A labeled dataset for building HVAC systems operating in faulted and fault-free states.

Open data is fueling innovation across many fields. In the domain of building science, datasets that can be used to inform the development of operational applications - for example new control algorithms and performance analysis methods - are extremely difficult to come by. This article summarizes the development and content of the largest known public dataset of building system operations in faulted and fault free states. It covers the most common HVAC systems and configurations in commercial buildings, across a range of climates, fault types, and fault severities. The time series points that are contained in the dataset include measurements that are commonly encountered in existing buildings as well as some that are less typical. Simulation tools, experimental test facilities, and in-situ field operation were used to generate the data. To inform more data-hungry algorithms, most of the simulated data cover a year of operation for each fault-severity combination. The data set is a significant expansion of that first published by the lead authors in 2020.

Datasets of a Multizone Office Building under Different HVAC System Operation Scenarios.

This study provides an open-source dataset of the measured weather data, building indoor data, and system data under the different test settings. The test building is the two-story Flexible Research Platform building at the US Department of Energy's Oak Ridge National Laboratory, in Oak Ridge, Tennessee. Four heating tests and three cooling tests were conducted. The 1-min interval of weather, building indoor data, and system data from each test setting are provided. Actual weather data were collected from a weather station installed on the roof. This paper describes information on the test building and installed sensors, data collection method, and data validation. The provided dataset can be employed to understand HVAC system conditions and building indoor conditions under different HVAC system operations and the performance of building envelope without HVAC system operation using free-floating test data. Additionally, it can be used for empirical validation of the building energy modelling engine.

High resolution dataset from a net-zero home that demonstrates zero-carbon living and transportation capacity.

This dataset includes high resolution, detailed end use data from a net-zero occupied home that demonstrates zero-carbon living and transportation capacity. The house is located in Davis, California, U.S., and the dataset includes full year data from 2020 with 1 minute time resolution. The data has been monitored with more than 230 sensors installed in the house, and there are total 332 channels available. The data includes detailed end use electricity data (e.g., HVAC system, lighting, plug load including major appliances), building's interior thermal conditions (e.g., indoor air temperatures in multiple rooms and relative humidity), HVAC system operation data (e.g., soil temperatures around ground bores and supply water temperatures), on-site power generation system data (e.g., PV power supply and PV surface temperatures) and etc. The original dataset from the house has been curated, and the data has been carefully reviewed for quality check. The data quality check revealed there are 156 minutes of data were missing in the month of April, and around 1,404 minutes of data was missing in August. The data gap was filled with linear interpolation in case the gap is less than continuous 6 hours. Otherwise, the data is filled with -9999. The data curation has been processed using the Tsdat framework (https://github.com/tsdat/tsdat). In addition, a semantic description for the dataset was generated by leveraging the Brick (https://brickschema.org/). The final curated and processed data as well as raw data are currently available through https://bbd.labworks.org/ds/bbd/hshus.

Dataset of low global warming potential refrigerant refrigeration system for fault detection and diagnostics.

HVAC and refrigeration system fault detection and diagnostics (FDD) has attracted extensive studies for decades; however, FDD of supermarket refrigeration systems has not gained significant attention. Supermarkets consume around 50 kWh/ft2 of electricity annually. The biggest consumer of energy in a supermarket is its refrigeration system, which accounts for 40%-60% of its total electricity usage and is equivalent to about 2%-3% of the total energy consumed by commercial buildings in the United States. Also, the supermarket refrigeration system is one of the biggest consumers of refrigerants. Reducing refrigerant usage or using environmentally friendly alternatives can result in significant climate benefits. A challenge is the lack of publicly available data sets to benchmark the system performance and record the faulted performance. This paper identifies common faults of supermarket refrigeration systems and conducts an experimental study to collect the faulted performance data and analyze these faults. This work provides a foundation for future research on the development of FDD methods and field automated FDD implementation.

Building fault detection data to aid diagnostic algorithm creation and performance testing.

It is estimated that approximately 4-5% of national energy consumption can be saved through corrections to existing commercial building controls infrastructure and resulting improvements to efficiency. Correspondingly, automated fault detection and diagnostics (FDD) algorithms are designed to identify the presence of operational faults and their root causes. A diversity of techniques is used for FDD spanning physical models, black box, and rule-based approaches. A persistent challenge has been the lack of common datasets and test methods to benchmark their performance accuracy. This article presents a first of its kind public dataset with ground-truth data on the presence and absence of building faults. This dataset spans a range of seasons and operational conditions and encompasses multiple building system types. It contains information on fault severity, as well as data points reflective of the measurements in building control systems that FDD algorithms typically have access to. The data were created using simulation models as well as experimental test facilities, and will be expanded over time.

Efficient Solar-Thermal Distillation Desalination Device by Light Absorptive Carbon Composite Porous Foam.

Solar-thermal driven desalination based on porous carbon materials has promise for fresh water production. Exploration of high-efficiency solar desalination devices has not solved issues for practical application, namely complicated fabrication, cost-effectiveness, and scalability. Here, direct solar-thermal carbon distillation (DS-CD) tubular devices are introduced that have a facile fabrication process, are scalable, and use an inexpensive but efficient microporous graphite foam coated with carbon nanoparticle and superhydrophobic materials. The "black" composite foam serving as a solar light absorber heats up salt water effectively to produce fresh water vapor, and the superhydrophobic surface of the foam traps the liquid feed in the device. Two proof-of-principle distillation systems are adopted, i.e., solar still and membrane distillation and the fabricated devices are evaluated for direct solar desalination efficiency. For the solar still, nanoparticle and fluorosilane coatings on the porous surface increase the solar energy absorbance, resulting in a solar-steam generation efficiency of 64% from simulated seawater at 1 sun. The membrane distillation demonstrates excellent vapor production (≈6.6 kg m-2 h-1) with >99.5% salt rejection under simulated 3 sun solar-thermal irradiation. Unlike traditional solar desalination, the adaptable DS-CD can easily be scaled up to larger systems such as high-temperature tubular modules, presenting a promising solution for solar-energy-driven desalination.