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22 pages, 7140 KiB  
Article
Impact of Phenological and Lighting Conditions on Early Detection of Grapevine Inflorescences and Bunches Using Deep Learning
by Rubén Íñiguez, Carlos Poblete-Echeverría, Ignacio Barrio, Inés Hernández, Salvador Gutiérrez, Eduardo Martínez-Cámara and Javier Tardáguila
Agriculture 2025, 15(14), 1495; https://doi.org/10.3390/agriculture15141495 - 11 Jul 2025
Viewed by 228
Abstract
Reliable early-stage yield forecasts are essential in precision viticulture, enabling timely interventions such as harvest planning, canopy management, and crop load regulation. Since grape yield is directly related to the number and size of bunches, the early detection of inflorescences and bunches, carried [...] Read more.
Reliable early-stage yield forecasts are essential in precision viticulture, enabling timely interventions such as harvest planning, canopy management, and crop load regulation. Since grape yield is directly related to the number and size of bunches, the early detection of inflorescences and bunches, carried out even before flowering, provides a valuable foundation for estimating potential yield far in advance of veraison. Traditional yield prediction methods are labor-intensive, subjective, and often restricted to advanced phenological stages. This study presents a deep learning-based approach for detecting grapevine inflorescences and bunches during early development, assessing how phenological stage and illumination conditions influence detection performance using the YOLOv11 architecture under commercial field conditions. A total of 436 RGB images were collected across two phenological stages (pre-bloom and fruit-set), two lighting conditions (daylight and artificial night-time illumination), and six grapevine cultivars. All images were manually annotated following a consistent protocol, and models were trained using data augmentation to improve generalization. Five models were developed: four specific to each condition and one combining all scenarios. The results show that the fruit-set stage under daylight provided the best performance (F1 = 0.77, R2 = 0.97), while for inflorescences, night-time imaging yielded the most accurate results (F1 = 0.71, R2 = 0.76), confirming the benefits of artificial lighting in early stages. These findings define optimal scenarios for early-stage organ detection and support the integration of automated detection models into vineyard management systems. Future work will address scalability and robustness under diverse conditions. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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21 pages, 2236 KiB  
Article
Behavioral Responses of Migratory Fish to Environmental Cues: Evidence from the Heishui River
by Jiawei Xu, Yilin Jiao, Shan-e-hyder Soomro, Xiaozhang Hu, Dongqing Li, Jianping Wang, Bingjun Liu, Chenyu Lin, Senfan Ke, Yujiao Wu and Xiaotao Shi
Fishes 2025, 10(7), 310; https://doi.org/10.3390/fishes10070310 - 30 Jun 2025
Viewed by 289
Abstract
Hydropower infrastructure has profoundly altered riverine connectivity, posing challenges to the migratory behavior of aquatic species. This study examined the post-passage migration efficiency of Schizothorax wangchiachii in a regulated river system, focusing on upstream and downstream reaches of the Songxin Hydropower Station on [...] Read more.
Hydropower infrastructure has profoundly altered riverine connectivity, posing challenges to the migratory behavior of aquatic species. This study examined the post-passage migration efficiency of Schizothorax wangchiachii in a regulated river system, focusing on upstream and downstream reaches of the Songxin Hydropower Station on the Heishui River, a tributary of the Jinsha River. We used radio-frequency identification (RFID) tagging to track individuals after fishway passage and coupled this with environmental monitoring data. A Cox proportional hazards model was applied to identify key abiotic drivers of migration success and to develop a predictive framework. The upstream success rate was notably low (15.6%), with a mean passage time of 438 h, while downstream success reached 81.1%, with an average of 142 h. Fish exhibited distinct diel migration patterns; upstream movements were largely nocturnal, whereas downstream migration mainly occurred during daylight. Water temperature (HR = 0.535, p = 0.028), discharge (HR = 0.801, p = 0.050), water level (HR = 0.922, p = 0.040), and diel timing (HR = 0.445, p = 0.088) emerged as significant factors shaping the upstream movement. Our findings highlight that fishways alone may not ensure functional connectivity restoration. Instead, coordinated habitat interventions in upstream tributaries, alongside improved passage infrastructure, are crucial. A combined telemetry and modeling approach offers valuable insights for river management in fragmented systems. Full article
(This article belongs to the Special Issue Behavioral Ecology of Fishes)
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24 pages, 1231 KiB  
Article
Hourly Daylight Illuminance Prediction Considering Seasonal and Daylight Condition-Based Meteorological Analog Intervals
by Zhiyi Zhu, Xingyu Wang, Jinghan Hao, Linkun Yang and Ying Yu
Sustainability 2025, 17(11), 4914; https://doi.org/10.3390/su17114914 - 27 May 2025
Viewed by 428
Abstract
With the growing global demand for energy optimization, particularly in the building sector, accurate daylight illuminance prediction plays a key role in enhancing energy efficiency through natural lighting and intelligent lighting systems. This study proposes a novel prediction model that integrates Meteorological Analog [...] Read more.
With the growing global demand for energy optimization, particularly in the building sector, accurate daylight illuminance prediction plays a key role in enhancing energy efficiency through natural lighting and intelligent lighting systems. This study proposes a novel prediction model that integrates Meteorological Analog Intervals with a hybrid TCN-Transformer-BILSTM architecture to address the issue of insufficient prediction accuracy caused by the influence of various complex factors on daylight illuminance, as well as sudden weather changes, fluctuating meteorological conditions, and short-term variations. The model uses Grey Relational Analysis and Cosine Similarity to select historical data similar to the target moment in terms of meteorological conditions and time attributes, and constructs Meteorological Analog Intervals by combining the preceding and following time steps, providing high-quality data for the subsequent model development. The model effectively combines the multi-scale feature extraction capability of TCN, the global correlation-capturing advantage of Transformer, and the bidirectional temporal modeling characteristic of BILSTM to predict the temporal dynamics of daylight illuminance. Based on the measured data from Xi’an in 2023, experiments show that the proposed MAIL-TCN-Trans-BILSTM model achieves RMSEs of 1425.83 Lux and 2581.45 Lux under optimal and suboptimal daylight conditions, respectively, with MAPE reductions of 9–12% and 4–6% compared to baseline models. The proposed Meteorological Analog Intervals method significantly enhances the prediction accuracy and robustness of the model, especially in scenarios with complex and variable meteorological conditions, providing data support for intelligent lighting control systems. Full article
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27 pages, 14209 KiB  
Article
Machine-Learning-Enhanced Building Performance-Guided Form Optimization of High-Rise Office Buildings in China’s Hot Summer and Warm Winter Zone—A Case Study of Guangzhou
by Xie Xie, Yang Ni and Tianzi Zhang
Sustainability 2025, 17(9), 4090; https://doi.org/10.3390/su17094090 - 1 May 2025
Viewed by 734
Abstract
Given their dominant role in energy expenditure within China’s Hot Summer and Warm Winter (HSWW) zone, high-fidelity performance prediction and multi-objective optimization framework during the early design phase are critical for achieving sustainable energy efficiency. This study presents an innovative approach integrating machine [...] Read more.
Given their dominant role in energy expenditure within China’s Hot Summer and Warm Winter (HSWW) zone, high-fidelity performance prediction and multi-objective optimization framework during the early design phase are critical for achieving sustainable energy efficiency. This study presents an innovative approach integrating machine learning (ML) algorithms and multi-objective genetic optimization to predict and optimize the performance of high-rise office buildings in China’s HSWW zone. By integrating Rhino/Grasshopper parametric modeling, Ladybug Tools performance simulation, and Python programming, this study developed a parametric high-rise office building model and validated five advanced and mature machine learning algorithms for predicting energy use intensity (EUI) and useful daylight illuminance (UDI) based on architectural form parameters under HSWW climatic conditions. The results demonstrate that the CatBoost algorithm outperforms other models with an R2 of 0.94 and CVRMSE of 1.57%. The Pareto optimal solutions identify substantial shading dimensions, southeast orientations, high aspect ratios, appropriate spatial depths, and reduced window areas as critical determinants for optimizing EUI and UDI in high-rise office buildings of the HSWW zone. This research fills a gap in the existing literature by systematically investigating the application of ML algorithms to predict the complex relationships between architectural form parameters and performance metrics in high-rise building design. The proposed data-driven optimization framework provides architects and engineers with a scientific decision-making tool for early-stage design, offering methodological guidance for sustainable building design in similar climatic regions. Full article
(This article belongs to the Section Green Building)
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13 pages, 982 KiB  
Article
Cathemerality and Insensitivity to Predatory Fish Cues in Pond Isopods (Caecidotea communis)
by Elizabeth C. Long and Erika V. Iyengar
Hydrobiology 2025, 4(2), 11; https://doi.org/10.3390/hydrobiology4020011 - 16 Apr 2025
Viewed by 387
Abstract
Because animals threatened by visually oriented predators may respond in sun-lit daytime but not at night, invertebrate responses to predatory challenges may yield varying results based on the time period within the 24 h daily cycle. We predicted that in laboratory experiments aquatic [...] Read more.
Because animals threatened by visually oriented predators may respond in sun-lit daytime but not at night, invertebrate responses to predatory challenges may yield varying results based on the time period within the 24 h daily cycle. We predicted that in laboratory experiments aquatic isopods exposed to kairomones from predatory fish would spend more time immobilized in daylight to avoid detection than those not exposed to kairomones but that this difference would disappear under the cover of nighttime darkness. We further predicted that isopods in the absence of kairomones would move at elevated rates in the daytime compared with night, seeking a precautionary proximity to shelters. However, contrary to our predictions, Caecidotea communis isopods exhibited consistent activity (movement rate and proportion of time spent moving) when exposed to kairomones or in the absence of such cues, at all of the three diurnal cycle periods examined. Thus, Caecidotea communis displayed cathemerality (sometimes called metaturnality), the first documented case of this behavior in crustaceans. Full article
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30 pages, 3465 KiB  
Article
Weather-Driven Cycling: Developing a Predictive Model for Urban Bicycle Usage Based on Five Key Weather Factors
by Nahid Falah, Nadia Falah and Jaime Solis-Guzman
Urban Sci. 2025, 9(2), 41; https://doi.org/10.3390/urbansci9020041 - 11 Feb 2025
Viewed by 1431
Abstract
Weather conditions significantly influence urban cycling, shaping both its frequency and intensity. This study develops a predictive model to evaluate the impact of five key meteorological factors, namely temperature, humidity, precipitation, wind speed, and daylight duration, on urban cycling trends. Using non-linear regression [...] Read more.
Weather conditions significantly influence urban cycling, shaping both its frequency and intensity. This study develops a predictive model to evaluate the impact of five key meteorological factors, namely temperature, humidity, precipitation, wind speed, and daylight duration, on urban cycling trends. Using non-linear regression analysis, the research examines cycling data from 2017 to 2019 in Hamburg, Germany, comparing predicted values for 2019 with actual data to assess model accuracy. The statistical analyses reveal strong correlations between weather parameters and cycling activity, highlighting each factor’s unique influence. The model achieved high accuracy, with R2 values of 0.942 and 0.924 for 2017 and 2019, respectively. To further validate its robustness, the model is applied to data from 2021 and 2023—years not included in its initial development—yielding R2 values of 0.893 and 0.919. These results underscore the model’s reliability and adaptability across different timeframes. This study not only confirms the critical influence of weather on urban cycling patterns, but also provides a scalable framework for broader urban planning applications. Beyond the immediate findings, this research proposes expanding the model to incorporate urban factors, such as land use, population density, and socioeconomic conditions, offering a comprehensive tool for urban planners and policymakers to enhance sustainable transportation systems. Full article
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57 pages, 16680 KiB  
Article
Generating High Spatial and Temporal Surface Albedo with Multispectral-Wavemix and Temporal-Shift Heatmaps
by Sagthitharan Karalasingham, Ravinesh C. Deo, Nawin Raj, David Casillas-Perez and Sancho Salcedo-Sanz
Remote Sens. 2025, 17(3), 461; https://doi.org/10.3390/rs17030461 - 29 Jan 2025
Cited by 1 | Viewed by 1233
Abstract
Surface albedo is a key variable influencing ground-reflected solar irradiance, which is a vital factor in boosting the energy gains of bifacial solar installations. Therefore, surface albedo is crucial towards estimating photovoltaic power generation of both bifacial and tilted solar installations. Varying across [...] Read more.
Surface albedo is a key variable influencing ground-reflected solar irradiance, which is a vital factor in boosting the energy gains of bifacial solar installations. Therefore, surface albedo is crucial towards estimating photovoltaic power generation of both bifacial and tilted solar installations. Varying across daylight hours, seasons, and locations, surface albedo is assumed to be constant across time by various models. The lack of granular temporal observations is a major challenge to the modeling of intra-day albedo variability. Though satellite observations of surface reflectance, useful for estimating surface albedo, provide wide spatial coverage, they too lack temporal granularity. Therefore, this paper considers a novel approach to temporal downscaling with imaging time series of satellite-sensed surface reflectance and limited high-temporal ground observations from surface radiation (SURFRAD) monitoring stations. Aimed at increasing information density for learning temporal patterns from an image series and using visual redundancy within such imagery for temporal downscaling, we introduce temporally shifted heatmaps as an advantageous approach over Gramian Angular Field (GAF)-based image time series. Further, we propose Multispectral-WaveMix, a derivative of the mixer-based computer vision architecture, as a high-performance model to harness image time series for surface albedo forecasting applications. Multispectral-WaveMix models intra-day variations in surface albedo on a 1 min scale. The framework combines satellite-sensed multispectral surface reflectance imagery at a 30 m scale from Landsat and Sentinel-2A and 2B satellites and granular ground observations from SURFRAD surface radiation monitoring sites as image time series for image-to-image translation between remote-sensed imagery and ground observations. The proposed model, with temporally shifted heatmaps and Multispectral-WaveMix, was benchmarked against predictions from models image-to-image MLP-Mix, MLP-Mix, and Standard MLP. Model predictions were also contrasted against ground observations from the monitoring sites and predictions from the National Solar Radiation Database (NSRDB). The Multispectral-WaveMix outperformed other models with a Cauchy loss of 0.00524, a signal-to-noise ratio (SNR) of 72.569, and a structural similarity index (SSIM) of 0.999, demonstrating the high potential of such modeling approaches for generating granular time series. Additional experiments were also conducted to explore the potential of the trained model as a domain-specific pre-trained alternative for the temporal modeling of unseen locations. As bifacial solar installations gain dominance to fulfill the increasing demand for renewables, our proposed framework provides a hybrid modeling approach to build models with ground observations and satellite imagery for intra-day surface albedo monitoring and hence for intra-day energy gain modeling and bifacial deployment planning. Full article
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20 pages, 3919 KiB  
Article
Drying Performance of a Combined Solar Greenhouse Dryer of Sewage Sludge
by Fatiha Berroug, Yassir Bellaziz, Zakaria Tagnamas, Younes Bahammou, Hamza Faraji, El Houssayne Bougayr and Naaila Ouazzani
Sustainability 2024, 16(22), 9925; https://doi.org/10.3390/su16229925 - 14 Nov 2024
Cited by 1 | Viewed by 1383
Abstract
The solar drying of sewage sludge in greenhouses is one of the most used solutions in wastewater treatment plants (WWTPs). However, it presents challenges, particularly in terms of efficiency and drying time. In this context, the present study explores the drying performances of [...] Read more.
The solar drying of sewage sludge in greenhouses is one of the most used solutions in wastewater treatment plants (WWTPs). However, it presents challenges, particularly in terms of efficiency and drying time. In this context, the present study explores the drying performances of an innovative Combined Solar Greenhouse Dryer (CSGD) for sewage sludge. The system integrates rock bed storage (RBS), a solar air collector (SAC), and a solar greenhouse dryer (SGD). A numerical model, developed using TRNSYS software, predicts the drying kinetics of sewage sludge through hourly dynamic simulations based on the climatic conditions of Marrakesh, Morocco. Experimental validation confirmed the accuracy of the model. The results reveal that integrating the SAC with the SGD during the day and the RBS with the SGD at night significantly enhances the drying efficiency of the sewage sludge. During daylight hours, the SAC generates hot air, reaching maximum temperatures of 64 °C in January and 109 °C in July. Concurrently, the outlet air temperature of the RBS rises notably during the day, corresponding to the charging phase of the storage unit. Moreover, during the night, the RBS air temperature exceeds ambient temperatures by approximately 7–16 °C in January and 11–37 °C in July. This integration leads to a substantial reduction in drying time. The reduction in sewage sludge water content from 4 kg/kg of dry solid (20% dry solid content) to 0.24 kg/kg of dry solid (80% dry solid content) is related to a decrease in the drying time from 121 h to 79 h in cold periods and from 47 h to 27 h in warm periods. The drying process is significantly enhanced within the greenhouse, both during daylight and nocturnal periods. The CSGD system proves to be energy-efficient, offering an effective, high-performance solution for sewage sludge management, while also lowering operational costs for WWTPs. This innovative solar drying system combines a thermal storage bed and a solar collector to enhance drying efficiency, even in the absence of sunlight. Full article
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36 pages, 60184 KiB  
Article
Poly-Methyl-Methacrylate Rods in Light-Transmitting Concrete: A Critical Investigation into Sustainable Implementation
by Adithya Shenoy, Gopinatha Nayak, Adithya Tantri, Kiran K. Shetty, Jasmin Anna Maxwell, B. H. Venkataram Pai and Laxman P. Kudva
Sustainability 2024, 16(18), 8033; https://doi.org/10.3390/su16188033 - 13 Sep 2024
Viewed by 1469
Abstract
The development of special concrete focussed on sustainability and energy conservation has been approached through the use of recycled materials, novel techniques and processes, and materials that harness natural energy. This paper presents the results of one such study on the development of [...] Read more.
The development of special concrete focussed on sustainability and energy conservation has been approached through the use of recycled materials, novel techniques and processes, and materials that harness natural energy. This paper presents the results of one such study on the development of light-transmitting concrete using a novel polymeric transmitting media, poly-methyl-methacrylate, and a detailed analysis of the results obtained. Four variants based on the diameter and number of rods have been studied, with 5 and 10 mm diameter rods incorporated into 100 mm cube samples. A positive correlation between the area of rods and transmittance has been established; however, a loss in compressive and flexural strength was observed. Seasonal and monthly variation results indicate higher transmittance in summer, with the highest transmittance being observed in the month of May and the monsoon having the lowest transmittance, specifically in the month of July. The results of a case study of the application of the material have also been presented. The cost of construction has been studied, and the prediction of electricity consumption during operations has been carried out. The results have indicated the feasibility of use, even with the high initial cost. Variants have been shown to return the investments in a period of 7–31 years. Additionally, three of the four variants showed a sharp decrease in total CO2 emissions by eliminating the need for energy for daylighting and eliminating the consumption of electricity throughout the service life. Variants have been shown to return the investments in a period of 7–31 years. Additionally, three of the four variants show a sharp decrease in total CO2 emissions by eliminating the need for energy for daylighting and eliminating the consumption of electricity throughout the service life. Full article
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36 pages, 12052 KiB  
Article
Building Information Modeling and AI Algorithms for Optimizing Energy Performance in Hot Climates: A Comparative Study of Riyadh and Dubai
by Mohammad H. Mehraban, Aljawharah A. Alnaser and Samad M. E. Sepasgozar
Buildings 2024, 14(9), 2748; https://doi.org/10.3390/buildings14092748 - 2 Sep 2024
Cited by 13 | Viewed by 4223
Abstract
In response to increasing global temperatures and energy demands, optimizing buildings’ energy efficiency, particularly in hot climates, is an urgent challenge. While current research often relies on conventional energy estimation methods, there has been a decrease in the efforts dedicated to leveraging AI-based [...] Read more.
In response to increasing global temperatures and energy demands, optimizing buildings’ energy efficiency, particularly in hot climates, is an urgent challenge. While current research often relies on conventional energy estimation methods, there has been a decrease in the efforts dedicated to leveraging AI-based methodologies as technology advances. This implies a dearth of multiparameter examinations in AI-driven extreme case studies. For this reason, this study aimed to enhance the energy performance of residential buildings in the hot climates of Dubai and Riyadh by integrating Building Information Modeling (BIM) and Machine Learning (ML). Detailed BIM models of a typical residential villa in these regions were created using Revit, incorporating conventional, modern, and green building envelopes (BEs). These models served as the basis for energy simulations conducted with Green Building Studio (GBS) and Insight, focusing on crucial building features such as floor area, external and internal walls, windows, flooring, roofing, building orientation, infiltration, daylighting, and more. To predict Energy Use Intensity (EUI), four ML algorithms, namely, Gradient Boosting Machine (GBM), Random Forest (RF), Support Vector Machine (SVM), and Lasso Regression (LR), were employed. GBM consistently outperformed the others, demonstrating superior prediction accuracy with an R2 of 0.989. This indicates that the model explains 99% of the variance in EUI, highlighting its effectiveness in capturing the relationships between building features and energy consumption. Feature importance analysis (FIA) revealed that roofs (29% in Dubai scenarios (DS) and 40% in Riyadh scenarios (RS)), external walls (19% in DS and 29% in RS), and windows (15% in DS and 9% in RS) have the most impact on energy consumption. Additionally, the study explored the potential for energy optimization, such as cavity green walls and green roofs in RS and double brick walls with VIP insulation and green roofs in DS. The findings of the paper should be interpreted in light of certain limitations but they underscore the effectiveness of combining BIM and ML for sustainable building design, offering actionable insights for enhancing energy efficiency in hot climates. Full article
(This article belongs to the Collection Renewable Energy in Buildings)
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26 pages, 15835 KiB  
Article
A Framework for Adaptive Façade Optimization Design Based on Building Envelope Performance Characteristics
by Ping Chen and Hao Tang
Buildings 2024, 14(9), 2646; https://doi.org/10.3390/buildings14092646 - 26 Aug 2024
Cited by 4 | Viewed by 3174
Abstract
The adaptive façades serve as the interface between the indoor and outdoor energy of the building. Adaptive façade optimization design can improve daylighting performance, the thermal environment, view performance, and solar energy utilization efficiency, thus reducing building energy consumption. However, traditional design frameworks [...] Read more.
The adaptive façades serve as the interface between the indoor and outdoor energy of the building. Adaptive façade optimization design can improve daylighting performance, the thermal environment, view performance, and solar energy utilization efficiency, thus reducing building energy consumption. However, traditional design frameworks often neglect the influence of building envelope performance characteristics on adaptive façade optimization design. This paper aims to reveal the potential functional relationship between building façade performance characteristics and adaptive façade design. It proposes an adaptive façade optimization design framework based on building envelope performance characteristics. The method was then applied to a typical office building in northern China. This framework utilizes a K-means clustering algorithm to analyze building envelope performance characteristics, establish a link to adaptive façade design, and use the optimization algorithm and machine learning to make multi-objective optimization predictions. Finally, Pearson’s correlation analysis and visual decision tools were employed to explore the optimization potential of adaptive façades concerning indoor daylighting performance, view performance, and solar energy utilization. The results showed that the optimized adaptive façade design enhances useful daylight illuminance (UDI) by 0.52%, quality of view (QV) by 5.36%, and beneficial solar radiation energy (BSR) by 14.93% compared to traditional blinds. In addition, each office unit can generate 309.94 KWh of photovoltaic power per year using photovoltaic shading systems. The framework provides new perspectives and methods for adaptive façade optimization design, which helps to achieve multiple performance objectives for buildings. Full article
(This article belongs to the Topic Building Energy and Environment, 2nd Edition)
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16 pages, 1862 KiB  
Article
Predicting Number of Vehicles Involved in Rural Crashes Using Learning Vector Quantization Algorithm
by Sina Shaffiee Haghshenas, Giuseppe Guido, Sami Shaffiee Haghshenas and Vittorio Astarita
AI 2024, 5(3), 1095-1110; https://doi.org/10.3390/ai5030054 - 8 Jul 2024
Cited by 3 | Viewed by 1688
Abstract
Roads represent very important infrastructure and play a significant role in economic, cultural, and social growth. Therefore, there is a critical need for many researchers to model crash injury severity in order to study how safe roads are. When measuring the cost of [...] Read more.
Roads represent very important infrastructure and play a significant role in economic, cultural, and social growth. Therefore, there is a critical need for many researchers to model crash injury severity in order to study how safe roads are. When measuring the cost of crashes, the severity of the crash is a critical criterion, and it is classified into various categories. The number of vehicles involved in the crash (NVIC) is a crucial factor in all of these categories. For this purpose, this research examines road safety and provides a prediction model for the number of vehicles involved in a crash. Specifically, learning vector quantization (LVQ 2.1), one of the sub-branches of artificial neural networks (ANNs), is used to build a classification model. The novelty of this study demonstrates LVQ 2.1’s efficacy in categorizing accident data and its ability to improve road safety strategies. The LVQ 2.1 algorithm is particularly suitable for classification tasks and works by adjusting prototype vectors to improve the classification performance. The research emphasizes how urgently better prediction algorithms are needed to handle issues related to road safety. In this study, a dataset of 564 crash records from rural roads in Calabria between 2017 and 2048, a region in southern Italy, was utilized. The study analyzed several key parameters, including daylight, the crash type, day of the week, location, speed limit, average speed, and annual average daily traffic, as input variables to predict the number of vehicles involved in rural crashes. The findings revealed that the “crash type” parameter had the most significant impact, whereas “location” had the least significant impact on the occurrence of rural crashes in the investigated areas. Full article
(This article belongs to the Section AI Systems: Theory and Applications)
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25 pages, 8390 KiB  
Article
Comparison of Simulation Methods for Glare Risk Assessment with Roller Shades
by Sichen Lu and Athanasios Tzempelikos
Buildings 2024, 14(6), 1773; https://doi.org/10.3390/buildings14061773 - 12 Jun 2024
Cited by 1 | Viewed by 1765
Abstract
Daylight discomfort glare evaluation is important when selecting shading properties. New standards recommend allowable glare frequency limits but do not specify the modeling accuracy required for annual glare risk assessment. Fast simulation tools allow users to perform hourly glare evaluations within minutes. However, [...] Read more.
Daylight discomfort glare evaluation is important when selecting shading properties. New standards recommend allowable glare frequency limits but do not specify the modeling accuracy required for annual glare risk assessment. Fast simulation tools allow users to perform hourly glare evaluations within minutes. However, reliable evaluation of glare through roller shades requires accurate modeling of their specular and diffuse transmission characteristics, affected by color, materials, and weaving technology. This study presents a systematic comparison between commonly used glare simulation methods against the “ground truth” Radiance ray-tracing tool rpict in terms of hourly daylight glare probability (DGP), hourly vertical illuminance (Ev), and annual visual discomfort frequency. The results are presented for two shade fabrics using light transmission models with and without a peak extraction algorithm (Radiance–aBSDF and Radiance–BSDF) for the specular component. The impact of sky/sun discretization on glare prediction is also discussed. The results show that the Radiance 5–Phase Method (5PM) is superior when modeling direct sunlight and DGP through shades, while other investigated methods (3–Phase Method, imageless DGP, ClimateStudio Annual Glare) are not as robust for that purpose. Users are encouraged to understand the underlying assumptions in the imageless methods to avoid errors when simulating glare, especially due to the contrast effects. Full article
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40 pages, 9003 KiB  
Article
A Comparative Analysis of Polynomial Regression and Artificial Neural Networks for Prediction of Lighting Consumption
by Pavol Belany, Peter Hrabovsky, Stefan Sedivy, Nikola Cajova Kantova and Zuzana Florkova
Buildings 2024, 14(6), 1712; https://doi.org/10.3390/buildings14061712 - 7 Jun 2024
Cited by 8 | Viewed by 3465
Abstract
This article presents a comparative analysis of two prominent machine learning techniques for predicting electricity consumption in workplace lighting systems: polynomial regression analysis and artificial neural networks. The primary objective is to assess their suitability and applicability for developing an accurate predictive model. [...] Read more.
This article presents a comparative analysis of two prominent machine learning techniques for predicting electricity consumption in workplace lighting systems: polynomial regression analysis and artificial neural networks. The primary objective is to assess their suitability and applicability for developing an accurate predictive model. After a brief overview of the current state of energy-saving techniques, the article examines several established models for predicting energy consumption in buildings and systems. These models include artificial neural networks, regression analysis and support vector machines. It then focuses on a practical comparison between polynomial regression analysis and an artificial neural network-based model. The article then looks at the data preparation process, outlining how the data is used within each model to establish appropriate prediction functions. Finally, it describes the methods used to evaluate the accuracy of the developed prediction functions. These functions allow the prediction of lighting consumption based on external lighting intensity. The article evaluates the accuracy of the developed prediction functions using the root mean square error, correlation coefficient and coefficient of determination values. The article compares these values obtained for both models, allowing a conclusive assessment of which model provides superior accuracy in predicting lighting consumption based on external lighting intensity. Full article
(This article belongs to the Section Building Energy, Physics, Environment, and Systems)
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14 pages, 6699 KiB  
Article
Research on the Design of Recessed Balconies in University Dormitories in Cold Regions Based on Multi-Objective Optimization
by Weidong Ji, Jian Sun, Huiyi Wang, Qiaqing Yu and Chang Liu
Buildings 2024, 14(5), 1446; https://doi.org/10.3390/buildings14051446 - 16 May 2024
Cited by 2 | Viewed by 1931
Abstract
Thermal comfort and daylighting are vital components of dormitory environments. However, enhancing indoor lighting conditions may lead to increased annual energy consumption and decreased thermal comfort. Therefore, it is crucial to identify methods to reduce buildings’ energy costs while maintaining occupants’ thermal comfort [...] Read more.
Thermal comfort and daylighting are vital components of dormitory environments. However, enhancing indoor lighting conditions may lead to increased annual energy consumption and decreased thermal comfort. Therefore, it is crucial to identify methods to reduce buildings’ energy costs while maintaining occupants’ thermal comfort and daylighting. Taking the dormitory building of Songyuan No. 2 at Shandong Jianzhu University of Architecture, which is located in a cold region, as an example, a field measurement analysis was conducted on the recessed balconies within the dormitory. The measured data were analyzed and utilized to simulate the annual energy consumption, thermal comfort predicted mean vote (PMV), and useful daylight illuminance (UDI) values of the dormitory units using the Grasshopper platform with the Ladybug and Honeybee plugins. The different depths of the balconies and window-to-wall ratios have a significant impact on the indoor physical environment and energy consumption, leading to the design of independent variables and the construction of a simplified parametric model. The simulation results underwent multi-objective optimization using genetic algorithm theory through the Octopus platform, resulting in a Pareto optimal solution set. Comparisons between the final-generation data and simulations of the original Song II dormitory unit indicate potential energy savings of up to 2.5%, with a 25% improvement in indoor thermal comfort satisfaction. Although there was no significant improvement in the UDI value, all the solution sets meet the minimum requirement of 300 lux specified by relevant regulations, according to the simulated average illuminance levels on the indoor work plane. Finally, the 60 optimal solution sets were further screened, filtering out sets deviating excessively from certain objectives, to identify 6 optimal solutions that are more balanced and exhibit a higher overall optimization rate. These findings offer detailed data references to assist in the design of dormitory buildings in cold regions. Full article
(This article belongs to the Section Building Energy, Physics, Environment, and Systems)
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