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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Demanding energy from carbon

Carbon is the central element driving the evolution of our human society towards prosperity over several historical stages. As for now, we are in a stage of blossoming sciences and technologies related to carbon materials, as a result of which our ever-growing energy demand has been largely satisfied. Yet, the expected rise of carbon energy consumption and the emerging environmental concerns have prevented us from being optimistic. To build a sufficiently powered future, we have been revolutionizing our ways of carbon energy utilization by discovering and designing new carbon structures, exploring and enhancing their unique physicochemical properties, and pursuing environmentally friendly strategies. Emerging structures such as graphene and sp-bonded C 18 have allowed us to discover carbon’s promising properties such as energy storage and superconductivity, while green energy solutions such as fuel cells and CO 2 reduction are working synergistically to purify the ecospheric carbon cycle. Therefore, this essay timely discusses related carbon sciences and technologies that have been the milestones shaping our energy consumption, based on which our energy future can be envisioned to be green and prosperous.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Improving Characterization of Miscellaneous Energy Loads in Energy Demand Models

Miscellaneous energy loads (MELs) comprise a significant and growing portion of total building energy consumption. However relatively little is known about the products that make up MELs end uses, and MELs are modeled with less granularity than major end uses in energy demand models such as the U.S. Department of Energy’s Scout energy efficiency impact analysis tool or the U.S. Energy Information Administration’s National Energy Modeling System (NEMS). This paper identifies differences in the way MELs versus major building end uses are modeled, and then reviews potential sources of MELs baseline technology data that could be used to improve their characterization in energy demand models. Case studies of dry distribution transformers, hot water circulation pumps, and Japanese-style automatic bidets are presented to compare and contrast the level of baseline data available for different end uses. We show that there is potential to use data from energy conservation standards and other sources to significantly improve representation of some MELs in energy demand models, while other emerging MELs require further data collection before they can be modeled precisely.

Miscellaneous Electric Loads, MELs, Models, Nation↗
DOE OSTI2019

Improving Characterization of Miscellaneous Energy Loads in Energy Demand Models

Miscellaneous energy loads (MELs) comprise a significant and growing portion of total building energy consumption. However relatively little is known about the products that make up MELs end uses, and MELs are modeled with less granularity than major end uses in energy demand models such as the U.S. Department of Energy's Scout energy efficiency impact analysis tool or the U.S. Energy Information Administration's National Energy Modeling System (NEMS). This paper identifies differences in the way MELs versus major building end uses are modeled, and then reviews potential sources of MELs baseline technology data that could be used to improve their characterization in energy demand models. Case studies of dry distribution transformers, hot water circulation pumps, and Japanese-style automatic bidets are presented to compare and contrast the level of baseline data available for different end uses. We show that there is potential to use data from energy conservation standards and other sources to significantly improve representation of some MELs in energy demand models, while other emerging MELs require further data collection before they can be modeled precisely.

Fares, Robert↗
DOE OSTI2018

Simulated building energy demand biases resulting from the use of representative weather stations

Numerical building models are typically forced with weather data from a limited number of “representative cities” or weather stations representing different climate regions. The use of representative weather stations reduces computational costs, but often fails to capture spatial heterogeneity in weather that may be important for simulations aimed at understanding how building stocks respond to a changing climate. We quantify the potential reduction in bias from using an increasing number of weather stations over the western U.S. The approach is based on deriving temperature and load time series using incrementally more weather stations, ranging from 8 to roughly 150, to capture weather across different seasons. Using 8 stations, one from each climate zone, across the western U.S. results in an average absolute summertime temperature bias of 7.2°F with respect to a spatially-resolved gridded dataset. The mean absolute bias drops to 2.8°F using all available weather stations. Temperature biases of this magnitude could translate to absolute summertime mean simulated load biases as high as 13.8%, a significant error for capacity expansion planners who may use these types of simulations. Increasing the size of the domain over which biases are calculated reduces their magnitude as positive and negative biases may cancel out. Using 8 representative weather stations can lead to a 20-40% overestimation of peak building loads during both summer and winter. Using weather stations close to population centers reduces both mean and peak load biases. This approach could be used by others designing aggregate building simulations to understand the sensitivity to their choice of weather stations used to drive the models.

Burleyson, Casey D.↗
DOE OSTI2018

The Building ENergy Demand (BEND) Model: A Comprehensive Composite Building Energy Modeling Framework

The Building ENergy Demand (BEND) model was developed at Pacific Northwest National Laboratory (PNNL) to simulate climate-dependent hourly building energy demand with the ability to disaggregate down to or aggregate up to any geographic area of interest, including counties, states, electric utility service territories, and census regions. The BEND modeling framework is designed to study the effects of climate change, economics, technology advancement, and various mitigation policies on aggregate building energy demand. To make the process computationally feasible, BEND reduces granularity in building characteristics and weather forcing while maintaining enough detail to minimize systematic differences (biases) between BEND results and data from the Energy Information Administration (EIA). Based on the Commercial Building Energy Consumption Survey (CBECS) and Residential Energy Consumption Survey (RECS) databases, BEND establishes a set of representative buildings with predefined sets of building types, sizes, and vintages. These representative buildings are defined for each of the four U.S. census regions and the five climate zones used by the EIA for building surveys (hereafter “EIA climate zones”). BEND’s representative buildings are based on a statistical analysis of the CBECS and RECS databases that infers detailed parameters such as construction type, thermal properties, internal loads, equipment and mechanical systems, and operation schedules from the high-level descriptors in those databases (building type, vintage, size, and location). The representative buildings are simplified and consolidated into a set of prototype buildings for the purpose of simulating building energy demand. Combining the prototype buildings with weather files from selected locations and years, BEND creates a set of simulation-ready building models to be run using the EnergyPlus simulation engine (EnergyPlus). The base spatial unit of each BEND simulation is a subregion that is the intersection of an International Energy Conservation Code climate zone, a census region, and an EIA climate zone; analysis for each spatial unit is influenced by the weather file BEND selects to represent the climate-similar area. The major simulation outputs of interest to BEND are hourly energy consumption values for each simulated year, possibly for multiple fuel types. Based on building-stock weights derived from the CBECS/RECS data, the hourly output streams are aggregated to a single, 8760-hour load profile for each of the major building categories—residential and commercial—with the possibility of segregating by fuel types. To correct for biases inherent in the use of a limited suite of representative buildings, BEND is calibrated against observational targets of total annual building energy consumption and hourly load profiles (including peak demand) obtained for the decision-relevant geographic scales of interest (e.g., total electric load data for individual balancing authorities available from EIA). Based on available data such as population distribution, BEND is able to aggregate both up and down scales, allowing the study of building energy demand over a range of spatial scales (e.g., states or counties within the Western Electricity Coordinating Council, Eastern Interconnection, or the entire U.S.) and/or subsets of building types (e.g., office buildings with particular size ranges).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

A multi-scale calibration approach for process-oriented aggregated building energy demand models

Long-term energy planning relies largely on projections of future energy demand and hourly load profiles. Aggregate building models are increasingly being utilized to characterize the sensitivity of current and future building stocks to changes in climate, population, and building technology on city-to-regional-to-national scales. Due to challenges in the availability of data, those analyses have been limited to projection of energy demand at a single scale, usually state or country, while long-term planning power system models and production cost models might operate at different spatial scales such as energy regions which are focused on ensuring adequate generation infrastructure. In this work, we propose and evaluate a novel method to calibrate an aggregate building energy demand model (PNNL’s BEND model) against the best available data at the spatial scale of balancing authorities. This approach extends previous work on aggregated building energy demand by facilitating analysis of building energy demand across scales, in particular policy and operational decision-making scales. We show that the bias-corrected model estimates building electric loads reasonably well compared with estimates from a statistical model, but has the additional feature of flexibility across spatial scales. While the calibration approach is presently U.S.-centric and associated with U.S. energy regions, it can be extrapolated to other worldwide regions with similar scale challenges between policy and operational implementation decision making. We discuss the significant challenges involved in formulating and calibrating a complex physical model based on simulations of roughly 100,000 individual buildings against available aggregate regional electric load data and highlight areas for potential future work and improved data collection.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

The Interplay Between Climate and Urban Expansion on Building Energy Demand in Morocco

Understanding building energy demand is critical for addressing climate uncertainty challenges and ensuring sustainable urban growth. This study develops a building energy demand (BED) model to explore how climate variation and urban expansion affect residential and commercial space heating and cooling demands in Morocco for three scenarios, namely, 2005, 2018, and 2018 + 1.5 °C. The results show that coastal cities have lower heating and cooling needs due to the oceanic influence, while interior cities require significantly higher heating demand per-unit-floorspace. Between 2005 and 2018, urban growth increased total heating and cooling demand by 218.8 GWh, particularly in northern and coastal regions, despite per-unit-floorspace reductions in milder climates and improved building efficiency in 2018. Residential heating remains the dominant energy use, though commercial demand is significant in urban centers. Under the 2018 + 1.5 °C hypothetical scenario, heating demand across Morocco declines by 335.8 GWh compared to 2018, with urban areas amplifying this trend. Meanwhile, cooling demand increases slightly by 44.4 GWh, with major cities experiencing relative increases of up to 50%. These findings highlight a trade-off where reduced winter heating needs are partly offset by increased summer cooling demands in densely urbanized areas. In conclusion, the study identifies key urban hotspots for targeted interventions, emphasizing the need for energy-efficient building designs, climate-adaptive urban planning, and resilient energy management strategies to sustainably address shifting seasonal energy patterns.

Morocco↗
DOE OSTI2020

Energy demand science for a decarbonized society in the context of the residential sector

To develop a decarbonized society, two contradictory requirements must be met: (1) reducing energy demand and (2) creating flexibility in energy demand in order to respond to fluctuations in renewable electricity generation. To help meet these requirements, conventional energy efficiency studies should be extended to incorporate “energy demand science.” This paper presents a definition of “energy demand science” and then reviews the related history and research questions of energy demand science in the context of the residential sector. It then examines three key areas that must be integrated into the next-generation energy demand science: (1) energy demand measurement with detailed granularity and analysis using cutting-edge technology, (2) energy demand modeling that helps clarify the formation mechanism of energy demand, and (3) identification of the factors that influence people's decision making, which represents typical human-dimension research. Energy demand science consists of technical, human, natural environment, demographic, and land-use dimensions, and their integration is key for the establishment of a decarbonized society.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Amplification of future energy demand growth due to climate change

Future energy demand is likely to increase due to climate change, but the magnitude depends on many interacting sources of uncertainty. We combine econometrically estimated responses of energy use to income, hot and cold days with future projections of spatial population and national income under five socioeconomic scenarios and temperature increases around 2050 for two emission scenarios simulated by 21 Earth System Models (ESMs). Here we show that, across 210 realizations of socioeconomic and climate scenarios, vigorous (moderate) warming increases global climate-exposed energy demand before adaptation around 2050 by 25–58% (11–27%), on top of a factor 1.7–2.8 increase above present-day due to socioeconomic developments. We find broad agreement among ESMs that energy demand rises by more than 25% in the tropics and southern regions of the USA, Europe and China. Socioeconomic scenarios vary widely in the number of people in low-income countries exposed to increases in energy demand.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Implications of different income distributions for future residential energy demand in the U.S.

Abstract Future income distribution will affect energy demand and its interactions with various societal priorities. Most future model simulations assume a single average consumer and thus miss this important demand determinant. We quantify long-term implications of alternative future income distributions for state-level residential energy demand, investment, greenhouse gas, and pollutant emission patterns in the United States (U.S.) by incorporating income quintiles into the residential energy sector of the Global Change Analysis Model with 50-state disaggregation. We find that if the income distribution within each U.S. state becomes more egalitarian than present, what means that the difference on income between the richest and poorest decreases over time, residential energy demand could be 10% (4%–14% across states) higher in 2100. This increase of residential energy demand will directly reduce energy poverty, with a very modest increment on economywide CO 2 emissions (1%–2%). On the other hand, if U.S. states transition to a less equitable income distribution than present, with the difference between richest and poorest increasing over time, residential energy demand could be 19% (12%–26% across states) lower. While this study focuses on a single sector, we conclude that to improve understanding of synergies and tradeoffs across multiple societal goals such as energy access, emissions, and investments, future model simulations should explicitly consider subregional income distribution impacts.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

Optimization Based Data Mining Approah for Forecasting Real-Time Energy Demand

The worldwide concern over environmental degradation, increasing pressure on electric utility companies to meet peak energy demand, and the requirement to avoid purchasing power from the real-time energy market are motivating the utility companies to explore new approaches for forecasting energy demand. Until now, most approaches for forecasting energy demand rely on monthly electrical consumption data. The emergence of smart meters data is changing the data space for electric utility companies, and creating opportunities for utility companies to collect and analyze energy consumption data at a much finer temporal resolution of at least 15-minutes interval. While the data granularity provided by smart meters is important, there are still other challenges in forecasting energy demand; these challenges include lack of information about appliances usage and occupants behavior. Consequently, in this paper, we develop an optimization based data mining approach for forecasting real-time energy demand using smart meters data. The objective of our approach is to develop a robust estimation of energy demand without access to these other building and behavior data. Specifically, the forecasting problem is formulated as a quadratic programming problem and solved using the so-called support vector machine (SVM) technique in an online setting. The parameters of the SVM technique are optimized using simulated annealing approach. The proposed approach is applied to hourly smart meters data for several residential customers over several days.

data mining↗
DOE OSTI2021

Energy demand reduction in the built environment using shallow geothermal integrated energy systems: Part II – Hybrid ground source heat pump for building heating

The research collection aims at finding the various possible opportunities for the effective integration of shallow geothermal energy (SGE) to decrease the energy demand in the built environment and to reduce emission associated with it. The direct utilization of SGE using a ground source heat pump (GSHP) has been reviewed in comprehensive review part I and part II. From the extensive review, we have found that the hybrid GSHP is needed to avoid ground thermal imbalance and peak demand. Hybrid GSHP can adopt various supplemental heat sources and sinks according to the local climatic conditions and the balance of energy demands. The primary focus on the integration of subsystems such as biomass, solar energy (PV, PVT, and collector), phase change material, micro gas turbine, and absorption heat pump with GSHP is presented for heating application. This comprehensive review part III highlights the recent research findings and a potential gap in hybrid GSHP for further research and developments

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Mitigating energy demand sector emissions: The integrated modelling perspective

Mitigating carbon emissions in the current energy system will require fundamental changes of both the energy supply and the energy demand sectors. Previous global model-based analyses, however, have focused mostly on energy supply transformations, while the energy demand sector changes are less well understood. In this study, this knowledge gap is addressed by analysing in detail the projected future energy demand projections, and the required demand-side changes to reach stringent mitigation targets using a suite of integrated assessment models. We examine industry, transport and buildings sector pathways across four models and three different reference scenarios from the Shared-Socioeconomic Pathway framework which is used as a set of common future perspectives by the climate research community. The demand side mitigation efforts are compared to a more detailed, sector-specific, technology-oriented assessments of abatement potential based on a literature review for the year 2030. The results indicate that strong emission growth in the industry and transport sector can be attributed to increasing final energy per capita and population growth. In the stringent mitigation scenarios energy efficiency, electrification and switching to low carbon fuel are all required in the short term. In the green growth SSP1 scenario the required emission reduction is significantly less than other scenarios showing that the demand growth and the technology development largely affects the sectors’ mitigation challenge. The technology assessment estimates that in particular in the transport and buildings sector there is a higher potential to reduce demand-side emissions through energy efficiency improvements than currently envisioned in the integrated assessment models.

Edelenbosch, Oreane Y.↗