Grantee Research Project Results

Final Report: Sensible Home: Micro-environmental control through wearable personal sensors

EPA Grant Number: SU836940
Title: Sensible Home: Micro-environmental control through wearable personal sensors
Investigators: Wang, Julian , Fan, Howard
Institution: University of Cincinnati
EPA Project Officer: Callan, Richard
Phase: II
Project Period: February 1, 2017 through January 31, 2019 (Extended to January 31, 2022)
Project Amount: $75,000
RFA: P3 Awards: A National Student Design Competition for Sustainability Focusing on People, Prosperity and the Planet - Phase 2 (2016) Recipients Lists
Research Category: P3 Awards , P3 Challenge Area - Chemical Safety , Sustainable and Healthy Communities

Objective:

Our long-term research goal is to bring the human-in-the-loop of residential building controls using personal wearable sensors and lead to new advances in building energy efficiency and indoor comfort. As a step toward this, this P3 Phase II project targets the development of minimally invasive data capture systems on individual micro-environmental conditions and subjective comfort ratings and in turn builds a reliable individual comfort model upon wearable sensor data, and determines the methods taking both individual comfort and behavioral pattern considerations into account. The experimental testing of the developed prototype consists of a personal wearable module and a computing module for continuous and autonomous monitoring of individual micro-environmental conditions and subjective comfort status. Built upon the theoretical demonstration in Phase I, the ultimate goal of this Phase II project is to improve and verify our concept and prepare it for implementation, in an effort to benefit individual indoor comfort (people), promote economic growth in smart buildings and healthcare (prosperity), and build energy efficiency (the planet).

Summary/Accomplishments (Outputs/Outcomes):

Four major research tasks were planned and conducted, including a) Indoor Behavior Characterization and Prediction, b) Wearable Comfort Monitoring System Development, c) Individual Comfort Model Development, and d) Model Integration and Energy Saving Prediction. The main findings achieved through conducting these tasks include:

Designed and established an automatic remote data collection system for individual micro-environmental conditions and physiological factors, which incorporates smartphones, wearable sensors, and online data exchange and tuning functions. The developed system allows the research team to minimize the intervention and survey requests to participants while maintaining high data quality.
Developed and validated a wearable-sensor-supported individual comfort model based on mini-batch online modeling techniques that can achieve high accuracy >90% by using much fewer input data relative to the traditional subjective comfort modeling procedures.
Examined a minimally invasive method to predict residential building energy use patterns and features upon readily available datasets in terms of weather, building profiles, and utility data. This method is built upon machine learning techniques and is able to support the potential energy savings by the micro-environmental control model proposed in this project.
Incorporated the designed individual comfort model into building energy and performance simulation, combined with indoor behavior and pattern predictions, enabling the analysis of annual energy savings and individualized comfort levels.
Demonstrated a potential multisensory interaction between thermal and visual aspects. In the conventional indoor comfort study, thermal and visual aspects were completely separated. However, the collective effects were found in our study. Also, this reveals some opportunities (e.g., manipulating the user thermal response by visual quality control by smart lighting systems) to further save building energy and enhance the indoor environmental quality for occupants. This is also aligned with the Agency’s mission on people and the environment.

Conclusions:

In this project year, a new data collection system consisting of wearable sensor modules and smartphone App for capturing individual micro-environmental conditions, physiological responses, and subjective comfort ratings has been developed and tested. The designed data collection system enables an autonomous, minimally invasive, and interactive online capture process, which is then used to build an individual comfort model based on the mini-batch learning technique. The individual micro-environmental comfort system and model developed in this work achieves 93.8% accuracy with fewer data input to predict individual comfort levels relative to the traditional subjective comfort data collection method. Accordingly, compared with a baseline model (built with the DOE prototypical residential model and schedule-based control strategy), the building simulation incorporated with the designed micro-environment-focused comfort models shows ~ 22% building energy savings and ~30% comfort (thermal and visual) level increase.

Journal Articles on this Report : 2 Displayed | Download in RIS Format

Publications Views
Other project views:	All 18 publications	8 publications in selected types	All 8 journal articles

Publications
Type	Citation	Project	Document Sources
Journal Article	Feng Y, Duan Q, Wang J, Baur S. Approximation of building window properties using in situ measurements. BUILDING AND ENVIRONMENT 2020;169(106590).	SU836940 (Final)	Full-text: ScienceDirect - Full Text HTML Exit
Journal Article	Feng Y, Wang J, Wang N, Chen C. Alert-based wearable sensing system for individualized thermal preference prediction. BUILDING AND ENVIRONMENT 2023;232(110047).	SU836940 (Final)	Full-text: ScienceDirect - Full Text HTML Exit

Supplemental Keywords:

Micro-environmental control; wearable sensors; personal comfort; building energy efficiency; personal factors; healthcare; data sensing and processing.

Progress and Final Reports:

Original Abstract

P3 Phase I:

Sensible Home: Micro-environmental control through wearable personal sensors | Final Report

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The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.