Green Buildings: Optimization and Adaptation

CIS-800 Spring 2011

Cource Description

Buildings account for 40% of all energy in the United States and 70% of all electricity. Recent information technology developments such as sensor/actuator networks, optimization, learning, and control, will be critical in reducing the energy consumption of green buildings. This special topics course will focus on state-of-the-art models, tools, and approaches and pose research projects for students. Topics that will be considered are:

  1. Building modeling and simulation

    • HVAC (Heating, Ventilating, and Air Conditioning) system

    • Lighting system

    • Interdependency: coupling HVAC-lighting-human

  2. Building & HVAC control

    • Conventional control

    • Advanced control

  3. Building sensing

    • Sensor network for buildings

    • Sensor placement

  4. Building-Human interaction

    • Occupancy sensors

    • Usage pattern learning

  5. Smart Grid-Building interaction

    • Dynamic pricing

    • Buildings as agents in the Smart Grid

    • Energy market

  6. Campus-wide optimization

    • Energy-aware scheduling

  7. Data centers and IT-heavy buildings

  8. Fault detection and diagnostics

    • Fault detection and diagnostics (FDD)

    • Fault-tolerant control

The class will also schedule guest lectures as well as site visits to building and campus energy management centers. This course is organized in light of a new Philadelphia-based federal project to design energy-efficient buildings (press release, website).

Instructors

Time and Location

For Spring 2011, class will meet on Mondays and Wednesdays, 2:00-3:30 PM, in Levine 307.

Pre-requisites

CIS-520 or ESE-504 or permission of instructors.

Grading and Course Requirements

Course grade will be based on:

  1. Reading of relevant papers

  2. Presentation of papers

  3. Class participation

  4. Class project: sample projects include but not limited to

    1. Using green building software tools

    2. Instrumenting existing buildings

    3. Literature survey

    4. Algorithm development