PhD Candidate Data Analysis and Building/neighborhood Energy Management

Eindhoven University of Technology, Netherlands is offering PhD position in mathematics. The initial contract for this position is four years. Applications will be evaluated on a continuous basis.

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 Departments 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 air-conditioning. 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 aims 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. 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 learning from a approach based on top-down big data of smart meters and bottom-up small data from home/building automation systems. Analytics of data and 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 (top layers). Within the project there will be other PhD candidates working on other aspects 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 cole Polytechnique Fdrale de Lausanne (EPFL) and Technische Universitt Mnchen (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).

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 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 the MV/LV level of the distribution neighborhood systems (Smart meters- Big Data) towards the buildings (Building Management Systems - Small data) . It uses both the data of the neighborhood's substation's smart meter data as well as that of the energy management system of 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 building 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 CO 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.

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 NEMS system.

We are looking for an excellent and highly motivated candidate with an MSc degree in Building Services, Mechanical or Electrical 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, Data analysis or (Big) Data Research is a plus.

We offer a stimulating and ambitious research environment.

The duration of the project is four years. The project is expected to start in February 2019. The duration of the project is four years. The gross monthly salary increases from 2222 Euro in the first year to 2840 Euro in the fourth year.

Besides this, the TU/e has an excellent package of attractive benefits for employees, a child-care facility and a modern sports complex. Assistance for finding accommodation can be given.

More information may be obtained from: Prof. ir. Wim Zeiler (

Please submit your application (motivation letter, recent CV) to our website (max. 5 × 2 MB) using the ‘APPLY NOW’ or ‘SOLLICITEER DIRECT’ button on the TU/e website. Do not send us your application by email.


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