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The curated fault experiment data set consists of tagged and fully described time series representing measured faults from the AFDD test building (ORNLs Flexible Research Platform [FRP]), including baseline performance and faulty performance. A total of 10 different faults are tested for 49 different faulted and unfaulted scenarios with various fault intensity levels. Additional Contacts: Principal investigator: Matt Leach Matt.Leach@nrel.gov Experiments coordinator: Piljae Im imp1@ornl.gov Document preparation: Janghyun Kim Janghyun.Kim@nrel.gov

The ResStock Analysis Tool was developed by NREL with support from the U.S. Department of Energy to provide a new approach to large-scale residential analysis by combining large public and private data sources, statistical sampling, detailed sub hourly building simulations, and high-performance computing. This combination achieves unprecedented granularity and accuracy in modeling the diversity of the housing stock and the distributional impacts of building technologies in different communities. The annual baseline energy results from a national-scale ResStock run use typical meteorological year 3 (TMY3) files for energy simulations. Results include heating and cooling loads for individual components of each building. Component loads describe the heating/cooling load that can be attributed to specific elements of a home, such as heat transfer through walls or internal gains. Additionally, these results include the standard ResStock outputs for housing characteristics and numerous energy outputs by end-use and fuel. A snapshot of the ResStock version used to produce this data, including a configuration file for the run can be found using the Source Code resource link.

This data set, compiled by the Fraunhofer Center for Sustainable Energy Systems, includes long-term 10-minute temperature and relative humidity data, and HVAC system state data for 79 apartments in a low-income housing complex in Revere, MA. The monitoring period spans two winters and one summer between 2011 and 2013. Data were collected as part of a project sponsored by the U.S. Department of Energy Building America program to evaluate the impact of programmable thermostat usability on occupant behavior. This project was done in conjunction with NREL as part of the US Department of Energy's Building America program.

Building project data for 75,110 single-family homes upgraded between July 1, 2010, and September 30, 2013 from the Better Building Neighborhood Program. This dataset includes a documentation file and three data tables. Reported data for some elements have been transformed and data for some upgraded homes have been omitted to protect privacy. See documentation file for details.

The United States is embarking on an ambitious transition to a 100% clean energy economy by 2050, which will require improving the flexibility of electric grids. One way to achieve grid flexibility is to shed or shift demand to align with changing grid needs. To facilitate this, it is critical to understand how and when energy is used. High quality end-use load profiles (EULPs) provide this information, and can help cities, states, and utilities understand the time-sensitive value of energy efficiency, demand response, and distributed energy resources. Publicly available EULPs have traditionally had limited application because of age and incomplete geographic representation. To help fill this gap, the U.S. Department of Energy (DOE) funded a three-year project, End-Use Load Profiles for the U.S. Building Stock, that culminated in this publicly available dataset of calibrated and validated 15-minute resolution load profiles for all major residential and commercial building types and end uses, across all climate regions in the United States. These EULPs were created by calibrating the ResStock and ComStock physics-based building stock models using many different measured datasets, as described in the "Technical Report Documenting Methodology" linked in the submission.

These datasets can be used to evaluate and benchmark the performance accuracy of Fault Detection and Diagnostics (FDD) algorithms or tools. It contains operational data from simulation, laboratory experiments, and field measurements from real buildings for seven HVAC systems/equipment (rooftop unit, single-duct air handler unit, dual-duct air handler unit, variable air volume box, fan coil unit, chiller plant, and boiler plant). Each dataset includes a .pdf file to document key information necessary to understand the content and scope, multiple csv files containing all the time-series data for faults at different severity levels and one fault-free case, and a ttl file to visualize the data according to BRICK schema. The dataset was created by LBNL, PNNL, NREL, ORNL and Drexel University.

Data were collected to characterize whole-house mechanical ventilation (WHMV) and indoor air quality (IAQ) in 55 homes in the Marine climate of Oregon and Cold-Dry climate of Colorado in the U.S. Sixteen homes were monitored for two weeks, with and without WHMV operating. Ventilation airflows; airtightness; time-resolved CO2, PM2.5 and radon; and time-integrated NO2, NOX and formaldehyde were measured. Participants provided information about IAQ-impacting activities, perceptions and ventilation use. All homes had operational cooktop ventilation and bathroom exhaust. Thirty homes had equipment that could meet the ASHRAE 62.2-2010 standard with continuous or controlled runtime and 34 had some WHMV operating as found. Thirty-five of 46 participants with WHMV reported they did not know how to operate it, and only half of the systems were properly labeled. Two-week homes had lower formaldehyde, radon, CO2, and NO (NOX-NO2) when operated with WHMV; and also had faster PM2.5 decays following indoor emission events. Overall IAQ satisfaction was similar in Oregon and Colorado, but more Colorado participants (19 vs. 3%) felt their IAQ could be improved and more reported dryness as a problem (58 vs. 14%). The collected data indicate that there are benefits of operating WHMV, even when continuous use may not be needed because outdoor pollutant concentrations are low and indoor sources do not present substantial challenges.

Le Department of Natural Resources and Renewables de la Nouvelle-Écosse s'engage à soutenir les projets locaux qui créent des réductions durables des gaz à effet de serre (GES), en plus d'aider les communautés à développer et à faire progresser des solutions innovantes dans les domaines de l'efficacité des bâtiments, de l'électricité propre et de l'énergie propre. les secteurs des transports. Le programme de subvention provinciale pour les communautés à faible émission de carbone (LCC) est conçu pour répondre au besoin croissant des communautés d'offrir des solutions à faible émission de carbone et d'atténuer les émissions de GES dans le secteur de l'énergie. The Nova Scotia Department of Natural Resources and Renewables is committed to supporting locally driven projects that create long-lasting greenhouse gas (GHG) reductions in addition to helping communities develop and advance innovative solutions in the building efficiency, clean electricity, and clean transportation sectors. The Low Carbon Communities (LCC) Provincial Grant Program is designed to respond to the growing need from communities to offer low carbon solutions and mitigate GHG emissions in the energy sector.

NREL has assembled a list of U.S. retail electricity tariffs and their associated demand charge rates for the Commercial and Industrial sectors. The data was obtained from the Utility Rate Database. Keep the following information in mind when interpreting the data: (1) These data were interpreted and transcribed manually from utility tariff sheets, which are often complex. It is a certainty that these data contain errors, and therefore should only be used as a reference. Actual utility tariff sheets should be consulted if an action requires this type of data. (2) These data only contains tariffs that were entered into the Utility Rate Database. Since not all tariffs are designed in a format that can be entered into the Database, this list is incomplete - it does not contain all tariffs in the United States. (3) These data may have changed since this list was developed (4) Many of the underlying tariffs have additional restrictions or requirements that are not represented here. For example, they may only be available to the agricultural sector or closed to new customers. (5) If there are multiple demand charge elements in a given tariff, the maximum demand charge is the sum of each of the elements at any point in time. Where tiers were present, the highest rate tier was assumed. The value is a maximum for the year, and may be significantly different from demand charge rates at other times in the year. Utility Rate Database: https://openei.org/wiki/Utility_Rate_Database