Search Results (274)

Many electricity providers are offering their customers an array of tariff options intended to discourage electricity consumption at specific times of the day. The problem facing a customer is whether to switch from their existing tariff to a new tariff. The aim of this paper is twofold: first, to develop two analytical methods that help residential customers evaluate when switching from a flat-rate tariff to time-varying pricing options, specifically the Time-of-Use (TOU) tariff and an event-based tariff, becomes economically beneficial, and second, to review customers’ experiences with the tariffs. The methods identify the specific consumption distributions at which the TOU or event-based tariffs are in energy- and cost-equilibrium with the domestic service tariff for residential customers. For the TOU structure, the analysis shows that customers must maintain a non-winter-to-winter-peak consumption ratio exceeding 3.0756 for cost neutrality, a condition rarely met by households with winter-dominant loads. In contrast, event-based structures require only minimal behavioral adjustments to achieve savings, with as little as 1.75% of annual consumption needing to be avoided during event periods to match domestic-service costs. Additional savings are observed with partial or full load shifting away from peak events. The findings highlight that while TOU may benefit households with high summer usage, event-based tariffs present a more practical and economically favorable option for residential customers living in the Canadian province of Nova Scotia. The paper concludes with implications for tariff selection and consumer behavior. This research will be of value to anyone considering designing a time-varying rate or having to choose between an existing flat-rate tariff and a time-varying tariff. Full article

Challenges related to energy security require support for investments in renewable energy sources. One of the most dynamically developing technologies in this area is photovoltaics. The literature provides numerous publications indicating PV development directions; however, strategic development planning remains fragmented between purely technological solutions and market-economic analyses. Systematic integration of both perspectives with customer needs is lacking. This study fills this gap: applying the Trends of Engineering System Evolution (TESE) methodology enables identification of PV system development trends with particular attention to PV user needs and consideration of market-economic and technological conditions. The TESE framework was used to identify the Main Parameter of Value (MPV), which indicates which technology features are important to consumers. Two key MPVs were identified: “profitability” and “independence.” These reflect the fundamental decision criteria of customers in residential and commercial segments. The analysis revealed that profitability is between stages 2 and 3 of the technology S-curve, while independence is at stage 2. As areas worth developing in terms of the indicated MPVs, the authors proposed: increasing panel efficiency, building integrated platforms containing PV, batteries, and an efficient management system (PV + ESS + EMS), and creating PV microgrids with energy storage. The integration of photovoltaic systems with energy storage solutions proved to be the most important strategic direction, simultaneously addressing both MPVs and enabling advanced energy management capabilities. The study provides manufacturers and technology developers with evidence-based recommendations concerning resource allocation in photovoltaic innovation. It combines the technology development approach and market demand through systematically verified evolutionary patterns. This methodology offers a repeatable framework for strategic technology planning in renewable energy sectors. Full article

This study investigates the effect of rotation step size on the performance of flat-plate solar collectors (FPSC) equipped with single-axis tracking. Numerical simulations were carried out in EnergyPlus, coupled with a custom Python interface enabling dynamic control of collector orientation. The analysis was carried out for the city of Kragujevac in Serbia, located in a temperate continental climate zone, based on five representative summer days (3 July–29 September) to account for seasonal variability. Three collector types with different efficiency parameters were considered, and inlet water temperatures of 20 °C, 30 °C, and 40 °C were applied to represent typical operating conditions. The results show that single-axis tracking increased the incident irradiance by up to 28% and the useful seasonal heat gain by up to 25% compared to the fixed configuration. Continuous tracking (ψ = 1°) achieved the highest energy yield but required 181 daily movements, which makes it mechanically demanding. Stepwise tracking with ψ = 10–15° retained more than 90–95% of the energy benefit of continuous tracking while reducing the number of daily movements to 13–19. For larger steps (ψ = 45–90°), the advantage of tracking decreased sharply, with thermal output only 5–10% higher than the fixed case. Increasing the inlet temperature from 20 °C to 40 °C reduced seasonal heat gain by approximately 30% across all scenarios. Overall, the findings indicate that relative single-axis tracking with ψ between 10° and 15° provides the most practical balance between energy efficiency, reliability, and economic viability, making it well-suited for residential-scale solar thermal systems. This is the first study to quantify how discrete rotation steps in single-axis tracking affect both thermal and economic performance of flat-plate collectors. The proposed EnergyPlus–Python model demonstrates that a 10–15° step offers 90–95% of the continuous-tracking energy gain while reducing actuator motion by ~85%. The results provide practical guidance for optimizing low-cost solar-thermal tracking in continental climates. Full article

Ensuring grid stability in the transition to renewable energy sources requires accurate power demand forecasting. This study addresses the need for precise forecasting by differentiating among industrial, commercial, and residential consumers through customer clusterisation, tailoring the forecasting models to capture the unique consumption patterns of each group. Feature selection incorporated temporal, socio-economic, and weather-related data obtained from the Copernicus Earth Observation (EO) program. A variety of AI and machine learning algorithms for short-term load forecasting (STLF) and very-short-term load forecasting (VSTLF) are explored and compared, determining the most effective approaches. With all that, the main contribution of this work are the new forecasting approaches proposed, which have demonstrated superior performance compared to simpler models, both for STLF and VSTLF, highlighting the importance of customized forecasting strategies for different consumer groups and demonstrating the impact of incorporating detailed weather data on forecasting accuracy. These advancements contribute to more reliable power demand predictions, with our novel forecasting approaches reducing the Mean Absolute Percentage Error (MAPE) by up to 1–3% for industrial and 1–10% for commercial consumers compared to baseline models, thereby supporting grid stability. Full article

This study investigates the influence of rear setback geometry and façade design parameters on microclimatic conditions, indoor thermal comfort, and energy performance in multi-story residential buildings in hot arid climates, addressing the growing need for climate-responsive design in regions with extreme temperatures and high solar radiation. Despite increasing interest in sustainable strategies, the combined effects of urban geometry and building envelope design remain underexplored in these environments. A coupled simulation framework was developed, integrating ENVI-met for outdoor microclimate modeling with Design Builder and EnergyPlus for dynamic building performance analysis. A total of 270 simulation scenarios were examined, combining three rear setback aspect ratios (1.5, 1.87, and 2.25), three window-to-wall ratios (10%, 20%, and 30%), three glazing types (single-, double-, and triple-pane), and two wall insulation states, using customized weather files derived from microclimate simulations. Global sensitivity analysis using rank regression and multivariate adaptive regression splines identified the glazing type as the most influential parameter (sensitivity index ≈ 0.99), especially for upper floors. At the same time, higher aspect ratios reduced peak Physiological Equivalent Temperature (PET) by up to 5 °C and decreased upper-floor cooling loads by 37%, albeit with a 9.3% increase in ground-floor cooling demand. Larger window-to-wall ratios lowered lighting energy consumption by up to 35% but had minimal impact on cooling loads, whereas wall insulation reduced annual cooling demand by up to 29,441 kWh. The results emphasize that integrating urban morphology with optimized façade components, particularly high-performance glazing and suitable aspect ratios, can significantly improve thermal comfort and reduce cooling energy consumption in hot arid residential contexts. Full article

The accelerating integration of electric vehicles (EVs) into contemporary transportation infrastructure has underscored significant limitations in traditional charging paradigms, particularly in accommodating heterogeneous user requirements within dynamic operational environments. This study presents a differentiated optimization framework for EV charging strategies through the systematic classification of user types. A multidimensional decision-making environment is established for three representative user categories—residential, commercial, and industrial—by synthesizing time-variant electricity pricing models with dynamic carbon emission pricing mechanisms. A bi-level optimization architecture is subsequently formulated, leveraging deep reinforcement learning (DRL) to capture user-specific demand characteristics through customized reward functions and adaptive constraint structures. Validation is conducted within a high-fidelity simulation environment featuring 90 autonomous EV charging agents operating in a metropolitan parking facility. Empirical results indicate that the proposed typology-driven approach yields a 32.6% average cost reduction across user groups relative to baseline charging protocols, with statistically significant improvements in expenditure optimization (p < 0.01). Further interpretability analysis employing gradient-weighted class activation mapping (Grad-CAM) demonstrates that the model’s attention mechanisms are well aligned with theoretically anticipated demand prioritization patterns across the distinct user types, thereby confirming the decision-theoretic soundness of the framework. Full article

Renewable energy sources are being progressively incorporated into modern power grids to increase sustainability, stability, and resilience. To ensure that residential, commercial, and industrial customers have a dependable and efficient power supply, the transmission system must deliver electricity to end-users via the distribution network. To improve the performance of the distribution system, this study employs distributed generator (DG) units and focuses on determining their optimal placement, sizing, and power factor. A novel metaheuristic algorithm, referred to as Rüppell’s fox optimizer (RFO), is proposed to address this optimization problem under various scenarios. In the first scenario, where the DG operates at unity power factor, it is modeled as a photovoltaic system. In the second and third scenarios, the DG is modeled as a wind turbine system with fixed and optimal power factors, respectively. The performance of the proposed RFO algorithm is benchmarked against five well-known metaheuristic techniques to validate its effectiveness and competitiveness. Simulations are conducted on the IEEE 33-bus and IEEE 69-bus radial distribution test systems to demonstrate the applicability and robustness of the proposed approach. Full article

Spores of filamentous fungi are common biological particles in indoor air that can negatively impact human health, particularly among immunocompromised individuals and patients with chronic respiratory conditions. Airborne viruses represent an equally pervasive threat, with some carrying the potential for pandemic spread, affecting both healthy individuals and the immunosuppressed alike. This study investigated the abundance and diversity of airborne fungal spores in both hospital and residential environments, using custom designed air samplers with or without the presence of negative air ions (NAIs) inside the sampler. The main purpose of investigation was the assessment of biological effects of NAIs on fungal spore viability, deposition, mycelial growth, and sporulation, as well as airborne viral load. The precise assessment of mentioned biological effects is otherwise difficult to carry out due to low concentrations of studied specimens; therefore, specially devised and designed, ion-bioaerosol interaction air samplers were used for prolonged collection of specimens of interest. The total fungal spore concentrations were quantified, and fungal isolates were identified using cultural and microscopic methods, complemented by MALDI-TOF mass spectrometry. Results indicated no significant difference in overall spore concentration between environments or treatments; however, presence of NAIs induced a delay in the sporulation process of Cladosporium herbarum, Aspergillus flavus, and Aspergillus niger within 72 h. These effects of NAIs are for the first time demonstrated in this work; most likely, they are mediated by oxidative stress mechanisms. A parallel experiment demonstrated a substantially reduced concentration of aerosolized equine herpesvirus 1 (EHV-1) DNA within 10–30 min of exposure to NAIs, with more than 98% genomic load reduction beyond natural decay. These new results on the NAIs interaction with a virus, as well as new findings regarding the fungal sporulation, resulted in part from a novel interaction setup designed for experiments with the bioaerosols. Our findings highlight the potential of NAIs as a possible approach for controlling fungal sporulation and reducing airborne viral particle quantities in indoor environments. Full article

Improving the energy efficiency of buildings is a critical pathway in mitigating greenhouse gas emissions and fostering sustainable urban development. This study introduces a simulation-based multi-objective optimization framework designed to enhance both the thermal and economic performance of residential buildings. A representative single-family dwelling located in Patras, Greece, served as a case study to demonstrate the application and scalability of the proposed methodology. The optimization simultaneously minimized two conflicting objectives: the building’s annual thermal energy demand and the cost of construction materials. The computational process was implemented using MATLAB’s Multi-Objective Genetic Algorithm, supported by a modular Excel interface that enables the dynamic customization of design parameters and climatic inputs. A parametric analysis across four optimization scenarios was conducted by systematically varying the key algorithmic hyperparameters—population size, mutation rate, and number of generations—to assess their impact on convergence behavior, Pareto front resolution, and solution diversity. The results confirmed the algorithm’s robustness in producing technically feasible and non-dominated solutions, while also highlighting the sensitivity of optimization outcomes to hyperparameter tuning. The proposed framework is a flexible, reproducible, and computationally tractable approach to supporting early-stage, performance-driven building design under realistic constraints. Full article

The inequity in the distribution of green spaces in megacities has a detrimental effect on the physical and mental well-being of their inhabitants, highlighting the necessity for careful and strategic urban planning, along with appropriate regulatory interventions. Nevertheless, scholarly articles addressing the equity of access to urban green spaces primarily concentrate on urban parks, with limited studies examining the influence of alternative types of green spaces. This research initially recognized and categorized informal green spaces (IGS) located within the Third Ring Road of Chengdu, utilizing the UGS-1m dataset and area of interest (AOI) data, in accordance with a well-defined classification framework. Then, the G2SFCA method and Gini coefficient were employed to assess the impact of IGS on the green space accessibility, especially scenario analysis of open and shared use of green space. The findings indicate that (1) IGS in the narrow sense constitute 21.2% of the overall green spaces within the study area, resulting in a reduction of the Gini coefficient by 0.103; (2) IGS in the broad sense, including public affiliated green spaces, shows an even more positive effect on improving the equity of green space supply, with a reduction of the Gini coefficient by 0.28; (3) there exists great spatial disparity in accessibility improvement effect by different types of IGS, so public policies must be customized to reflect local circumstances, taking into account the practicality and associated costs of management and maintenance of various IGS as well as accessibility enhancement; (4) certain older residential areas may not be amenable to effective enhancement through the use of IGS alone, and these should then adopt a multidimensional greening strategy such as green-roof. The findings of this research offer valuable insights for the planning and management of green spaces in densely populated urban environments, thereby aiding in the development of more refined models for the development of “Garden Cities”. Full article

The management of energy demand in systems lacking smart metering presents a significant challenge for electric distributors, primarily due to the absence of real-time data. This research assesses the efficacy of the K-Means algorithm when applied to the monthly billing records of 221,401 residential customers from Empresa Eléctrica Ambato Regional Centro Norte S.A. (EEASA) (Ecuador) over the period 2023–2024. The methodology encompassed data cleaning, Z-score normalization, and validation employing the Silhouette (0.55) and Davies–Bouldin (0.51) indices. Additionally, linear regression (LR) and Random Forest (RF) models were utilized to forecast demand, with the latter yielding an R² of 0.67. The findings delineated eight distinct clusters, facilitating the formulation of more representative rates, the identification of outliers through the interquartile range (IQR) method, and the enhancement of consumption estimation. It is concluded that this unsupervised segmentation approach constitutes a robust and cost-effective tool for energy planning in network environments devoid of smart infrastructure. Full article

The study examines how design quality, indoor air quality, and energy efficiency affect customer satisfaction in eco-friendly houses in Shanghai, China. Further, it examines how environmental awareness mediates community participation and resident satisfaction. A stratified sampling technique is used to collect the data from 742 eligible respondents in public and private eco-residential complexes. The results show that design, air quality, and energy efficiency improve customer satisfaction. At the same time, community engagement partially mediates these correlations, stressing the importance of social cohesion in enhancing residential area quality. Environmental awareness moderated the effects and boosted the happiness benefits of energy efficiency and indoor air quality. This research uses a comprehensive framework that includes psychological, environmental, and social components to make it stand out. Instead of studying green housing benefits in general, it accomplishes this inside China’s urban sustainability program. The results help policymakers, urban planners, and housing authorities make megacity green housing more desirable and livable. Full article

When power system equipment fails due to disasters, resulting in the isolation of parts of the network, the loads within the isolated system cannot be guaranteed a continuous power supply. However, for critical loads—such as hospitals or data centers—continuous power supply is of utmost importance. While distributed energy resources (DERs) within the network can supply power to some loads, outages may lead to compensation and fairness issues regarding the unsupplied loads. In response, this study proposes a methodology to determine the appropriate power contract price for key customers by estimating the unsupplied power demand for critical loads in isolated networks and incorporating both outage compensation costs and voltage stability penalties. The microgrid under consideration comprises DERs—including electric vehicles (EVs), fuel cell electric vehicles (FCEVs), photovoltaic (PV) plants, and wind turbine (WT) plants—as well as controllable resources such as battery energy storage systems (BESS) and hydrogen energy storage systems (HESS). It serves both residential load clusters and critical loads associated with social infrastructure. The proposed methodology is structured in two stages. In normal operating conditions, optimal scheduling is simulated using second-order conic programming (SOCP). In the event of a fault, mixed-integer SOCP (MISOCP) is employed to determine the optimal load shedding strategy. A case study is conducted using the IEEE 123 bus test node system to simulate the outage compensation cost calculation and voltage penalty assessment processes. Based on this analysis, a contract price for key customers that considers both disaster-induced outages and voltage impacts is presented. Full article

This study investigates the effectiveness of active structural control in mitigating seismic damage of inelastic structures. A fuzzy control algorithm is integrated into a custom-developed finite element routine to examine the relationship between maximum control force requirements and the resulting structural damage state. Consequently, a series of nonlinear dynamic simulations was conducted on 3D inelastic numerical models representing five building typologies—three residential, one office, and one school using seismic inputs from two historical earthquakes. Structural damage was quantified using the Park–Ang damage index. Key findings show that active control can reduce structural damage of inelastic structures, but its effectiveness depends on seismic input and the complexity structural layout. Lower forces are adequate for low-rise or simple buildings, while taller or complex structures require substantially higher forces, which may be challenging to apply in real applications. Moreover, the results emphasize how local seismic conditions and variations in building dynamic characteristics impact the demands in control forces. Full article

The traditional folk houses in Pengcheng Town preserve the architectural style of the southern part of Hebei Province and integrate local living customs and cultural traditions. These folk houses reflect the deep cultural origins of Pengcheng and the Cizhou Kiln. “Wang’s Old House” is the largest and tallest building among numerous residential settlements in Pengcheng Town. It is not only the residence of the Wang family but also the epitome of Cizhou Kiln culture. Taking “Wang’s Old House” in Pengcheng Town as an example of a location in the process of restoration, this paper uses field investigation, oral history methods, and digital technology, combined with an analysis of the overall architectural style of Handan folk houses, oral historical materials, and existing architectural sites, to carry out detailed research and prediction on the plane layout, facade modeling, construction structure, and decorative details of Wang’s Old House, in order to restore the original appearance of Wang’s Old House. This study provides valuable information on and experience in restoring traditional dwellings in the vicinity of Cizhou Kiln, so that we can have a deeper understanding of traditional dwellings’ historical and cultural connotations. Full article