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Morton, April M.

Publications and source records attributed to Morton, April M..

7 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

City energysheds and renewable energy in the United States

Cities are powerful political and economic entities, and for many cities cultivating renewable energy penetration is sound economic policy. Many power plants in the United States will need to be replaced in the coming decades, so opportunities for renewable energy development are imminent. Although numerous cities have committed to sourcing 100% of their electricity needs from renewables, those cities represent a very small portion of cities across the United States. Due to their high energy consumption, cities have environmental impacts in areas far from their centre but are often unaware of these impacts and are thus unmotivated to undergo a transition to more sustainable energy sources. Herein we present a spatial framework for matching the supply of energy to demand across the electricity grid that allows for allocation of city energysheds. A city energyshed comprises the network of power plants that supply a given city and the amount of energy drawn from each plant. As a result, the power plant–city links revealed by energysheds allow for estimations of a city’s energy mix and environmental footprint from electricity consumption, and provide urban areas with insights into their energy sources that can catalyse renewable energy penetration for cities seeking to expand their renewable portfolio.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Evaluation of Factors that Influence Residential Solar Panel Installations

Though rooftop photovoltaic (PV) systems are the fastest growing source of distributed generation, detailed information about where they are located and who their owners are is often known only to installers and utility companies. This lack of detailed information is a barrier to policy and financial assessment of solar energy generation and use. To bridge the described data gap, Oak Ridge National Laboratory (ORNL) was sponsored by the Department of Energy (DOE) Office of Energy Policy and Systems Analysis (EPSA) to create an automated approach for detecting and characterizing buildings with installed solar panels using high-resolution overhead imagery. Additionally, ORNL was tasked with using machine learning techniques to classify parcels on which solar panels were automatically detected in the Washington, DC, and Boston areas as commercial or residential, and then providing a list of recommended variables and modeling techniques that could be combined with these results to identify attributes that motivate the installation of residential solar panels. This technical report describes the methodology, results, and recommendations in greater detail, including lessons learned and future work.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

A high resolution agent-based model to support walk-bicycle infrastructure investment decisions: A case study with New York City

Active transportation modes--walk and bicycle--are central for low carbon transport, healthy living, and complete streets initiative. Building a community with amenable walk and bicycle facilities asks for smart planning and investments. It is critical to investigate the impact of infrastructure building or expansion on the overall walk and bicycle mode usage prior to making investment choices utilizing public tax money. This research developed an agent-based model to support investment decisions that allows to assess the impact of changes in walk-bike infrastructures at a high spatial resolution (e.g., block group level). The agent-based model (ABM) utilizes data from a synthetic population simulator generating agents with corresponding socio-demographic characteristics, and integrates facility attributes regarding walking and bicycling (e.g., sidewalk width, bike lane length) into the mode choice decision making process. Moreover, the ABM accounts for the effect of social interactions among agents who live and work at the same geographic locations. Finally, GIS-based maps are developed at block group resolution that allows exploring the effect of walk-bike infrastructure related investments. The results from New York City case study indicate that infrastructure investments such as widening sidewalk and increasing bike lane network can positively influence the active transportation mode choices. In addition, the level of impact varies with geographic locations--different boroughs of New York City will have different impacts. Lastly, social promotions resulting in higher social interaction among agents can reinforce the impacts of infrastructure changes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

US cities can manage national hydrology and biodiversity using local infrastructure policy

Significance We introduce a unique and detailed data-driven approach that links cities’ hard infrastructures to their distal ecological impacts on streams. Although US cities concentrate most of the nation’s population, wealth, and consumption in roughly 5% of the land area, we find that city infrastructures influence habitats for over 60% of North America’s fish, mussel, and crayfish species and have contributed to local and complete extinctions in 260 species. We also demonstrate that city impacts are not proportionate to city size but reflect infrastructure decisions; thus, as US urbanization trends continue, local government and utility companies have opportunities to improve regional aquatic ecosystem conditions outside city boundaries through their hard infrastructure policies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Exploring the impact of walk–bike infrastructure, safety perception, and built-environment on active transportation mode choice: a random parameter model using New York City commuter data

Here, this study finds the effects of traffic safety, walk-bike network facilities, and land use attributes on walk and bicycle mode choice decision in the New York City for home-to-work commute. Applying the flexible econometric structure of random parameter models, we capture the heterogeneity in the decision making process and simulate scenarios considering improvement in walk-bike infrastructure such as sidewalk width and length of bike lane. Our results indicate that increasing sidewalk width, total length of bike lane, and proportion of protected bike lane will increase the likelihood of more people taking active transportation mode This suggests that the local authorities and planning agencies to invest more on building and maintaining the infrastructure for pedestrians. Furthermore, improvement in traffic safety by reducing traffic crashes involving pedestrians and bicyclists will increase the likelihood of taking active transportation modes. Our results also show positive correlation between number of non-motorized trips by the other family members and the likelihood to choose active transportation mode. The findings will help to make smart investment decisions in context of building sustainable transportation systems accounting for active transportation.

42 ENGINEERING↗
DOE OSTI2016

A Bayesian Machine Learning Model for Estimating Building Occupancy from Open Source Data

Understanding building occupancy is critical to a wide array of applications including natural hazards loss analysis, green building technologies, and population distribution modeling. Due to the expense of directly monitoring buildings, scientists rely in addition on a wide and disparate array of ancillary and open source information including subject matter expertise, survey data, and remote sensing information. These data are fused using data harmonization methods which refer to a loose collection of formal and informal techniques for fusing data together to create viable content for building occupancy estimation. In this paper, we add to the current state of the art by introducing the Population Data Tables (PDT), a Bayesian based informatics system for systematically arranging data and harmonization techniques into a consistent, transparent, knowledge learning framework that retains in the final estimation uncertainty emerging from data, expert judgment, and model parameterization. PDT probabilistically estimates ambient occupancy in units of people/1000ft2 for over 50 building types at the national and sub-national level with the goal of providing global coverage. The challenge of global coverage led to the development of an interdisciplinary geospatial informatics system tool that provides the framework for capturing, storing, and managing open source data, handling subject matter expertise, carrying out Bayesian analytics as well as visualizing and exporting occupancy estimation results. We present the PDT project, situate the work within the larger community, and report on the progress of this multi-year project.Understanding building occupancy is critical to a wide array of applications including natural hazards loss analysis, green building technologies, and population distribution modeling. Due to the expense of directly monitoring buildings, scientists rely in addition on a wide and disparate array of ancillary and open source information including subject matter expertise, survey data, and remote sensing information. These data are fused using data harmonization methods which refer to a loose collection of formal and informal techniques for fusing data together to create viable content for building occupancy estimation. In this paper, we add to the current state of the art by introducing the Population Data Tables (PDT), a Bayesian model and informatics system for systematically arranging data and harmonization techniques into a consistent, transparent, knowledge learning framework that retains in the final estimation uncertainty emerging from data, expert judgment, and model parameterization. PDT probabilistically estimates ambient occupancy in units of people/1000 ft 2 for over 50 building types at the national and sub-national level with the goal of providing global coverage. The challenge of global coverage led to the development of an interdisciplinary geospatial informatics system tool that provides the framework for capturing, storing, and managing open source data, handling subject matter expertise, carrying out Bayesian analytics as well as visualizing and exporting occupancy estimation results. We present the PDT project, situate the work within the larger community, and report on the progress of this multi-year project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗