Ongeveer 11 uur geleden - Technische Universiteit Eindhoven - Eindhoven
The Department offers a BSc in Building Science (Bouwkunde) and MSc programmes in Architecture, Building and Planning and Construction Management and Engineeri…
The Department offers a BSc in Building Science (Bouwkunde) and MSc programmes in Architecture, Building and Planning and Construction Management and Engineering. Education and research in the Department focus on the development and use of technology for the design and construction of a comfortable, healthy and sustainable built environment. The Department's philosophy is 'Beyond Building', which reflects our multidisciplinary, integral and innovative approach in the construction, evaluation and improvement of buildings and urban areas. Our research is based on fundamental scientific insights and methods and their application for the built environment.
The research will be embedded in the group 'Building Services (BS)' of the unit Building Physics & Services (BPS). Research and teaching at this unit aim at creating and maintaining a sustainable, healthy, comfortable and productive indoor and outdoor environment. The focus lies on energy aspects and processes including heat and mass transfer in the indoor and outdoor built environment, indoor and outdoor air quality, lighting, heating, ventilation and airconditioning. The unit has about 80 scientific staff (including 60 PhD researchers) and ample physical laboratory facilities (supported by 5 technicians). The multidisciplinary staffing of the unit enables the aforementioned approach and makes the unit rather unique.
Towards achieving the 2050 National energy and sustainability targets, local governments such as the city of Breda aim to become CO2 neutral by 2044. This energy transition would warrant significant changes in the existing energy infrastructure and to buildings, which account for a significant amount of energy consumption. Buildings account for well over 40% of energy consumption in the EU which makes buildings a key player in the future energy infrastructure and within the smart energy system. Like in most sectors nowadays, the proliferation of ICT has facilitated the availability of an enormous amount and variety of data ranging from building users to systems and component level. Whilst the analyse and use of these data from the Heating Ventilation Air Condition and Lighting (HVAC-L) has in most cases being limited to individual buildings, analytics of these data sets in a much broader scope particularly on the neighborhood level can enhance the design, planning and management of the future energy infrastructure (which includes energy generation, storage and energy transport systems). In addition, it would yield useful insights to facilitate the achievement of various grid stability and environmental sustainability goals for process control as well as for urban energy infra restructuring planning. Therefore, S&B NEDMIS aims to develop functional data clustering and transformation by deep learning from a bottom-up approach based on big data of smart meters and small data from home/building automation systems. Analytics of data and deep learning from home/building automation and management systems (Small Data) as well as smart energy meters (Big Data) can provide insights into the interactions and correlations between user behaviors (bottom layer) and the changing/future energy infrastructure (middle and top layers). Especially the combination of Small data, the small set of specific attributes produce typically a small set of sensor data such as temperature, air speed, humidity and status, focusing on the what combined with the big data more focused on why leads to new opportunities. Given that user behavior is recognized as a major factor that influences energy performance and given its stochasticity and the uncertainty it introduces, clearer understanding of its impact on the changing energy infrastructure can facilitate an integrated energy management approach on the user-level through the neighborhood level in such a way that is efficient, sustainably effective, flexible and resilient. Within the project there will be other PhD candidates working on other aspect for example Demand driven energy flexibility of buildings. This as well as a PhD candidate on Machine learning at the Faculty of Electrical Engineering from the TU Eindhoven and a PostDoc on gossiping agents at the Centre of Mathematics and Informatics Amsterdam.
On the European level, a network of scientific excellence is built in The European University Alliance of Science and Engineering through cooperation with Denmark Tekniske Universitet (DTU), School of Architecture, Civil and Environmental Engineering (ENAC) at École Polytechnique Fédérale de Lausanne (EPFL) and Technische Universität München (TUM) on the fields of Computational Building & Systems Performance Prediction (TU/e), Human Comfort and Productivity Research (DTU), and Integrated Building & Systems Design and Applications (TUM, DTU, TU/e).
Furthermore, there is a strong connection with a number of other research projects such as the STW Perspectief project Smart Energy Systems in the Built Environment and the EBC IEA Annex 67 (energy flexibility of buildings).
The proposed solution will work on neighborhood level in the existing built environment and acts as a middle out solution to couple the Smart Grid on HV/MV level and the MV/LV level of the distribution neighborhood systems towards the buildings. It uses both the data of the neighborhood's substations as well as that of the energy management and smart meters of the individual buildings. This allows to use the potential energy flexibility of the building structures and building services installations to optimize the interaction between both energy infrastructure levels. The approach of combining the individual houses and buildings on neighborhood level enables to optimize buildings on a higher level of integration with more ways of optimizing energy flexibility use overall than based on individual optimization.
The goal of the municipality Breda is to reach a zero CO2 built environment in 2044. This can only be done by using all available technologies and options. One of these options is to use the energy flexibility of individual houses and buildings and to let them optimally exchange energy among each other or with the Smart Grid. This will make it possible to even out surplus and minuses of local produced renewable energy without the need of additional external power supply or exchange to the neighborhood.
Main research areas in energy and the built environment reveals a stark separation of the topics based on the scale of analysis: individual buildings and the urban scale. The first scale of analysis, the individual building scale, is concerned only with the building itself and omits any relationship of the building to the larger levels within the built environment, such as for example neighborhoods, districts or cities. It is related to the architectural design and operational systems. The second scale of analysis, the urban scale, focuses on entire energy infrastructures within the built environment rather than individual elements such as buildings. This scale is related to the urban form and infrastructural networks. This separation per scale is problematic, as it ignores the actual pattern of operation and energy use: the building within existing cities. Assessing these currently missing patterns is crucial for a holistic analysis of energy use in the built environment and achieving future environmental goals. A combined bottom-up and top-down approach will be used to address the current missing interaction between approaches on the two different scales. The proposal aims to develop a strategy for energy system integration (ESI) by aggregated value from the combination of small and big data. It forms a new holistic approach to the scientific context of the problem on how effectively use data within the energy system of the built environment on different scales from building process control (small data) to urban energy planning (big data).
The research methodology includes both analytical and experimental methods. The research will start with a literature review on the latest developments in the domain of neighborhood energy management systems, deep learning and gossiping algorithms. On the building level first the Kropman Breda building will be used to verify concepts of data integration on building level. On the neighborhood level a neighborhood of Breda is selected, Pricenhage, as test case in the early stage of the research. A database using a subset of buildings, combining smart meters and BEMS with machine learning methods will be created and used for pattern/behavior recognition and training. We will in particular draw on mathematical methodologies that support data-driven clustering of time series in order to identify underlying patterns and corresponding outliers. Functional energy control modules will be implemented in a multi agent platform supported NEMS system. This will be implemented in a building, where the individual rooms act as a sort of mini buildings. A subset of buildings of the neighborhood will be used to test the concept.
We are looking for an excellent and highly motivated candidate with an MSc degree in Building Services, Building Physics, Mechanical or Environmental Engineering, or equivalent. Experience and interest in applications of Data analysing technology as well as interest in the information processing in the built environment is essential. Experience in Building Services, Building Management Systems, Reasoning with uncertainty,(Big) data or Thermal comfort Research is a plus.
We offer a stimulating and ambitious research environment. To complement this environment and for the specific project mentioned above, we are looking for an outstanding candidate that meets the following requirements: