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At least 1,531 records · Page 85

Understanding Commercial Building Energy Use in the Huntsville-Florence Area: Building Stock Segmentation for Retrofit Planning

This report is part of the second phase of a publication series focusing on approximately 100 different local geographies, or "clusters." Each report provides characteristic features and energy data for commercial buildings in a specific area to help policy makers at the city, county, and state level better understand building energy use and emissions. This report breaks down the energy consumption and emissions of the building stock in the counties shown in Figure 2 by building type, building size, end use, energy consumption, emissions, and segment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Understanding Commercial Building Energy Use in the Duluth-Grand Forks-Fargo Area: Building Stock Segmentation for Retrofit Planning

This report is part of the second phase of a publication series focusing on approximately 100 different local geographies, or "clusters." Each report provides characteristic features and energy data for commercial buildings in a specific area to help policy makers at the city, county, and state level better understand building energy use and emissions. This report breaks down the energy consumption and emissions of the building stock in the counties shown in Figure 2 by building type, building size, end use, energy consumption, emissions, and segment.

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Understanding Commercial Building Energy Use in the Omaha-Lincoln Area: Building Stock Segmentation for Retrofit Planning

This report is part of the second phase of a publication series focusing on approximately 100 different local geographies, or "clusters." Each report provides characteristic features and energy data for commercial buildings in a specific area to help policy makers at the city, county, and state level better understand building energy use and emissions. This report breaks down the energy consumption and emissions of the building stock in the counties shown in Figure 2 by building type, building size, end use, energy consumption, emissions, and segment.

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City and County Energy Profiles

The City and County Energy Profiles lookup table provides modeled electricity and natural gas consumption and expenditures, on-road vehicle fuel consumption, vehicle miles traveled, and associated emissions for each U.S. city and county. Please note this data is modeled and more precise data may be available from regional, state, or other sources. The modeling approach for electricity and natural gas is described in Sector-Specific Methodologies for Subnational Energy Modeling: https://www.nrel.gov/docs/fy19osti/72748.pdf. This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and builds on Cities-LEAP energy modeling, available at the "EERE Cities-LEAP Page" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.

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The Design and Implementation of a Secure Datastore Based on Ethereum Smart Contract

In this paper, we present a secure datastore based on an Ethereum smart contract. Our research is guided by three research questions. First, we will explore to what extend a smart-contract-based datastore should resemble a traditional database system. Second, we will investigate how to store the data in a smart-contract-based datastore for maximum flexibility while minimizing the gas consumption. Third, we seek answers regarding whether or not a smart-contract-based datastore should incorporate complex processing such as data encryption and data analytic algorithms. The proposed smart-contract-based datastore aims to strike a good balance between several constraints: (1) smart contracts are publicly visible, which may create a confidentiality concern for the data stored in the datastore; (2) unlike traditional database systems, the Ethereum smart contract programming language (i.e., Solidity) offers very limited data structures for data management; (3) all operations that mutate the blockchain state would incur financial costs and the developers for smart contracts must make sure sufficient gas is provisioned for every smart contract call, and ideally, the gas consumption should be minimized. Our investigation shows that although it is essential for a smart-contract-based datastore to offer some basic data query functionality, it is impractical to offer query flexibility that resembles that of a traditional database system. Furthermore, we propose that data should be structured as tag-value pairs, where the tag serves as a non-unique key that describes the nature of the value. We also conclude that complex processing should not be allowed in the smart contract due to the financial burden and security concerns. The tag-based secure datastore designed this way also defines its applicative perimeter, i.e., only applications that align with our strategy would find the proposed datastore a good fit. Those that would rather incur higher financial cost for more data query flexibility and/or less user burden on data pre- and post-processing would find the proposed database too restrictive.

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U.S. Light-Duty Vehicle Air Conditioning Fuel Use and Impact of Solar/Thermal Control Technologies

To reduce fuel consumption and carbon dioxide (CO2) emissions from mobile air conditioning (A/C) systems, 'U.S. Light-Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards' identified solar/thermal technologies such as solar control glazings, solar reflective paint, and active and passive cabin ventilation in an off-cycle credit menu. National Renewable Energy Laboratory (NREL) researchers developed a sophisticated analysis process to calculate U.S. light-duty A/C fuel use that was used to assess the impact of these technologies, leveraging thermal and vehicle simulation analysis tools developed under previous U.S. Department of Energy projects. Representative U.S. light-duty driving behaviors and weighting factors including time-of-day of travel, trip duration, and time between trips were characterized and integrated into the analysis. In addition, U.S. weather conditions weighted based on light-duty vehicle registrations were generated and used for the analysis. Three representative vehicle types for the light-duty fleet were selected based on registration data containing vehicle size information. These key inputs were used to support a weighted parametric analysis that quantified vehicle thermal load, vehicle accessory load, and vehicle fuel use. This analysis estimates that 7.6 billion gallons of fuel a year are used in the United States for vehicle A/C, equivalent to 6.1% of the total national light-duty vehicle fuel use. This equates to 30.0 gallons/year per vehicle or 23.5 grams CO 2 per mile (g/mi) for an average U.S. light-duty vehicle. A/C is a significant contributor to national fuel use; therefore, technologies that reduce A/C loads have an opportunity to reduce fuel consumption, reduce imported oil, and improve energy security. Implementing solar control glass reduced the CO 2 emissions by 2.0 g/mi and the reduction due to solar reflective paint was 0.8 g/mi. The active and passive ventilation strategies as defined in this analysis only reduced emissions by 0.1 and 0.2 g/mi, respectively. This analysis shows the potential to reduce operational costs, A/C fuel use, and CO 2 emissions by implementing advanced vehicle climate control technologies including the solar/thermal technologies.

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In-Use and Vehicle Dynamometer Evaluation and Comparison of Class 7 Hybrid Electric and Conventional Diesel Delivery Trucks

This study compared fuel economy and emissions between heavy-duty hybrid electric vehicles (HEVs) and equivalent conventional diesel vehicles. In-use field data were collected from daily fleet operations carried out at a FedEx facility in California on six HEV and six conventional 2010 Freightliner M2-106 straight box trucks. Field data collection primarily focused on route assessment and vehicle fuel consumption over a six-month period. Chassis dynamometer testing was also carried out on one conventional vehicle and one HEV to determine differences in fuel consumption and emissions. Route data from the field study was analyzed to determine the selection of dynamometer test cycles. From this analysis, the New York Composite (NYComp), Hybrid Truck Users Forum Class 6 (HTUF 6), and California Air Resource Board (CARB) Heavy Heavy-Duty Diesel Truck (HHDDT) drive cycles were chosen. The HEV showed 31% better fuel economy on the NYComp cycle, 25% better on the HTUF 6 cycle and 4% worse on the CARB HHDDT cycle when compared to the conventional vehicle. The in-use field data indicates that the HEVs had around 16% better fuel economy than the conventional vehicles. Dynamometer testing also showed that the HEV generally emitted higher levels of nitric oxides than the conventional vehicle over the drive cycles, up to 77% higher on the NYComp cycle (though this may at least in part be attributed to the different engine certification levels in the vehicles tested). The conventional vehicle was found to accelerate up to freeway speeds over ten seconds faster than the HEV.

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Impacts of Electric Space Heating in Multifamily Apartments in Juneau, Alaska [Slides]

The study objective was to understand the change in electrical consumption and cost after the addition of a ductless mini-split air-source heat pump (ASHP) across rental apartments heated by electrical resistance heating systems in multifamily buildings in Juneau, Alaska. Alaska Electric Light and Power Company (AELP), the utility in Juneau, Alaska, funded installation of 19 heat pumps across four buildings. Using electrical meter interval data, including the total apartment consumption and submeter data for each heating appliance (electric resistance baseboard and heat pump), the National Laboratory of the Rockies (NLR) calculated energy use metrics before and after heat pump installation and visualized results. Through this research, NLR compares electrical use (total and specific to heating appliances), adjusted for weather and apartment size, and electrical demand in the year before and after installation of an ASHP across study units. The results will inform the utility and residents of documented energy and cost savings from transitioning building heating systems to a higher-efficiency electricity-based appliance, deliver a basis for building energy modeling across other multifamily housing dwellings, and enable estimates of potential electricity savings to AELP grid under widespread adoption scenarios.

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EIA Thermoelectric Water Cooling Data

The U.S. Energy Information Administration (EIA) collects water cooling data for the electric power industry in the United States. This submission includes annual data from 2014 to 2019. Each spreadsheet details the generator type, fuel consumption, water consumption, cooling type, and equipment status, location, and water source for each plant.

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Premature Deaths in Africa Due to Particulate Matter Under High and Low Warming Scenarios

Sustainable development and climate change mitigation can provide enormous public health benefits via improved air quality, especially in polluted areas. We use the latest state-of-the-art composition-climate model simulations to contrast human exposure to fine particulate matter in Africa under a “baseline” scenario with high material consumption, population growth, and warming to that projected under a sustainability scenario with lower consumption, population growth, and warming. Evaluating the mortality impacts of these exposures, we find that under the low warming scenario annual premature deaths due to PM2.5 are reduced by roughly 515,000 by 2050 relative to the high warming scenario (100,000, 175,000, 55,000, 140,000, and 45,000 in Northern, West, Central, East, and Southern Africa, respectively). This reduction rises to ∼800,000 by the 2090s, though by that time much of the difference is attributable to the projected differences in population. By contrast, during the first half of the century benefits are driven predominantly by emissions changes. Depending on the region, we find large intermodel spreads of ∼25%–50% in projected future exposures owing to different physics across the ensemble of 6 global models. The spread of projected deaths attributable to exposure to fine particulate matter, including uncertainty in the exposure-response function, are reduced in every region to ∼20%–35% by the non-linear exposure-response function. Differences between the scenarios have an even narrower spread of ∼5%–25% and are highly statistically significant in all regions for all models. These results provide valuable information for policy-makers to consider when working toward climate change mitigation and sustainable development goals.

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COMPARATIVE STUDY AMONG HYBRID GROUND SOURCE HEAT PUMP SYSTEM, COMPLETE GROUND SOURCE HEAT PUMP AND CONVENTIONAL HVAC SYSTEM

In this paper, a hotel with hybrid geothermal heat pump system (HyGSHP) in the Pensacola is selected and simulated by the transient simulation software package TRNSYS [1]. To verify the simulation results, the validations are conducted by using the monthly average entering water temperature, monthly facility consumption data, and etc. And three types of HVAC systems are compared based on the same building model and HVAC system capacity. The results are presented to show the advantages and disadvantages of HyGSHP compared with the other two systems in terms of energy consumptions, life cycle cost analysis.

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On the Road to Transportation Efficiency

Reducing emissions and oil consumption are crucial worldwide goals. Reducing transportation emissions, in particular, is key to reducing overall emissions. Electric vehicles driving on electrified roadways could be a significant part of the solution. E-roadways offer a variety of benefits: reduce petroleum consumption (electricity is used instead of gasoline), decrease vehicular operating costs (from about 12 cents per mile to 4 cents per mile), and extend the operational range of electric vehicles. Plus, e-roadway power can come from renewable sources.

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Building America Case Study: Solar Water Heating in Multifamily Buildings, Greenfield, Massachusetts

Solar domestic hot water (SDHW) systems have been installed on buildings for decades, but because of relatively high costs they have not achieved significant market penetration in most of the country. As more buildings move towards zero net energy consumption, however, many designers and developers are looking more closely at SDHW. In multifamily buildings especially, SDHW may be more practical for several reasons: (1) When designing for zero net energy consumption, solar water heating may be part of the lowest cost approach to meet water heating loads. (2) Because of better scale, SDHW systems in multifamily buildings cost significantly less per dwelling than in single-family homes. (3) Many low-load buildings are moving away from fossil fuels entirely. SDHW savings are substantially greater when displacing electric resistance water heating. (4) In addition to federal tax incentives, some states have substantial financial incentives that dramatically reduce the costs (or increase the benefits) of SDHW systems in multifamily buildings. With support from the U.S. DOE Building America program, the Consortium for Advanced Residential Buildings (CARB) worked with a developer in western Massachusetts to evaluate a SDHW system on a 12-unit apartment building. Olive Street Development completed construction in spring of 2014, and CARB has been monitoring performance of the water heating systems since May 2014.

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Analysis of Different Methods for Computing Source Energy in the Context of Zero Energy Buildings

Building energy consumption can only be measured at the site or at the point of utility interconnection with a building. Often, to evaluate the total energy impact, this site-based energy consumption is translated into source energy, that is, the energy at the point of fuel extraction. Consistent with this approach, the U.S. Department of Energy's (DOE) definition of zero energy buildings uses source energy as the metric to account for energy losses from the extraction, transformation, and delivery of energy. Other organizations, as well, use source energy to characterize the energy impacts. Four methods of making the conversion from site energy to source energy were investigated in the context of the DOE definition of zero energy buildings. These methods were evaluated based on three guiding principles--improve energy efficiency, reduce and stabilize power demand, and use power from nonrenewable energy sources as efficiently as possible. This study examines relative trends between strategies as they are implemented on very low-energy buildings to achieve zero energy. A typical office building was modeled and variations to this model performed. The photovoltaic output that was required to create a zero energy building was calculated. Trends were examined with these variations to study the impacts of the calculation method on the building's ability to achieve zero energy status. The paper will highlight the different methods and give conclusions on the advantages and disadvantages of the methods studied.

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Leveraging Shore-Side, Building Energy Simulation Tools for Use in the Shipboard Environment

Within the confines of the Maritime Prescreening Assessment of Conservation Technologies program, the U.S. Department of Energy's National Renewable Energy Laboratory, in collaboration with the Naval Surface Warfare Center Carderock and Philadelphia Divisions, the U.S. Department of Transportation's Maritime Administration, and the Massachusetts Maritime Academy, recently developed a physics-based modeling engine to evaluate non-propulsive ship energy consumption under different operational scenarios and technology applications, in order to enhance the Navy's ability to optimize ship operational reach and tactical performance. To accomplish this, a mature thermophysical modeling software, EnergyPlus TM , was modified for ship-related applications. This software, originally developed for use in whole-building energy consumption studies, is a free and open-source simulation program that has been used for more than two decades. The team evaluated the accuracy of the modified version of EnergyPlus by comparing results to direct measurements taken over a 6-week underway period onboard the Maritime Administration's Training Ship Kennedy. The team developed an energy model of a prescribed area of the ship and compared its performance to measured results obtained from 235 sensor points. EnergyPlus behaved well with respect to solar loading and convective heat transfer to seawater while the ship was underway. However, results suggest that additional work remains to accurately simulate seawater heat transfer while the ship is stationary.

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Is Every kWh the Same? How Do Energy Efficiency Measures Stack Up Across Regions?

The U.S. residential building sector is responsible for thirty-eight percent of electricity use, as well as for 49%, 8%, 19% and 2% of U.S. SO2, NOx, CO2 and PM2.5 emissions, respectively. The residential building sector is also a key target of customer funded energy efficiency programs, which have typically been designed to reduce energy consumption (kWh). Today, energy efficiency is also being used to meet air emissions goals. However, not every kWh saved has the same mitigation potential. The goal of this work is to evaluate energy efficiency potential in the U.S. residential building sector and determine how energy conservation measures (ECMs) that reduce electricity consumption correlate to air emissions reductions. To assess energy efficiency potential, this analysis uses Scout, a software program developed by the U.S. Department of Energy that estimates the energy and CO2 impact potential of various ECMs on the U.S. residential and commercial building sectors. Here, Scout is first used to assess the total long term national energy (electricity, gas, and oil), CO2, and cost savings of deploying residential ECM portfolios. Then, electricity savings results from Scout in 2021 are used to assess the regional differences in emissions reductions with the Avoided Emissions and geneRation Tool (AVERT) model from different end uses. Better understanding the relationship between ECMs and cost-effective emission reductions will enable states, municipalities and other interested parties to meet multiple policy objectives through energy efficiency.

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Charging Decision Making for Automated Electric Vehicles

Abstract: Overview/Significance With the emerging of autonomous driving and its application in electric vehicles, the charging decision making should be taken over by the vehicles. Autonomous electric vehicles will require the capability to make charging decisions according to the battery energy state, the travel demand and also the available charging infrastructure. The introduction of autonomous driving technology would remove the challenge of co-locating charging infrastructure with driver destinations and presents a driver-free method for EVs to reach nearby charging stations. This will significantly change the charging behavior of electric vehicles. EV driver will no longer need to be present at charging stations for charging actions. Automated EVs can drive to nearby charging stations to perform charging actions by themselves when necessary. Meanwhile, connected vehicles technology makes electric vehicles have the capability to sense and obtain pertinent information from nearby charging station networks and then calculate the corresponding costs and availability for charging. All these promising capabilities bring new opportunities to construct an automatic charging decision making system and improve energy efficiency of charging actions for automatic electric vehicles. Abstract: Methodology This research introduces optimization tools and case studies on charging decision making for automated electric vehicles under personal usage scenario. The designed optimization tools aim to provide charging strategies, i.e. the optimal choice of charging station outside home and the amount of charged energy, by considering constraints from personal daily itineraries and existing charging infrastructure. Abundant real world EV data is utilized to construct stochastic energy consumption prediction model under realistic driving conditions. Advanced dynamic programming technology is utilized to model and solve the potential multi-stage decision making process by considering realistic uncertainties of EV energy cost and dynamics of itinerary information. Large-scale simulation studies using Chicago itinerary data are demonstrated to illustrate energy benefits from automatic charging decision making for future autonomous EVs. Abstract: Results/Conclusions Optimal automatic charging decision making and its energy benefits have been studied for personal automated electric vehicles. A data-driven method based on large amount of real-world data and the corresponding real-time update algorithm have been proposed to construct multi-channel stochastic energy consumption prediction model. Based on the energy cost prediction, dynamic multi-stage charging decision-making models are established for optimal strategies during a daily itinerary. Derived charging strategies can minimize the monetary and energy cost of charging actions for autonomous vehicles. Simulations and case studies show the potential ability of autonomous electric vehicles to reduce the range anxiety and charging infrastructure dependency. Automatic charging decision making can mitigate the pain from electric vehicle charging necessity outside home. In conclusion, automated electric vehicles equipped with an appropriate automatic charging decision making system will help EV driver to relieve from heavy time cost of recharging, reduce the energy cost of performing charging actions and improve the energy efficiency in future electrified transportation system.

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