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DOE OSTI2015

Realizing High-Performance Buildings

High-performance buildings (HPBs) are exceptional examples of both design and practice. Their energy footprints are small, and these are buildings that people want to work in because of their intelligent structure, operations, and coincident comfort. However, the operation of most buildings, even ones that are properly constructed and commissioned at the start, can deviate significantly from the original design intent over time, particularly due to control system overrides and growing plug and data center loads. With early planning for systems such as submetering and occupant engagement tools, operators can identify and remedy the problems. This guide is a primer for owners and owners' representatives who are pursuing HPBs. It describes processes that have been successful in the planning, procurement, and operation of HPBs with exceptional energy efficiency. Much of the guidance offered results from a series of semi-structured conference calls with a technical advisory group of 15 owners and operators of prominent HPBs in the United States. The guide provides a prescription for planning, achieving, and maintaining an HPB. Although the guide focuses on the operations stage of buildings, many of the operations practices are specified during the planning stage.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2016

Building Technologies Office FY 2017 Budget At-A-Glance

Buildings and homes use more than 73% of the electrical energy consumed in the United States. They also consume 40% of the nation’s total energy, with an annual energy bill of $430 billion. These energy bills can be cost effectively reduced by 20%–50% or more through various energy-efficient technologies and techniques. The Building Technologies Office (BTO) will continue to develop and demonstrate advanced building efficiency technologies and practices to make buildings in the United States more efficient, affordable, and comfortable.

Building, Budget, Finance↗

DOE OSTI2016

Can High-Performance Equipment Lead to a Low-Performance Building?

The performance-based compliance alternative available in most energy codes, intended to provide energy efficiency equivalent to that of prescriptive compliance while allowing innovation and design flexibility, can instead result in sub-standard energy performance in both the short and the long term. The potential deficiencies in modeled buildings originate with subtleties in the energy modeling rules, allowing building systems that consume more energy than their real-world, prescriptively-designed counterparts. This performance gap is exacerbated over subsequent decades as less efficient permanent features of the building remain while elements with shorter lives are regularly upgraded in most buildings. This paper summarizes an investigation into the topic for Pacific Northwest National Laboratory and the City of Seattle, including identification of the principal deficiencies exploited in the modeling path, and several potential code amendments that could resolve these deficiencies and establish better equivalency between prescriptive and performance compliance paths. The study, focusing on Seattle and Washington State energy codes, offers lessons and implications for other jurisdictions and energy codes.

Building Energy Codes↗

DOE OSTI2017

IN2 Profile: Reducing Energy Prices for Commercial Buildings with NETenergy’s Black ICE Technology

To cool a commercial building properly and cost-effectively, the owners of NETenergy have created a thermal battery that works much like an electrical battery except instead of storing energy electrically, it stores energy thermally. Through the Wells Fargo Innovation Incubator (IN²) program, NETenergy will be able to advance modeling of their system and increase product development. The IN² program launched in October 2014 and is part of Wells Fargo’s 2020 Environmental Commitment to provide $100 million to environmentally-focused nonprofits and universities. The goal is to create an ecosystem that fosters and accelerates the commercialization of promising commercial buildings technologies that can provide scalable solutions to reduce the energy impact of buildings. According to the Department of Energy, nearly 40 percent of energy consumption in the U.S. today comes from buildings at an estimated cost of $413 billion.

25 ENERGY STORAGE↗

DOE OSTI2017

NREL’s Advanced Analytics Research for Energy-Efficient Buildings

At NREL, we believe in building better buildings. More importantly, high-performance buildings that can do more and be smarter than ever before. Forty percent of the total energy consumption in the United States comes from buildings. Working together, we can dramatically shrink that number. But first, it starts with the research: our observations, experiments, modeling, analysis, and more. NREL’s advanced analytics research has already proven to reduce energy use, save money, and stabilize the grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2017

Federal New Buildings Handbook for Net Zero Energy, Water, and Waste

In 2015, the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) defined zero energy buildings as "an energy-efficient building where, on a source energy basis, the actual annual delivered energy is less than or equal to the on-site renewable exported energy." This document is focused on applying EERE’s definition of zero energy buildings to federal sector new buildings. However, it is not intended to replace, substitute, or modify any statutory or regulatory requirements and mandates.

federal new buildings↗

DOE OSTI2017

Federal Existing Buildings Handbook for Net Zero Energy, Water, and Waste

In 2015, the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) defined zero energy buildings as "an energy-efficient building where, on a source energy basis, the actual annual delivered energy is less than or equal to the on-site renewable exported energy." This handbook is focused on applying the EERE definition of zero energy buildings to existing buildings in the federal sector. However, it is not intended to replace, substitute, or modify any statutory or regulatory requirements and mandates.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2018

FloorspaceJS - A New, Open Source, Web-Based Geometry Editor for Building Energy Modeling (BEM): Preprint

Across most industries, desktop applications are being rapidly migrated to web applications for a variety of reasons. Web applications are inherently cross platform, mobile, and easier to distribute than desktop applications. Fueling this trend are a wide range of free, open source libraries and frameworks that make it incredibly easy to develop powerful web applications. The building energy modeling community is just beginning to pick up on these larger trends, with a small but growing number of building energy modeling applications starting on or moving to the web. This paper presents a new, open source, web based geometry editor for Building Energy Modeling (BEM). The editor is written completely in JavaScript and runs in a modern web browser. The editor works on a custom JSON file format and is designed to be integrated into a variety of web and desktop applications. The web based editor is available to use as a standalone web application at: https://nrel.github.io/openstudio-geometry-editor/. An example integration is demonstrated with the OpenStudio desktop application. Finally, the editor can be easily integrated with a wide range of possible building energy modeling web applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2018

How Building America Research is Removing Major Roadblocks to High-Performance Homes: Preprint

Today the U.S. housing industry is critically challenged to deliver homes that safely and effectively meet demanding new performance requirements of homeowners and modern building codes. But the housing industry must learn to better manage real and perceived risks that can impact occupant health, comfort, and building durability. It is clear from decades of applied research through the U.S. Department of Energy's (DOE) Building America program that many building energy efficiency and performance improvements will not be adopted by the market or industry standards if the costs and perceived risks of change outweigh the known benefits, especially when the benefits do not accrue directly to businesses who must take those risks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2018

An Open, Cloud-Based Platform for Whole-Building Fault Detection and Diagnostics

Small commercial buildings in the U.S. waste an estimated 300 Trillion BTU (approximately $6 billion in energy costs) annually due to faults, but lack cost-effective automated fault detection and diagnosis (AFDD) tools. NREL and GE Global Research are partnering to develop hybrid AFDD algorithms tailored to the unique needs of small commercial buildings (</=10,000 sq. ft.). The approach combines physics-based energy modeling tools with state-of-the-art machine learning algorithms to enable diagnostics using modeled input data. NREL, in partnership with Purdue University, has adapted DOE's physics-based modeling software EnergyPlus and OpenStudio to simulate common building faults. Cloud-based implementations of these physics-based models were used to generate data for multiple climate zones. The data were then used to train machine learning algorithms, such as those available in GE's Predix platform, to detect and diagnose building faults. Key features of the system including the automated, cloud-implementation of EnergyPlus, genetic algorithms for automated model feature selection, multiclass detection and diagnosis models, and anomaly detection algorithms will be presented.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2018

An Interactive Visualization Tool for Large-Scale Building Stock Modeling

Recent advancements in data science and high-performance computing are making it easier to run millions of building simulations, but meaningful visualization of such large datasets remains a challenge. This paper presents a new tool developed to view the results of large-scale OpenStudio simulations of national, regional, or local building stocks. The tool processes millions of simulations to calculate measure savings, utility bills, carbon emissions, primary energy, and cost-effectiveness metrics at a high geographic resolution. Interactive visualizations of the building characteristics, consumption, and measure savings data include proportional symbol maps and histogram plots and can be filtered by any building characteristic.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2018

FloorspaceJS - A New, Open Source, Web-Based Geometry Editor for Building Energy Modeling (BEM)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2018

How Building America Research is Removing Major Roadblocks to High-Performance Homes

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

End-Use Load Profiles for the U.S. Building Stock

The End-Use Load Profiles for the U.S. Building Stock project uses New ResStock and ComStock models to statistically represent the energy use of U.S. buildings. The project's hybrid approach combines best-available ground truth data, such as submetering studies and statistical disaggregation of whole-building interval meter data, with the reach, cost-effectiveness, and granularity of physics-based and data-driven building stock modeling to deliver a nationally-comprehensive data set at a fraction of the historical cost.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Grid-Interactive Multi-Zone Building Control Using Reinforcement Learning with Global-Local Policy Search: Preprint

In this paper, we develop a grid-interactive multi-zone building controller based on a deep reinforcement learning (RL) approach. The controller is designed to facilitate building operation during normal conditions and demand response events, while ensuring occupants comfort and energy efficiency. We leverage a continuous action space RL formulation, and devise a two-stage global-local RL training framework. In the first stage, a global fast policy search is performed using a gradient-free RL algorithm. In the second stage, a local fine-tuning is conducted using a policy gradient method. In contrast to the state-of-the-art model predictive control (MPC) approach, the proposed RL controller does not require complex computation during real-time operation and can adapt to non-linear building models. We illustrate the controller performance numerically using a five-zone commercial building.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2021

Resilient Energy Platform. Fact Sheet: Power Sector Cybersecurity Building Blocks

The Power Sector Cybersecurity Building Blocks, developed through the USAID-NREL Partnership and the Resilient Energy Platform, are designed to help a variety of stakeholders improve security for the electrical grid. This effort grows out of USAID and NREL's discussions with utilities around the world, as well as past cybersecurity assessments performed by NREL on dozens of utilities and government agencies, with a focus on the cybersecurity challenges faced by small and under-resourced utilities. This document outlines eleven building blocks for power sector cyber security. It functions as a guide to help organizations develop a robust, balanced cybersecurity defense program. Individually, each building block represents a cluster of related activities within cybersecurity on which an organization should focus. Using the building blocks, organizations can effectively prioritize their cybersecurity efforts to best thwart a wide range of potential cyberattacks.

building blocks↗

DOE OSTI2021

Building a Just Transition to a Sustainable Energy Future

Residential and commercial buildings consume roughly 74% of U.S. electricity and are responsible for approximately 35% of carbon emissions. As a result, there is no pathway to a sustainable energy future that does not prioritize a transformation in how we use, store, and generate energy in our nation’s buildings. Consequently, the importance and timeliness of research, development, demonstration, and deployment of clean energy building technologies is more pressing than ever. However, inequities that permeate society are acutely prevalent in the energy economy, particularly in the buildings sector. Therefore, as we transition to a sustainable energy future, we should prioritize a “just transition,” where we ensure the benefits, as well as costs, are more equitably distributed. As a scientific and technical community, we should take the lead in centering equity in clean energy technology innovation by permeating equity throughout the RDD&D spectrum. This talk will discuss pathways to achieving a just transition toward a sustainable energy future. It will highlight current Department of Energy and national laboratory RDD&D efforts that advance clean energy goals. The talk will conclude by challenging the ASME community to look through a lens of equity that prioritizes an equitable distribution of benefits and costs for our sustainable energy future.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2020

Physics-constrained Deep Recurrent Neural Models of Building Thermal Dynamics

We develop physics-constrained and control-oriented predictive deep learning models for the thermal dynamics of a real-world commercial office building. The proposed method is based on the systematic encoding of physics-based prior knowledge into a structured recurrent neural architecture. Specifically, our model mimics the structure of the building thermal dynamics model and leverages penalty methods to model inequality constraints. Additionally, we use constrained matrix parameterization based on the Perron-Frobenius theorem to bound the eigenvalues of the learned network weights. We interpret the stable eigenvalues as dissipativeness of the learned building thermal model. We demonstrate the effectiveness of the proposed approach on a dataset obtained from an office building with $20$ thermal zones.

Building Energy, structured neural networks↗