• Energy Research

Vor dem Hintergrund des Klimawandels und begrenzter Energieressourcen wird es immer wichtiger, die energetischen Anforderungen und Bedarfe des Gebäudebestandes mit Hilfe energetischer Quartiersmodellierung zu analysieren und zu optimieren. Ziel dieser Studie ist es, die Auswirkungen verschiedener Eingangsparameter, wie z.B. die U-Werte der thermischen Gebäudehülle, und damit verbundener Unsicherheiten, wie beispielsweise eine eventuell bereits erfolgte energetische Sanierung, zu analysieren. Durch die Kombination der Analyse von Haupteffekten mit Interaktionseffekten wird die Reaktion des Modells auf seine Zielgröße, den Heizwärmebedarf, umfassend analysiert. Die Ergebnisse tragen zu einem besseren Verständnis von Unsicherheiten in den Eingangsdaten und den daraus resultierenden Interaktionen bei. Proceedings Bauphysiktage in Weimar 2024: Bauphysik in Forschung und Praxis

The operation of wind turbines is subject to numerous natural and human-related factors that can cause inequality of aerodynamic loads on the rotor blades. Wind is a chaotic and stochastic phenomenon, posing challenges in accurately determining wind speed and its direction. Similar points over the blades of a rotating wind turbine may experience different wind speeds due to turbulent wind regimes, icing, wind shear, and other extreme meteorological phenomena. Moreover, human errors in the installation of blades with different pitch angles or manufacturing errors of the blades can further exacerbate the issue of unequal blade loading. Despite these common occurrences in the operation of commercial-scale wind turbines, most wind turbine analyses assume that the rotor blades are identical and rotate with equal aerodynamic loading. However, inequality of loads on wind turbines can have significant effects on the system's operation, reliability, and efficiency. To address this issue, a mathematical analysis has been developed to study the effects of inequality of loads on the rotor and the related consequences on the main operating parameters of wind turbines. One of the most widely used and well-known theories for designing wind turbines is Blade Element Momentum Theory (BEM). In the present research work, this theory has been extended to consider the steady-state unequal contributions to the induced velocities and other parameters that affect the operation of a horizontal-axis wind turbine. This extension has provided the model for the Unequal Blade Element Model (UBEM), which includes extra terms and imposes more complexity into the calculation. To examine the accuracy of the mathematical model, a wind turbine (MoWiTO-06) with different pitch angles was tested experimentally in ForWind's wind tunnel at the University of Oldenburg, Germany. The experimental data obtained from MoWiTO-06 is the only measurement that has been done on an unequally loaded rotor and the outcomes of simulation obtained from UBEM are the first computational investigation of such rotors. The results of computational and experimental investigations have demonstrated that inequality can lead to remarkable changes in torque, thrust, and extracted power. Also, it has been demonstrated that more experiments on different wind turbines operating under different forms on inequality are necessary to examine the UBEM model. Installing instruments on all blades of wind turbines would enable the monitoring of the unequal aerodynamic loads on the blades. Furthermore, the computational cost of UBEM has been assessed to determine whether it is possible to have a faster analysis since UBEM is not as fast as BEM. Different approaches have been examined and among them, replacing the conventional Prandtl tip loss factor instead of finite blade functions for high tip speed ratios enhanced the speed of simulation but affected its accuracy negatively. Prandtl tip loss factor saves 70% of the computational cost. Reducing the distance between trailing vortices and each blade element was considered as another potential solution. However, the simulations did not show a considerable reduction in computational cost. To speed up calculations, only the vortices located in the tip and hub regions were considered, but the results were not in agreement with the most accurate results. Therefore, according to what has been studied in the present research, there is no solution that can reduce the execution time of UBEM with acceptable accuracy for all tip speed ratios. Overall, the findings of this research work underscore the importance of considering the effects of inequality of loads on the operation of wind turbines. By providing a mathematical model to study the impact of unequal blade loading on the rotor, this research could help improve the reliability, efficiency, and overall performance of wind turbines. The results highlight the need for further investigation and refinement of computational models to accurately capture the effects of load inequality in wind turbine design and operation.

Urban Building Energy Models (UBEMs) are increasingly important tools for national and local authorities seeking to understand and manage their carbon emissions. As such tools move from the preserve of research into the more general application, interest in learning about their application is increasing. The quantity of data inherent in a UBEM and its complexity, means that educating students in the underpinning principles and their application requires teaching a wide body of knowledge. This presents significant challenges for educators required to fit within predefined limits for teaching courses. The authors have now taught urban-scale building energy modelling to 3 cohorts of students in India, Peru and the UK using two different approaches. This paper summarises the contents of the courses and the approach taken to delivering the required learning in the limited time available. It details student feedback, outcomes and lessons learned for the different approaches and the challenges which remain. Highlights • The first publication on efforts to deliver education on urban building energy modelling • Results of teaching initiatives on three continents are presented • Three key challenges for UBEM education are identified • The importance of using real examples with personal relevance for students is highlighted

Urban densification increases the number of people living in urban areas and is hypothesized to be a more efficient use of available land than urban sprawl. The objective of this study was to quantify the operational and embodied emissions created as a result of densification. A ‘Business as usual’ and a ‘Concentrated’ densification strategy were investigated. When densifying at the neighbourhood level, existing buildings can either be replaced or extended to accommodate the additional inhabitants. The densification strategies were applied to two reference urban design neighbourhoods in Switzerland. The ‘Typical’ approach assumed that all the buildings were demolished and rebuilt and the ‘Preserve-existing’ approach involved the extension of existing buildings as much as possible. Construction material choice and modification of the built form were the sources of embodied emissions considered for each strategy. Urban building energy modelling was used to calculate the emissions incurred by heating the buildings and the embodied emissions were calculated using building standards. The operational performance was simulated assuming both a gas boiler and an electric heat pump to determine the influence of the heating system type on the operational emissions. This study found that savings of approximately 30% in embodied emissions can be achieved by extending the existing building stock rather than rebuilding. However, these savings represent a relatively small percentage of the total emissions incurred throughout a building's lifetime and the savings further diminish in the concentrated densification strategies. Energy and Buildings, 276 ISSN:0378-7788 ISSN:1872-6178

La sociedad contemporánea se enfrenta a crisis simultáneas —climática, energética, económica y sanitaria—, cuya confluencia puede desencadenar un cambio de paradigma con un nuevo modelo energético. El consumo energético de los edificios supone un tercio del consumo energético total en los países occidentales. En España, sólo el consumo energético de energía final del sector residencial supone el 18,1% del total. A pesar de la importancia del Consumo Energético Residencial (CER) en las políticas de lucha contra el cambio climático, falta información, estudios y estadísticas accesibles sobre su naturaleza, a una escala adecuada. Sin embargo, compañías energéticas sí gestionan y explotan esos datos, sin que esa información trascienda a la sociedad para generar conocimiento. La tesis ofrece una alternativa a la carencia de información sobre el CER proponiendo modelos predictivos basados en datos abiertos —open data—actualizados y procesos de código abierto —open source—, que abarcan desde la escala regional-municipal (municipios de una región) a la urbana (edificios de un municipio). Ese es su principal objetivo. Los datos se estructuran fundamentalmente en tres categorías de información implicadas en el CER: lugar, población y parque residencial. Los modelos se aplican a Andalucía y Jaén, región y ciudad considerados como casos de estudio. La metodología empleada se adapta a la naturaleza de los datos y las escalas de estudio. Para ello se desarrollan procesos de ciencia de datos, incluyendo minería de datos (clustering) y técnicas de aprendizaje automático —machine learning—. Para gestionar la información y elaborar los modelos en ambas escalas se combina el uso de sistemas de información geográfica (GIS) y modelizado energético de edificios a escala urbana (UBEM). El hecho de que la metodología propuesta se base en la explotación de datos abiertos, así como en el uso de herramientas de código abierto, favorece su implementación en otras regiones y ciudades mediterráneas y europeas, trascendiendo así los límites presentes en trabajos de investigación afines. Como principales aportaciones de la tesis, a escala regional-municipal, se incluye la agrupación de municipios de Andalucía en clústeres conforme a su perfil característico del que se deduce un CER potencial común. Dicho CER potencial sirve de base para la elaboración de un modelo predictivo bottom-up del CER a escala regional y provincial para Andalucía. A escala urbana, se obtiene mediante GIS y UBEM, a partir de la cartografía catastral, un modelo predictivo de la demanda de calefacción y refrigeración de los edificios residenciales de un sector urbano. Sus resultados, una vez validados, sirven como conjunto de entrenamiento para la elaboración de un modelo predictivo mediante machine learning para los edificios residenciales de toda la ciudad de Jaén.

Abstract There is no doubt that during recent years, the developing countries are in urgent demand of energy, which means the energy generation and the carbon emissions increase accumulatively. The 40 % of the global energy consumption per year comes from the building stock. Considering the predictions regarding future climate due to climate change, a good understanding on the energy use due to future climate is required. The aim of this study was to evaluate the impact of future weather in the heating demand and carbon emissions for a group of buildings at district level, focusing on two areas of London in the United Kingdom. The methodological approach involved the use of geospatial data for the case study areas, processed with Python programming language through Anaconda and Jupyter notebook, generation of an archetype dataset with energy performance data from TABULA typology and the use of Python console in QGIS to calculate the heating demand in the reference weather data, 2050 and 2100 in accordance with RCP 4.5 and RCP 8.5 scenarios. A validated model was used for the district level heating demand calculation. On the one hand, the results suggest that a mitigation of carbon emissions under the RCP4.5 scenario will generate a small decrease on the heating demand at district level, so slightly similar levels of heating generation must continue to be provided using sustainable alternatives. On the other hand, following the RCP 8.5 scenario of carbon emission carrying on business as usual will create a significant reduction of heating demand due to the rise on temperature but with the consequent overheating in summer, which will shift the energy generation problem. The results suggest that adaptation of the energy generation must start shifting to cope with higher temperatures and a different requirement of delivered energy from heating to cooling due to the effect of climate change.

Abstract. This document introduces the process for the creation of a testbed for energy applications based on a semantic 3D city model for the municipality of Rijssen-Holten in The Netherlands. The creation of this dataset requires the consolidation from multiple data sources as well as a lot of manual work so the authors can warranty as much as possible the quality of the dataset so in can be used in several use cases. The data is stored following the OGC standard CityGML v2.0 and contain the geometrical and semantical information of CityObjects from the thematic modules Building, Vegetation and Relief. This data set consolidates the open weather data from the closest weather station to the study area located in Heino in the Netherlands. We discuss the decisions taken during the manual data collection process and we present some use cases that have already consume the dataset at the time of writing this document.

Cities are currently responsible for an important part of energy consumption and greenhouse gas emissions, justifying the need to develop measures to help them become more sustainable. One of those measures can be to address under-utilized assets in cities, such as derelict buildings with high potential for rehabilitation, and the establishment of new residence hubs within cities. Consequently, this work establishes a novel framework for evaluating the impact of rehabilitating these buildings in an urban area in Lisbon, considering the energy consumption associated with the usage of the dwelling as well as the impact on mobility, since it was considered that these buildings will be occupied by people who currently work nearby but live in the outskirts of Lisbon, favouring an urban planning of proximity between home and work. To this extent, a methodology was developed for selecting the buildings to be analysed and the commuting movements to be replaced. Then, buildings were simulated in an urban building energy modelling (UBEM) tool, considering three rehabilitation scenarios, and the required primary energy, CO2 emissions, and costs were calculated. Regarding mobility, three new scenarios were compared with the current scenario. The results obtained confirm the high potential savings from the rehabilitation of derelict buildings and in the best-case scenario—corresponding to the rehabilitation considering envelope insulation, the installation of efficient windows, and the adoption of a heat pump together with a mobility standard targeting 15 min cities—reductions of 76% in primary energy and 84% in CO2 emissions were achieved.

New modelling tools are required to accelerate the decarbonisation of the building sector. Urban building energy modelling (UBEM) has recently emerged as an attractive paradigm for analysing building energy performance at district and urban scales. The balance between the fidelity and accuracy of created UBEMs is known to be the cornerstone of the model’s applicability. This study aimed to analyse the impact of traditionally implicit modeller choices that can greatly affect the overall UBEM performance, namely, (1) the level of detail (LoD) of the buildings’ geometry; (2) thermal zoning; and (3) the surrounding shadowing environment. The analysis was conducted for two urban areas in Stockholm (Sweden) using MUBES—the newly developed UBEM. It is a bottom-up physics-based open-source tool based on Python and EnergyPlus, allowing for calibration and co-simulation. At the building scale, significant impact was detected for all three factors. At the district scale, smaller effects (<2%) were observed for the level of detail and thermal zoning. However, up to 10% difference may be due to the surrounding shadowing environment, so it is recommended that this is considered when using UBEMs even for district scale analyses. Hence, assumptions embedded in UBEMs and the scale of analysis make a difference.