Diane J. Graziano

Evaluation of calibration efficacy under different levels of uncertainty

This paper examines how calibration performs under different levels of uncertainty in model input data. It specifically assesses the efficacy of Bayesian calibration to enhance the reliability of EnergyPlus model predictions. A Bayesian approach can be used to update uncertain values of parameters, given measured energy-use data, and to quantify the associated uncertainty. We assess the efficacy of Bayesian calibration under a controlled virtual-reality setup, which enables rigorous validation of the accuracy of calibration results in terms of both calibrated parameter values and model predictions. Case studies demonstrate the performance of Bayesian calibration of base models developed from audit data with differing levels of detail in building design, usage, and operation.

Understanding Variability To Reduce the Energy and GHG Footprints of U.S. Ethylene Production

Recent growth in U.S. ethylene production due to the shale gas boom is affecting the U.S. chemical industrys energy and greenhouse gas (GHG) emissions footprints. To evaluate these effects, a systematic, first-principles model of the cradle-to-gate ethylene production system was developed and applied. The variances associated with estimating the energy consumption and GHG emission intensities of U.S. ethylene production, both from conventional natural gas and from shale gas, are explicitly analyzed. A sensitivity analysis illustrates that the large variances in energy intensity are due to process parameters (e.g., compressor efficiency), and that large variances in GHG emissions intensity are due to fugitive emissions from upstream natural gas production. On the basis of these results, the opportunities with the greatest leverage for reducing the energy and GHG footprints are presented. The model and analysis provide energy analysts and policy makers with a better understanding of the drivers of energy use and GHG emissions associated with U.S. ethylene production. They also constitute a rich data resource that can be used to evaluate options for managing the industrys footprints moving forward.

Using Agent-Based Modeling to Inform Regional Health Care System Investment and Planning

An agent-based model is used to simulate a developing region population, disease burden, health care infrastructure and estimate the impact of resource investment decisions on population health and health care costs. In this approach, the primary agents are individual health care facilities, capturing population characteristics, facility catchment population, and facility diagnostic capacity and strategies. Health facility investment decisions are represented by new hospital placement and capacity in selected jurisdictions. Impact on outcomes is simulated over a time horizon of up to 20 years. Data visualization is applied and used to compare multiple scenarios to help inform public health planning, investment and policy decision-making.

Environmental and Economic Implications of Distributed Additive Manufacturing: The Case of Injection Mold Tooling

Summary Additive manufacturing (AM) holds great potentials in enabling superior engineering functionality, streamlining supply chains, and reducing life cycle impacts compared to conventional manufacturing (CM). This study estimates the net changes in supply-chain lead time, life cycle primary energy consumption, greenhouse gas (GHG) emissions, and life cycle costs (LCC) associated with AM technologies for the case of injection molding, to shed light on the environmental and economic advantages of a shift from international or onshore CM to AM in the United States. A systems modeling framework is developed, with integrations of lead-time analysis, life cycle inventory analysis, LCC model, and scenarios considering design differences, supply-chain options, productions, maintenance, and AM technological developments. AM yields a reduction potential of 3% to 5% primary energy, 4% to 7% GHG emissions, 12% to 60% lead time, and 15% to 35% cost over 1 million cycles of the injection molding production depending on the AM technology advancement in future. The economic advantages indicate the significant role of AM technology in raising global manufacturing competitiveness of local producers, while the relatively small environmental benefits highlight the necessity of considering trade-offs and balance techniques between environmental and economic performances when AM is adopted in the tooling industry. The results also help pinpoint the technological innovations in AM that could lead to broader benefits in future.

Energy Impacts of Wide Band Gap Semiconductors in U.S. Light-Duty Electric Vehicle Fleet

Silicon carbide and gallium nitride, two leading wide band gap semiconductors with significant potential in electric vehicle power electronics, are examined from a life cycle energy perspective and compared with incumbent silicon in U.S. light-duty electric vehicle fleet. Cradle-to-gate, silicon carbide is estimated to require more than twice the energy as silicon. However, the magnitude of vehicle use phase fuel savings potential is comparatively several orders of magnitude higher than the marginal increase in cradle-to-gate energy. Gallium nitride cradle-to-gate energy requirements are estimated to be similar to silicon, with use phase savings potential similar to or exceeding that of silicon carbide. Potential energy reductions in the United States vehicle fleet are examined through several scenarios that consider the market adoption potential of electric vehicles themselves, as well as the market adoption potential of wide band gap semiconductors in electric vehicles. For the 2015-2050 time frame, cumulative energy savings associated with the deployment of wide band gap semiconductors are estimated to range from 2-20 billion GJ depending on market adoption dynamics.

STOCHASTIC ENERGY SIMULATION FOR RISK ANALYSIS OF ENERGY RETROFITS

Building energy modeling is a common procedure for the analysis of energy efficiency retrofits. Smaller retrofits of isolated systems, such as equipment motors and lighting systems, can often be made without the need for complete energy modeling; however, when the retrofit affects multiple systems, such as those involving the building envelope or the heating or cooling system, or when the retrofits of motors and lighting systems are so significant that they affect the heating and cooling load of the building, a more complete energy analysis is necessary. Because the exact inputs to building energy models are never known, and some inputs to the model are stochastic in nature (e.g., occupancy, plug-loads, lighting loads, weather), deterministic prediction of energy use is not only invariably inaccurate, it is actually inappropriate. When simple deterministic energy savings without uncertainty are used in economic analyses (e.g., return on investment), it is difficult to analyze the risk/benefit of the retrofit investment with true accuracy. A stochastic simulation, which includes the effects of input uncertainty and stochastic inputs, is a more appropriate way to predict the building energy use. In this paper, we present a method for stochastic energy simulation that propagates probability characterizations of the input values through a computational engine to create probable energy use predictions. When this probable energy use is combined with forecasts of energy and construction costs, a probable estimate of return on energy efficiency measure investment is generated, and an economic risk/benefit analysis of the investment can be made. Such information is especially important to the growing energy service company market. The computational engine is based on the CEN/ISO monthly building energy calculation standards so its accuracy is well researched and validated, and the computational simplicity allows for efficient stochastic analysis.

Financial Analysis of Experimental Releases Conducted at Glen Canyon Dam during Water Year 2013

This report examines the financial implications of experimental flows conducted at the Glen Canyon Dam (GCD) in water year 2013. It is the fifth report in a series examining the financial implications of experimental flows conducted since the Record of Decision (ROD) was adopted in February 1997 (Reclamation 1996). A report released in January 2011 examined water years 1997 to 2005 (Veselka et al. 2011), a report released in August 2011 examined water years 2006 to 2010 (Poch et al. 2011), a report released June 2012 examined water year 2011 (Poch et al. 2012), and a report released April 2013 examined water year 2012 (Poch et al. 2013).