Indoor green walls (GW) are increasingly adopted as Nature-based Solutions (NbS) to improve buildings Indoor Environmental Quality (IEQ). However, their widespread adoption is limited by high maintenance costs, particularly in indoor environments where artificial lighting, irrigation, and drainage are required. In this context, continuous and non-destructive monitoring techniques are essential to support efficient maintenance strategies and future automation. This study investigates the application of reflectance hyperspectral imaging (HSI) as a monitoring tool for indoor GWs, with the aim of defining best practices for data acquisition and processing. Two living walls (LWs), an intensive type of GW, composed of four different plant species were installed inside a climatic chamber, where air temperature, relative humidity, and CO 2 concentration were controlled and monitored over a 5-week campaign. Hyperspectral data were acquired weekly and processed using six vegetation indices (VIs) targeting plant vigour, pigment content, photosynthetic efficiency, and water status. Two lighting configurations were tested during acquisition: halogen-only illumination, and a combination of the previous with LED lighting optimal for plant growth. Preliminary results show that vegetation remained healthy throughout the monitoring period, confirming successfully plant adaptation to the controlled indoor environment. VIs captured species physiological characteristics and subtle temporal variations, providing insights into the evolution of LWs. CO 2 decay experiments demonstrated enhanced photosynthetic activity under LED lighting, confirming the active role of LWs in CO 2 absorption when adequate photosynthetically active radiation (PAR) is provided. However, lighting conditions influenced spectral measurements, with plant-growth LED lighting introducing spectral noise. These findings highlight the strong potential of HSI for indoor GW monitoring and provide quantitative data for future automated maintenance systems aimed at improving GWs performance, reducing operational costs, and supporting sustainable building design.
Adaptive-reuse heritage spaces require improved daylight conditions, while fa & ccedil;ade modifications are constrained by minimal intervention and reversibility. This study presents a simulation-based workflow supported by machine learning surrogate models to optimise reversible ceiling-mounted reflectors while considering the daylight-glare trade-off. A reflector is defined by six vertical control points, each taking five discrete Z-levels, yielding 15,625 geometries. Reflector placement was fixed through an XY pre-screening study (n = 119) using annual spatial daylight autonomy (sDA) and mean workplane illuminance, after which a location was selected by two-objective Pareto screening. Annual ClimateStudio simulations are completed for a 7500-sample subset of the factorial space, producing sDA and mean illuminance under consistent room, material and sensor-grid settings. Multiple surrogate families (random forest, gradient-boosted trees and multilayer perceptron) are benchmarked using hold-out evaluation with error metrics root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R 2) and ranking metrics Spearman correlation, top-K recall (Recall@ TopK) to reflect the design-screening objective. The best-performing surrogate is retrained on all simulated samples and used to predict performance for the un-simulated candidates, enabling full-space Pareto screening to shortlist high-performing geometries. A confirmation set of simulations, including top-ranked and randomly selected un-simulated candidates, is used to verify surrogate reliability before selection. The workflow incorporates annual glare screening using spatial disturbing glare (sDG) via a stratified glare subset and glare surrogate, and demonstrates scalable two-reflector screening through shortlist-based shape pairing and controlled 2D placement sweeps. Validation simulations showed that the selected single-reflector design increased mean workplane illuminance from 375 to 403.6 lx and sDA300/50% from 43.1% to 47.3%. The selected two-reflector configuration achieved 412 lx and 53.2% sDA, while annual glare remained comparable to the baseline, with sDG values of 8.0% for the single-reflector case, 8.8% for the two-reflector case and 8.6% for the baseline.
This paper presents a methodology for integrating the orographic context of micro-locations into building energy modelling (BEM) for topographically diverse regions. Dynamic energy simulations rely on high-resolution weather data from weather stations that represent specific areas. When the micro-location of the simulated building differs from the weather reference site, topographic shading can trigger deviations. This study developed a three-dimensional reproduction of the micro-location’s surrounding terrain as a simplified ring model, based on horizon height data retrieved from a Digital Elevation Model (DEM). Within a 20 km radius, four Slovenian cities from the same weather-representative area were analysed and compared with a baseline calculation. When comparing solar gains (SG) at selected micro-locations using average elevated horizon angles, a clear tendency emerges: the lower the Sky-View Factor (SVF), the greater the SG deficit. By capturing the elevated horizon more precisely through the proposed methodology, the results demonstrate that—depending on azimuthal orientation and barrier height—solar gains may vary by up to 12% even for locations exhibiting nearly identical SVF values. This approach improves the precision of BEM in topographically diverse regions and can inform better design and planning decisions.
To assess the energy efficiency of wall prototypes with advanced designs, real scale experimental demonstrators are difficult and expensive projects. To address this issue, this study develops a reduced scale experimental demonstrator based on similarity laws established for a problem of transient two-dimensional heat transfer with boundary conditions varying in time and space. Based on the dimensionless formulation of the problem, scaling factors are defined to link the real model and the reduced scale demonstrator. Here, a geometry and a kinetic similarity change are applied to build a reduced experimental demonstrator using 3D printing technology. To simulate external winter climate conditions, a heat flux is imposed using a heater pad at the top of the wall. It replicates the incoming radiation flux that partially heats the sunlit part of the wall. Two wall prototypes are investigated: one with a classical planar external boundary and one with a curved external boundary. The latter results from shape optimization to enhance the energy efficiency. Measurements of temperature are used to verify the reliability of the similarity laws. A satisfactory agreement is observed between the model predictions and the experimental results. Finally, the energy efficiency of the curved and plane wall prototypes is assessed based on the experimental data.
Ensuring thermal comfort in rural housing is critical for public health, energy efficiency, and sustainable development, particularly in regions like Luuka District, Uganda, where households predominantly rely on locally available materials and biomass energy. This study evaluated the thermal performance of local building materials and assessed community awareness, energy use, and adaptation strategies in 20 randomly selected rural households. Data were collected through structured questionnaires, observational assessments, and literature review, focusing on wall, roof, floor, and insulation materials, as well as indoor temperature, ventilation, and energy consumption patterns. Findings indicate that burnt bricks (50%), mud and wattle (30%), and iron sheet roofing (60%) dominate construction, yet poor insulation, lack of ceilings (70%), and suboptimal ventilation contribute to significant indoor temperature fluctuations excessive heat during the day and cold at night. While 65% of respondents were aware of thermal insulation and recognized its importance, practical implementation was limited by insufficient technical knowledge and skilled labor. Households primarily depended on firewood (65%) and charcoal (30%), incurring substantial energy costs. The study identified locally available materials such as clay, straw, bamboo, and corrugated iron sheets with insulating linings as promising solutions to improve thermal performance. Recommendations include community training on energy-efficient construction, promotion of thermally efficient materials, technical and financial support, and integration of renewable energy solutions to reduce reliance on biomass. Overall, enhancing the thermal efficiency of rural houses in Ikumbya Village, Luuka District is feasible through optimized use of local materials, improved design practices, and community empowerment, supporting both energy conservation and occupant comfort.
