1. Fuzzy Logic Control of HVAC Systems Drew Brunning

2. Motivation Buildings consume ≈ 50% of world’s energy Fuzzy logic control more efficient Still being researched

3. Types of HVAC Controls Two-Position Control (Most Common) Floating Control PID ANN Fuzzy Logic

4. Comparison Comparing to other research Comparing to Two-Position model

5. Approach Error function – T Desired – T Measured (t) = Error(t) Membership functions for error – Change fan speed based on error membership

6. Simplistic Building Model Model as rectangular prism Makes modest assumptions about air pressure/density Interacts via conduction and convection Constants averaged among brick, glass, and wood Not terribly important to the controller

7. Expected Results Less energy consumption – Dependent on model and assumptions Less fluctuation about T desired Same or better time to temperature range