This series is home to publications and data sets from the Center for Environmental Research and Technology at the University of California, Riverside.
Owing to the call for energy efficiency, the need to optimize the energy
consumption of commercial buildings-- responsible for over 40% of US energy
consumption--has recently gained significant attention. Moreover, the ability
to participate in the retail electricity markets through proactive demand-side
participation has recently led to development of economic model predictive
control (EMPC) for building's Heating, Ventilation, and Air Conditioning (HVAC)
system. The objective of this paper is to develop a price-sensitive operational
model for building's HVAC systems while considering inflexible loads and other
distributed energy resources (DERs) such as photovoltaic (PV) generation and
battery storage for the buildings. A Nonlinear Economic Model Predictive
Controller (NL-EMPC) is presented to minimize the net cost of energy usage by
building's HVAC system while satisfying the comfort-level of building's
occupants. The efficiency of the proposed NL-EMPC controller is evaluated using
several simulation case studies.
Recent technological advances in both air sensing technology and Internet of Things (IoT) connectivity have enabled the development and deployment of remote monitoring networks of air quality sensors. The compact size and low power requirements of both sensors and IoT data loggers allow for the development of remote sensing nodes with power and connectivity versatility. With these technological advancements, sensor networks can be developed and deployed for various ambient air monitoring applications. This paper describes the development and deployment of a monitoring network of accurate ozone (O3) sensor nodes to provide parallel monitoring in an air monitoring site relocation study. The reference O3 analyzer at the station along with a network of three O3 sensing nodes was used to evaluate the spatial and temporal variability of O3 across four Southern California communities in the San Bernardino Mountains which are currently represented by a single reference station in Crestline, CA. The motivation for developing and deploying the sensor network in the region was that the single reference station potentially needed to be relocated due to uncertainty that the lease agreement would be renewed. With the implication of siting a new reference station that is also a high O3 site, the project required the development of an accurate and precise sensing node for establishing a parallel monitoring network at potential relocation sites. The deployment methodology included a pre-deployment co-location calibration to the reference analyzer at the air monitoring station with post-deployment co-location results indicating a mean absolute error (MAE) < 2 ppb for 1-h mean O3 concentrations. Ordinary least squares regression statistics between reference and sensor nodes during post-deployment co-location testing indicate that the nodes are accurate and highly correlated to reference instrumentation with R2 values > 0.98, slope offsets < 0.02, and intercept offsets < 0.6 for hourly O3 concentrations with a mean concentration value of 39.7 ± 16.5 ppb and a maximum 1-h value of 94 ppb. Spatial variability for diurnal O3 trends was found between locations within 5 km of each other with spatial variability between sites more pronounced during nighttime hours. The parallel monitoring was successful in providing the data to develop a relocation strategy with only one relocation site providing a 95% confidence that concentrations would be higher there than at the current site.
This article considers the subject of information losses arising from the finite data sets used in the training of neural classifiers. It proves a relationship between such losses as the product of the expected total variation of the estimated neural model with the information about the feature space contained in the hidden representation of that model. It then bounds this expected total variation as a function of the size of randomly sampled data sets in a fairly general setting, and without bringing in any additional dependence on model complexity. It ultimately obtains bounds on information losses that are less sensitive to input compression and in general much smaller than existing bounds. This article then uses these bounds to explain some recent experimental findings of information compression in neural networks that cannot be explained by previous work. Finally, this article shows that not only are these bounds much smaller than existing ones, but they also correspond well with experiments.
© 2019 Elsevier Ltd The rapid adoption of cloud storage and computing services led to unprecedented growth of data centers in the world. As bulk energy consumers, large-scale data centers in the U.S. rack up billions in electricity costs annually. Fortunately, the operational flexibility of data centers can be leveraged to provide valuable frequency regulation services in smart grids to mitigate the indeterminacy of the renewable generation resources. Specifically, this paper aims to leverage computational flexibility provided by servers, such as dynamic voltage frequency scaling and dummy loads. This paper develops a comprehensive framework for data center's frequency regulation service provision in both hour-ahead market and real-time operations. A risk constrained hour-ahead bidding strategy along with a real-time data center power consumption control algorithm are developed to minimize electricity bills and the total response time of the requests. The introduction of dummy load, realistic bi-linear server power consumption model, and probabilistic forecast of electricity and frequency regulation service prices enable the data center to accurately follow frequency regulation signals, while reducing the financial risks associated with electricity market participation. The simulation results show that the proposed frequency regulation provision framework results not only in significant cost reduction for data centers, but also limits degradation in quality of service. Meanwhile, the stability and reliability of a power grid will be improved by the frequency regulation service provision.
© 2019 American Chemical Society. An addition to the Acknowledgments of our paper is required. It is as follows: This research used resources of the Advanced Light Source, which is a DOE Office of Science User Facility under Contract No. DE-AC02-05CH11231.
Air pollution poses a significant threat to the environment and human health. Most in vivo health studies conducted regarding air pollutants, including particulate matter (PM) and gas phase pollutants, have been either through traditional medical intranasal treatment or using a tiny chamber, which limit animal activities. In this study, we designed and tested a large, whole-body, multiple animal exposure chamber with uniform dispersion and exposure stability for animal studies. The chamber simultaneously controls particle size distribution and PM mass concentration. Two different methods were used to generate aerosol suspension through either soluble material (Alternaria extract), liquid particle suspension (nanosilica solution), or dry powder (silica powder). We demonstrate that the chamber system provides well controlled and characterized whole animal exposures, where dosage is by inhalation of particulate matter.
[This corrects the article DOI: 10.1186/s13068-019-1353-7.].
© Author(s) 2018. Biomass-burning organic-aerosol (OA) emissions are known to exhibit semi-volatile behavior that impacts OA loading during plume transport. Because such semi-volatile behavior depends in part on OA composition, improved speciation of intermediate and semi-volatile organic compounds (I/SVOCs) emitted during fires is needed to assess the competing effects of primary OA volatilization and secondary OA production. In this study, 18 laboratory fires were sampled in which a range of fuel types were burned. Emitted I/SVOCs were collected onto Teflon filters and solid-phase extraction (SPE) disks to qualitatively characterize particulate and gaseous I/SVOCs, respectively. Derivatized filter extracts were analyzed using comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC×GC-TOFMS). Quality control tests were performed using biomass-burning relevant standards and demonstrate the utility of SPE disks for untargeted analysis of air samples. The observed chromatographic profiles of I/SVOCs in coniferous fuel-derived smoke samples were well correlated with each other, but poorly correlated with other fuel types (e.g., herbaceous and chaparral fuels). Emissions of benzenediol isomers were also shown to be fuel dependent. The combined Teflon and SPE filter data captured differences in gas-particle partitioning of the benzenediol isomers, with hydroquinone having a significantly higher particle-phase fraction than catechol due to its lower volatility. Additionally, the speciated volatility distribution of I/SVOCs in smoke from a rotten-log fire was estimated to evaluate the composition of potentially volatilized primary OA, which was entirely attributed to oxygenated (or other heteroatomic) compounds. The isomer-dependent partitioning and the speciated volatility distributions both suggest the need for better understanding of gas-phase and heterogenous reaction pathways of biomass-burning-derived I/SVOCs in order to represent the atmospheric chemistry of smoke in models.