Search Results (2,581)

Energy poverty poses a critical threat to global sustainable development by undermining household well-being and deepening social inequality. This study draws on data from 17,778 households across six waves of the China Family Panel Studies (CFPS) from 2012 to 2022 to examine the dynamics, determinants, and predictive patterns of household energy poverty in China. Our study also enhances and optimises the four-quadrant classification framework within the Low-Income, High-Cost (LIHC) framework, which jointly evaluates income and energy expenditure using dynamic thresholds. This approach enables us to identify not only households experiencing energy poverty but also those facing heightened vulnerability. In the sample, 7.96% were classified as energy-poor, 29.10% as at risk of energy poverty, 24.14% as at risk of income poverty, and 38.81% as not at risk, indicating that the number of households facing hidden risks far exceeds that of households identified as poor using traditional binary diagnostic methods. Next, we implement a Bayesian-optimised Extreme Gradient Boosting (XGBoost) model to improve predictive accuracy. Thus, the trained model achieved a prediction accuracy of 78%. We employ Shapley Additive exPlanations (SHAP) analysis to interpret the relative importance and interaction of explanatory variables. Our findings reveal three key patterns. First, households at risk of energy insecurity substantially outnumber those already in energy poverty, indicating a large latent vulnerable population that conventional measures often overlook. Second, housing conditions and energy expenditures remain the dominant structural drivers of energy poverty; however, financial pressures related to healthcare, education, and other non-energy expenditures increasingly intensify vulnerability. Third, Bayesian optimisation significantly enhances the model’s capacity to capture nonlinear relationships and complex household heterogeneity. By integrating dynamic measurement with interpretable machine learning, this study advances methodological approaches to energy poverty assessment and provides robust empirical evidence for early-warning systems, differentiated governance strategies, and targeted policy design in the context of China’s energy transition. Full article

When a fuel briquette is pressed using solar electricity in summer and burned for heating in winter, the briquette functions as a seasonal energy store—without batteries, self-discharge, or capital investment in storage infrastructure. This paper quantifies such “indirect energy storage” at an operating briquette production facility in Sumy, Ukraine, using 2024 operational data from a 34 kW hybrid solar power plant integrated into the production process without battery storage under continental climate conditions (50°55′ N) and full-scale military conflict. The objective was to determine the contribution of the solar power plant (SPP) to energy supply, analyse the structure of electricity consumption, and quantify the mechanism of indirect accumulation of renewable energy through transformation into solid biofuels. The study tested two hypotheses: (H1) that integration of a solar power plant into industrial daytime operation (6:00–22:00) achieves a self-consumption rate close to 100%, displacing grid electricity without curtailment or storage losses; and (H2) that the solar fraction embedded in produced briquettes constitutes a quantifiable mechanism of indirect seasonal energy storage despite a temporal mismatch between solar peaks (summer) and product demand (winter). Methods included statistical analysis of monthly and intraday operational data; Pearson correlation analysis between solar generation and production cycles; energy audit of production processes; decomposition of specific consumption into pressing and packaging components; and a simple economic assessment (NPV, IRR, LCOE, payback) with sensitivity analysis. Annual production reached 1222.975 t of briquettes. Total specific electricity consumption (including two short packaging campaigns in June and July only) was 141.3 ± 12.6 kWh/t (CV = 8.9%). After deducting 4962 kWh of dedicated packaging electricity (2.9% of annual consumption), the specific consumption for briquette pressing alone was 136.7 ± 5.0 kWh/t (CV = 3.7%)—within the European benchmark range of 80–150 kWh/t for wood densification, with tight monthly variation indicating a stable, well-tuned pressing operation throughout the year. The SPP supplied 18.3% of total annual electricity, peaking at 33.06% in May and averaging 29.95% from March to August. Intraday analysis of 530 five-minute intervals confirmed a 100% self-consumption rate across all seasons (H1 supported). A total of 223.4 t of briquettes containing accumulated solar energy were produced during the spring–summer period. A weak negative correlation (r = −0.28) between monthly SPP generation and briquette production was observed but did not reach statistical significance (p = 0.385); this descriptive—rather than causal—relationship is consistent with the expected temporal shift between summer surpluses and winter demand, and is itself a signature of indirect rather than direct energy coupling (H2 supported in a descriptive sense). The compound efficiency along the solar-to-stored-fuel chain was estimated at approximately 68%, providing a quantitative indicator for the indirect-storage concept. Economic analysis yielded a simple payback period of about 3 years, NPV (20 yr, 12%) ≈ 1.15 million UAH, IRR ≈ 33%, and LCOE ≈ 3.28 UAH/kWh—61% below the prevailing industrial tariff of 8.45 UAH/kWh—with sensitivity analysis showing positive NPV across ±20% variation in electricity price and ±15% in CAPEX. To the best of the authors’ knowledge, this is the first empirical quantification of biomass-solar integration as a seasonal energy buffer operating without battery storage. The solar energy accumulated in briquettes is sufficient to heat 56–74 households for a full winter season. Regional scaling of the present configuration—under explicit assumptions of comparable facility sizes and operating regimes—could in principle provide fuel for 15,000–20,000 households (8–12% of regional heating needs during energy crises). These findings are directly relevant to post-conflict energy recovery and to regions where attacks on energy infrastructure have left solid biofuels as the primary available heating source. Full article

Building-integrated photovoltaics (BIPVs) offer significant potential for energy-efficient building renovations by seamlessly integrating renewable energy generation into the built environment. This work highlights the strategic opportunity for BIPV within the current European and international context, where the building stock faces an increasingly urgent need for large-scale rehabilitation. BIPV solutions and products for building retrofit are reviewed holistically considering thermal insulation, solar control, daylighting, architectural design, aesthetics, and electrical performance to optimise energy savings and increase social acceptance. A selection of nine international case studies illustrates the versatility of BIPV across diverse building typologies, including projects focused on heritage preservation for which targeted measures are proposed. Despite the opportunities, BIPV adoption remains limited, primarily due to regulatory, economic, and socio-cultural barriers. The specific challenges of BIPV retrofitting in heritage and protected buildings are also examined. Multiple studies have demonstrated BIPV cost-effectiveness, especially when fiscal incentives, environmental co-benefits, and architectural factors are considered. Nonetheless, affordability remains a barrier for many households, highlighting the need for comprehensive financial support to accelerate market uptake. This review is intended to provide a broad audience—including researchers, architects, building professionals, and decision-makers—with a comprehensive, structured overview of BIPV renovation opportunities. Full article

Despite Mozambique’s substantial natural gas reserves, most households rely on solid biomass for cooking, with serious consequences for public health, livelihoods, and the environment. The domestic use of these resources could improve energy efficiency, security, and sustainable development. This mixed-methods study uses household interviews, descriptive statistics, multinomial, and conditional logit models, analyzing data from a random survey of 434 households in energy-rich peripheries of northern Inhambane and Maputo City to ascertain the determinants of household cooking energy choice. Results reveal that rising income increases the odds of choosing electricity, LPG, and biomass over natural gas. In energy-rich peripheries, the odds of selecting biomass over natural gas are reduced by 96.2% compared to non-energy-rich regions. Educational and urban habitation are positively correlated with the adoption of electricity and liquefied petroleum gas (LPG). Price serves as a significant negative predictor of fuel selection (OR ≈ 0.000001), whereby each unit increase in price per GJ substantially diminishes the likelihood of opting for alternatives over domestic gas. Monthly fuel expenditure positively predicts electricity, LPG, and biomass adoption (OR = 1.0042), with effects accumulating meaningfully across realistic spending ranges. Households that experienced energy system incidents were more than twice as likely to switch away from natural gas (OR = 2.072), reflecting the critical role of infrastructure reliability in fuel choice. Given natural gas’s potential as a clean cooking transition fuel, the government should prioritize investment in gas infrastructure, expand domestic supply, and promote public awareness of the health and environmental benefits of clean cooking energy. Full article

Background: Lifestyle factors such as diet and physical activity significantly impact on the risk of obesity in individuals with Down syndrome (DS). However, in the absence of national nutritional guidelines in individuals with DS, further work is needed to understand their dietary and physical activity patterns. In this work we retrieved caregivers’ responses on those aspects. Methods: We analyzed data from a cross-sectional online survey of caregivers of individuals with DS conducted as part of the GO-DS21 project and reported in the accompanying paper (nutrients-4216283) (n = 764). We explored physical activity patterns, dietary habits, beliefs around weight-loss interventions and caregiver confidence that family members with DS would engage in a healthier lifestyle. Associations were examined using correlation analysis, and cumulative and binary logistic regression models. Results: Caregivers reported that most individuals with DS exercised 1–3 times per week, with frequency declining with age. Males were more likely to exercise daily than females. Caregiver exercise frequency was positively correlated with that of their DS family member (ρ = 0.521, p < 0.001), suggesting clustering of shared health behaviors within households. In adjusted models, caregivers who exercised regularly had up to thirteen-fold higher odds of having a physically active family member with DS (aOR = 13.02, 95% CI: 7.40–24.06, p < 0.001). Fried food consumption and higher snack frequency were independently associated with perceived obesity status, while sugar-sweetened beverage consumption was not. Caregivers favored exercise as a weight-loss strategy, while anti-obesity drugs were endorsed by only 11% of caregivers primarily and were more likely to be endorsed when obesity was perceived (aOR = 4.21, 95% CI: 2.44–7.39, p < 0.001). Finally, caregiver confidence that their family member with DS would engage in healthier behaviors was associated with perceived obesity status and strongly associated with higher physical activity levels (aOR 14.68, 95% CI: 6.59–33.40, p < 0.001). Conclusions: In this large European caregiver survey, reported consumption of selected energy-dense foods was generally low, although fried food intake and higher snack frequency were associated with perceived obesity. Physical activity patterns were closely aligned between caregivers and individuals with DS, suggesting shared household health behaviors. These findings highlight the importance of involving caregivers and family environments in lifestyle interventions aimed at supporting physical activity and weight management in individuals with DS. Full article

The transition to decentralized, clean energy systems is essential for sustainable development, particularly in rural South African communities where grid extension costs can exceed R300,000 per km. This paper presents a comprehensive analysis of Artificial Intelligence (AI) integration into hybrid solar-battery systems to address challenges of intermittency, load variability, and unreliable demand. We propose a model incorporating Long Short-Term Memory (LSTM) networks for energy forecasting and Reinforcement Learning (RL) for real-time optimization. Mathematical formulations for photovoltaic (PV) generation, battery state-of-charge dynamics, and a multi-objective cost function minimizing Levelized Cost of Energy (LCOE), carbon emissions, and reliability loss are derived with appropriate citations. A fairness metric is introduced as an operational constraint to mitigate algorithmic bias in energy allocation. Simulation results, calibrated with South African data, demonstrate a 20% improvement in forecasting accuracy (RMSE), a 30% reduction in diesel generator use, and a decrease in LCOE from R7.80 to R5.50/kWh. Furthermore, our fairness-constrained optimization reduced the Gini coefficient for load shedding from 0.38 to 0.19, ensuring more equitable access across households. This study concludes that AI-driven microgrids are technically viable, environmentally beneficial, and ethically sound for advancing equitable rural electrification in South Africa. Full article

The emergence of citizen energy communities under the European Clean Energy Package is creating new opportunities for neighboring households to collectively reduce electricity costs through local energy sharing. This paper presents a distributed multi-agent energy management system for a two-household residential energy community in which each household is equipped with photovoltaic generation and a battery energy storage system operating under realistic hourly-varying electricity prices. Each household is managed by an independent Deep Q-Learning agent that learns a cost-optimal charging and discharging policy using only local observations. In parallel, a coordination agent, implemented on the SPADE platform with XMPP-based messaging, oversees real-time peer-to-peer energy transfers between households, enabling energy exchange whenever one household has surplus generation and another faces a deficit. The two households are deliberately configured with complementary profiles: one has higher PV generation capacity while the other has higher energy consumption. This setup creates natural opportunities for local energy sharing between them. Performance is assessed through a three-level evaluation framework: (i) individual household economics (cost reduction, battery management, grid exchanges), (ii) coordination efficiency (transfer frequency, direction, and volume), and (iii) aggregate community performance, which isolates the added value of peer-to-peer sharing beyond what each household achieves through individual BESS optimization. Numerical experiments using GEFCom2014 solar generation data, synthetic residential load profiles calibrated following documented consumption patterns, and day-ahead price signals representative of the Spanish electricity market demonstrate that both Deep Q-Learning agents independently learn effective charge/discharge strategies aligned with price signals and PV availability. They also show that the coordination layer further reduces community grid dependence by routing surplus energy locally rather than exchanging it with the main grid at less favorable rates. The results confirm that a well-engineered integration of decentralized reinforcement learning with a lightweight coordination protocol can deliver measurable economic benefits in realistic residential energy communities without requiring centralized training, shared data, or complex multi-agent reinforcement learning architectures. Full article

Long-term decarbonization of urban residential buildings in southern China depends on the joint evolution of building stock, end-use efficiency, and electricity carbon intensity. This study develops a dynamic stock-energy-carbon framework for urban residential buildings in China’s hot-summer warm-winter region from 2010 to 2060, using Guangdong, Guangxi, Fujian, and Hainan as case provinces. The model links demographic and housing-space change with stock survival, retrofit of the base-year stock, cohort-specific performance levels for post-2022 new construction, and time-varying provincial grid emission factors. EnergyPlus simulations of seven high-rise residential archetypes show that nearly zero-energy performance reduces province-level EUI by 19.2–26.5% relative to the baseline, with cooling-load reductions forming the dominant part of the improvement in the warmer provinces. Across coupled demand-side scenarios, stricter new-build performance standards reduce 2026–2060 cumulative operational energy by 5.3–10.1% relative to the conservative demand-side setting, while increasing retrofit intensity provides a smaller but consistent additional reduction. Carbon outcomes are more sensitive to electricity-sector assumptions: under the main demand-side setting, moving from the conservative to the accelerated grid pathway advances the operational-carbon peak by 8–15 years across the four provinces and lowers 2060 residual emissions by about 71%. A comparison with available observed provincial household-electricity statistics is added as a plausibility check; it confirms the relevant order of magnitude but also indicates that absolute demand estimates should be interpreted cautiously because of boundary and EUI-representation differences. These results suggest that demand-side efficiency policies must be coordinated with rapid provincial power-sector decarbonization if the residential sector in Hot-Summer Warm-Winter Region is to reach earlier carbon peaks and lower residual operational emissions. Full article

In this study, we investigate the impact of electric energy storage (EES) on phase line power flow symmetry and photovoltaic (PV) energy utilization in prosumer three-phase four-wire integrated household systems. The analysis is based on high-time-resolution (1 s) experimental data collected from a real household grid and subsequent simulations of energy flows using MATLAB/Simulink software. Two converter operation strategies were evaluated: the conventional symmetric mode and the asymmetric mode developed by the authors based on an adaptive power flow management algorithm. For both strategies, the impact of EES capacity on imbalance in the distribution system operator (DSO) grid was investigated. The methodology analyzes energy flows in each phase line separately, allowing for a detailed assessment of the imbalance between phase line phenomena and their impact on local energy consumption. Key performance parameters used for the efficiency evaluation include the self-consumption and self-sufficiency rates, which quantify the share of locally generated energy consumed within the household and the degree of independence from the DSO grid. The results show that combining adaptive asymmetric inverter control with appropriately sized energy storage allows for more efficient on-site utilization of PV energy, which, at the same time, improves the load symmetry of the phase lines in the DSO grid. Full article

Series arc faults on the DC side of photovoltaic (PV) systems are a critical hazard that can trigger system fires. Conventional contact-based detection methods suffer from cumbersome installation and high retrofit cost, whereas existing non-contact approaches mostly rely on megahertz-level high-frequency sampling and therefore require expensive radio-frequency instrumentation or high-performance computing platforms. As a result, it remains difficult to simultaneously achieve strong interference immunity and real-time performance on low-cost embedded devices with limited resources. To address this engineering paradox between high-frequency sampling and constrained computational capability, this paper proposes a fully embedded, non-contact arc fault detection system based on a 12–80 kHz low-frequency sub-band selection strategy. By exploiting the physical characteristic of broadband energy elevation induced by arc faults, the proposed strategy avoids dependence on high-bandwidth hardware. Guided by this strategy, a Moebius-topology coaxial shielded loop antenna is employed as the near-field sensor, while an ultra-simplified passive analog front end is constructed directly by using the on-chip programmable gain amplifier and analog-to-digital converter of the microcontroller unit, enabling efficient signal acquisition and fast Fourier transform processing within the target sub-band. To cope with complex background noise in the low-frequency range, an environment-adaptive baseline mechanism based on exponential moving average and exponential absolute deviation is developed for dynamic decoupling. In addition, a lightweight INT8-quantized multilayer perceptron is introduced as a nonlinear auxiliary module, thereby forming a robust hybrid decision architecture with complementary rule-based and artificial intelligence components. Experimental results show that, under the tested household, laboratory, and PV-site conditions, the proposed system achieved an overall detection rate of 97%, while the remaining 3% mainly corresponded to failed ignition or non-sustained arc attempts rather than persistent false triggering during normal monitoring. Full article

In Brazil, more than 80% of households rely on electricity for water heating, representing approximately 13% of residential electricity consumption and significantly contributing to peak grid demand. As a prominent alternative for supplying household thermal energy and reducing grid stress, this study experimentally evaluates, under tropical climate conditions, the performance of a modular water-based photovoltaic–thermal (PVT) system and compares it with a conventional photovoltaic (PV) system operating simultaneously under identical environmental conditions. The PVT system, based on commercial PV modules coupled to roll-bond heat exchangers, a storage tank, and a shower outlet, was tested under three hydraulic regimes: natural thermosiphon, closed-loop, and Forced circulation. A dedicated ESP32-based data acquisition system, integrated with a cloud platform, continuously monitors electrical, thermal, and meteorological variables. Results show that PVT modules exhibit a small electrical efficiency reduction due to increased cell temperatures, which is largely compensated by the simultaneous thermal generation, yielding overall efficiency gains of 74.04%, 76.53%, and 7.62% over the reference PV system for Normal, Forced, and Closed circulation, respectively. The comparative analysis identifies Forced-circulation scheduling and the matching between thermal generation and consumption as key factors for performance optimization. The findings provide practical guidelines for deploying PVT systems to replace electric showers in tropical regions, reducing residential electricity consumption and mitigating peak-demand stress on the grid. Full article

Rooftop photovoltaic (PV) potential assessments have advanced significantly through high-resolution geospatial methods. However, most studies remain focused on well-planned urban environments and primarily consider geometric or radiative factors, often neglecting material constraints and deployment realism in heterogeneous cities of the Global South. This study addresses these gaps by developing an automated LiDAR- and GIS-based methodology to estimate rooftop PV potential in Cartagena, Colombia, explicitly integrating cadastral constraints, geometric feasibility, and roof material exclusion. The workflow combines LiDAR-derived elevation data, parcel-based segmentation, slope and aspect filtering, and post-processing techniques to identify PV-suitable rooftops, validated against 482 manually delineated polygons. The optimal configuration (45° slope threshold; 0.25 m buffer) achieved RMSE values of 6.79° (slope) and 20.95° (aspect). A geometry-constrained panel fitting algorithm estimated 3,599,631 panels across 146,091 rooftops, representing 7.06 km² of suitable area. Compared to simple area-based methods, this approach reduced capacity estimates by approximately 15.3%, demonstrating the importance of geometric realism. A key contribution is the integration of asbestos-cement (AC) roof exclusion, which reduced suitable rooftop area by ~65%, resulting in a final capacity of 1,281,202 panels. Estimated annual generation decreased from 1891.9 GWh/year to 673.4 GWh/year, equivalent to supplying 53.4–126.8% of Cartagena’s households. The proposed methodology provides a scalable framework for realistic urban PV assessment and introduces a dual-purpose planning tool that enables authorities to both prioritize solar deployment and identify areas requiring roof remediation, supporting safer and more controlled energy transitions in developing-country cities. Full article

This article studies why the same Bank of Canada tightening is reflected differently in provincial CPI inflation. It combines monthly provincial data from January 1991 to December 2024 with interacted local projections and public-data measures of common national monetary movements. The design estimates reduced-form provincial loadings in a common monetary environment, rather than structural responses to a single externally identified surprise. The main result is a housing-sensitive gap between headline inflation and inflation excluding shelter. Provinces with larger shelter weights and higher household debt–service exposure show a stronger headline response than non-shelter response after a common tightening. The evidence does not reduce to rent or to basket arithmetic alone: debt–service exposure is the more stable standalone component, while shelter weights tie the differential to measured CPI. Outside shelter, no single provincial characteristic dominates. Internal trade integration is associated with smaller baseline deviations from the national non-shelter response, but energy-related provincial composition is at least as informative in the competing-factor specifications. The paper therefore identifies shelter and household debt as the clearest sources of provincial incidence under one policy rate, while treating non-housing deviations from the national response as a broader provincial-structure result. Full article

This study presents a comprehensive clustering analysis of residential prosumer profiles for optimizing control and peer-to-peer (P2P) energy trading in community renewable energy systems (CRES). Using data from 25 prosumer households equipped with rooftop solar photovoltaic (PV) systems and electric vehicle (EV) charging capabilities, this study implements and compares k-means and spectral clustering algorithms to identify optimal segmentation strategies for prosumer energy management. K-means clustering identifies seven practical prosumer categories with a silhouette coefficient of 0.17, while spectral clustering achieves superior mathematical separation with a silhouette coefficient of 0.275 in ten clusters, though producing six singleton outliers. The k-means solution demonstrates three primary prosumer categories: net producers, net consumers, and balanced profiles. Cluster size variation requires adaptive optimization, while singleton outliers need custom strategies. EV ownership impact consumption, so future proliferation demands dynamic clustering, and these findings will guide metaheuristic algorithms for energy trading and pricing. Full article

Residential buildings are a major source of urban carbon emissions, yet the uptake of smart home retrofitting remains far below the level required to meet decarbonization and sustainability targets. While technical solutions for energy-efficient renovation are well established, less is known about how behavioral, psychological, and institutional factors jointly shape household retrofit decisions and their broader sustainability implications. This study develops an integrated analytical framework that combines UTAUT2 with perceived risk, trust, innovativeness, and regulatory pressure, interpreted through a socio-technical systems perspective, to examine smart home retrofitting in Thailand and its contribution to Sustainable Community Development Goals (SCDG). Survey data were collected from 448 households in Bangkok and Chonburi and analyzed using structural equation modeling. The results show that traditional UTAUT2 predictors such as performance expectancy, effort expectancy, and social influence do not significantly influence adoption intention in this high-cost retrofit context. Instead, innovativeness, trust, price value, perceived risk, and regulatory pressure emerge as key behavioral and institutional drivers, while facilitating conditions and habits shape actual use behavior. Actual retrofit behavior is found to generate significant economic, environmental, socio-cultural, technological, and public-policy sustainability outcomes aligned with SCDG. These findings demonstrate the limitations of conventional technology acceptance models in infrastructure-based contexts and provide a mechanism-based explanation of how retrofit adoption is driven in high-cost sustainability contexts. Full article