A pure phase of VO2(B) nanorods have been synthesized through an energy-efficient microwave hydrothermal reaction and used as cathode materials of lithium ion batteries, which exhibit promising specific capacity (e.g., 130 mAh g-1 even after 100 charge/discharge cycles) and rate capacity (e.g., ~130 mAh g-1 at a high current of 400 mA g-1). The excellent cyclability originates from the structural reversibility of VO2(B) upon lithiation/delithiation that is confirmed by the in situ high-energy synchrotron X-ray diffraction (HEXRD) and in situ x-ray adsorption near-edge spectroscopy (XANES) of the VO2 nanorods in operating battery cells. The real-time results reveal that discharge forces lithium ions to insert firstly into the tunnels with the largest size along b direction followed by the second largest tunnels along c direction, which is completely reversible in the charge process.
a b s t r a c tThe increasing interest in retrofitting of existing buildings is motivated by the need to make a major contribution to enhancing building energy efficiency and reducing energy consumption and CO 2 emission by the built environment. This paper examines the relevance of calibration in model-based analysis to support decision-making for energy and carbon efficiency retrofits of individual buildings and portfolios of buildings. The authors formulate a set of real retrofit decision-making situations and evaluate the role of calibration by using a case study that compares predictions and decisions from an uncalibrated model with those of a calibrated model. The case study illustrates both the mechanics and outcomes of a practical alternative to the expert-and time-intense application of dynamic energy simulation models for large-scale retrofit decision-making under 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.
IntroductionThe objective of calibrating building energy models is to improve predictions by determining feasible values for uncertain model parameters that are typically unattainable from a pool of available data. Generally, calibration of energy simulation models is applied to reliably evaluate energy-saving potentials for energy-efficiency measures (EEMs) (Yoon, Lee, and Claridge 2003;Reddy and Maor 2006). Also, current standards, including the international performance measurement and verification protocol (EVO 2012) and ASHRAE Guideline 14 (ASHRAE 2002), endorse the whole-building calibrated simulation approach for measuring and verifying energy savings achieved from EEMs implemented for existing buildings.For measurement and verification of energy retrofits of individual buildings, ASHRAE Guideline 14 provides a standard analysis procedure for the calibrated simulation approach. First, information about the building is obtained through audits (e.g. building dimensions, construction specifications, system nameplates information, occupancy and operation schedules, and whole-building utility data). From these audit data, an energy simulation model is constructed for the building. Then, uncertain parameter values are estimated by comparing model outcomes with measured data until discrepancies between predicted and monitored energy use meet an acceptable tolerance. For validating a calibrated model, ASHRAE Guideline 14 stipulates that the coefficient of variation of the root-mean-square error (CVRMSE)
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.