Occupancy Based HVAC ControlCS3720 Final Presentation

Keren Ye, Debarun Das, and Xiaoyu Liang

Outline

Motivations

Method

Experiments & Evaluation

Conclusion

Future Scopes

Motivations

● Lack of user comprehension of the behavior of HVAC systems○ Improvement of Occupant Comfort needed

● Largest consumer of energy in buildings are the HVAC systems○ 26.1% and 53.4% of total energy usage in residential and commercial

applications, respectively○ Energy Consumption Saving

● Usage of new, more efficient ventilation technologies are expensive○ Need to use existing technology more efficiently

Motivations -- Contd.Occupancy Based HVAC systems

● Energy usage can be reduced by reducing heating and cooling when occupants are away [1].● The number of occupants in a room affect the room temperature [2].

Occupancy Counting [3]

● Determine the total number of people in a room at a given time. ● Sensor based methods

○ Need to deploy multiple special sensors within the building○ Accuracy is questionable

● Camera based methods○ Capability of achieving high accuracy○ Require significant computation resources ○ Is often criticized of the privacy concerns

1. Elahe Soltanaghaei and Kamin Whitehouse: Practical occupancy detection for programmable and smart thermostats.2. J.S. Sage-Lauck and D.J. Sailor: Evaluation of phase change materials for improving thermal comfort in a super-insulated residential building

3. Kemal Akkaya, et.al.: IoT-based Occupancy Monitoring Techniques for Energy-Efficient Smart Buildings

Raspberry Pi 3, Model B

Arduino UNO

Sensors

● Raspberry Pi camera● HC-SR04 Ultrasonic Sensor Distance Module● DHT11 temperature / humidity sensor

Actuators

● SG90 Micro Servo Motor - I am the FAN● Arduino LCD module

Method - Hardwares

Pi Camera

Distance Sensor

Temperature& Humidity

Sensor

Fan

LCD

Method - SoftwaresOpenFace

● Amos, Brandon, Bartosz Ludwiczuk, and Mahadev Satyanarayanan. "Openface: A general-purpose face recognition library with mobile applications." CMU School of Computer Science (2016).

OpenCV

● Bradski, Gary B. Open source computer vision library. Springer, 2004.

Method - Motion Detection

Detect the motion of passing through the door

● Potential changes of the number of people in the room

Activate image processing module

Method - Person Detection & OccupancyPerson-detection and counting

● Per-frame person (face) detection● Count & vote on consecutive frames

2

Method - Person Detection & Occupancy

Face detection

● OpenCV face detection● OpenFace face detection

Method - Person Detection & Occupancy

Approaches

● Majority Vote approach● Clustering using facial features

Clustering based counting

Method - Decision Making

Aggregation of sensor data

● Occupancy data from Raspberry Pi module● Temperature & humidity information

Decision making

● HVAC control depends on:○ Current Temperature○ Number of Occupants

Assumptions● Insulated. Outdoor Temperature ignored● Avg mass of Human body = 70 kg● Avg Specific Heat of Human body = 3470 J/(kg °C)● Temperature Range = 18°C - 30°C● Equation

Sleep Activity

HVAC Cooling Rate (°C/hr)

Avg Temp. (°C)

Std. Dev

Temp. In Range %

5 33.17 6.08 54.17

10 25.67 3.47 100.00

15 24.63 4.93 83.33

20 21.92 6.74 66.67

Light Activity

HVAC Cooling Rate

(°C/hr)

Avg Temp. (°

C)

Std. Dev

Temp. In Range %

5 43.42 17.86 50.00

10 26.75 3.12 95.83

15 24.05 5.44 83.33

20 22.59 7.14 66.67

Moderate Activity

HVAC Cooling Rate

(°C/hr)

Avg Temp. (°C)

Std. Dev

Temp. In Range %

5 56.04 34.20 45.83

10 35.42 14.76 54.17

15 23.13 6.84 41.67

20 16.67 6.31 33.33

Experiments & EvaluationMotion detection

● Energy consumption

Image processing module

● Accuracy of the counting

Experiments & Evaluation - Motion Detection Module

Strategies to activate image processing module

● Without motion detection - activate periodically● With motion detection - delayed activation once human motion is detected

Energy consumption of the Raspberry Pi

● Assumes the energy consumption is proportional to CPU utilization

Experiments & Evaluation - Motion Detection Module

Experiments & Evaluation - Image processing module

Accuracy of the image processing module

● Web images○ Downloaded from the web○ Capture them by camera

● Real-time video data○ System deployed in Room 6410

Metrics to evaluate performance of detection

Web images

Real-time video data

Experiments & Evaluation - Image Processing Module

Accuracy of the image processing module

● Web images (avg: 11.1 people)○ Downloaded from the web○ Capture them by camera

● Real-time video data (avg: 2.1)○ System deployed in Room 6410

Web images Real-time video

Methods Accuracy RMSE Accuracy RMSE

Majority vote 12.5% 3.37 36.11% 1.11

Clustering 0 4.14 NA NA

Web images

Real-time video data

DEMO

Conclusion

● HVAC Control based on occupancy

● Face Detection Algorithm to count the no. of occupants○ Majority Vote○ Triggered by Distance Sensor

● HVAC Control - Selecting rate and time of cooling based on ○ No. of Occupants○ Current Temperature

Future Scopes

● Improvement in accuracy of face detection algorithm

● Application of HVAC Control rules○ Study of actual changes in room temperature○ Inclusion of more parameters for HVAC control

● Usage of user feedback for thermal comfort

Thank you!