Christopher Tull

Christopher Tull

Data and software for empowered local government.

Publications

You can also find my articles on my Google Scholar profile.

A data-driven predictive model of city-scale energy use in buildings

Published in Applied Energy, 2017

We use statistical models to predict the energy use of 1.1 million buildings in New York City using the physical, spatial, and energy use attributes of a subset derived from 23,000 buildings required to report energy use data each year. Linear regression (OLS), random forest, and support vector regression (SVM) algorithms are fit to the city’s energy benchmarking data and then used to predict electricity and natural gas use for every property in the city. Model accuracy is assessed and validated at the building level and zip code level. Read more

Recommended citation: Kontokosta, C. E., & Tull, C. (2017). A data-driven predictive model of city-scale energy use in buildings. Applied Energy, 197, 303-317. http://www.sciencedirect.com/science/article/pii/S0306261917303835

How Much Water Does Turf Removal Save? Applying Bayesian Structural Time-Series to California Residential Water Demand

Published in KDD Workshop on Data Science for Food, Energy and Water, 2016

Monthly water savings from turf removal are estimated at the household level as the difference between actual usage and a synthetic control and then aggregated using a mixed-effects regression model to investigate the determinants of water savings. Read more

Recommended citation: Tull, C., Schmitt, E., Atwater, P., (2016). How Much Water Does Turf Removal Save? Applying Bayesian Structural Time-Series to California Residential Water Demand. Presented at the KDD Workshop on Data Science for Food, Energy and Water, San Francisco, CA. https://www.researchgate.net/publication/306219830_How_Much_Water_Does_Turf_Removal_Save_Applying_Bayesian_Structural_Time-Series_to_California_Residential_Water_Demand

Web-Based Visualization and Prediction of Urban Energy Use from Building Benchmarking Data

Published in Bloomberg Data for Good Exchange 2015, 2015

Details two projects to increase both the availability and comprehensiveness of building energy data through interactive visualization and extrapolation with predictive models. Received ‘Best Paper’ award at the Bloomberg Data for Good Exchange 2015. Read more

Recommended citation: Kontokosta, C., Tull, C., Marulli, D., Pingerra, R., & Yaqub, M. (2015) Web-Based Visualization and Prediction of Urban Energy Use from Building Benchmarking Data. Presented at Bloomberg Data for Good Exchange 2015 New York, NY. https://www.researchgate.net/profile/Christopher_Tull/publication/282781435_Web-Based_Visualization_and_Prediction_of_Urban_Energy_Use_from_Building_Benchmarking_Data/links/561c6ab808ae6d17308b1843.pdf