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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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At least 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities

Buildings account for 35% of the global final energy demand. Efficiency improvements and advanced control strategies have a significant impact in the reduction of energy costs and CO2 emissions. Building energy simulation is widely used to help planners, contractors, and building owners analyse diverse options regarding the planning and management of energy consumption in buildings. Furthermore, recent advances in data processing and computing have led to the development of sophisticated machine learning algorithms that can learn from large datasets, e.g., sensor data from buildings, and use them to develop building-specific adaptive and automatic energy controllers. Control algorithms, such as deep reinforcement learning can tune themselves, are model-free, and economical to implement. In this paper, we introduce an integrated simulation environment that combines CitySim, a fast building energy simulator, and TensorFlow, a platform for efficient implementation of advanced machine learning algorithms. The integration is achieved via Keras - an API for TensorFlow - and a set of text and csv files for data transfer between the applications. This new environment will allow researchers to investigate novel learning control algorithms, and demonstrate their robustness and potential for diverse applications in the built environment. We present two case studies for energy savings and demand response, respectively.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2014

Retrofit California: Better Buildings Program Overview

Energy efficiency retrofits (also called upgrades) are widely recognized as a critical component to achieving energy savings in the building sector to help lower greenhouse gas (GHG) emissions. To date, however, upgrades have accounted for only a small percentage of aggregate energy savings in building stock, both in California and nationally. Although the measures and technologies to retrofit a building to become energy efficient are readily deployed, establishing this model as a standard practice remains elusive. Retrofit California sought to develop and test new program models to increase participation in the energy upgrade market in California. The Program encompassed 24 pilot projects, conducted between 2010 and mid-2013 and funded through a $30 million American Recovery and Reinvestment Act (ARRA) grant from the U.S. Department of Energy’s (DOE) Better Buildings Neighborhood Program (BBNP). The broad scope of the Program can be seen in the involvement of the following regionally based Grant Partners: Los Angeles County (as prime grantee); Association of Bay Area Governments (ABAG), consisting of: StopWaste.org for Alameda County, Regional Climate Protection Authority (RCPA) for Sonoma County, SF Environment for the City and County of San Francisco, and City of San Jose; California Center for Sustainable Energy (CCSE) for the San Diego region; Sacramento Municipal Utilities District (SMUD). Within these jurisdictions, nine different types of pilots were tested with the common goal of identifying, informing, and educating the people most likely to undertake energy upgrades (both homeowners and contractors), and to provide them with incentives and resources to facilitate the process. Despite its limited duration, Retrofit California undoubtedly succeeded in increasing awareness and education among home and property owners, as well as contractors, realtors, and community leaders. However, program results indicate that a longer timeframe will be needed to transform the market and establish energy retrofits as the new paradigm. Innovations such as Flex Path, which came about because of barriers encountered during the Program, have already shown promise and are enabling increased participation. Together, the pilots represent an unprecedented effort to identify and address market barriers to energy efficiency upgrades and to provide lessons learned to shape future program planning and implementation. The statistics reflects the scope of the marketing and outreach campaigns, which tested a variety of approaches to increase understanding of the benefits of energy upgrades to drive participation in the Program. More traditional methods such as TV and radio advertisements were complimented by innovative community based social marketing campaigns that sought to leverage the trusted status of neighborhood organizations and leaders in order to motivate their constituents to undertake retrofits. The remainder of this report provides an overview of Retrofit California including brief summaries of the pilots’ main components and highlights, followed by the major findings or takeaway lessons from the approaches that were tested. Eleven of the pilots will be continued, with modifications, under the ratepayer-funded Regional Energy Networks. Involvement in the RENS by many of the Retrofit California partners will ensure that early lessons learned are carried forward to guide future programs for energy upgrades in California.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Linking human-building interactions in shared offices with personality traits

Occupant behavior influences office building energy performance. The level of human-building interactions (HBIs) in shared offices strongly influences building energy use and occupant well-being. This study explored the link between occupant personality types and their behaviors of sharing energy and environment control systems and interactions with their colleagues. Inspired by the Five-Factor Model (FFM), we classified HBI behaviors into four dimensions: willingness to share control, knowledge of control, group decision behavior, and adaptive strategies. These four variables can be mapped to the four personality traits proposed by the FFM: agreeableness, openness, extraversion, and conscientiousness. Our cluster analysis identified six behavioral patterns: average (17.7%), reserved (15.3%), environmentally friendly (16.6%), role model (24.2%), self-centered (17.2%), and mechanist (9.0%). We further applied association rules, a widely utilized machine learning technique, to discover how demographics, building-related contextual factors, and perception-attitudinal factors influence HBI behaviors. Country, control feature accessibility, and group dynamics were found to be the three most influential factors that determine occupants’ HBI behaviors. Finally, the study provides insights about building design and operation, as well as policy to promote socially and environmentally desirable HBI behaviors in a shared office environment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2009

The Applications of NASA Mission Technologies to the Greening of Human Impact

I will give an overview talk about flight software systems, robotics technologies and modeling for energy minimization as applied to vehicles and buildings infrastructures. A dominant issue in both design and operations of robotic spacecraft is the minimization of energy use. In the design and building of spacecraft increased power is acquired only at the cost of additional mass and volumes and ultimately cost. Consequently, interplanetary spacecrafts are designed to have the minimum essential power and those designs often incorporate careful timing of all power use. Operationally, the availability of power is the most influential constraint for the use of planetary surface robots, such as the Mars Exploration Rovers. The amount of driving done, the amount of science accomplished and indeed the survivability of the spacecraft itself is determined by the power available for use. For the Mars Exploration Rovers there are four tools which are used: (1) models of the rover and it s thermal and power use (2) predictive environmental models of power input and thermal environment (3) fine grained manipulation of power use (4) optimization modeling and planning tools. In this talk I will discuss possible applications of this methodology to minimizing power use on Earth, especially in buildings.

Sims, Michael H.↗
DOE OSTI2017

IEA EBC annex 53: Total energy use in buildings—Analysis and evaluation methods

One of the most significant barriers to achieving deep building energy efficiency is a lack of knowledge about the factors determining energy use. In fact, there is often a significant discrepancy between designed and real energy use in buildings, which is poorly understood but are believed to have more to do with the role of human behavior than building design. Building energy use is mainly influenced by six factors: climate, building envelope, building services and energy systems, building operation and maintenance, occupants’ activities and behavior, and indoor environmental quality. In the past, much research focused on the first three factors. However, the next three human-related factors can have an influence as significant as the first three. Annex 53 employed an interdisciplinary approach, integrating building science, architectural engineering, computer modeling and simulation, and social and behavioral science to develop and apply methods to analyze and evaluate the real energy use in buildings considering the six influencing factors. Finally, outcomes from Annex 53 improved understanding and strengthen knowledge regarding the robust prediction of total energy use in buildings, enabling reliable quantitative assessment of energy-savings measures, policies, and techniques.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Residential retrofits at scale: opportunity identification, saving estimation, and personalized messaging based on communicating thermostat data

Basic insulation and heating system retrofits of existing homes could achieve annual energy savings of up to $4–5 billion in the USA. However, current US utility energy efficiency (EE) programs are costly and challenging to scale. Customer acquisition occurs primarily through energy bill mailers, mass media, and on-line advertising that lack specificity about particular home retrofit opportunities, expected energy savings, and cost-effectiveness. Specific retrofit opportunities are identified via on-site home energy assessments that can be inconvenient to homeowners, expensive, and of variable accuracy. Our paper discusses using communicating thermostat data to significantly increase the customer uptake of energy conservation measures (ECMs) by identifying homes with the most significant retrofit opportunities, estimating post-retrofit energy savings, and formulating home-specific outreach. We extended our previously developed gray-box building model to identify physical building parameters corresponding to the target retrofit opportunities, i.e., whole-home R value, air leakage characteristics, and heating system efficiency. Here, the estimated R values and air leakage characteristics compare favorably with the ground truth. The validated algorithms can calculate home-specific energy savings estimate for each ECM, and the algorithm outputs can be used by utility EE programs to formulate home-specific retrofit offers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Making Better Use of On-Site PV Generation: Direct Distribution of DC Power in Buildings

With the increasing availability of Direct Current (DC) power from on-site solar systems, building designers now have the option of direct distribution of DC power to energy storage systems and end-use loads such as lighting, electronics, and motors. Research shows that direct-DC can reduce building distribution losses by 5-15%, while improving power reliability. A readily available way to implement direct-DC distribution is Power over Ethernet (PoE), which typically has provided low-voltage DC to power office equipment such as WiFi access points. With recent updates to the PoE standard, equipment such as network-controlled LED lighting, thin client and computer workstations, HVAC controllers, fans, motors, and actuators can now be powered by PoE and are commercially available. This paper presents results from a DOE-funded project that is demonstrating PoE technologies and describes a DC modeling and design tool that is intended to help building designers properly evaluate and design DC distribution systems in buildings. The tool provides designers of DC power systems with estimates of both power system losses and indirect effects on building heating and cooling. The results of this research demonstrate that the design methods and products are available to implement DC power in buildings today.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

A network model for natural ventilation simulation in deep buried underground structures

The study of underground natural ventilation opportunities has become increasingly significant in recent years with its promise of wide application in underground structures such as underground hydro power stations, metro stations, underground parking and laboratories. It is recognized that deployment of natural ventilation constitutes a passive technology that can lead to significant energy conservation if applied judiciously. This paper focuses on underground buildings that are buried deeply and typically consist of an underground complex network of connected structures. The other characteristic is that these structures house machinery and devices that generate heat, leading to elevated internal air temperatures. Combined with the deep location, it implies that buoyancy forces are significant which make natural ventilation through vertical shaft openings a viable option. These characteristics demand a study of the heat transfer processes between ambient conditions, soil and underground buildings. In this paper, we present a dynamic flow network model with loops for multizone airflow and apply it to deep buried underground structures considering the dominant heat transfer characteristics, not only through the elements of the network but also the heat exchange with the envelope and adjacent soil mass. Finally, a small-scale experiment of occurring airflow is conducted and compared with the outcomes of the dynamic simulation of the proposed model. The comparison serves as validation and illustration of the application potential of the network model for natural ventilation investigation and consecutive optimization of its use in underground buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Integrated Dynamic Facade Control with an Agent-based Architecture for Commercial Buildings

Dynamic façades have significant technical potential to minimize heating, cooling, and lighting energy use and peak electric demand in the perimeter zone of commercial buildings, but the performance of these systems is reliant on being able to balance complex trade-offs between solar control, daylight admission, comfort, and view over the life of the installation. As the context for controllable energy-efficiency technologies grows more complex with the increased use of intermittent renewable energy resources on the grid, it has become increasingly important to look ahead towards more advanced approaches to integrated systems control in order to achieve optimum life-cycle performance at a lower cost. This study examines the feasibility of a model predictive control system for low-cost autonomous dynamic façades. A system architecture designed around lightweight, simple agents is proposed. The architecture accommodates whole building and grid level demands through its modular, hierarchical approach. Automatically-generated models for computing window heat gains, daylight illuminance, and discomfort glare are described. The open source Modelica and JModelica software tools were used to determine the optimum state of control given inputs of window heat gains and lighting loads for a 24-hour optimization horizon. Penalty functions for glare and view/ daylight quality were implemented as constraints. The control system was tested on a low-power controller (1.4 GHz single core with 2 GB of RAM) to evaluate feasibility. The target platform is a low-cost ($35/unit) embedded controller with 1.2 GHz dual-core cpu and 1 GB of RAM. Configuration and commissioning of the curtainwall unit was designed to be largely plug and play with minimal inputs required by the manufacturer through a web-based user interface. An example application was used to demonstrate optimal control of a three-zone electrochromic window for a south-facing zone. The overall approach was deemed to be promising. Further engineering is required to enable scalable, turnkey solutions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

Flexible Residential Test Facility: Impact of Infiltration and Ventilation on Measured Heating Season Energy and Moisture Levels

Two identical laboratory homes designed to model existing Florida building stock were sealed and tested to 2.5 ACH50. Then, one was made leaky with 70% leakage through the attic and 30% through windows, to a tested value of 9 ACH50. Reduced energy use was measured in the tighter home (2.5 ACH50) in the range of 15% to 16.5% relative to the leaky (9 ACH50) home. Internal moisture loads resulted in higher dew points inside the tight home than the leaky home. Window condensation and mold growth occurred inside the tight home. Even cutting internal moisture gains in half to 6.05 lbs/day, the dew point of the tight home was more than 15 degrees F higher than the outside dry bulb temperature. The homes have single pane glass representative of older Central Florida homes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

Flexible Residential Test Facility: Impact of Infiltration and Ventilation on Measured Heating Season Energy and Moisture Levels

Two identical laboratory homes designed to model existing Florida building stock were sealed and tested to 2.5 ACH50. Then, one was made leaky with 70% leakage through the attic and 30% through windows, to a tested value of 9 ACH50. Reduced energy use was measured in the tighter home (2.5 ACH50) in the range of 15% to 16.5% relative to the leaky (9 ACH50) home. Internal moisture loads resulted in higher dew points inside the tight home than the leaky home. Window condensation and mold growth occurred inside the tight home.

BA-PIRC↗
DOE OSTI2020

Deep Reinforcement Learning for Residential HVAC Control with Consideration of Human Occupancy

The Artificial Intelligence (AI) development described herein uses model-free Deep Reinforcement Learning (DRL) to minimize energy cost during residential heating, ventilation, and air conditioning (HVAC) operation. Building cooling loads and HVAC operation are difficult to accurately model due to complexity, lack of measurements and data, and model specific performance, so online machine learning is used to allow for real-time readjustment in performance. Energy costs for the multi-zone cooling unit shown in this work are minimized by scheduling on/off commands around dynamic prices. By taking advantage of precooling events that take place when the price is low, the agent is able to reduce operational cost without violating user comfort. The DRL controller was tested in simulation where the learner achieved a 43.89% cost reduction when compared to traditional, fixed-setpoint operation. The system is now ready for the next phase of testing in a live, real-time home environment.

Mckee, Evan↗
DOE OSTI2026

MeshedResevoir v1.0

This is a small Modelica library and python scripts that are used to run numerical experiments for a journal paper. The software is meant to be shared so that readers can reproduce our results. The models are a highly simplified representation of a district energy system, using components from the Modelica Buildings Library (BSD-licensed, LBL owned) and new models for an expansion vessel.

Wetter, Michael [Lawrence Berkeley National Labora↗
DOE OSTI2018

Energy Auditor and Quality Control Inspector Competency Model

The Energy Auditor (EA) and Quality Control Inspector (QCI) Competency model was developed to identify the soft skills, foundational competencies and define the levels of Knowledge, Skills, and Abilities (KSAs) required to successfully perform the tasks defined in the EA and QCI Job Task Analysis (JTAs), the U.S. Department of Energy (DOE) used the U.S. Department of Labor's (DOL) Competency Model Clearinghouse resources to develop a QCI and EA Competency Model. To keep the QCI and EA competency model consistent with other construction and energy management competency models, DOE and the National Renewable Energy Laboratory used the existing 'Residential Construction Competency Model' and the 'Advanced Commercial Building Competency Model' where appropriate.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Identification of aggregate building thermal dynamic model and unmeasured internal heat load from data

This paper is on the problem of simultaneously identifying the parameters of an aggregate thermal dynamic model of a multi-zone building and unknown disturbances from input-output data. An aggregate model is a single-zone equivalent of a multi-zone building, and is useful for many purposes, including model based control of large heating, ventilation and air conditioning (HVAC) equipment that delivers thermal energy to the entire building. A key challenge in identification is the presence of unknown disturbance since it is not measurable but non-negligible.We first present a principled method to aggregate a multizone building model into a single zone model. We then provide a method to identify thermal parameters and the unknown disturbance for this aggregate (single-zone) model. Finally, we test our proposed identification algorithm to data generated from a virtual building. A key insight provided by the aggregation method allows us to recognize under what conditions the estimation of the disturbance signal will be necessarily poor and uncertain.

Guo, Zhong↗
DOE OSTI2019

Development of a topology analysis tool for fifth-generation district heating and cooling networks

Fifth generation district heating and cooling networks are characterized by supply temperatures in the ambient range of 15-25 degrees C, which not only reduces heat loss but also allows for integrating various kinds of low-temperature waste heat sources. The ability of these networks to absorb waste heat that is normally unrecoverable makes them an attractive solution for the future energy supply of urban areas. To enhance the adoption of these networks, this paper describes the development of a software tool to analyze the feasibility of fifth-generation of district heating and cooling systems in both new and existing districts. The research attempts to answer the question: 'Which buildings should be connected to a low-temperature hydraulic network given both the incremental benefits and costs relative to the scenario of decentralized (dedicated) heating and cooling systems for each building?' Therefore, all possible network layouts of the buildings are considered where the heating and cooling demands of each building are met by the fifth-generation of district heating and cooling network. For the heating and cooling load characterization of the buildings, reduced-order models are used while renewable energy and waste heat sources are included in the network. The focus of the research lies in the development of a hydraulic model for flexible use in the urban energy modeling and the optimization of the fifth-generation of district heating and cooling network topology for a given urban district. This research displays simulation results that quantify the performance of the fifth-generation of district heating and cooling network based on various output metrics, including primary energy usage, carbon dioxide emissions, and network implementation cost.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Public Housing: A Tailored Approach to Energy Retrofits

Over one million HUD-supported public housing units provide rental housing for eligible low-income families across the country. A survey of over 100 public housing authorities (PHAs) across the country indicated that there is a high level of interest in developing low-cost solutions that improve energy efficiency and can be seamlessly included in the refurbishment process. Further, PHAs, have incentives (both internal and external) to reduce utility bills. ARIES worked with four PHAs to develop packages of energy efficiency retrofit measures the PHAs can cost-effectively implement with their own staffs in the normal course of housing operations at the time when units are refurbished between occupancies. The energy efficiency turnover protocols emphasized air infiltration reduction, duct sealing, and measures that improve equipment efficiency. ARIES documented implementation in 18 housing units. Reductions in average air leakage were 16% and duct leakage reductions averaged 23%. Total source energy consumption savings due to implemented measures was estimated at 3-10% based on BEopt modeling with a simple payback of 1.6 to 2.5 years. Implementation challenges were encountered mainly related to required operational changes and budgetary constraints. Nevertheless, simple measures can feasibly be accomplished by PHA staff at low or no cost. At typical housing unit turnover rates, these measures could impact hundreds of thousands of units per year nationally.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗