Accurately modeling the growth process of plants in interaction with their environment is important for predicting their biophysical characteris-tics, referred to as phenotype prediction. Most models are described by dis-crete dynamic systems in general state-space representation with important domain-specific characteristics: First, plant model parameters have usually clear functional meanings and may be of genetic origins, thus necessitating a precise estimation. Second, critical growth variables, specifically biomass production and dynamic allocation to organs, are hidden variables not acces-sible to measure. Finally, the difficulty to assess the local plant environment may imply the introduction of process noises in models. Therefore, a pre-cise understanding of the system's behavior requires the joint estimation of functional parameters, hidden states, and noise parameters. In this paper we describe how a full Bayesian method of estimation can accurately estimate all these key model variables using Markov chain Monte Carlo (MCMC) tech-niques. In the presence of both process and observation noises, it requires to use adequate particle MCMC (PMCMC) algorithms to efficiently sample the hidden states which, consequently, allows for a precise estimation of all noise parameters involved. Thanks to the Bayesian framework, appropriate choices of prior distributions for the noise parameters have enabled analytical pos-terior distributions and only simple updates are required. Furthermore, this estimation strategy can be easily generalized and adapted to different types of plant growth models, such as organ-scale or compartmental, provided that they are formulated as hidden Markov models. Our estimation method im-proves on those classically used in plant growth modeling in several aspects: First, by building upon a general probabilistic framework the estimation re-sults allow proper statistical analyses. It is useful in prediction, no only for uncertainty and risk analysis (e.g., for crop yield prediction) but also to an-alyze the results of experimental trials, for example, to compare genotypes in breeding. Moreover, the care taken in the estimation of hidden variables opens new perspectives in the understanding of inner growth processes, no-tably the balance and interaction between biomass production and allocation (referred to as source-sink dynamics). Applications of this estimation proce-dure are demonstrated on the GreenLab model for Arabidopsis thaliana and the Log-Normal Allocation and Senescence (LNAS) model for sugar beet, on both synthetic and real data.

This report seeks to inform the potential for motor systems to support decarbonization by estimating their potential energy, electricity cost, and CO2 emissions reduction potential from adoption of proven energy efficiency actions and advanced technologies. This is the third and final report in a series of reports disseminating the findings of the U.S. Department of Energy’s (DOE’s) Motor System Market Assessment (MSMA). The MSMA and this report focus on polyphase motor systems greater than or equal to 1 horsepower (hp) in the industrial and commercial sectors. In the U.S. Industrial and Commercial Motor System Market Assessment Report Volume 1: Characteristics of the Installed Base (Volume 1 report), it was determined that these motor systems consume more than 1,000 terawatt-hours (TWh) annually. This equates to 29% of the U.S. electric grid load and results in 765 million metric tons (MMT) of CO2 emissions and $166 billion in electricity costs. This report finds that substantial reductions to these energy, cost, and emissions impacts are possible, with three areas of significant opportunity being (1) improved load matching, (2) replacing older inefficient motors with more efficient motors, and (3) improving the condition of fluid (e.g., air, water, compressed air) distribution systems. A summary of the savings potential from these three opportunities placed within the context of the overall consumption for motor systems is illustrated in Figure ES 1 (industrial) and Figure ES 2 (commercial).

Due to lack of nationally and regionally representative water rate data, water conservation programs and policies may have difficulty assessing their economic impacts. Based on historical data, the proposed approach could fill in data gaps of water rates by establishing regionally-specific rate trends. The obtained prediction model outperforms the CPI for water and sewerage describing the pace of increasing water rates, and can project rates for years in the absence of available data. It can also serve as a data validation tool to assess the consistency, robustness and representativeness of regional rates when new rates data are available.

Hot water savings from water-efficient lavatory fittings lead to reductions in water heating energy consumption, and ultimately to decreases in carbon emissions. This paper characterizes existing and proposed approaches used to estimate hot water savings and carbon emissions reductions stemming from the U.S. Environmental Protection Agency's WaterSense program. Also described are refinements that improve the accuracy of residential hot water use percentage estimates of lavatory fittings. The authors conclude that (1) hot water percentages for showers and faucets calculated using up-to-date, publicly available national data are consistent with those found by regional studies and household-level models of water use; (2) the accuracy of heating energy savings estimates attributable to WaterSense-labeled lavatory products, as well as associated emissions reductions, can be refined by modifying the existing energy factor/uniform energy factor (EF/UEF)-based estimation approach with available data. The refined approach accounts for more nuanced conditions than the EF/UED-based approach but depends on data not always available; and (3) the approaches described and intermediate outputs can be generalized for other water conservation programs or estimating purposes.

Motor systems are an integral part of our industrial and commercial facilities. They provide the motive force behind the fans, pumps, compressors, chillers, and conveyors in these facilities. Given their centrality to any facility’s operations, they are a critical energy end use to understand, particularly when developing technologies and policies to meet sustainability goals, improve productivity, and enhance resilience. In the late 1990s, the U.S. Department of Energy (DOE) conducted two seminal studies to better understand the installed stock and energy savings opportunities of industrial and commercial motor systems. In the industrial sector, The United States Industrial Electric Motor Systems Market Opportunities Assessment used primary data collected through onsite assessments and led to a greater understanding of the installed base of motor systems, their characteristics, and the opportunities for energy savings (U.S. Department of Energy, 2002). Notable findings included the following: Industrial motor systems consumed 679 billion kilowatt-hours (kWh) in 1994, representing 23 percent of U.S. electricity consumption. Cost-effective energy efficiency measures could result in 62-104 billion kWh of energy savings annually. Sixty-two percent of the energy savings potential was from fan, pump, and compressor end use equipment. Nearly half of motor system electricity consumption was attributable to approximately 3,500 facilities, or 1.5 percent of U.S. manufacturing facilities. Opportunities for Energy Savings in the Residential and Commercial Sectors with High Efficiency Electric Motors provided an evaluation of the installed stock of motor driven equipment in U.S. commercial and residential buildings and opportunities for utilization of high efficiency motors and variable speed technologies (Arthur D. Little, 1999). Notable findings included the following: Commercial motor systems consumed 343 billion kWh in 1995, with refrigeration and space conditioning constituting 93 percent of the total. Cost-effective energy efficiency measures could result in 51 billion kWh of energy savings annually. Due in no small part to these seminal studies, motor system technologies and usage characteristics have changed drastically since the late 1990s. Greater awareness of cost-effective strategies for reducing motor system electricity consumption have been developed and deployed. This includes several software tools, literature, and utility and government programs promoting energy efficiency improvements in motor driven systems. Additionally, several rounds of energy efficiency standards have been enacted, resulting in improved installed motor efficiency. The cost of variable speed drives has dropped substantially, and combined with utility rebate programs, has led to their greater adoption. Further, since these results were published, the U.S. manufacturing sector has undergone a massive transformation. Due to global competition, some sectors have relocated operations overseas. Others have brought operations onshore to avail low cost and abundant natural gas. Additionally, automation and robotics have pervaded the entire sector. Consequently, these two reports likely do not represent the current state of motor driven systems in U.S. industrial and commercial facilities. As cited in recent studies, the lack of current information on motor system electricity consumption and use characteristics limits the ability to conduct analysis on energy savings potential, develop technologies to address energy and productivity gaps, and develop programs to promote energy efficiency practices and technologies for motor systems (International Energy Agency, 2007; UNIDO, 2010; McKane and Hasanbeigi, 2011; Waide and Brunner, 2011). Specifically, the lack of information affects a range of stakeholders: Governments must rely on outdated information when setting research agendas, developing policies, and designing energy efficiency programs and offerings. Utilities and energy efficiency programs cannot identify the current market needs or potential impact when designing rebate and energy efficiency programs. Electric grid planners cannot identify motor system usage characteristics when developing plans to support the resilience of the electric grid. Manufacturers of motors, motor driven equipment, and drives are hampered when developing technologies to meet the needs of their market. Motor system end users are limited in their ability to identify energy saving opportunities within their own facilities because they do not have reliable benchmark information. In response to the lack of current information and analysis on industrial and commercial motor systems, the DOE initiated an update to these two studies. Launched in 2016 and led by Lawrence Berkeley National Laboratory (LBNL), the Motor System Market Assessment (MSMA) provides an updated, more comprehensive assessment of the installed stock of motor systems in both the industrial and commercial sectors, a review of the supply chains supporting motor and drives in the U.S., and the performance improvement opportunity available from using best available technologies and maintenance and operation practices. The outcomes of the MSMA are documented in three U.S. Industrial and Commercial Motor System Market Assessment reports, with this report being the first listed: 1. Volume 1: Characteristics of the Installed Base (this report) documents the findings on the installed base of motor systems in the U.S. industrial and commercial sectors. Quantification of energy savings potential is not documented in this report but in Volume 3. 2. Volume 2: Motors and Drives Supply Chain Review reviews the state of supply chains for motors and drives installed in U.S. industrial and commercial facilities, focusing on advanced motor and drive technologies and their constituent materials. 3. Volume 3: Energy Savings Opportunity analyzes the energy performance improvement opportunity for the installed base of U.S. industrial and commercial motor systems. This report has been prepared as a reference for motor system stakeholders. It provides factual information as could be best determined by the assessment results and avoids speculating on any findings.

Author(s): Chen, Yuting; Fuchs, Heidi; Schein, Jonah; Franco, Victor; Stratton, Hannah; Dunham, Camilla

Since 2006, the U.S. Environmental Protection Agency (EPA) has operated WaterSense® in partnership with manufacturers, utilities, and consumer groups. Similar to EPA's ENERGY STAR® role for energy-efficient products, WaterSense® employs a labeling system to identify water-efficient products, homes, and services. As of 2015, the WaterSense® program can claim credit for a total savings of 1.5 trillion gallons of water and $32.6 billion in consumer water and energy bills. Savings are tracked in the National Water Savings (NWS) model that combines innovative analyses with methodologies established in the energy sector. Merging life-cycle cost and national impact analysis models, the NWS model estimates savings from a bottom-up accounting method for individual products. The model extends those savings to the national level by employing parameters such as frequency of product use by number of people and building type, product lifetime, stock accounting, and market saturation. The NWS model tracks the water and consumer monetary savings of WaterSense-labeled products for residential and commercial water use both indoors and out.

Holistic energy management system business practices, such as the framework detailed in ISO 50001 – Energy management system, requirements and guidance for use , are centrally based upon the concept of energy performance improvement. For the purposes of ISO 50001, energy performance improvement can be determined for boundaries 1 and following a process that is best suited for the implementing organization. Many organization and government ISO 50001 based programs, including the United States Department of Energy Superior Energy Performance (SEP) program, find value in demonstrating organizational energy performance improvement as the difference in energy consumption between two time periods within physically defined facility-boundaries. To make the difference in energy consumption for the two time periods meaningful, the amount of energy consumed must be adjusted to account for relevant variables. Relevant variables, such as metrics of production and weather conditions, affect directly the amount of energy consumed but are independent of the facility’s energy performance. Therefore, it is crucial to adjust the observed energy consumption by the relevant variables identified by the facility, so that the energy savings resulted from the energy performance improvement actions can be isolated and determined, which is the purpose of this program. To tackle this adjustment issue, the SEP measurement and verification (M&V) protocol specifies four energy consumption adjustment modeling methods for use; forecast, backcast, standard conditions, and chaining. Application of a single set of energy consumption and relevant variable data from a manufacturing facility to the four different energy consumption adjustment modeling methods produces four different energy savings values. Variation in the energy savings values is the result of inevitable changes in operation and conditions between the baseline and reporting periods, which affects the evaluation results significantly. The lack of agreement in the calculated energy savings values, while all meeting the requirements of the SEP M&V Protocol, indicates that additional context and analysis is required to understand which modeling method, and subsequent result, best represents the actual energy performance improvement of an organization. This report describes how each adjustment model method can be implemented and provides guidelines of how to choose an appropriate adjustment method. A variety of statistical tests were made use to reveal which of the four methods best reflects the energy performance improvement of a given organization. In the study case of this paper, all of the four adjustment methods were applied. The resulting four savings estimates ranging from -1091.4 to 142,248.0 MMBtu, and the four SEP Energy Performance Indicator (

Water and wastewater treatment and delivery is the most capital-intensive of all utility services. Historically underpriced, water and wastewater rates have exhibited unprecedented growth in the past fifteen years. Steep annual increases in water and wastewater rates that outpace the Consumer Price Index (CPI) have increasingly become the norm across the United States. In this paper, we analyze water and wastewater rates across U.S. census regions between 2000 and 2014. We also examine some of the driving factors behind these rate increases, including drought, water source, required infrastructure investment, population patterns, and conservation effects. Our results demonstrate that water and wastewater prices have consistently increased and have outstripped CPI throughout the study period nationwide, as well as within each census region. Further, evaluation of the current and upcoming challenges facing water and wastewater utilities suggests that sharp rate increases are likely to continue in the foreseeable future.

Green lawns and landscaping are archetypical of the populated American landscape, and typically require irrigation, which corresponds to a significant fraction of residential, commercial, and institutional water use. In North American cities, the estimated portion of residential water used for outdoor purposes ranges from 22-38% in cooler climates up to 59-67% in dry and hot environments, while turfgrass coverage within the United States spans 11.1-20.2 million hectares (Milesi et al. 2009). One national estimate uses satellite and aerial photography data to develop a relationship between impervious surface and lawn surface area, yielding a conservative estimate of 16.4 (± 3.6) million hectares of lawn surface area in the United States—an area three times larger than that devoted to any irrigated crop (Milesi et al. 2005). One approach that holds promise for cutting unnecessary outdoor water use is the increased deployment of “smart” irrigation controllers to increase the water efficiency of irrigation systems. This report describes the methodology and inputs employed in a mathematical model that quantifies the effects of the U.S. Environmental Protection Agency’s WaterSense labeling program for one such type of controller, weather-based irrigation controllers (WBIC). This model builds off that described in “Methodology for National Water Savings Model and Spreadsheet Tool–Outdoor Water Use” and uses a two-tiered approach to quantify outdoor water savings attributable to the WaterSense program for WBIC, as well as net present value (NPV) of that savings. While the first iteration of the model assessed national impacts using averaged national values, this version begins by evaluating impacts in three key large states that make up a sizable portion of the irrigation market: California, Florida, and Texas. These states are considered to be the principal market of “smart” irrigation controllers that may result in the bulk of national savings. Modeled water savings and net present value for these three states should be more accurate and representative than the averaged national values given state-specific inputs such as lot size, water price, and housing stock. To complete the picture of national impacts, the remaining WBIC shipments not assigned to these three states are assessed using the original methodology based on the averaged national values.

Water and wastewater treatment and delivery are the most capital‐intensive of all utility services. The literature indicates that historically underpriced water and wastewater rates have exhibited steadily high growth in the past 15 years, while the consumer price index (CPI) of water and sewage maintenance has outpaced the general CPI by an increasingly wide margin. This article employs a chained analysis method to examine water and wastewater rates for a group of utilities across US Census regions between 2000 and 2014. Results demonstrate that water and wastewater prices for this sample group have consistently increased and have surpassed CPI growth since 2006. Current and upcoming challenges facing water and wastewater utilities suggest that rate increases are likely to continue in the foreseeable future.

Parameter estimation in complex models arising in real data applications is a topic which still attracts a lot of interest. In this article, we study a specific data and parameter augmentation method which gives us the opportunity to estimate more easily the parameters of the initial model. For this reason, the notion of Gaussian randomization of a model with respect to some of its parameters is introduced. The initial model can be regarded as a submodel of the resulting extended incomplete data model. Under the assumption that the initial model has a unique maximum likelihood estimator (MLE) and that the likelihood function is continuous we prove that the extended model has a unique MLE with common values for the parameters of the MLE which correspond to the initial model. We also prove the reverse direction. Moreover, an appropriate stochastic version of an EM (Expectation-Maximization) algorithm is suggested to make parameter estimation feasible. In particular, we describe how the regularized particle filter of Musso and Oudjane ( 1998 ) can be used in this frequentist-based approach to perform the Monte Carlo E-step at each iteration of the stochastic EM algorithm. This regularized version is particularly adapted to the framework of Gaussian randomization since the last iterations of the EM algorithm are characterized by low variance in the parameter distributions. A toy example with available analytic solutions, a synthetic example and a real data application with scarce observations to the LNAS (Log-Normal Allocation and Senescence) model of sugar beet growth are presented to highlight some theoretical and practical aspects of the proposed methodology.

In this paper we present a platform that mixes a domain-specific language for stochastic discrete dynamical models implemented with LLVM and a C++ multi-threaded library for the simulation, analysis and statistical evaluation of these models. More precisely, the user can easily implement a dynamic model, specifying the state and exogenous variables, parameters, state and observation functions, noises, and then run simple simulations, sensitivity analysis, parameter estimation, data assimilation or uncertainty analysis on computation clusters. We believe that this platform can foster good modeling practices since it simplifies the management of data, models and simulations on clusters and it demonstrates the different steps for a proper model design and evaluation. This platform was initially developed for the plant growth modeling community, for which such methodological tools are deeply needed, since a large variety of models coexist in the literature with generally an absence of benchmarking between the different approaches and insufficient model evaluation. However, the software can be used in any scientific field for which discrete dynamical models are developed.

Plant growth is understood through the use of dynamical systems involving many interacting processes and model parameters whose estimation is therefore a crucial issue, all the more so since experimental data obtained from agronomical systems are most of the time characterized by their scarcity and their heterogeneity owing to the complex underlying acquisition. One of the approaches used to solve this kind of problem within a Bayesian paradigm involves Markov Chain Monte Carlo (MCMC) algorithms, one of the drawbacks of the latter being that they lead to some intensive computation because of the statistical framework employed, which is why the efficiency of the implemented computing methods is of particular importance. In this paper, we compare three implementations of a generic MCMC-based algorithm for Bayesian estimation in C++, R and Julia so as to compare the performance and precision of these languages. Here, genericness means that the estimation algorithm can be used for any dynamic model provided that it is implemented in a given modeling template. Such genericness is of crucial importance in a scientific field such as plant growth modeling for which no reference model exists and new models are constantly developed and evaluated. The tests are conducted for the particular cases of Lotka--Volterra model and the Log-Normal Allocation and Senescence model for sugar beet.

As many regions of the United States experience rising temperatures, consumers have come to rely increasingly on cooling appliances (including portable air conditioners) to provide a comfortable indoor temperature. Home occupants sometimes use a portable air conditioner (PAC) to maintain a desired indoor temperature in a single room or enclosed space. Although PACs in residential use are few compared to centrally installed and room air conditioning (AC) units, the past few years have witnessed an increase of PACs use throughout the United States. There is, however, little information and few research projects focused on the energy consumption and performance of PACs, particularly studies that collect information from field applications of PACs. The operation and energy consumption of PACs may differ among geographic locations and households, because of variations in cooling load, frequency, duration of use, and other user-selected settings. In addition, the performance of building envelope (thermal mass and air leakage) as well as inter-zonal mixing within the building would substantially influence the ability to control and maintain desirable indoor thermal conditions. Lawrence Berkeley National Laboratory (LBNL) conducted an initial field-metering study aimed at increasing the knowledge and data related to PAC operation and energy consumption in the United States.

A three-step data assimilation approach is proposed in this paper to enhance crop model predictive capacity in various environmental conditions. The most influential parameters are first selected by global sensitivity analysis and then estimated in a Bayesian framework. The posterior distribution of the estimation step is then considered as prior information for data assimilation. In this last step, a filtering method is sequentially applied to update state and parameter estimates, with the purpose of improving model prediction and assessing the prediction uncertainty.The estimation and assimilation steps are based on the Convolution Particle Filtering, whose features make it particularly suitable for data assimilation in crop models: the method is easy to adapt to any general state-space models (both probabilistic and deterministic ones) with very few tuning parameters, no approximation needs to be made for nonlinear models, and it remains robust in situations with irregular and sparse datasets.With the aim of illustrating the robustness and adaptive capacity of the proposed approach, its predictive performance is evaluated with two crop models, the STICS model for winter wheat and the LNAS model for sugar beet. The two models are built with different perspectives. STICS is deterministic and provides a very detailed description of the ecophysiological processes driving crop-environment interactions, while LNAS is designed to describe only the essential ecophysiological processes of plant biomass budget in a probabilistic framework, so as to put emphasis on the uncertainty assessment.In order to evaluate the approach, five datasets obtained in various experimental conditions were used for the sugar beet LNAS model, and three datasets for the winter wheat STICS model. In both studies, one dataset was used for a priori parameter estimation and the others were used to test the model predictive capacity, both with and without data assimilation. The CPF-based data assimilation approach showed promising predictive capacity and provided robust and reduced credibility intervals in various test configurations (different years for calibration and prediction by assimilation, different experimental sites, different cultivars, different crop densities, different levels of water stresses), which suggests that the combination of such an approach with both types of crop models (simple probabilistic model or complex deterministic model) is quite reliable and can therefore be regarded as a potential tool for yield prediction applications in agriculture. (C) 2014 Elsevier B.V. All rights reserved.

The complexity of plant growth models and the scarcity of experimental data make the application of conventional data assimilation techniques rather difficult. In this paper, we use the Convolution Particle Filter (CPF) and an iterative adaptation, the Iterative Convolution Particle Filter (ICPF) for nonlinear parameter estimation. Both methods provide prior distributions in the Bayesian framework for data assimilation. CPF is sequentially used to update state and parameter estimates in order to improve model prediction and to assess the predictive uncertainty. The predictive performances of the two methods are evaluated by an application to the LNAS sugar beet growth model with three sets of real measurements, one used for parameter estimation and the two others used to test the model predictive capacity, both with and without data assimilation. Despite the low accuracy and the scarcity of the early data used for assimilation, the CPF-based data assimilation approach with the prior distribution based on ICPF estimations showed promising predictive capacities and provided robust confidence intervals. The method can therefore be considered as a potential candidate for yield prediction applications in agriculture.

OBJECTIVE To propose an original method of benchmarking regions based on their prevalence of healthcare-associated infections (HAIs) and to identify regions with unusual results. DESIGN To study between-region variability with a three-level hierarchical logistic regression model and a Bayesian non-parametric method. SETTING French 2006 national HAIs point prevalence survey. PARTICIPANTS A total of 336 858 patients from 2289 healthcare facilities in 27 regions. Patients with an imported HAI (1% of the data, 20.7% of infected patients), facilities with <5 patients and patients who had at least one missing value for the variables taken into account were excluded (5.0% of patients). MAIN OUTCOME MEASURE Binary outcome variable indicates whether a given patient was infected. RESULTS Two clusters of regions were identified: one cluster of five regions had a lower adjusted prevalence than the other one of 22 regions, while no region with unusually high prevalence could be identified. Nevertheless, the degree of heterogeneity of odds ratios between facilities for facility-specific effects of use of invasive devices was more important in some regions than in others. CONCLUSIONS The adjusted regional prevalence of HAIs can serve as an adequate benchmark to identify regions with concerning results. Although no outlier regions were identified, the proposed approach could be applied to the data of the 2012 national survey to benchmark regional healthcare policies. The estimation of facility-specific effects of use of invasive devices may orient future regional action plans.