vissza a tantárgylistához   nyomtatható verzió    

    Construction Information Technology Programming

    A tantárgy neve magyarul / Name of the subject in Hungarian: Építmény-informatikai programozás

    Last updated: 2023. október 16.

    Budapest University of Technology and Economics
    Faculty of Electrical Engineering and Informatics
    (HUN) MSc Építmény-informatikai mérnök
    (ENG) MSc Construction Information Technology Engineering
    Course ID Semester Assessment Credit Tantárgyfélév
    VIAUM051   6/1/4/f 6  
    3. Course coordinator and department Dr. Kovács Tibor,
    4. Instructors

    Dr. Kovács Tibor

    Faculty of Electrical Engineering and Informatics

    Department of Automation and Applied Informatics

    Békési Gergő Bendegúz

    Faculty of Electrical Engineering and Informatics

    Department of Automation and Applied Informatics 
    5. Required knowledge

    Python programming

    6. Pre-requisites
    Ajánlott:
    Programming (BMEVIHIA061 or BMEEODH001)
    7. Objectives, learning outcomes and obtained knowledge
    The aim of the course for students is to apply and deepen previously got programming knowledge (object-oriented Python programming) in Building Management Systems field. Students get familiar in application of IT methods in BMS field based on simplified models of widely used solutions, services of buildings. Resource-effective and secure applications are implemented while perfection is got in terminology, typical and specific tasks, standards of BMS.

    Potential projects:
    1. Geoinformatics, Image Processing: the base is a spatial point cloud of a building (or building part), students need to use programming tools to clean the point cloud, identify simple, typical building objects, derive dimensions.
    2. Energetics: based on a software of hardware (IoT) environment of a building, students create energy management system. The goal is a building model consist of scalable environmental impacts (temperature, illumination), energy loss of the building/walls, and the heating system (traditional combined with the one using renewable energy). Students need to harmonize these systems, create appropriate control management, and prove its working by reports/queries.
    3. Intelligent building: the model described above is to be extended by the elements of an intelligent house, i.e., sensors, safety, and comfort services (e.g., wind sensor, actuators for shading screens, cameras and window/door sensors and actuators, controlling lighting and entertainment systems. Student will obtain creative services in this context/environment.
    4. Building safety/security: Security and safety solutions (security sensors, fire detectors to minimize damage and improve safety level).
    5. Facility management: students are modelling the aging of the building, the deterministic and stochastic events, are estimating operating costs, scheduling maintenance actions (considering Industry 4.0 concepts).
    6. IoT-based Building Management: IoT tools monitoring the building and its environment are to be used for the above-mentioned tasks, solutions. From the acquired data the accuracy level of estimates can be improved to save the quality of the building, its safety level, considering comfort and effective operation. Students learn the basics and strengths of Big Data analysis and Deep Learning.

    Projects are supported by hardware and software modelling elements, practically a building simulation environment is prepared. Students must create software solutions in above fields using sensors and activators of the model building starting from prepared application structures.

    8. Synopsis

    The programme bellow is tentative and subject to changes due to calendar variations and other reasons specific to the actual semester. Consult the effective detailed course schedule of the course on the subject website.

    Week

    Topics of lectures and/or exercise classes

    1.

    Begin by revisiting Numpy's pivotal role in numerical operations, then swiftly transition into an overview of Pandas for adept data manipulation and Matplotlib for creating insightful visualizations, ensuring a solid foundation in Python-based data handling and visualization tools.

    2.

    Delve into fundamental data visualization techniques with Matplotlib, exploring various chart types, and proceed to harness both Numpy and Pandas in executing elementary data analysis, exploring basic statistical and visual methods to extract preliminary insights from datasets.

    3.

    Navigate through K-means clustering, starting with a practical exploration of its implementation in 1D data, advancing to a more complex application in 2D, and finally transitioning to linear regression, unraveling its predictive capabilities and exploring its usage in predicting outcomes based on varying input variables.

    4.

    Dive into bridge vibration data using Pandas for data handling and Matplotlib for visualization. Employ Fourier analysis to detect dominant vibration frequencies.

    5.

    Delve into Hungarian government housing expenditure data manipulation and analysis with Python, utilizing Pandas for data handling and Matplotlib for visualization. Navigate through data extraction from ZIP files and resolve CSV parsing errors, while ensuring data consistency and alignment. Apply data cleaning techniques to manage non-numeric and misaligned entries, ensuring accurate analysis. Leverage data visualization to explore financial trends, examine class imbalances, and derive insights. Engage with exercises and visualization tasks to gain practical knowledge and insights into data preparation and exploration for real-world applications.

    6.

    Practice exercises for deepening knowledge.

    7.

    Practice exercises for deepening knowledge.

    8.

    BTC project week at Balatonfüred

    9.

    RC-based energy performance modelling of buildings

    10.

    Visual programming and EnergyPlus-based energy analysis of buildings

    11.

    Energy, comfort and summer overheating modelling of buildings

    12.

    Case study: Schneider Electric building automation

    13.

    Case study: MOL tower building automation

    14.

    Project presentation
    9. Method of instruction Module with associated contact hours
    10. Assessment
    General rules

     
    The final grade results from
    • preparation of the project task in cooperative form (specification, coding, documentation, acceptance by lecturers) 
    • presenting the project at the end of the semester
     
    Evaluation system
     
    Project task: 70%
    Project presentation: 30%
    Sum 100%
     
    Requirements and validity of signature

    The requirement of the signature is successfully preparing and presenting project task.

    Grading system
     
    Excellent P > = 92
    Good 91 > P >= 84
    Satisfactory 84 > P >= 76
    Pass 76 > P >= 68
    Fail 68 > P
     
    The project task preparation gives 70 points, the oral and documented presentation gives 30 points.

    11. Recaps Based on electronic notes published for the subject.
    12. Consultations Consultations will be provided as needed, based on prior agreement.
    13. References, textbooks and resources
    Mandatory literature:
    1. Krishnan Saravanan (Author, Editor), Golden Julie (Editor), Harold Robinson (Editor): Handbook of Research on Implementation and Deployment of IoT Projects in Smart Cities (Advances in Civil and Industrial Engineering) 1st Edition, 2019, ISBN 978-1522591993
    2. Digital source for used libraries (listed during the course)

     
    Recommended literature:
    3. Adriana X Sanchez, Keith Hampson, Geoffrey London: Integrating Information in Built Environments, 2018, ISBN 9781138706323
    4. Peter Wentworth, Jeffrey Elkner, Allen B. Downey, Chris Meyers: How to think like a Computer Scientist, 2012.
    14. Required learning hours and assignment
    Contact classes84
    Midterm preparation26
    Preparation of the project task70
    Learning from the assigned supplementary material40
    Preparation for the presentation20
     
    Sum240
    15. Syllabus prepared by

    Békési Gergő Bendegúz

    Dr. Kovács Tibor