Note to students

This website is organized as follows. The side navigation serves a static menu to navigate across pages on this website. We provide you with a template of what we want to see from your project pages, the contents can be edited on a per project basis.

Abstract

This project aims to develop an ontology that recommends a viable solution to improve IEQ for occupants and minimize energy use in an office. According to reports written by U.S. Energy Information Administration, heating, cooling, ventilation, and lighting account for 46.2% of the energy use in commercial and residential buildings. This energy is used for enhancing indoor environmental quality (IEQ). In a room, IEQ is affected by many factors: temperature, humidity, airflow, air quality, etc.; however, each building is under different environmental conditions including weather, outdoor air quality, direction/location of the building, etc. Additionally, every occupant has different a clothing level and metabolic rate. Potential solutions may be available; but, they have different influences on IEQ. In this project, we propose an ontology find an optimal solution to minimize energy use by combining several sets of knowledge: 1) thermal comfort based on temperature, humidity, air speed, metabolic rate, and clothing level, 2) occupancy behavior for IEQ, 3) indoor air quality, 4) interior illumination level.

Workflow Diagram

Add a representative diagram of your project such as the below workflow diagram illustrating the flow between the components.

List of Resources

List resources you think a reader would benefit from to use your project. We list some examples you could make available below.

Resources	Links
1. Ontology	(a) Your Ontology
2. Term List	(a) Mapped Vocabularies
2. Competency Questions	(a) SPARQL Queries
3. Presentations:	(a) Project presentations during class

Acknowledgements

Please acknowledge people who have helped you in this work. An example is below

This work is undertaken as a part of the Health Empowerement by Analytics, Learning and Semantics (HEALS) project , and is partially supported by IBM Research AI through the AI Horizons Network. We thank our colleagues from IBM Research, Dan Gruen, Morgan Foreman and Ching-Hua Chen, and from RPI, John Erickson, Alexander New, Neha Keshan and Rebecca Cowan, who provided insight and expertise that greatly assisted the research.