Search Results (265)

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

The structure and interpersonal interactions of traditional residential communities have also been impacted and recreated as a result of the fast development of urban space and related communities. This study explores the interrelationship between neighborhood social cohesion and the life satisfaction of urban adult residents through the mediating effect of place attachment. A comprehensive theoretical model was constructed to analyze the action mechanism among these variables. Data were collected through an online questionnaire platform (n = 301), and structural equation modeling (PLS-SEM) was employed for analysis. The findings revealed a significant positive relationship between neighborhood social cohesion and residents’ place attachment. Place attachment appeared to play a mediating role between neighborhood social cohesion and life satisfaction, in which place dependence was also a potential effective mediator between the three dimensions of neighborhood social cohesion (neighborliness, sense of community, and neighborhood attractiveness) and life satisfaction. The results suggest that enhancing community cohesion may contribute to urban adult residents’ well-being by strengthening their functional dependence on the community. Full article

This study defines evidence-based minimum internal floor areas required to support long-term residential use across different household types. It addresses the following question: what is the smallest viable floor area that supports sustained occupancy without persistent stress, conflict, or turnover? An integrative review method was employed, drawing from behavioural studies in environmental psychology, international regulatory standards, and real-world market data. The analysis focuses on essential domestic functions including sleep, hygiene, food preparation, storage, social interaction, and work. Quantitative findings from tenancy surveys, post-occupancy research, and market performance data indicate that residential units below 30 square metres for single occupants and 45 square metres for couples are consistently associated with reduced satisfaction and shorter tenancies. Regulatory minimums across diverse jurisdictions tend to converge near these same thresholds. The study proposes technical minimums of 30, 45, and 60 square metres for one-, two-, and three-person households, respectively. These values reflect functional lower bounds rather than ideal or aspirational sizes and are intended to inform performance-based housing standards. Full article

Form-based design codes have emerged as a planning tool aimed at shaping the physical form of neighborhoods to reinforce local character and enhance sense of place (SoP). However, their effectiveness in delivering these outcomes remains underexplored. This study investigates the extent to which Buffalo’s Green Code—a form-based zoning ordinance—enhances SoP in residential environments, using Elmwood Village as a case study. A multi-scalar analytical framework assesses SoP at the building, street, and neighborhood levels. Empirical data were gathered through an online survey, while the neighborhood was systematically mapped into street segment blocks categorized by Green Code zoning. The study consolidates six Green Code classifications into three overarching categories: mixed-use, residential, and single-family. SoP satisfaction is analyzed through a two-step process: first, comparative assessments are conducted across the three zoning groups; second, k-means clustering is applied to spatially map satisfaction levels and evaluate SoP at different scales. Findings indicate that mixed-use areas are most closely associated with place identity, while residential and single-family zones (as defined by the Buffalo Green Code) yield higher satisfaction overall—though satisfaction varies significantly across spatial scales. These results suggest that while form-based codes can strengthen SoP, their impact is uneven, and more scale-sensitive zoning strategies may be needed to optimize their effectiveness in diverse urban contexts. This research overall offers an empirically grounded, multi-scalar assessment of zoning impacts on lived experience—addressing a notable gap in the planning literature regarding how form-based codes perform in established, rather than newly developed, neighborhoods. Full article

In recent decades, university dormitories have gradually evolved from traditional residential spaces into educationally meaningful venues that support informal learning. However, limited research has explored how supportive environmental factors within dormitories influence students’ informal learning experiences. This study aims to evaluate key environmental factors that affect students’ satisfaction with informal learning in dormitory settings. Based on a comprehensive literature review, two types of informal learning behaviors—individual and collaborative—were defined, and a multi-dimensional evaluation framework comprising five categories and 26 environmental indicators was established. Field observations and structured questionnaires were employed to assess students’ satisfaction with each environmental factor and their overall informal learning experiences. Quantitative analyses were conducted to examine the relationships between environmental conditions and learning satisfaction. Results show that all five-factor categories—spatial designs, natural environments, physical settings, social aspects, and resources—positively influence informal learning, with resources being the most impactful. While environmental influences on individual and collaborative learning exhibit minor differences, the overall patterns are consistent. Compared to other informal learning spaces on campus, dormitory users place greater emphasis on spatial controllability. This study further demonstrates the cognitive and emotional value of dormitory environments and proposes targeted directions for optimizing them as informal learning spaces. Full article

Despite notable advancements in smart home technologies, residential energy management continues to face critical challenges. These include the complex integration of intermittent renewable energy sources, issues related to data latency, interoperability, and standardization across diverse systems, the inflexibility of centralized control architectures in dynamic environments, and the difficulty of accurately modeling and influencing occupant behavior. To address these challenges, this study proposes an intelligent multi-agent system designed to accurately estimate and control energy consumption in residential buildings, with the overarching objective of optimizing energy usage while maintaining occupant comfort and satisfaction. The methodological approach employed is a hybrid framework, integrating multi-agent system architecture with system dynamics modeling and agent-based modeling. This integration enables decentralized and intelligent control while simultaneously simulating physical processes such as heat exchange, insulation performance, and energy consumption, alongside behavioral interactions and real-time adaptive responses. The system is tested under varying conditions, including changes in building insulation quality and external temperature profiles, to assess its capability for accurate control and estimation of energy use. The proposed tool offers significant added value by supporting real-time responsiveness, behavioral adaptability, and decentralized coordination. It serves as a risk-free simulation platform to test energy-saving strategies, evaluate cost-effective insulation configurations, and fine-tune thermostat settings without incurring additional cost or real-world disruption. The high fidelity and predictive accuracy of the system have important implications for policymakers, building designers, and homeowners, offering a practical foundation for informed decision making and the promotion of sustainable residential energy practices. Full article

The growing global demand for energy has resulted in a demand for innovative strategies for residential energy management. This study explores a novel framework—MELISSA (Modern Energy LLM-IoE Smart Solution for Automation)—that integrates Internet of Things (IoT) sensor networks with Large Language Models (LLMs) to optimize household energy consumption through intelligent automation and personalized interactions. The system combines real-time monitoring, machine learning algorithms for behavioral analysis, and natural language processing to deliver personalized, actionable recommendations through a conversational interface. A 12-month randomized controlled trial was conducted with 100 households, which were stratified across four socioeconomic quintiles in metropolitan areas. The experimental design included the continuous collection of IoT data. Baseline energy consumption was measured and compared with post-intervention usage to assess system impact. Statistical analyses included k-means clustering, multiple linear regression, and paired t-tests. The system achieved its intended goal, with a statistically significant reduction of 5.66% in energy consumption (95% CI: 5.21–6.11%, $p<0.001$) relative to baseline, alongside high user satisfaction (mean = 7.81, SD = 1.24). Clustering analysis ($k=4$, silhouette = 0.68) revealed four distinct energy-consumption profiles. Multiple regression analysis ($R^2=0.68$, $p<0.001$) identified household size, ambient temperature, and frequency of user engagement as the principal determinants of consumption. This research advances the theoretical understanding of human–AI interaction in energy management and provides robust empirical evidence of the effectiveness of LLM-mediated behavioral interventions. The findings underscore the potential of conversational AI applications in smart homes and have practical implications for optimization of residential energy use. Full article

Uncoordinated electric vehicle (EV) charging can significantly complicate power system operations. In this paper, we develop a hybrid EV charging method that seamlessly integrates centralized EV charging and distributed control schemes to address EV energy demand challenges. The proposed method includes (1) a centralized day-ahead optimal scheduling mechanism and EV shifting process based on mixed-integer linear programming (MILP) and (2) a distributed control strategy based on a genetic algorithm (GA) that dynamically adjusts the charging rate in real-time grid scenarios. The MILP minimizes energy imbalance at overloaded slots by reallocating EVs based on supply–demand mismatch. By combining full and minimum charging strategies with MILP-based shifting, the method significantly reduces network stress due to EV charging. The centralized model schedules time slots using valley-filling and EV-specific constraints, and the local GA-based distributed control adjusts charging currents based on minimum energy, system availability, waiting time, and a priority index (PI). This PI enables user prioritization in both the EV shifting process and power allocation decisions. The method is validated using demand data on a radial feeder with residential and commercial load profiles. Simulation results demonstrate that the proposed hybrid EV charging framework significantly improves grid-level efficiency and user satisfaction. Compared to the baseline without EV integration, the average-to-peak demand ratio is improved from 61% to 74% at Station-A, from 64% to 80% at Station-B, and from 51% to 63% at Station-C, highlighting enhanced load balancing. The framework also ensures that all EVs receive energy above their minimum needs, achieving user satisfaction scores of 88.0% at Stations A and B and 81.6% at Station C. This study underscores the potential of hybrid charging schemes in optimizing energy utilization while maintaining system reliability and user convenience. Full article

The rapid growth of electric vehicle (EV) adoption necessitates advanced energy management strategies to ensure sustainable, reliable, and efficient operation of charging infrastructure. This study proposes a hybrid AI-based framework for optimizing residential EV charging systems through the integration of Reinforcement Learning (RL), Linear Programming (LP), and real-time grid-aware scheduling. The system architecture includes smart wall-mounted chargers, a 120 kWp rooftop solar photovoltaic (PV) array, and a 60 kWh lithium-ion battery energy storage system (BESS), simulated under realistic load conditions for 800 residential units and 50 charging points rated at 7.4 kW each. Simulation results, validated through SCADA-based performance monitoring using MATLAB/Simulink and OpenDSS, reveal substantial technical improvements: a 31.5% reduction in peak transformer load, voltage deviation minimized from ±5.8% to ±2.3%, and solar utilization increased from 48% to 66%. The AI framework dynamically predicts user demand using a non-homogeneous Poisson process and optimizes charging schedules based on a cost-voltage-user satisfaction reward function. The study underscores the critical role of intelligent optimization in improving grid reliability, minimizing operational costs, and enhancing renewable energy self-consumption. The proposed system demonstrates scalability, resilience, and cost-effectiveness, offering a practical solution for next-generation urban EV charging networks. Full article

Effective management of urban development requires robust decision-support tools, including land suitability analysis and its visual communication. This study introduces and evaluates seven 3D geovisualization methods—Horizontal Planes, Point Cloud, 3D Surface, Vertical Planes, 3D Graduated Symbols, Prism Map, and Voxels—for visualizing land suitability for residential development in Jihlava, Czechia. Using five raster-based data layers derived from a multi-criteria evaluation (Urban Planner methodology) across three time horizons (2023, 2028, 2033), the visualizations were implemented in ArcGIS Online and assessed by 19 domain experts via a structured questionnaire. The evaluation focused on clarity, usability, and accuracy in interpreting land suitability values, with the methods being rated on a five-point scale. Results show that the Horizontal Planes method was rated highest in terms of interpretability and user satisfaction, while 3D Surface and Vertical Planes were considered the least effective. The study demonstrates that visualization methods employing visual variables (e.g., color and transparency) are better suited for land suitability communication. The methodological contribution lies in systematically comparing 3D visualization techniques for thematic spatial data, providing guidance for their application in planning practice. The results are primarily intended for urban planners, designers, and local government representatives as supportive tools for efficient planning of future built-up area development. Full article

This study aims to photograph, design, and digitally document the surviving residential buildings on the island of Halki (Heybeliada), within the Princes’ Islands. This documentation focuses on the architectural, urban, and historical aspects of Halki, highlighting the significant material evidence of the Greek social and economic presence. It also examines the urban cultural heritage as depicted in Turkish literature of that period to understand how Turkish writers perceived and presented Halki, referencing the Princes’ Islands only for background context. The methodology includes the collection of material from residents through bibliographic and field research conducted on Halki. Based on these findings, a mobile augmented reality (AR) application was developed using the TaleBlazer platform, designed specifically for use on Halki. The application provides a virtual tour with multimedia-supported thematic layers of architectural and historical information. Its usability and learnability were evaluated using a questionnaire completed by students. The results showed high usability, user satisfaction, and perceived value of learning, with the majority of results close to a median score of 4 out of 5. The students identified the occurrence of immersive experience, ease of use, and the emotional stimulation created by the integration of spatial storytelling and multimedia. This paper also shows how the convergence of cultural content (history, architecture, and literature) can enhance interpretations and experiences with mobile AR technologies. Full article

Despite the growing body of literature on the built environment and social capital, there remains a significant gap in understanding the mediating role of subjective residential satisfaction. Examining how residents perceive and experience their environment offers a fresh angle for understanding the intricate relationship between physical spaces and social dynamics. Our study assessed social capital and subjective residential satisfaction through an extensive questionnaire survey conducted across 60 communities, involving 1684 participants in Tianjin’s metropolitan area, China. We evaluated the elements of the built environment using the ‘5D’ framework, and the pathways of influence were examined using a multilevel structural equation model. Our results reveal a notable mediating effect, with subjective residential satisfaction being a key factor in the intricate process by which the built environment affects social capital. The results also indicate that land use diversity negatively impacts social capital, while population density, access to facilities and services, and public transport density have positive effects. These insights offer practical guidance for fostering social capital and community development by considering subjective perceptions. The results enhance our understanding of effective strategies for building social capital through improved socio-spatial interventions. 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

With the goal of sustainable urbanization, eco-cities have garnered significant global attention in recent decades. Unlike eco-city renovation or renewal, the construction of a new eco-city represents a comprehensive urbanization process that integrates environmental sustainability with livability. To evaluate the outcomes of new eco-city construction in China, this study employs a dual approach combining objective achievements and residents’ subjective satisfaction to systematically examine the Sino-Singapore Tianjin Eco-City. The analysis encompasses five dimensions: environmental amenity, life safety, residential functionality, traffic capability, and economic well-being, with the relative weights of specific indicators determined through the entropy method, expert scoring, and analytic hierarchy process. The findings reveal that based on objective indicators, the eco-city’s overall performance nearly doubled during its first phase of development, with life safety showing the most notable improvements. However, subjective assessments revealed that overall resident satisfaction remained below 70%, with residential functionality receiving the highest rating. The annual progress of the eco-city did not consistently align with residents’ needs, and no clear correlation was found between the eco-city’s current state and public sentiment. For sustainable development, the eco-city must address its shortcomings and better cater to residents’ demands across various dimensions through targeted and effective strategies. Full article

The large-scale integration of renewable energy and the use of high-energy-consuming equipment in agricultural parks have a great influence on the security of rural distribution networks. To ensure reliable power delivery for residential and agricultural activities and sustainable management of distributed energy resources, this paper develops a distributed generation-supported interactive optimization framework coordinating distribution networks and agricultural parks. Specifically, a wind–photovoltaic scenario generation method based on Copula functions is first proposed to characterize the uncertainties of renewable generation. Based on the generated scenario, a bi-level interactive optimization framework consisting of a distribution network and agricultural park is constructed. At the upper level, the distribution network operators ensure the security of the distribution network by reconfiguration, coordinated distributed resource dispatch, and dynamic price compensation mechanisms to guide the agricultural park’s electricity consumption strategy. At the lower level, the agricultural park users maximize their economic benefits by adjusting controllable loads in response to price compensation incentives. Additionally, an improved particle swarm optimization combined with a Gurobi solver is proposed to obtain equilibrium by iterative solving. The simulation analysis demonstrates that the proposed method can reduce the operation costs of the distribution network and improve the satisfaction of users in agricultural parks. Full article