employment creation, and sustainable development, this research aims to provide timely and actionable insights for policymakers, industry stakeholders, and researchers seeking to optimize the employment benefits of renewable energy growth in these regions. The purpose of this study is to investigate the impact of disaggregated renewable energy (solar, hydro, wind, nuclear, and other renewables including bioenergy) on employment dynamics in BRICS nations, so as to provide empirical evidence on the employment effects of renewable energy growth in these regions. The key findings from the study are summarized as follows: Hydro contributes positively to employment creation in BRICS nations, with FMOLS (0.78%), DOLS (2.06%), and PCSE (0.61%) results showing significant positive effects. Solar contributes positively to employment creation in BRICS nations, with FMOLS (1.99%) and DOLS (9.60%) results showing significant positive effects, although country-specific results are mixed. Economic growth contributes positively to employment creation in BRICS nations, with FMOLS (32.93%), DOLS (36.86%), and PCSE (27.68%) results showing significant positive effects. Wind contributes negatively to employment creation in BRICS nations at the aggregate level (FMOLS, −0.66%), but has positive effects in some countries (Brazil, China, Russia, and South Africa). Nuclear contributes negatively to employment creation in BRICS nations at the aggregate level (FMOLS, −0.47%; PCSE, −1.04%), but has positive effects in some countries (Russia, India, China, and South Africa). Other Renewables contribute negatively to employment creation in BRICS nations, with FMOLS (−2.57%) and PCSE (−4.77%) results showing significant negative effects. Policymakers in BRICS nations should prioritize investments in hydropower, solar power, and wind power to leverage their job creation potential and promote sustainable economic growth. Additionally, governments should implement policies to support the development of other renewable energy sources, such as bioenergy, geothermal, and tidal power, to increase their job creation potential. Furthermore, policymakers should promote economic growth through green investments and sustainable development initiatives to maximize employment creation in the renewable energy sector. Full article

To promote sustainable food choices, it is essential to provide easily understandable information that integrates health, environmental impacts and planetary boundaries. For this purpose, the Planet Health Conformity Index (PHC) was developed and tested. Current labels, such as the Nutri-Score for health and the Eco-Score for environmental impacts, provide separate information, which may result in consumers receiving conflicting messages. The PHC combines these dimensions into a single label, aligning with consumer demand for clearer guidance and fostering sustainable food consumption and development. Methods: The PHC assesses 18 nutrients and five environmental impacts—Global Warming Potential (GWP), cropland use, freshwater use, nitrogen application (N-min) and phosphorus application (P-min)—within the framework of planetary boundaries. Six different algorithm designs, varying in capping and weighting, were tested on 125 food products from the German market. The analysis compared mass-, energy- and multi-nutrient-based functional units. Results: Under mass- and energy-based units, many products meet planetary boundaries. However, incorporating nutrient profiles often leads to exceeding these boundaries (exceedance rate PHC: GWP: 38% of products transgressed the boundary, cropland use: 41%, freshwater use: 27%, N-min: 34%, P-min: 71%). Accordingly, the PHC contextualizes nutritional strengths and weaknesses environmentally. Moreover, it disaggregates the Planetary Health Diet (PHD) at the nutrient level, facilitating adaptation to individual nutritional needs. Conclusions: Traditional food Life Cycle Assessments should include nutrients in the functional unit and consider planetary boundaries to enable more accurate food comparisons. The PHC presented here takes these aspects into account. In addition, its dual-factor approach, integrating health and environmental metrics, ensures broad applicability. Thus, the PHC Index can be applied not only to single food items but also to recipes, dishes, menus and entire diets. Full article

The momentum of the Fourth Industrial Revolution is driving increased demand for certain specific metals. These include copper, silver, gold, and platinum group metals (PGMs), which have important applications in renewable energies, green hydrogen, and electronic products. However, the continuous extraction of these metals is leading to a rapid decline in their ore grades and, consequently, increasing the environmental impact of extraction. Hence, obtaining metals from secondary sources, such as waste electrical and electronic equipment (WEEE), has become imperative for both environmental sustainability and ensuring their availability. To evaluate the sustainability of the process, this paper proposes using an exergy approach, which enables appropriate allocation among co-products, as well as the assessment of exergy losses and the use of non-renewable resources. As a case study, this paper analyzes the recycling process of waste printed circuit boards (PCBs) by disaggregating the exergy cost into renewable and non-renewable sources, employing different exergy-based cost allocation methods for the mentioned metals. It further considers the complete life cycle of metals using the Circular Thermoeconomics methodology. The results show that, when considering the entire life cycle, between 47% and 53% of the non-renewable exergy is destroyed during recycling. Therefore, delaying recycling as much as possible would be the most desirable option for minimizing the use of non-renewable resources. Full article

Climate change, primarily driven by human activities such as burning fossil fuels, is causing significant long-term changes in temperature and weather patterns. To mitigate these impacts, there is an increased focus on renewable energy sources. However, optimizing power consumption through effective usage control and waste recycling also offers substantial potential for reducing energy demands. This study explores non-intrusive load monitoring (NILM) to estimate disaggregated energy consumption from a single household meter, leveraging advancements in deep learning such as convolutional neural networks. The study uses the UK-DALE dataset to extract and plot power consumption data from the main meter and identify five household appliances. Convolutional neural networks (CNNs) are trained with transfer learning using VGG16 and MobileNet. The models are validated, tested on split datasets, and combined using ensemble methods for improved performance. A new voting scheme for ensembles is proposed, named weighted average confidence voting (WeCV), and it is used to create combinations of the best 3 and 5 models and applied to NILM. The base models achieve up to 97% accuracy. The ensemble methods applying WeCV show an increased accuracy of 98%, surpassing previous state-of-the-art results. This study shows that CNNs with transfer learning effectively disaggregate household energy use, achieving high accuracy. Ensemble methods further improve performance, offering a promising approach for optimizing energy use and mitigating climate change. Full article

Recently, various strategies for energy management have been proposed to improve energy efficiency in smart grids. One key aspect of this is the use of microgrids. To effectively manage energy in a residential microgrid, advanced computational tools are required to maintain the balance between supply and demand. The concept of load disaggregation through non-intrusive load monitoring (NILM) is emerging as a cost-effective solution to optimize energy utilization in these systems without the need for extensive sensor infrastructure. This paper presents an energy management system based on NILM and the Internet of Things (IoT) for a residential microgrid, including a photovoltaic (PV) plant and battery storage device. The goal is to develop an efficient load management system to increase the microgrid’s independence from the traditional electrical grid. The microgrid model is developed in the electromagnetic transient program PSCAD/EMTDC to analyze and optimize energy performance. Load disaggregation is obtained by combining artificial neural networks (ANNs) and particle swarm optimization (PSO) to identify appliances for demand-side management. An ANN is applied in NILM as a load identification task, and PSO is used to optimize the ANN algorithm. This combination enhances the NILM technique’s accuracy, which is verified using the mean absolute error method to assess the difference between the predicted and measured power consumption of appliances. The NILM output is then transferred to consumers through the ThingSpeak IoT platform, enabling them to monitor and control their appliances to save energy and costs. Full article

With the development of smart grids and new power systems, the combination of non-intrusive load identification technology and smart home technology can provide users with the operating conditions of home appliances and equipment, thus reducing home energy loss and improving users’ ability to demand a response. This paper proposes a non-intrusive load decomposition model with a parallel multiscale attention mechanism (PMAM). The model can extract both local and global feature information and fuse it through a parallel multiscale network. This improves the attention mechanism’s ability to capture feature information over long time periods. To validate the model’s decomposition ability, we combined the PMAM model with four benchmark models: the Long Short-Term Memory (LSTM) recurrent neural network model, the Time Pooling-based Load Disaggregation Model (TPNILM), the Extreme Learning Machine (ELM), and the Load Disaggregation Model without Parallel Multi-scalar Attention Mechanisms (UNPMAM). The model was trained on the publicly available UK-DALE dataset and tested. The models’ test results were quantitatively evaluated using a confusion matrix. This involved calculating the F1 score of the load decomposition. A higher F1 score indicates better model decomposition performance. The results indicate that the PMAM model proposed in this paper maintains an F1 score above 0.9 for the decomposition of three types of electrical equipment under the same household user, which is 3% higher than that of the other benchmark models on average. In the cross-household test, the PMAM also demonstrated a better decomposition ability, with the F1 score maintained above 0.85, and the mean absolute error (MAE) decreased by 5.3% on average compared with that of the UNPMAM. Full article

For many years, energy monitoring at the most disaggregate level has been mainly sought through the idea of Non-Intrusive Load Monitoring (NILM). Developing a practical application of this concept in the residential sector can be impeded by the technical characteristics of case studies. Accordingly, several databases, mainly from Europe and the US, have been publicly released to enable basic research to address NILM issues raised by their challenging features. Nevertheless, the resultant enhancements are limited to the properties of these datasets. Such a restriction has caused NILM studies to overlook residential scenarios related to geographically-specific regions and existent practices to face unexplored situations. This paper presents applied research on NILM in Quebec residences to reveal its barriers to feasible implementations. It commences with a concise discussion about a successful NILM idea to highlight its essential requirements. Afterward, it provides a comparative statistical analysis to represent the specificity of the case study by exploiting real data. Subsequently, this study proposes a combinatory approach to load identification that utilizes the promise of sub-meter smart technologies and integrates the intrusive aspect of load monitoring with the non-intrusive one to alleviate NILM difficulties in Quebec residences. A load disaggregation technique is suggested to manifest these complications based on supervised and unsupervised machine learning designs. The former is aimed at extracting overall heating demand from the aggregate one while the latter is designed for disaggregating the residual load. The results demonstrate that geographically-dependent cases create electricity consumption scenarios that can deteriorate the performance of existing NILM methods. From a realistic standpoint, this research elaborates on critical remarks to realize viable NILM systems, particularly in Quebec houses. Full article

As a low-cost demand-side management application, non-intrusive load monitoring (NILM) offers feedback on appliance-level electricity usage without extra sensors. NILM is defined as disaggregating loads only from aggregate power measurements through analytical tools. Although low-rate NILM tasks have been conducted by unsupervised approaches based on graph signal processing (GSP) concepts, enhancing feature selection can still contribute to performance improvement. Therefore, a novel unsupervised GSP-based NILM approach with power sequence feature (STS-UGSP) is proposed in this paper. First, state transition sequences (STS) are extracted from power readings and featured in clustering and matching, instead of power changes and steady-state power sequences featured in other GSP-based NILM works. When generating graph in clustering, dynamic time warping distances between STSs are calculated for similarity quantification. After clustering, a forward-backward power STS matching algorithm is proposed for searching each STS pair of an operational cycle, utilizing both power and time information. Finally, load disaggregation results are obtained based on STS clustering and matching results. STS-UGSP is validated on three publicly accessible datasets from various regions, generally outperforming four benchmarks in two evaluation metrics. Besides, STS-UGSP estimates closer energy consumption of appliances to the ground truth than benchmarks. Full article

This study aims to investigate the impacts and identify the causal links between tourism expansion and the environment among countries of the Eurozone from 1996 to 2019 in the context of the environmental Kuznets curve (EKC). To achieve this end, we used a new set of untested tourism proxies when elaborating the EKC. We disaggregated the tourism phenomenon and highlighted its heterogenous nature by including specific and high-impact market segments such as business and leisure tourism spending as well as capital investment spending. The research findings indicate the pivotal role that tourism proxies have on environmental degradation in terms of greenhouse gas emissions (GHGs). Specifically, the identified reciprocal causalities between leisure and investment spending and environmental degradation suggest some complementarities between these variables. In the case of business tourism spending, an increase (decrease) in this variable leads to an increase (decrease) in environmental degradation. The last two feedback hypotheses indicate that the primary and final energy consumption Granger cause GHGs and vice versa. Such a result offers evidence for incorporating the concept of energy efficiency in tourism. Practical implications should motivate supply and demand dimensions within the tourism system to improve efficiency in tourism flow management. The supply side should transfer the environmental message to visitors to spend wisely and consume smarter, whereas the demand side should perform pro-environmental behavior by spending wisely and acting responsibly at destinations. Full article

Insightful information on energy use encourages home residents to conduct home energy conservation. This paper proposes an experimental design for an energy disaggregation system based on the low-computational-cost approaches of multi-target classification and multi-target regression, which are under the multi-target learning framework. The experiments are set up to determine the optimal learning algorithm and model parameters. In addition, the designated system can provide inference of the appliance power state and the estimated power consumption from both approaches. The kernel density estimation technique is utilized to formulate the appliance power state as a finite-state machine for the multi-target classification approach. Multi-target regression can directly provide the estimation of appliance power demand from the aggregate data, and this work unifies the system’s design together with multi-target classification. The predictive performances obtained through the F-score (micro-averaged) and power estimation accuracy index for the power state inference and the estimated power demand, respectively, are shown to outperform a deep-learning-based denoising autoencoder network under the same data settings from both approaches. The results lead to a recommendation to apply the approach in home energy monitoring, which is mainly based on the characteristics of appliance power and the information that the residents wish to perceive. Full article

Economic expansion leads to higher CODe₂ emissions, which puts pressure on environmental degradation. More than 30% of carbon emissions are contributed by the top0polluting countries in the world through their energy consumption. Therefore, the current study examines the association between CO₂ emissions, energy consumption, GDP and industrial production, along with other control variables at the aggregated and disaggregated levels for the top emitter countries for the 1990–2019 period. The short- and long-term results indicate that CO₂ emissions are positively and significantly linked with energy consumption, except carbon emissions from the gas model, by employing the PARDL model using pooled mean group (PMG) analysis. Thus, gas consumption is less polluting to the environment than other sources of energy; therefore, countries need to reduce the consumption of coal and oil, which will lead to a decrease in CO₂ emissions. This refers to the composition effect, which focuses on the use of clean energy instead of dirty energy in the production and consumption processes. The shift from oil or coal to gas in the production process will help to reduce the oil demand, which ultimately controls its consumption and prices, which may help to control the prices of various other goods and services. Full article