Search Results (822)

To address the issues of neglecting population heterogeneity and the difficulties in determining constraint parameters in residential load adjustable capacity forecasting, this paper proposes a data-driven forecasting method that considers profile heterogeneity. First, K-means++ is utilized to extract diverse user electricity consumption profiles. Second, to solve the problem of real response data scarcity, the difference-in-differences (DID) method is employed to empirically calibrate the true physical constraint boundaries of different clusters, and high-quality response samples are generated in batches based on an electricity cost minimization model. Finally, a Long Short-Term Memory (LSTM) time-series forecasting model is constructed to achieve the precise quantitative evaluation of adjustable capacity. Case studies demonstrate that after introducing user profile labels, the three accuracy metrics of the predictive model are improved by 16.29%, 24.52%, and 20.21%, respectively. Although the practical application of synthetic labels faces minor limitations caused by uncertain user behaviors, this scalable framework supports seamless incremental retraining using future empirical response data to realize continuous model evolution and persistent accuracy improvement, thereby providing technical support for load aggregators’ market bidding and the precise dispatch of power grid demand response. Full article

The rapid expansion of online real estate (RE) platforms has intensified information overload, making property search and decision-making increasingly complex. Real estate recommendation systems (RERSs) have emerged as essential decision-support tools; however, their development has not kept pace with advances in explainable artificial intelligence (XAI), transfer learning (TL), and fairness-aware machine learning. This PRISMA-compliant systematic review synthesizes 59 peer-reviewed studies published between 2005 and 2025 to critically examine algorithmic approaches, data modalities, evaluation practices, and ethical considerations in RERS research. Our analysis reveals a substantial lag in the adoption of state-of-the-art AI techniques: While deep learning is employed in 15% of studies, no reviewed work implements state-of-the-art post hoc XAI or TL frameworks, despite their relevance for addressing interpretability and data scarcity challenges. Furthermore, we identify systemic research biases, including reliance on proprietary datasets (80%), geographic concentration in Asia (56%), the dominance of residential property studies (91%), and limited fairness auditing despite documented discrimination risks in housing markets. To address these gaps, we propose a trust-based evaluation (T-EVAL) framework that integrates predictive accuracy, user trust, fairness, and market efficiency, and introduces a comprehensive nine-layer conceptual architecture for transparent, ethical, and data-efficient next-generation RERS. This review establishes an empirical benchmark for technology adoption gaps and outlines a research agenda for advancing responsible AI in RE decision-support systems. Full article

This data descriptor presents CINES-REC-CITY, an open synthetic dataset providing high-resolution load profiles for energy community research. The dataset represents a typical German urban district with 70 apartments across eight multi-family buildings, including diverse socioeconomic characteristics. Three main components are provided at 15 min resolution for a full year: non-controllable residential electricity consumption for all apartments, charging profiles for 17 battery electric vehicles with trip information, and heat pump operation data for both variable-speed and hysteresis-controlled ground-source systems. All profiles were generated using validated bottom-up stochastic simulation models accounting for realistic user behavior, mobility patterns, and thermal building physics. The modular structure allows for selective combination of components, enabling investigation of different technology penetration scenarios. The dataset serves as a reference benchmark for reproducible research, allowing for direct comparison of optimization approaches, business models, and control strategies using identical underlying consumption patterns. It is suitable for techno-economic analysis, algorithm development for flexible load control, and grid impact assessment. All data is provided in CSV format with weather data for consistent extensions. Full article

The digitization of existing building stock often depends on legacy 2D raster floor plans (scanned drawings, PDF exports, or photographs) because structured building information models are frequently unavailable for older properties. Manual measurement and visual inspection of such documents are time consuming and error prone. This paper presents an integrated deep learning pipeline that extracts semantic information from unstructured two-dimensional floor plan images of existing structures and supports preliminary compliance screening via locally deployed large language models. The pipeline employs YOLOv8 for the localization and classification of 18 architectural symbols and furniture items, and a U-Net with a ResNet34 encoder for the semantic segmentation of walls and interior room spaces. To translate pixel-level predictions into physical metrics, we implement an area calculation module based on user-defined reference scale calibration. An LLM evaluation module, deployed locally via Ollama with a retrieval-augmented generation pipeline, interprets extracted room metrics and flags potential non-compliance against referenced residential design guidelines; it is intended for the assessment of existing layouts rather than generative co-design. We expand a core dataset of 101 manually annotated source floor plans to 303 augmented instances using label-aligned geometric transformations, while reporting generalization in terms of the 101 unique source plans. On the held-out validation split (10 source plans), YOLOv8 achieves 92.3% mAP50 versus 87.2% for a Faster R-CNN reference model on the same data split (detection baselines differ in training epochs and pretraining; see Experiments); U-Net achieves 95.71% mIoU, surpassing DeepLabv3+ (93.2%) under matched segmentation training settings. The system is deployed as an interactive web application for legacy building survey and preliminary regulatory review when only two-dimensional documentation is available. Full article

Urban industrial heritage is increasingly embedded in urban regeneration, public space provision, and community governance, yet existing studies have insufficiently examined how heterogeneous publics perceive its value through everyday digital discourse. Taking the Guangzhou Iron and Steel Plant industrial heritage site (hereafter, the Guanggang industrial heritage site) as a case study, this study used user-generated content from Rednote posts and local WeChat public-account comments to identify platform-mediated expressions of public value perception. A corpus of 745 valid samples comprising 51,459 Chinese characters was constructed after data collection, screening, and text preprocessing. Word-frequency analysis, semantic network analysis, and sentiment analysis were conducted using ROST CM 6.0. The results show that the two retrieved platform-contextual corpora foregrounded different concerns. Rednote discourse foregrounded ruin landscapes, industrial aesthetics, photography-based check-ins, and exploratory experiences, whereas WeChat comments emphasized park construction, public facilities, governance responsiveness, safety, and the residential environment. At the corpus level, lexicon-based sentiment classification indicated that Rednote texts were dominated by positive and neutral categories, while WeChat comments contained a higher proportion of texts classified as negative. This study conceptualizes dual foregrounding as a bounded selection process through which platform affordances, user self-selection, and users’ relationships with the site influence which concerns become visible in each corpus; it does not treat the observed differences as a causal platform effect. It argues that industrial heritage regeneration must translate historical, technological, and aesthetic values into public values that are interpretable, accessible, usable, and trusted by local communities. Full article

Accurate customer baseline load (CBL) estimation is crucial for incentive allocation and flexibility potential assessment in demand response (DR) programs. However, residential electricity consumption is highly stochastic, and long-duration DR events often result in missing critical load segments, making it difficult for traditional regression-based and daily load-profile clustering methods to accurately capture the counterfactual baseline pattern. To address this issue, this paper proposes a CBL estimation method that integrates a physics-/domain-informed response-consistency constraint with a conditional generative adversarial network. In the proposed framework, deep soft clustering is employed to extract weekly scale load modes, while mutual information (MI) and autocorrelation coefficient (ACC) are quantified as user-specific conditioning fingerprints to characterize intrinsic consumption behaviors. Comparative experiments on a publicly available real-world dataset demonstrate that the proposed method provides strong event-period accuracy among the recurrent and attention-based benchmark models considered in the main comparison. Under matched response-consistency budgets, PI-ICGAN achieves the lowest constrained DR-period MAE at the tested NRR targets, and the ablation results show that the attention, fingerprint, response-consistency, and GradNorm components contribute to different aspects of the accuracy–consistency trade-off. Full article

The integration of renewable energy and rising electricity demand strain system flexibility. While price-based demand response (PBDR) improves flexibility through pricing signals, its efficacy hinges critically on accurate consumer modeling. Recognizing this pivotal role, this paper provides a comprehensive review of consumer models in PBDR and their applications to electric vehicles (EVs). First, a unified conceptual framework is presented, delineating the energy, information and financial flows among the system operator (SO), load aggregators (LAs), and end-users, and highlighting the central position of consumer modeling. Second, existing modeling approaches are systematically classified into four categories, namely rule-based, optimization-based, data-driven, and hybrid, to facilitate the selection of appropriate models by researchers and stakeholders for diverse scenarios. Furthermore, the application and adaptation of these models to EVs are critically analyzed, accounting for unique vehicular constraints. Subsequently, a systematic summary of the key characteristics and existing research gaps is provided. Finally, key directions for future research are proposed accordingly, aimed at incorporating bounded rationality into behavioral models, developing individualized consumer modeling coupled with user-specific dynamic pricing, and extending consumer modeling to residential multi-energy prosumers in integrated energy systems. Full article

The increasing integration of renewable energy sources, smart appliances, and distributed energy technologies has significantly increased the complexity of residential energy systems, necessitating advanced Home Energy Management Systems (HEMS). Optimization techniques play a critical role in achieving key objectives, including energy cost reduction, load balancing, minimizing the peak-to-average ratio, and enhancing user comfort. This paper presents a comprehensive review and critical analysis of optimization techniques employed in HEMS, including mathematical methods, metaheuristic algorithms, artificial intelligence (AI)-based approaches, and rule-based strategies. These techniques are systematically classified and compared based on scalability, computational complexity, uncertainty handling, and real-time applicability. The analysis reveals that while conventional methods provide reliable solutions for structured problems, AI-based techniques offer superior adaptability and performance in dynamic and data-driven environments. Furthermore, key research gaps are identified, including limited multi-objective optimization, inadequate consideration of uncertainty and electric vehicle integration, and the lack of real-world implementation. Finally, future research directions are outlined, emphasizing hybrid optimization frameworks and intelligent, IoT-enabled energy management systems. Full article

The inequitable redistribution of electricity price cross-subsidies constitutes a critical issue, as it compromises the implementation efficiency of tiered electricity pricing (TEP) policies and impedes the equalization of basic public services in the power sector. Drawing on residential TEP data from Hebei Province spanning 2016 to 2020, this paper employs the Gini coefficient method and reveals that high-income residential users receive substantially larger electricity price cross-subsidies than their low-income counterparts. Overall, the degree of such inequality has been rising annually. Furthermore, both high-income and low-income groups exhibit greater inequity in subsidy allocation relative to the middle-income group. Against this backdrop, this paper proposes a more rational tiering framework for TEP by adopting the rank-sum ratio (RSR) method, thereby identifying a viable pathway for residential users across all income brackets to share electricity costs equitably. This research contributes to the sound management of electricity price cross-subsidies, mitigates the inequity in subsidy distribution, and guides residents toward rational electricity consumption behaviors. Full article

Demand-side management plays a critical role in the secure and efficient operation of smart grids. Traditional real-time pricing generally takes social welfare maximization as the only objective, while ignoring the benefit balance among electricity suppliers, grid company and users. This will lead to uneven benefit distribution among stakeholders and impair the long-term stable operation of power systems. To solve this problem, a bilevel real-time pricing strategy based on tripartite welfare equilibrium is proposed in this paper. The upper-level model minimizes the welfare differences among electricity suppliers, grid company and users to ensure fair benefit allocation, and the lower-level model maximizes the total social welfare so as to guarantee the economic efficiency of the system. The model adopts different utility functions for residential and industrial users to describe user heterogeneity. By using the Karush–Kuhn–Tucker conditions, the original bilevel model is transformed into a single-level optimization problem with complementarity constraints. The CHKS smoothing function and pseudo-Huber function are introduced to deal with complementarity constraints and absolute-value objective functions respectively. Combined with the central difference method, a modified rolling penalty function algorithm is developed for numerical solution. The 24 h simulation results show that the prices of four time periods converge steadily to equilibrium values as iterations proceed. Compared with the total social welfare maximization model, the proposed bilevel model effectively reduces the peak-to-average load ratio. It reduces the welfare disparities among the three stakeholders while maintaining the total social welfare at a stable level. Furthermore, it still maintains excellent applicability and robustness when the user scale is expanded. Full article

Industrial new towns have become important spatial instruments for regional economic development in China, yet many continue to struggle to attract and retain a stable workforce, support long-term settlement, and provide a complete urban living environment. Taking the Chenglingji New Port Area in central China as a case study, this paper applies the Industry–People–City analytical framework to examine the relationship among industrial development, residents’ everyday behaviours, and public service provision. The study combines field investigations, activity-diary interviews with 60 local residents, semi-structured interviews with 12 enterprise managers, and point-of-interest data on public service facilities. These materials are used for a primarily qualitative analysis supported by GIS-based spatial evidence. The findings show that the Chenglingji New Port Area has developed a clear basis of industrial agglomeration and four functional sectors, but public service provision remains mismatched with the everyday needs of different population groups. Managerial personnel and some technical workers continue to rely heavily on Yueyang’s main urban area for residence, consumption, leisure, and higher-order services, while locally based residents face combined deficits in commerce, transport, healthcare, education, cultural and recreational services, and public open spaces. The contribution of this study is twofold. First, it provides an empirically grounded assessment of the living conditions of labour and related residential groups in a resource-constrained inland industrial new town. Second, it demonstrates how the Industry–People–City analytical framework can be used to diagnose structural imbalances among industrial growth, population behaviour, and urban service provision. The study argues that improving the livability of industrial new towns should not depend solely on industrial expansion or one-off investment in high-standard facilities. Instead, phased, sector-specific, and user-oriented public service provision is needed to help industrial new towns gradually transform from mono-functional production-oriented growth poles into more complete and sustainable urban living nodes. Full article

This study proposes a comprehensive techno-economic methodology to assess the economic viability and optimal sizing of grid-connected residential photovoltaic (PV) self-consumption systems without storage in emerging economies. The model uses net present value (NPV) as the optimization criterion and estimates internal rate of return (IRR) and discounted payback time (DPBT) as complementary profitability indicators. It integrates hourly PV generation, synthesized hourly demand profiles, local tariff structures, surplus-energy remuneration, investment and operating costs, inflation, performance losses, and discount-rate assumptions, while explicitly accounting for context-specific limitations related to data availability, storage-free operation, and financing assumptions. The methodology is applied to 30 Colombian residential scenarios, covering five cities and six socioeconomic strata, and is complemented with a replicability case in Jaén, Spain. In Colombia, PV self-consumption is economically viable in all cases, but profitability is highly uneven: maximized NPV ranges from 2.8 € in the least favorable low-income case to 2816 € in the best high-income case, IRR ranges from 5.0% to 14.7%, and DPBT ranges from 8 to 24 years. From an energy-justice perspective, tariff subsidies improve affordability but may reduce PV attractiveness for low-income users, highlighting the need for capital grants, low-interest loans, or community solar schemes. Full article

The real estate sector has increasingly shifted toward digital platforms, where content sentiment plays a crucial yet understudied role in driving user engagement. While sentiment analysis has been widely applied in retail and finance, its impact on real estate web traffic remains poorly understood, particularly in competitive digital marketplaces. This study examines the relationship between sentiment in web content and the traffic it attracts on residential real estate websites in South Africa. Specifically, it examines how different sentiments associated with the type of content (articles versus property listings) influence total monthly web traffic and user engagement. A quantitative analysis of six years (2017–2023) of scraped data from Property24, Remax, and Private Property employed R (rvest, sentimentr, and stats packages) for web scraping, sentiment analysis, and ANOVA testing to evaluate relationships between content sentiment, type (listings vs. articles), and web traffic metrics. The analysis revealed a significant impact of sentiment on web traffic, indicating that the sentiment of web content influences visitor numbers. Specifically, property listings generated a total of 16,780,623 monthly visitors, significantly surpassing the 13,407,521 visitors attracted by articles. This study contributes empirical evidence regarding the influence of content sentiment and content type on web traffic within the South African real estate market. It highlights the critical role of sentiment in shaping web traffic and potentially user engagement and provides actionable insights for real estate developers and marketers seeking to optimize their content strategies to improve user attraction and retention. Full article

Dhaka, one of the world’s most densely populated megacities, has faced a severe ecological decline, with green cover plummeting from 44.80% in 1975 to approximately 24.50% by 2005. In response, urban rooftop farming has emerged as a vital adaptation strategy to mitigate the urban heat island effect and air pollution. Objective: This study evaluates the transition from “pixels to policy” by testing automated identification methods for URF to support evidence-based urban governance, specifically the 10.00% holding tax rebate offered by the Dhaka North City Corporation. Utilizing high-resolution (3 cm) drone imagery across three diverse areas of interest—representing planned, organic, and mixed-use urban fabrics, the research compares the performance of Support Vector Machines, U-Net, and Text-Segment Anything Model. Accuracy was validated using a confusion matrix based on 1000 randomly stratified sample points. The SVM model emerged as the most reliable, achieving a Kappa index of 0.74 and 100.00% user accuracy for identifying rooftop vegetation, significantly outperforming the U-Net model (Kappa 0.14). Spatial analysis quantified a distinct “green divide,” revealing that while planned residential zones achieved over 7.50% rooftop greening coverage, dense organic settlements were limited to 6.00%. The study concludes that high-accuracy SVM-based identification provides a scalable foundation for automating fiscal incentives. To bridge the socio-spatial green divide, policy interventions must shift toward inclusive greening strategies, such as vertical farming, and formal integration of URF into Dhaka’s blue-green infrastructure networks. Full article

This paper introduces the integration of smart appliances and Internet of Things technologies for the local balancing of low-voltage power distribution networks, particularly in response to the proliferation of prosumer renewable energy sources. The primary objective is the incorporation of the Elastic Energy Management algorithm with Mixed Reality and Augmented Reality interfaces to facilitate intuitive demand-side management. The methodology employs the GRASP heuristic algorithm alongside advanced on-device 3D point cloud segmentation, enabling the system to identify physical energy consumers within a residential environment. Simulation results demonstrate high algorithmic convergence and the capacity for the system to provide real-time updates to visual interfaces. The findings indicate that the utilization of AR and MR goggles significantly enhances interaction with energy infrastructure by providing hands-free operation and overlaying digital data directly onto physical components. This approach enables more effective grid balancing and increased self-consumption of renewable energy while maintaining user comfort and reducing the technical knowledge required for efficient household energy management. Full article