Project Abstract

Buildings consume around 40% of total US energy use, while heating, ventilation, and air conditioning (HVAC) systems account for 74% of building energy consumption. Current HVAC systems often rely on a fixed schedule, which typically results in conditioning of indoor spaces unnecessarily, without knowing the actual flow of the users. In this project, we directly measure the occupancy of university indoor spaces using a distributed sensor network. We then investigate correlation between the performance of the HVAC system and actual occupancy of these spaces to provide insights into building use patterns for adaptive control strategies of the HVAC system.

Project Description & Overview

Without knowing the exact occupancy, building HVAC control systems may set air flow rates for ventilation at a high percentage of maximum air flow rate unnecessarily. Overventilation results in significant energy use and discomfort for occupants.

We plan to use a Reconfigurable Environmental Intelligence Platform (REIP) and a set of 4-6 existing sensors with video and edge computing capabilities to directly measure the user occupancy in public areas of NYU CUSP facilities. For privacy reasons, no video data will be stored but only the outputs of live object (e.g person/user) detection. Students will need to extend the sensor capabilities to environmental (i.e. temperature & humidity) sensing by designing a simple hardware module based on Arduino microcontroller and implementing a corresponding REIP software block.

A total of a couple weeks of data collection (mid-project milestone) will then be used to correlate the occupancy of the spaces (strategically chosen using the floor plans) with the performance of the HVAC system (i.e. temperature & humidity at that time). Our findings about the building usage patterns could help reduce energy waste, carbon and environmental footprint of the building via suggested adjustments to the air conditioning regime. The project is also aiming to demonstrate feasibility of live detection of indoor spaces occupancy using REIP platform, that could be used in dynamically controllable HVAC systems for even better performance and energy efficiency.

Datasets

The data will be acquired by the students with an option of cross-checking with the data from the building HVAC system (upon availability).

Competencies

An ideal size of the team working on this project would be three students with a collective prior experience in at least one of:

1. Work with physical sensors (Arduino / C);
2. Python programming language;
3. Data analysis and visualization (e.g. Pandas / Matplotlib, R or MatLab).

Learning Outcomes & Deliverables

After completing the project, students will learn how to design physical sensors for measuring air temperature & humidity, and extend REIP by providing corresponding hardware and software components. Students will acquire weeks worth of user occupancy data and analyze it using Python programming language and common libraries, such as numpy, pandas, matplotlib, etc.

Students

Danna Alamer, Kaiyue Feng, Haosheng Jiang, Yipeng Qiu