Research Projects

City-scale / Urban Energy Modeling under Climate Change

Cities are facing unprecedented growth with an increase in population and urbanization. As development in dense urban areas continues, the scientific community must continue to observe, analyze, and interpret the effects of dense urbanization, including climate change impacts on urban sustainability, particularly buildings. The challenge is to test the feasibility of implementing green building technologies on a city-wide scale for energy policy decision-making. In this report, we discuss a novel physics-based approach to Urban Energy Modeling (UEM) using the City of Gainesville, Florida, USA as a case study. This city is the fourth largest city in Florida with a population of over 125,000.

City-scale Physics-based / White-box Models: The City of Gainesville, FL, comprises of over 40,000 residential buildings. Data related to construction type, building system efficiencies, etc., were obtained from open-source county appraiser website. Extensive data cleaning and preparation was completed using ArcGIS. The data for the building footprint were obtained as GIS files for each property in the city. Several algorithms were developed to extract data to seamlessly create EneryPlus™ input files (.IDF) using python script. Data related to thermo-physical properties were automatically populated in the IDF files based on when the building was built complying with Florida Building Code – Residential. To reduce the overall time for simulation, we custom-built software tool to execute all 40,000+ EneryPlus™ models. Essentially, we ran multiple instances of EneryPlus™on multiple cores to reduce the overall time for simulation. Our physics-based UEM approach can be used to virtually test the feasibility of implementing green building technologies on a city-wide scale for energy policy decision-making. Such a dynamic tool can be used by utility providers to accurately predict the demand of these communities in the future and mitigate risk. Refer to our recent publications for more information.

Black-box Models: Although many building energy prediction models have been developed, only a few have focused on climate change and its impacts. We developed building energy prediction models by using machine learning: lasso regression, multiple regression, and relative importance with bootstrapping confidence interval. The independent numeric variables used as inputs are building characteristics, temporal variables and environmental variables. The outputs are electricity and chilled water for 2054. Prior to modeling, matrix plots and histograms are used to identify correlations between variables. For the purpose of predicting energy consumption owing to climate change, we used weather data that represents 2054. Refer to our recent publications for more information, particularly works of Haekyung Im and Soheil Fathi.

Gray-box Models: We are conducting preliminary research in combining physics-based and data-driven models which will address both computational and reliability challenges. Refer to our recent publications for more information.

Research Support & Team:
UF-City of Gainesville Research Awards
PI: Ravi Srinivasan
Students: Baalaganapathy Manohar, Rahul Aggarwal, Akshay Padwal, Nikhil Asok Kumar, Vahid Danesmand
Distributed Computing: Jose Fortes, Renato Figueiredo

Acoustics-based HVAC Maintenance

Centralized HVAC systems are the primary means to control the indoor climate and maintain occupants’ comfort in over 88% of the commercial buildings in the USA. Due to the lack of an effective, low-cost, and continuous assessment and prognosis mechanism for detecting underperforming HVAC units, it is extremely difficult to determine whether a repair, retrofit, or permanent retirement of an HVAC system is warranted. For a systematic prognosis and lifecycle management of centralized HVAC systems, what we need is a robust, inexpensive, and easily deployable system, so that impending failures can be detected early. Such a system will save money, and help us breathe healthily.

We developed a Smart Audio SEnsing-based Maintenance (SASEM) system; SASEM has a single unifying intellectual focus, i.e., enabling predictive maintenance of building equipment by autonomously monitoring and analyzing their acoustic emissions. Using audio signatures to predict equipment failure requires more than simply connecting a microphone to a digital signal processor; it requires the development of novel hardware and software that are low cost, low maintenance, easy to deploy and take into consideration the variations in noises produced by different equipment, acoustically hostile building environments, false positives and negatives during classification, and privacy issues. We developed effective machine learning-based classifiers to identify acoustic characteristics of building equipment. Refer to our recent publications for more information.

Research Support & Team:
National Science Foundation
PIs: Ravi Srinivasan, Nirjon Shahriar (UNC-Chapel Hill)
Students: Zeyu Wang, Mohammed Islam, Tamzeed Islam

Solar Windows Project

There is a need to improve the energy efficiency of building envelopes as they are the primary factor governing the heating, cooling, lighting, and ventilation requirements of buildings –influencing 53% of building energy usage. In particular, windows contribute significantly to the overall energy performance of building envelopes, thus there is a need to develop advanced energy-efficient windows. This project has found a way to use PV modules to energize homes through windows while still ensuring visible transmittance through clear panels. They’re fitted with a photovoltaic module that contains solar cells to capture solar energy. This solar energy is then converted into electricity that can power the electrical appliances in the property. With these forms of solar energy, homeowners and companies can save tremendous amounts of money and reduce their carbon footprint. If a step-change in the energy efficiency and performance of buildings is to be achieved, there is a clear need to bring PV module solar windows to the marketplace. This project addressed the need to accelerate the widespread introduction of Solar windows into buildings and thus maximize the total energy savings in the US and worldwide.

Research Support & Team:
Winbuild Inc. (Bipin Shah)
PIs: Ravi Srinivasan
Students: Damilola Onatayo, Rahul Aggarwal

Dynamic- Sustainability Information Modeling (dSIM)

In the age of the digital, Smart Cities uses Information and Communication Technologies (ICT) to become innovative and resource efficient providing intelligent solutions to urban problems such as transportation infrastructure (re-routing owing to traffic), human health (indoor and outdoor air quality management), and building (relaying near-realtime energy use data to owners). However, to date, there is no holistic and robust environmental accounting platform for the management of Smart Cities, its neighborhoods and buildings other than the reliance of green rating systems (e.g., LEED, Green Globes, BREEAM) that are either incomplete or operate in silos. A business-as-usual approach to manage material and energy resources of Smart Cities and all their elements is entirely inadequate to avoid the pitfalls of previous attempts at Smart City growth and development. Besides, there is a lack of holistic metrics and evaluation criteria to perform an integrated environmental accounting to assess, innovate, and shape Smart Cities. What is needed is a robust, cloud-based platform to model, simulate, and visualize sustainability performance of buildings and other infrastructure in order to produce auditable reports, sustainability strategies, and a roadmap towards integrative environmentalism of Smart Cities.

Currently, under development is the dSIM platform that offers a sound solution to the problems discussed above by being integrative and collaborative using an extensible environment spanning seamlessly across a variety of scales — from city to campus-scale, and from campus to building-scale. This ability to cut across scales is what allows for this platform to achieve its ultimate goal: to model, simulate, and visualize the linkages and interplay of all elements of the urban fabric of Smart Cities.

Research Support & Team:
University of Florida
PI: Ravi Srinivasan

CODY: Co-Design for You, to enhance the ability of persons with Parkinson’s Disease

This research designed, developed, and tested CODY (co–design for you),a haptic-enabled Virtual Reality (VR) tool and Application Programming Interface (API), which uses an immersive, interactive environment for using, experiencing, and co-designing home alterations.  The ‘co-design’ nature of CODY denotes that persons with movement disorders (notably, Parkinson’s  Disease, or PD) can virtually interact with and experience a home alteration/modification (HM) in a virtual, simulated environment; have multiple variations of a HM that the user can choose from; and is able to assess and manipulate the HM for appropriateness to one’s condition. The two aims of this research were: (1) To develop the haptic-enabled Virtual Reality-based CODY tool and corresponding API; and (2) To assess the efficacy of using CODY to aid and enhance the ability of persons with PD to experience and choose appropriate home modifications. 

Research Support & Team:
US Veteran Affairs (VA)
PIs: Sherry Ahrentzen, Ravi Srinivasan, Shabboo Valipoor, Sriram Kalyanaraman, Michael Okun, Leo Almedia, Adolfo Zamora, Charles Levy, Sherrilene Classen
Students: Jithin Gopinadhan, Siddhesh Gupte, Farah Akiely, Xiaojie Lu, Mahshad Kazemzadeh, Sivani Korukonda, Julie Emminger, Luz Semeah, Eleyn Fangonilo

Re-envision Simulation Testing

For homes with spatial and structural constraints, one approach to enhancing accessibility is that of repurposing:  i.e., replacing or adapting problematic fixtures or spaces with others that were not originally intended for that purpose. For small-scale (2-4 units) attached housing, how best to repurpose fixtures and rooms to make them more accessible, affordable and appealing? Aim: The aim is to develop a series of redesigned interior layouts and fixtures of prototypical SSAH for persons with mobility or visual impairments; simulate those designs in a Virtual Reality (VR) environment; and by having research participants experience and “move through” the different design scenarios with a VR headset, hand glove, and leg and arm movement sensors, test these designs for participant accessibility and perceived attractiveness using multiple assessment tools  Research Design:  We will use a repeated measures (i.e. within-subjects) experimental research design for testing the accessibility and attractiveness of the redesigned prototypes with a maximum of 60 participants.  Participants will be recruited and screened for mobility and visual impairments (see later section on recruitment).  There will be a total of 4 design simulations tested:  two for bedroom-to-bathroom transition area, and two for kitchen. Accessibility will be measured by movement patterns (partially measured by sensors and completion of pre-determined tasks) and perceived ease of accessibility (via questionnaires).  Perceived attractiveness will be measured by a card sorting task by the participants. 

Research Support & Team:
US Department of Housing and Urban Development (HUD)
PIs: Sherry Ahrentzen, Ravi Srinivasan, Robert Ries, William O’Dell, Stephen Bender, Erin Cunningham, Linda Struckmeyer, Sherrilen Classen, Randall Cantrell, Boyi Hu
Students: Jithin Gopinadhan, Farah Akiely, Mahshad Kazemzadeh, Xiaojie Lu, Xu Jin, Yuhao Chen, Yue Luo