Phillip N. Price

Quantifying Changes in Building Electricity Use, With Application to Demand Response

We present methods for analyzing commercial and industrial facility 15-min-interval electric load data. These methods allow building managers to better understand their facilitys electricity consumption over time and to compare it to other buildings, helping them to “ask the right questions” to discover opportunities for demand response, energy efficiency, electricity waste elimination, and peak load management. We primarily focus on demand response. Methods discussed include graphical representations of electric load data, a regression-based electricity load model that uses a time-of-week indicator variable and a piecewise linear and continuous outdoor air temperature dependence and the definition of various parameters that characterize facility electricity loads and demand response behavior. In the future, these methods could be translated into easy-to-use tools for building managers.

All maps of parameter estimates are misleading.

Maps are frequently used to display spatial distributions of parameters of interest, such as cancer rates or average pollutant concentrations by county. It is well known that plotting observed rates can have serious drawbacks when sample sizes vary by area, since very high (and low) observed rates are found disproportionately in poorly-sampled areas. Unfortunately, adjusting the observed rates to account for the effects of small-sample noise can introduce an opposite effect, in which the highest adjusted rates tend to be found disproportionately in well-sampled areas. In either case, the maps can be difficult to interpret because the display of spatial variation in the underlying parameters of interest is confounded with spatial variation in sample sizes. As a result, spatial patterns occur in adjusted rates even if there is no spatial structure in the underlying parameters of interest, and adjusted rates tend to look too uniform in areas with little data. We introduce two models (normal and Poisson) in which parameters of interest have no spatial patterns, and demonstrate the existence of spatial artefacts in inference from these models. We also discuss spatial models and the extent to which they are subject to the same artefacts. We present examples from Bayesian modelling, but, as we explain, the artefacts occur generally.

A Method to Estimate the Chronic Health Impact of Air Pollutants in U.S. Residences

Background: Indoor air pollutants (IAPs) cause multiple health impacts. Prioritizing mitigation options that differentially affect individual pollutants and comparing IAPs with other environmental health hazards require a common metric of harm. Objectives: Our objective was to demonstrate a methodology to quantify and compare health impacts from IAPs. The methodology is needed to assess population health impacts of large-scale initiatives—including energy efficiency upgrades and ventilation standards—that affect indoor air quality (IAQ). Methods: Available disease incidence and disease impact models for specific pollutant–disease combinations were synthesized with data on measured concentrations to estimate the chronic heath impact, in disability-adjusted life-years (DALYs) lost, due to inhalation of a subset of IAPs in U.S. residences. Model results were compared with independent estimates of DALYs lost due to disease. Results: Particulate matter ≤ 2.5 μm in aerodynamic diameter (PM2.5), acrolein, and formaldehyde accounted for the vast majority of DALY losses caused by IAPs considered in this analysis, with impacts on par or greater than estimates for secondhand tobacco smoke and radon. Confidence intervals of DALYs lost derived from epidemiology-based response functions are tighter than those derived from toxicology-based, interspecies extrapolations. Statistics on disease incidence in the United States indicate that the upper-bound confidence interval for aggregate IAP harm is implausibly high. Conclusions: The approach demonstrated in this study may be used to assess regional and national initiatives that affect IAQ at the population level. Cumulative health impacts from inhalation in U.S. residences of the IAPs assessed in this study are estimated at 400–1,100 DALYs lost annually per 100,000 persons.

Bayesian prediction of mean indoor radon concentrations for Minnesota counties.

Past efforts to identify areas with higher than average indoor radon concentrations by examining the statistical relationship between local mean concentrations and physical parameters such as the soil radium concentration have been hampered by the variation in local means caused by the small number of homes monitored in most areas. In this paper, indoor radon data from a survey in Minnesota are analyzed to minimize the effect of finite sample size within counties, to determine the true county-to-county variation of indoor radon concentrations in the state, and to find the extent to which this variation is explained by the variation in surficial radium concentration among counties. The analysis uses hierarchical modeling, in which some parameters of interest (such as county geometric mean radon concentrations) are assumed to be drawn from a single population, for which the distributional parameters are estimated from the data. Extensions of this technique, known as random effects regression and mixed effects regression, are used to determine the relationship between predictive variables and indoor radon concentrations; the results are used to refine the predictions of each countys radon levels, resulting in a great decrease in uncertainty. The true county-to-county variation of geometric mean radon levels is found to be substantially less than the county-to-county variation of the observed geometric means, much of which is due to the small sample size in each county. The variation in the logarithm of surficial radium content is shown to explain approximately 80% of the variation of the logarithm of geometric mean radon concentration among counties. The influences of housing and measurement factors, such as whether the monitored home has a basement and whether the measurement was made in a basement, are also discussed. The statistical method can be used to predict mean radon concentrations, or applied to other geographically distributed environmental parameters.

Models, assumptions and model checking in ecological regressions

Ecological regression is based on assumptions that are untestable from aggregate data. However, these assumptions seem more questionable in some applications than in others. There has been some research on implicit models of individual data underlying aggregate ecological regression modelling. We discuss ways in which these implicit models can be checked from aggregate data. We also explore the differences in applications of ecological regressions in two examples: estimating the effect of radon on lung cancer in the United States and estimating voting patterns for different ethnic groups in New York City.

WORKER PERFORMANCE AND VENTILATION: ANALYSES OF INDIVIDUAL DATA FOR CALL-CENTER WORKERS

We investigated the relationship between ventilation rates and work performance in a call center. We randomized the ventilation controls and measured ventilation rate, differential carbon dioxide ({Delta}CO{sub 2}) concentration, temperature, humidity, occupant density, degree of under-staffing, shift length, time of day, and time required to complete two different work performance tasks (talk and wrap-up). {Delta}CO{sub 2} concentrations ranged from 13 to 611 ppm. We used multi-variable regression to model the association between the predictors and the responses. We found that agents performed talk tasks fastest when the ventilation rate was highest, but that the relationship between talk performance and ventilation was not monotone. We did not find a statistically significant association between wrap-up performance and ventilation. At high temperatures agents were slower at both the talk and wrap-up tasks. Agents were slower at wrap-up during long shifts and when the call center was under-staffed.

Novel approach for tomographic reconstruction of gas concentration distributions in air: Use of smooth basis functions and simulated annealing

Optical remote sensing and iterative computed tomography (CT) can be applied to measure the spatial distribution of gaseous pollutant concentrations. We conducted chamber experiments to test this combination of techniques using an open path Fourier transform infrared spectrometer (OP-FTIR) and a standard algebraic reconstruction technique (ART). Although ART converged to solutions that showed excellent agreement with the measured ray-integral concentrations, the solutions were inconsistent with simultaneously gathered point-sample concentration measurements. A new CT method was developed that combines (1) the superposition of bivariate Gaussians to represent the concentration distribution and (2) a simulated annealing minimization routine to find the parameters of the Gaussian basis functions that result in the best fit to the ray-integral concentration data. This method, named smooth basis function minimization (SBFM), generated reconstructions that agreed well, both qualitatively and quantitatively, with the concentration profiles generated from point sampling. We present an analysis of two sets of experimental data that compares the performance of ART and SBFM. We conclude that SBFM is a superior CT reconstruction method for practical indoor and outdoor air monitoring applications.

Performance of Installed Cooking Exhaust Devices

UNLABELLED   The performance metrics of airflow, sound, and combustion product capture efficiency (CE) were measured for a convenience sample of 15 cooking exhaust devices, as installed in residences. Results were analyzed to quantify the impact of various device- and installation-dependent parameters on CE. Measured maximum airflows were 70% or lower than values noted on product literature for 10 of the devices. Above-the-cooktop devices with flat-bottom surfaces (no capture hood)--including exhaust fan/microwave combination appliances--were found to have much lower CE at similar flow rates, compared to devices with capture hoods. For almost all exhaust devices and especially for rear-mounted downdraft exhaust and microwaves, CE was substantially higher for back compared with front burner use. Flow rate, and the extent to which the exhaust device extends over the burners that are in use, also had a large effect on CE. A flow rate of 95 liters per second (200 cubic feet per minute) was necessary, but not sufficient, to attain capture efficiency in excess of 75% for the front burners. A-weighted sound levels in kitchens exceeded 56 dB* when operating at the highest fan setting for all 14 devices evaluated for sound performance. PRACTICAL IMPLICATIONS Natural gas cooking burners and many cooking activities emit pollutants that can reach hazardous levels in homes. Venting range hoods and other cooking exhaust fans are thought to provide adequate protection when used. This study demonstrates that airflows of installed devices are often below advertised values and that less than half of the pollutants emitted by gas cooking burners are removed during many operational conditions. For many devices, achieving capture efficiencies that approach or exceed 75% requires operation at settings that produce prohibitive noise levels. While users can improve performance by preferentially using back burners, results suggest the need for improvements in hood designs to achieve high pollutant capture efficiencies at acceptable noise levels.

An algorithm for real-time tomography of gas concentrations, using prior information about spatial derivatives

We present a new computed tomography method, the low third derivative (LTD) method, that is particularly suited for reconstructing the spatial distribution of gas concentrations from path-integral data for a small number of optical paths. The method finds a spatial distribution of gas concentrations that (1) has path integrals that agree with measured path integrals, and (2) has a low third spatial derivative in each direction, at every point. The trade-off between (1) and (2) is controlled by an adjustable parameter, which can be set based on analysis of the path-integral data. The method produces a set of linear equations, which can be solved with a single matrix multiplication if the constraint that all concentrations must be positive is ignored; the method is therefore extremely rapid. Analysis of experimental data from thousands of concentration distributions shows that the method works nearly as well as smooth basis function minimization (the best method previously available), yet is about 100 times faster.

Ventilation Behavior and Household Characteristics in NewCalifornia Houses

LBNL 59620 E RNEST O RLANDO L AWRENCE B ERKELEY N ATIONAL L ABORATORY Ventilation Behavior and Household Characteristics in New California Houses Phillip N. Price and Max H. Sherman Environmental Energy Technologies Division April 2006 This work was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Building Technologies Program, of the U.S. Department of Energy under contract No. DE-AC02-05CH11231. The research reported here was also funded by the California Energy Commission and Air Resources Board through the University of California’s Survey Research Center.