Jonathan Roth

Jonathan Roth
Stanford University | SU · Department of Civil and Environmental Engineering

Ph.D. Candidate

About

15
Publications
6,476
Reads
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193
Citations
Introduction
Additional affiliations
October 2019 - April 2020
National University of Singapore
Position
  • Research Associate
June 2019 - September 2019
AutoGrid
Position
  • Data Science Intern
June 2018 - September 2018
Électricité de France (EDF)
Position
  • Data Science Intern
Education
June 2017 - June 2020
Stanford University
Field of study
  • Civil Engineering
September 2015 - June 2017
Stanford University
Field of study
  • Sustainable Design and Construction
August 2011 - May 2015
Cornell University
Field of study
  • Civil Engineering

Publications

Publications (15)
Article
Full-text available
As new grid edge technologies emerge—such as rooftop solar panels, battery storage, and controllable water heaters—quantifying the uncertainties of building load forecasts is becoming more critical. The recent adoption of smart meter infrastructures provided new granular data streams, largely unavailable just ten years ago, that can be utilized to...
Article
Full-text available
Cities officials are increasingly interested in understanding spatial and temporal energy patterns of the built environment to facilitate their city's transition to a low-carbon future. In this paper, a new Augmented-Urban Building Energy Model (A-UBEM) is proposed that combines data-driven and physics-based simulation methods to produce synthetic...
Article
Full-text available
Energy management information systems (EMIS) play a critical role in providing actionable insights into building operations, timely feedback, and—ultimately—large energy savings. Current EMIS technologies often focus on industrial applications or require large upfront investments and trained operators, therefore greatly limiting its penetration int...
Article
Full-text available
In late 2019, ASHRAE hosted the Great Energy Predictor III (GEPIII) machine learning competition on the Kaggle platform. This launch marked the third energy prediction competition from ASHRAE and the first since the mid-1990s. In this updated version, the competitors were provided with over 20 million points of training data from 2,380 energy meter...
Preprint
Full-text available
In late 2019, ASHRAE hosted the Great Energy Predictor III (GEPIII) machine learning competition on the Kaggle platform. This launch marked the third energy prediction competition from ASHRAE and the first since the mid-1990s. In this updated version, the competitors were provided with over 20 million points of training data from 2,380 energy meter...
Article
Full-text available
Buildings are by far the largest source of urban energy consumption. In an effort to reduce energy use, cities are mandating that buildings undergo energy benchmarking—the process of measuring building energy performance in order to identify buildings that are inefficient. In this paper, we examine the feasibility of using city-specific, public ope...
Preprint
Full-text available
Buildings are by far the largest source of urban energy consumption. In an effort to reduce energy use, cities are mandating that buildings undergo energy benchmarking—the process of measuring building energy performance in order to identify buildings that are inefficient. In this paper, we examine the feasibility of using city-specific, public ope...
Preprint
Full-text available
Energy management information systems (EMIS) play a critical role in providing actionable insights into building operations, timely feedback, and-ultimately-large energy savings. Current EMIS technologies often focus on industrial applications or require large upfront investments and trained operators, therefore greatly limiting its penetration int...
Conference Paper
Full-text available
Understanding the spatial and temporal distribution of energy consumption in cities is critical to facilitate the identification of potential energy saving opportunities and planning of new renewable and integrated district energy systems. Previous work analyzing urban building energy usage has been largely limited to either modeling of individual...
Conference Paper
Full-text available
New and emerging data streams, from public databases to smart meter infrastructure, contain valuable information that presents an opportunity to develop more robust data-driven models for benchmarking energy use in buildings. In this paper, we propose a new Data-driven, Multi-metric, and Time-varying (DMT) energy benchmarking framework that utilize...
Article
Full-text available
We propose a new building energy use benchmarking system to rank buildings via quantile regression. This methodology addresses several leading issues with current benchmarking practices by constructing a data-driven probabilistic model of performance, reducing outlier-effects, determining the varying effect of inputs across the distribution, and cr...
Article
Full-text available
With the world rapidly urbanizing, addressing the energy intensive urban built environment is becoming increasingly important. Cities across the United States and the world are turning to energy benchmarking as a means of understanding the relative energy efficiency of their building stock and identifying potential opportunities to reduce energy us...
Conference Paper
Full-text available
Cities across the country (20 to date) are rapidly passing laws to mandate the collection and disclosure of energy usage data with the hopes that such data could be utilized to benchmark building energy performance and provide a basis for designing and deploying efficiency measures. We present an integrated data-driven method based on recursive par...
Conference Paper
Cities across the country (20 to date) are rapidly passing laws to mandate the collection and disclosure of energy usage data with the hopes that such data could be utilized to benchmark building energy performance and provide a basis for designing and deploying efficiency measures. However, numerous municipalities are struggling to translate such...

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Projects

Projects (3)
Project
Crowdsource the most accurate long-term energy prediction models for buildings
Project
This project explores various methods to understand energy use in cities using publicly available open data sources.
Project
This project explores use-cases, benefits, and models for using smart meter data for building energy benchmarking to enhance energy management practices.