1,720,973 research outputs found
Buildings' Energy modeling and Platforms for a Sustainable Development of Cities and Communities
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Urban-Scale Building Air Change Rate Estimation Using Corrected Wind Speeds and Three-Zone Building Modeling
Get PDFThis study presents a scalable, time-efficient methodology for estimating building-specific air change rates by incorporating local urban morphology and wind conditions. The presented methodology is used to develop a QGIS-based plugin (Quantum Geographic Information System) to automate the integration of aerodynamic parameters derived from the Urban Multi-scale Environmental Predictor, with a simplified three-zone lumpedparameter model and multizone airflow simulations using CONTAM. The plugin calculates façade-specific wind-speed modifiers across 30° directional intervals. These modifiers are then used in a three-zone CONTAM model to calculate hourly building air change rates. The results were incorporated into an hourly energy consumption model for space heating and validated against measured energy-use from residential buildings in Turin, Italy. Results show that replacing constant air change rates with site-specific estimates reduced the Mean Absolute Percentage Error by 11% to 20%. In particular, the error was reduced from 49% -+ 9% to 32% -+ 6.5% in January, corresponding to a relative error reduction of approximately 34%. The proposed method demonstrates improved accuracy in simulating building energy consumption considering the influence of an accurate shape of the urban environment on air change rates; moreover, it offers an automated framework for urban-scale assessments of ventilation, infiltration, and energy performance
Modeling and mapping solar energy production with photovoltaic panels on Politecnico di Torino university campus
Get PDFEducational institutions have significant impacts on the society and environment they are inhabiting, and they can have a big role in influencing various development fields, including sustainability. The environmental sustainability of universities was critically analyzed recently. These bodies can contribute to the sustainability of cities due to their social role in shaping the future generations. The aim of this work is to analyze Urban Building Energy Modeling with a place-based approach using the open-source software QGIS in predicting energy production with photovoltaic solar technologies on the rooftops of the central university campus of Politecnico di Torino. This modeling can help in assessing the energy security and affordability of current and future sustainable scenarios considering their impact on climate change. This study evaluates the accuracy of urban scale QGIS-based energy modeling with a comparison of measured data available from the monitoring activity of LivingLab of Politecnico di Torino, the free tool PVGIS, and the web tools of ENEA. The QGIS modeling accuracy depends on the different precisions of the Digital Surface Model used to describe the built environment (i.e., 1 m or 5 m) and the climate input data (monthly and annual diffuse-to-global radiation and Linke turbidity factor). Moreover, this assessment can be used to map the results of new photovoltaic systems improving the energy and environmental performance of university campuses. The results of this work shed light on the significance of different input data for energy simulation tools at neighborhood-urban scale. The result shown accuracies in PV production of 10 to 37% with different spatial resolutions of the 3D built environment and of 14 to 15.2% for temporal resolution of solar irradiation variables
Urban Building Energy Modeling for Self-Sufficiency Scenarios with solar technologies in Turin, Italy
No full textUnderstanding the complexities of building energy dynamics is crucial as it offers a pathway to reducing energy consumption and associated emissions. By using Urban Building Energy Modeling (UBEM), this work aims to drive sustainable urban development, by integrating renewable energy sources in district heating networks. This study focuses on UBEM as an effective scale for analyzing buildings within their specific characteristics that influence their consumptions and limit their production from renewables. The analysis focuses on utilizing solar thermal collectors (STC) for space heating consumption in residential buildings connected to the district heating network. The aim is to allow the integration of renewables into district heating network by testing lower supply temperatures. The validated model is used to analyze the potential self-sufficiency achieved through STC production for all residential buildings in three analyzed zones. The results indicate the potential of achieving self-sufficiency using STCs with different inlet and outlet temperatures; a self-sufficiency of 24% with 90°C and 21% with 105°C supply temperatures in March while for October it could only reach 9% and 7% respectively. The results of this work are crucial for optimizing solar energy production and to explore a distributed production system for individual and collective self-consumption strategies, offering insights into sustainable energy scenarios and policies
Urban Building Energy Modeling: A Comparative Study of Process-Driven and Data-Driven Models
Get PDFThis study investigates the predictive capabilities of process-driven (PD) energy modeling and Machine Learning techniques, specifically Light Gradient Boosting Machine (LGBM) and Random Forest (RF) algorithms, in analyzing building energy consumption patterns. Leveraging a comprehensive dataset encompassing diverse building characteristics, energy-related variables, and operational configurations, the comparative performances of these methodologies is explored. Results reveal that while all approaches demonstrate promising predictive accuracies, LGBM exhibits a slight advantage over RF and the process-driven model. Moreover, the process-driven model showcases efficacy in colder seasons and for buildings of extreme ages, while encountering limitations in accurately modeling energy consumption for structures constructed during 1970s to 1990s. Conversely, Machine Learning models demonstrate consistent performance (with relative errors of 5-10%) across varied building ages, underscoring their adaptability and potential for capturing nuanced energy dynamics. However, a notable constraint lies in the availability of sufficient data for training Machine Learning models, posing challenges for model testing. These findings contribute to advancing our understanding of energy modeling methodologies at urban scale and offer insights for optimizing building energy efficiency strategies for a sustainable development of urban environments
Design and Modeling Renewable Energy Communities: A Case Study in Cagliari (Italy)
Get PDFRenewable energy communities (RECs) are non-profit organizations made up of members who join to produce and exchange clean energy for sustainable development. This work analyzes different REC scenarios, considering energetic, economic, and environmental perspectives. The case study is a typical condominium of eight apartments with a low energy class in Cagliari (Italy). This study considers the condominium with different energy efficiency levels before and after retrofit interventions together with solar technologies to produce energy. Future scenarios include both the share of energy between the eight apartments within the condominium and a REC composed of two neighboring condominiums. At condominium scale, results showed better outcomes in aggregating the energy share from the PV generation into a single point of sharing (PoS). In the REC scenario with a neighboring building, and after retrofit interventions, the self-sufficiency index was increased by 26% with a decrease of 23% in GHG emissions, which shows the importance of having retrofitted and smart buildings boosting the renewable energy sources in achieving a more sustainable built environment. The methodology of this work with a new software can be a useful decision-making tool to test the effectiveness of RECs and it can be applied to building, neighborhood, or district scales
Estimating Building Air Change Rates with Multizone Models at Urban Scale: Comparative Case Studies
Get PDFAccurate estimation of building-specific air change rates is important for reliable urban scale energy modeling, particularly in densely populated regions where airflow calculations must account for complex boundary conditions associated with urban geometry. This study applied lumped-parameter airflow models to simulate interzone airflow by calculating the internal pressures using simplified building representations. Air change rates were calculated by solving a system of nonlinear equations, with boundary conditions defined by localized wind inputs corrected using aerodynamic parameters extracted from three dimensional urban geometry. By linking these wind-related boundary conditions with lumped-parameter airflow models, the methodology describes spatial variability in natural infiltration across a broad range of urban densities. Two cities were compared to test the variability in building air change rates using local boundary conditions: New York City, a dense modern city, and Turin, a typical medium-density European city. Moreover, verifying the lumped-parameter model against CONTAM (Version 3.4.0.6) showed accurate results, with a mean absolute percentage error of 1.2% across 120 simulated weather scenarios. Furthermore, comparing energy consumption predictions using building-specific air change rates to those using fixed air change rates showed improved accuracy, resulting in an average error reduction of 27% over the entire heating season for a sample building.This scalable, automated approach enables more accurate assessments of ventilation-driven energy use in compact urban areas
Lumped-Parameter Models Comparison for Natural Ventilation Analyses in Buildings at Urban Scale
Get PDFThis study validates a three-zone lumped-parameter airflow model for Urban Building Energy Modeling, focusing on its accuracy in estimating air change rates caused by natural ventilation, referred to here as air change rate. The model incorporates urban-scale variables like canyon geometry and roughness elements for the accurate prediction of building infiltration, which is an important variable in building energy consumption. Air change rate predictions from the three-zone lumped-parameter model are compared against results from a three-zone CONTAM model across a range of weather scenarios. The study also examines the impact of building level of detail on air change rates. Results demonstrate that the three-zone lumped-parameter model achieves reasonable accuracy, with a maximum Mean Absolute Error of 0.1 h−1 in winter and 0.03 h−1 in summer compared to three-zone CONTAM model, while maintaining computational efficiency for urban-scale energy consumption simulations. However, its applicability is limited to buildings within urban canyons rather than detached structures, due to the assumptions made in the methodology of the three-zone lumped-parameter model. The results also showed that the model had lower errors for low to mid-rise buildings since the simplification of a detailed high-rise building into a three-zone model alters the buoyancy effect; a 4-story building showed Mean Absolute Percentage Error of 7% and 5% for a typical winter and summer day respectively when a detailed and simplified three-zone models are compared, while the error for a 16-story building were 18% and 12%. The results of building air change rates are used as input data in an hourly energy consumption model at urban scale and validated against measured hourly consumption to test the effect of the calculated urban-scale hourly air change rates
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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