Search Results (408)

Residential buildings in Mediterranean regions remain major contributors to energy consumption and greenhouse gas emissions. Existing studies often assess renewable energy technologies or innovative building solutions in isolation, with limited attention to their combined performance across different residential typologies. This study evaluates the integrated impact of solar renewable energy systems and smart building technologies on the energy performance of residential buildings in Cyprus. A typology-based methodology is applied to three representative residential building types—detached, semi-detached, and apartment buildings—using dynamic energy simulation and scenario analysis. Results show that solar photovoltaic systems achieve higher standalone reductions than solar thermal systems, while smart building technologies significantly enhance operational efficiency and photovoltaic self-consumption. Integrated solar–smart scenarios achieve up to 58% reductions in primary energy demand and 55% reductions in CO₂ emissions, and 25–30 percentage-point increases in PV self-consumption, enabling detached and semi-detached houses to approach national nearly zero-energy building (nZEB) performance thresholds. The study provides climate-specific, quantitative evidence supporting integrated solar–smart strategies for Mediterranean residential buildings and offers actionable insights for policy-making, design, and sustainable residential development. Full article

This paper focuses on whether smart-related built environments are associated with improved housing affordability for economically disadvantaged groups. Smart growth is a planning theme that aims to address the unintended negative consequences of urban sprawl through combining diverse dimensions across land-use diversity, housing diversity, accessibility, and compact development. Focusing on Clackamas County, Multnomah County, and Washington County within the Portland metropolitan area, the analysis uses census-tract-level data to assess both contemporaneous associations in 2013 and changes in affordability between 2013 and 2019. Overall, the findings suggest that smart-growth tools exhibit both potential and limitations with respect to housing affordability. Greater housing-type diversity and lower reliance on single-family residential land use are consistently associated with higher shares and subsequent increases in affordable housing units for low-income groups. In contrast, other smart-growth features—such as land-use mix and accessibility—show weaker or uneven relationships. These findings suggest that smart growth can contribute to expanding affordable housing supply primarily through housing-related components, while other dimensions of smart growth appear to play a limited role. The results underscore that housing-focused strategies play an important role in shaping affordability outcomes under smart growth. Full article

With growing community awareness of greenhouse gas emissions and their environmental consequences, distributed rooftop photovoltaic (PV) systems have emerged as a sustainable energy alternative in residential settings. However, the high penetration of these systems without effective operational strategies poses significant challenges for local distribution grids. Specifically, the estimation of surplus energy production from these systems, closely linked to complex outdoor weather conditions and seasonal fluctuations, often lacks an accurate forecasting approach to effectively capture the temporal dynamics of system output during peak periods. In response, this study proposes a recurrent neural network (RNN)- based forecasting framework to predict rooftop PV surplus in the context of micro-residential communities over time horizons not exceeding 48 h. The framework includes standard RNN, long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and gated recurrent unit (GRU) networks. In this context, the study employed estimated surplus energy datasets from six single-family detached houses, along with weather-related variables and seasonal patterns, to evaluate the framework’s effectiveness. Results demonstrated the significant effectiveness of all framework models in forecasting surplus energy across seasonal scenarios, with low MAPE values of up to 3.02% and 3.59% over 24-h and 48-h horizons, respectively. Simultaneously, BiLSTM models consistently demonstrated a higher capacity to capture surplus energy fluctuations during peak periods than their counterparts. Overall, the developed data-driven framework demonstrates potential to enable short-term smart energy scheduling in micro-residential communities, supporting electric vehicle charging from single-family detached houses through efficient rooftop PV systems. It also provides decision-making insights for evaluating renewable energy contributions in the residential sector. Full article

The development of integrated renewable energy and high-density thermal energy storage systems has been fueled by the need for environmentally friendly heating and cooling in buildings. In this paper, MiniStor, a hybrid thermochemical and phase-change material storage system, is presented. It is equipped with a heat pump, advanced electronics-enabled control, photovoltaic–thermal panels, and flat-plate solar collectors. To optimize energy flows, regulate charging and discharging cycles, and maintain operational stability under fluctuating solar irradiance and building loads, the system utilizes state-of-the-art power electronics, variable-frequency drives and modular multi-level converters. The hybrid storage is safely, reliably, and efficiently integrated with building HVAC requirements owing to a multi-layer control architecture that is implemented via Internet of Things and SCADA platforms that allow for real-time monitoring, predictive operation, and fault detection. Data from the MiniStor prototype demonstrate effective thermal–electrical coordination, controlled energy consumption, and high responsiveness to dynamic environmental and demand conditions. The findings highlight the vital role that digital control, modern electronics, and Internet of Things-enabled supervision play in connecting small, high-density thermal storage and renewable energy generation. This strategy demonstrates the promise of electronics-driven integration for next-generation renewable energy solutions and provides a scalable route toward intelligent, robust, and effective building energy systems. Full article

With smart buildings being widely adopted in urban digital transformation, interactive semantic question answering (QA) systems serve as a crucial bridge between user intent and environmental response. However, they still face substantial challenges in semantic understanding and dynamic reasoning. Most existing systems rely on static frameworks built upon Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), which suffer from rigid prompt design, breakdowns in multi-step reasoning, and inaccurate generation. To tackle these issues, we propose LoopRAG, a multi-agent RAG architecture that incorporates a Plan–Do–Check–Act (PDCA) closed-loop optimization mechanism. The architecture formulates a dynamic QA pipeline across four stages: task parsing, knowledge extraction, quality evaluation, and policy feedback, and further introduces a semantics-driven prompt reconfiguration algorithm and a heterogeneous knowledge fusion module. These components strengthen multi-source information handling and adaptive reasoning. Experiments on HotpotQA, MultiHop-RAG, and an in-house building QA dataset demonstrate that LoopRAG significantly outperforms conventional RAG systems in key metrics, including context recall of 90%, response relevance of 72%, and answer accuracy of 88%. The results indicate strong robustness and cross-task generalization. This work offers both theoretical foundations and an engineering pathway for constructing trustworthy and scalable semantic QA interaction systems in smart building settings. Full article

Cities increasingly utilise digital technologies to tackle climate risks and urban shocks, yet their real impact on resilience remains uncertain. This paper systematically reviews 115 peer-reviewed studies (2012–2024) to explore how smart city technologies engage with planning instruments, governance arrangements, and social processes, following PRISMA 2020 and combining bibliometric co-occurrence mapping with a qualitative synthesis of full texts. Three themes organise the findings: (i) urban planning and design, (ii) smart technologies in resilience, and (iii) strategic planning and policy integration. Across these themes, Internet of Things (IoT) and geographic information system (GIS) applications have the strongest empirical support for enhancing absorptive and adaptive capacities through risk mapping, early warning systems, and infrastructure operations, while artificial intelligence, digital twins, and blockchain remain largely at pilot or conceptual stages. The review also highlights significant geographical and hazard biases: most cases come from high-income cities and concentrate on floods and earthquakes, while slow stresses (such as heat, housing insecurity, and inequality) and cities in the Global South are under-represented. Overall, the study promotes a “smart–resilience co-production” perspective, demonstrating that resilience improvements rely less on technology alone and more on how digital systems are integrated into governance and participatory practices. Full article

An effective housing policy must ensure affordability for individuals across all income levels by integrating advanced technological innovations with comprehensive socioeconomic strategies. Affordable housing fosters social inclusion, whereas sustainability supports long-term environmental protection and economic stability. The success and long-term sustainability of affordable housing initiatives are heavily influenced by current socioeconomic conditions, emphasizing the need for context-specific, inclusive, and sustainable housing solutions. Benchmarks are crucial in affordable housing to determine if it is climate-positive, aligning with the goals of the United Nations’ Sustainable Development Goal 11.1, which seeks to provide affordable and sustainable housing for everyone by 2030. This study uses the Scopus database to perform a scientometric analysis of 595 publications (2015–2024) on sustainability and affordability in housing. Using R-Studio 2025.05.1 + 513.pro3 and VOSviewer 1.6.20, it examines bibliographic trends, research gaps, and collaboration patterns across countries and journals. This study highlights performance thresholds related to economic, environmental, energy, territorial, and climatic factors. However, cost and ecological objectives can cause conflict with each other practically, and hence a balanced approach including green practices, efficient materials, and subsidies is crucial. There is a need for policymakers to address market gaps to prevent socially exclusive or environmentally harmful outcomes, maintain long-term urban resilience, and ensure sustained urban resilience and equitable access to affordable, sustainable housing by 2030. Integrating sustainable materials, circular and climate-resilient design, smart technologies, inclusive governance, and evidence-based policies is crucial for advancing affordable, equitable, and resilient housing. This approach guides future research and policy toward long-term social, economic, and environmental benefits. The findings and recommendations promote sustainable, affordable housing, emphasizing the need for further research on climate-resilient, energy-efficient, and cost-effective building solutions. Full article

Most current second-hand housing sales, contract signing, and other processes require the participation of intermediaries. However, suppose the intermediary refuses to disclose all information to the parties involved in the transactions. In that case, this traditional model can lead to weak supervision and punishment, adverse selection, moral hazards, and weak contract enforcement. Blockchain technology can not only secure the information intermediaries share, encouraging them to disclose information, but can also generate irreversible records of housing transactions for data traceability. Therefore, this study aims to develop a framework based on blockchain technology for the trading of second-hand housing. In this study, a second-hand housing online trading framework (SHHOTF) based on smart contract development is proposed for the second-hand housing business process, aiming to promote second-hand housing transactions. The contributions of this study lie in (1) determining the framework requirements, (2) proposing the functional module of a framework based on the blockchain and designing a complete business process, (3) developing an architecture for integrating blockchain and second-hand housing transaction processes, and developing technical components that support the framework functions, and (4) demonstrating the use case in Britain, analyzing the effectiveness and innovation of the framework. Furthermore, the framework demonstrated a 24% increase in transaction speed compared to the traditional Ethereum public network. The proposed process is highly adaptable within the current second-hand housing domain, and the developed framework can serve as a reference for introducing blockchain technology into other industries or application scenarios. Full article

To ensure reliable, efficient and sustainable operation of modern power networks, accurate load forecasting is an important task in system planning and control. It is also a crucial task for the efficient operation of smart grids to maintain a balance between load shifting, load management and power dispatch. In this regard, this research study aims to investigate the efficiency of various machine learning models for whole-house energy consumption prediction and appliance-level load disaggregation using Non-Intrusive Load Monitoring (NILM). The primary objective is to determine which model offers the most accurate forecasts for both individual appliance consumption patterns and the total amount of energy used by the household. The empirical study presents comparative performance analysis of machine learning models, i.e., Random Forest, Decision Tree, K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Gradient Boosting and Support Vector Regressor (SVR) for load forecasting and load disaggregation. This research is conducted on PRECON: Pakistan Residential Electricity Dataset consisting of 42 Pakistani households. The dataset was recorded originally as one minute per sample, but the proposed study aggregated it to hourly samples to evaluate models’ alignment with the typical sampling rate of smart meters in Pakistan. It enables the models to more accurately depict implementation scenarios in real-world settings. The statistical measures MAE, MSE, RMSE and R² have been employed for performance evaluation. The proposed Random Forest algorithm out-performs all other employed models, with the lowest error values (MAE: 0.1316, MSE: 0.0367, RMSE: 0.1916) and the highest R² score of 0.9865. Furthermore, for detecting appliance events from aggregate power data, ensemble models such as Random Forest performed better than other models for ON/OFF prediction. To evaluate the suitability of machine learning models for real-time, appliance-level energy forecasting using Non-Intrusive Load Monitoring (NILM), this study presents a novel evaluation framework that combines learning speed and edge adaptability with conventional performance metrics (e.g., R², MAE). This paper introduces a NILM-based approach for load forecasting and appliance-level ON/OFF prediction, representing its capacity to improve residential energy efficiency and encourage sustainable energy consumption, while emphasizing operational metrics for implementation in embedded smart grid systems—an area mainly neglected in prior NILM-based research articles. The results provide useful information for improving demand-side energy management, facilitating more effective load disaggregation, and maximizing the energy efficiency and responsiveness of smart grids. Full article

Continuous and personalized monitoring are beneficial for patients suffering from neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease and multiple sclerosis. However, such levels of monitoring are seldom ensured by traditional models of care. This paper presents NeuroPredict, a secure edge–cloud Internet of Medical Things (IoMT) platform that addresses this problem by integrating commercial wearables and in-house sensors with cognitive and behavioral evaluations. The NeuroPredict platform links high-frequency physiological signals with periodic cognitive tests through the use of a modular architecture with lightweight device connectivity, a semantic integration layer for timestamp alignment and feature harmonization across heterogeneous streams, and multi-timescale data fusion. Its use of encrypted transport and storage, role-based access control, token-based authentication, identifier separation, and GDPR-aligned governance addresses security and privacy concerns. Moreover, the platform’s user interface was built by considering human-centered design principles and includes role-specific dashboards, alerts, and patient-facing summaries that are meant to encourage engagement and decision-making for patients and healthcare providers. Experimental evaluation demonstrated the NeuroPredict platform’s data acquisition reliability, coherence in multimodal synchronization, and correctness in role-based personalization and reporting. The NeuroPredict platform provides a smart system infrastructure for eHealth and remote monitoring in neurodegenerative care, aligned with priorities on wearables/IoMT integration, data security and privacy, interoperability, and human-centered design. Full article

Buildings consume energy and are responsible for a significant portion of greenhouse gas emissions in Australia. Increased standards are being set for building thermal performance. Given the rising demand for energy-efficient housing solutions, this work explores the potential application of innovative technologies to enhance the thermal performance. Since modular construction is attracting popularity owing to numerous advantages, including its efficiency and cost-effectiveness, optimising the thermal performance is a way to further improve its popularity, particularly in diverse Australian climates. Smart materials are unique and have desirable properties when subjected to a change in the external environment. Integration of smart insulation materials in prefabricated buildings forecasts a potential to expand the horizon of thermal performance of prefabricated buildings and subsequently lead towards an enhanced energy performance. This work investigates the effects of aerogel, phase change materials (PCMs), and electrochromic glazing. To assess their potential to improve the thermal performance of a modular house, building energy performance simulations were conducted for three different climatic conditions in Australia. Individual implementation of innovative technologies and their combined effects were also quantified. The combination of the three innovative technologies has yielded total annual energy savings of 15.6%, 11.2%, and 6.1% for Melbourne, Perth, and Brisbane, respectively. Full article

Accurate short-term load forecasting is vital for smart-city energy management, enabling real-time grid stability and sustainable demand response. This study introduces a cloud-enabled hybrid forecasting framework that integrates Seasonal Autoregressive Integrated Moving Average with Exogenous variables (SARIMAX), Random Forest (RF), and Long Short-Term Memory (LSTM) models, unified through a residual-correction mechanism to capture both linear seasonal and nonlinear temporal dynamics. The framework performs fine-grained 5 min forecasting at both appliance and aggregate levels, revealing that the aggregate forecast achieves higher stability and accuracy than the sum of appliance-level predictions. To ensure operational resilience, three independent hybrid models are deployed across distinct cloud platforms with a two-out-of-three voting scheme, that guarantees continuity if a single-cloud interruption occurs. Using a real residential dataset from a house in Summerlin, Las Vegas (2022), the proposed system achieved a Root Mean Squared Logarithmic Error (RMSLE) of 0.0431 for aggregated load prediction representing a 35% improvement over the next-best model (Random Forest) and maintained consistent prediction accuracy during simulated cloud outages. These results demonstrate that the proposed framework provides a scalable, fault-tolerant, and accurate energy forecasting. Full article

This study designed a message queuing telemetry transport (MQTT)-based communication framework to acquire environmental data with stable, low-latency response (soft real-time capability) and detect anomalies in smart livestock housing. We validated the performance of the proposed framework using actual sensor data. It comprises environmental sensor nodes, a Mosquitto MQTT broker, and a GRU-based anomaly detection model, with data transmission via a WiFi-based network. Comparing quality of service (QoS) levels, the QoS 1 configuration demonstrated the most stable performance, with an average latency of ~150 ms, a data collection rate ≥ 99%, and a packet loss rate ≤ 0.5%. In the sensor node expansion experiment, responsiveness (≤200 ms) persisted for 10–15 nodes, whereas latency increased to 238.7 ms for 20 or more nodes. The GRU model proved suitable for low-latency analysis, achieving 97.5% accuracy, an F1-score of 0.972, and 18.5 ms/sample inference latency. In the integrated experiment, we recorded an average end-to-end latency of 185.4 ms, a data retention rate of 98.9%, processing throughput of 5.39 samples/s, and system uptime of 99.6%. These findings demonstrate that combining QoS 1-based lightweight MQTT communication with the GRU model ensures stable system response and low-latency operation (soft real-time capability) in monitoring livestock housing environments, achieving an average end-to-end latency of 185.4 ms. Full article

Alongside technological innovations, the energy transition requires notable behavioral changes in the residential sector. Smart technologies (STs) can support this shift by promoting transparency, energy-conscious behavior, and automated efficiency gains; their adoption depends on user acceptance. This study investigates the determinants shaping adoption patterns of different STs in German households. Based on a standardized online survey of 284 participants within the SmartQuart project (2022 and 2023), the analysis examined the motivational, sociodemographic, and housing-related factors influencing usage. The investigation was guided by a conceptual framework adapted from the Unified Theory of Acceptance and Use of Technology 2. The results revealed that efficiency- and control-related motives mainly drive the adoption of energy-oriented technologies, such as energy monitoring and home energy management systems. In contrast, indoor air quality monitoring and smart home systems are primarily used to enhance residential comfort. Regression analyses demonstrated that education and building type have a significant impact on energy-oriented technologies, while income, age, and living space influence comfort-oriented applications. The findings highlight the importance of differentiated communication and user-centered technology design. Despite limited generalizability, this study offers relevant insights into the target group-specific adoption dynamics essential for promoting behavioral energy efficiency in the residential sector. Full article

The rapid global ageing population highlights the pressing need for age-friendly housing that supports independent and healthy ageing in place. Indoor environmental quality (IEQ), encompassing thermal comfort, air quality, acoustic environment, lighting, and humidity, is increasingly recognized as a critical determinant of the health and well-being of older adults. Despite this, existing standards and research methodologies often inadequately address the physiological sensitivities and subjective perceptions specific to older populations. This systematic review synthesizes empirical studies published between 2016 and 2025 on IEQ in age-friendly housing. Following PRISMA guidelines, 31 studies were rigorously screened and analyzed using thematic synthesis. Key findings indicate that older adults’ thermal comfort ranges diverge from standard models, indoor air quality and noise levels often fall short of their needs, and their subjective satisfaction remains low. Effective interventions include improved ventilation, enhanced insulation, noise reduction strategies, and the adoption of smart home technologies. Taken together, these findings reveal a significant gap between existing IEQ standards and the needs of older adults and highlight the necessity of both longitudinal, integrated assessments of objective and subjective factors and participatory design strategies to optimize IEQ in age-friendly housing. Full article