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Elmore, Ryan

Publications and source records attributed to Elmore, Ryan.

15 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

A data mining approach to estimating rooftop photovoltaic potential in the US

This paper aims to quantify the amount of suitable rooftop area for photovoltaic (PV) energy generation in the continental United States (US). The approach is data-driven, combining Geographic Information Systems analysis of an extensive dataset of Light Detection and Ranging (LiDAR) measurements collected by the Department of Homeland Security with a statistical model trained on these same data. The model developed herein can predict the quantity of suitable roof area where LiDAR data is not available. This analysis focuses on small buildings (1000 to 5000 square feet) which account for more than half of the total available rooftop space in these data (58%) and demonstrate a greater variability in suitability compared to larger buildings which are nearly all suitable for PV installations. This paper presents new results characterizing the size, shape and suitability of US rooftops with respect to PV installations. Overall 28% of small building roofs appear suitable in the continental United States for rooftop solar. Nationally, small building rooftops could accommodate an expected 731 GW of PV capacity and generate 926 TWh/year of PV energy on 4920 km2 of suitable rooftop space which equates to 25% the current US electricity sales.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Prediction and characterization of application power use in a high-performance computing environment

Power use in data centers and high-performance computing (HPC) facilities has grown in tandem with increases in the size and number of these facilities. Substantial innovation is needed to enable meaningful reduction in energy footprints in leadership-class HPC systems. In this paper, we focus on characterizing and investigating application-level power usage. We demonstrate potential methods for predicting power usage based on a priori and in situ characteristics. Lastly, we highlight a potential use case of this method through a simulated power-aware scheduler using historical jobs from a real scientific HPC system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings

Commercial buildings often experience faults that produce undesirable behavior in building systems. Building faults waste energy, decrease occupants' comfort, and increase operating costs. Automated fault detection and diagnosis (FDD) tools for buildings help building owners discover and identify the root causes of faults in building systems, equipment, and controls. Proper implementation of FDD has the potential to simultaneously improve comfort, reduce energy use, and narrow the gap between actual and optimal building performance. However, conventional rule-based FDD requires expensive instrumentation and valuable engineering labor, which limit deployment opportunities. This paper presents a hybrid, automated FDD approach that combines building energy models and statistical learning tools to detect and diagnose faults noninvasively, using minimal sensors, with little customization. We compare and contrast the performance of several hybrid FDD algorithms for a small security building. Our results indicate that the algorithms can detect and diagnose several common faults, but more work is required to reduce false positive rates and improve diagnosis accuracy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings: Preprint

Commercial buildings often experience faults that produce undesirable behavior in building systems. Building faults waste energy, decrease occupants' comfort, and increase operating costs. Automated fault detection and diagnosis (FDD) tools for buildings help building owners discover and identify the root causes of faults in building systems, equipment, and controls. Proper implementation of FDD has the potential to simultaneously improve comfort, reduce energy use, and narrow the gap between actual and optimal building performance. However, conventional rule-based FDD requires expensive instrumentation and valuable engineering labor, which limit deployment opportunities. This paper presents a hybrid, automated FDD approach that combines building energy models and statistical learning tools to detect and diagnose faults noninvasively, using minimal sensors, with little customization. We compare and contrast the performance of several hybrid FDD algorithms for a small security building. Our results indicate that the algorithms can detect and diagnose several common faults, but more work is required to reduce false positive rates and improve diagnosis accuracy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Analysis of Application Power and Schedule Composition in a High Performance Computing Environment

As the capacity of high performance computing (HPC) systems continues to grow, small changes in energy management have the potential to produce significant energy savings. In this paper, we employ an extensive informatics system for aggregating and analyzing real-time performance and power use data to evaluate energy footprints of jobs running in an HPC data center. We look at the effects of algorithmic choices for a given job on the resulting energy footprints, and analyze application-specific power consumption, and summarize average power use in the aggregate. All of these views reveal meaningful power variance between classes of applications as well as chosen methods for a given job. Using these data, we discuss energy-aware cost-saving strategies based on reordering the HPC job schedule. Using historical job and power data, we present a hypothetical job schedule reordering that: (1) reduces the facility's peak power draw and (2) manages power in conjunction with a large-scale photovoltaic array. Lastly, we leverage this data to understand the practical limits on predicting key power use metrics at the time of submission.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Rooftop Solar Photovoltaic Technical Potential in the United States. A Detailed Assessment

How much energy could be generated if PV modules were installed on all of the suitable roof area in the nation? To answer this question, we first use GIS methods to process a lidar dataset and determine the amount of roof area that is suitable for PV deployment in 128 cities nationwide, containing 23% of U.S. buildings, and provide PV-generation results for a subset of those cities. We then extend the insights from that analysis to the entire continental United States. We develop two statistical models--one for small buildings and one for medium and large buildings--and populate them with geographic variables that correlate with rooftop's suitability for PV. We simulate the productivity of PV installed on the suitable roof area, and present the technical potential of PV on both small buildings and medium/large buildings for every state in the continental US. Within the 128 cities covered by lidar data, 83% of small buildings have a location suitable for a PV installation, but only 26% of the total rooftop area of small buildings is suitable for development. The sheer number of buildings in this class, however, gives small buildings the greatest technical potential. Small building rooftops could accommodate 731 GW of PV capacity and generate 926 TWh/year of PV energy, approximately 65% of rooftop PV's total technical potential. We conclude by summing the PV-generation results for all building sizes and therefore answering our original question, estimating that the total national technical potential of rooftop PV is 1,118 GW of installed capacity and 1,432 TWh of annual energy generation. This equates to 39% of total national electric-sector sales.

14 SOLAR ENERGY↗
DOE OSTI2016

Rooftop Solar Photovoltaic Technical Potential in the United States

How much energy could we generate if PV modules were installed on all of the suitable roof area in the nation? To answer this question, we first use GIS methods to process a lidar dataset and determine the amount of roof area that is suitable for PV deployment in 128 cities nationwide, containing 23% of U.S. buildings, and provide PV-generation results for a subset of those cities. We then extend the insights from that analysis to the entire continental United States. We develop two statistical models -- one for small buildings and one for medium and large buildings -- and populate them with geographic variables that correlate with rooftop's suitability for PV. We simulate the productivity of PV installed on the suitable roof area, and present the technical potential of PV on both small buildings and medium/large buildings for every state in the continental US. Within the 128 cities covered by lidar data, 83% of small buildings have a location suitable for a PV installation, but only 26% of the total rooftop area of small buildings is suitable for development. The sheer number of buildings in this class, however, gives small buildings the greatest technical potential. Small building rooftops could accommodate 731 GW of PV capacity and generate 926 TWh/year of PV energy, approximately 65% of rooftop PV's total technical potential. We conclude by summing the PV-generation results for all building sizes and therefore answering our original question, estimating that the total national technical potential of rooftop PV is 1,118 GW of installed capacity and 1,432 TWh of annual energy generation. This equates to 39% of total national electric-sector sales.

14 SOLAR ENERGY↗
DOE OSTI2015

Evaluation of Methods for Comparison of Spatiotemporal and Time Series Datasets

Data used by the National Renewable Energy Laboratory (NREL) in energy analysis are often produced by industry and licensed or purchased for analysis. While this practice provides needed flexibility in selecting data for analysis it presents challenges in understanding the differences among multiple, ostensibly similar, datasets. As options for source data become more varied, it is important to be able to articulate why certain datasets were chosen and to ensure those include the data that best meet the boundaries and/or limitations of a particular analysis. This report represents the first of three phases of research intended to develop methods to quantitatively assess and compare both input datasets and the results of analyses performed at NREL. This capability is critical to identifying tipping points in the costs or benefits of achieving high spatial and temporal resolution of input data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Power/energy use cases for high performance computing

Power and Energy have been identified as a first order challenge for future extreme scale high performance computing (HPC) systems. In practice the breakthroughs will need to be provided by the hardware vendors. But to make the best use of the solutions in an HPC environment, it will likely require periodic tuning by facility operators and software components. This document describes the actions and interactions needed to maximize power resources. It strives to cover the entire operational space in which an HPC system occupies. The descriptions are presented as formal use cases, as documented in the Unified Modeling Language Specification [1]. The document is intended to provide a common understanding to the HPC community of the necessary management and control capabilities. Assuming a common understanding can be achieved, the next step will be to develop a set of Application Programing Interfaces (APIs) to which hardware vendors and software developers could utilize to steer power consumption.

97 MATHEMATICS AND COMPUTING↗