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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Sixteen Years of Terra MODIS On-Orbit Operation, Calibration, and Performance

Terra MODIS has successfully operated for more than 16 years since its launch in December 1999. From its observations, many science data products have been generated in support of a broad range of research activities and remote sensing applications. Terra MODIS has operated in a number of configurations and experienced a few anomalies, including spacecraft and instrument related events. MODIS collects data in 36 spectral bands that are calibrated regularly by a set of on-board calibrators for their radiometric, spectral, and spatial performance. Periodic lunar observations and long-term radiometric trending over well-characterized ground targets are also used to support sensor on-orbit calibration. Dedicated efforts made by the MODIS Characterization Support Team (MCST) and continuing support from the MODIS Science Team have contributed to the mission success, enabling well-calibrated data products to be continuously generated and routinely delivered to users worldwide. This paper presents an overview of Terra MODIS mission operations, calibration activities, and instrument performance of the past 16 years. It illustrates and describes the results of key sensor performance parameters derived from on-orbit calibration and characterization, such as signal-to-noise ratio (SNR), noise equivalent temperature difference (NEdT), solar diffuser (SD) degradation, changes in sensor responses, center wavelengths, and band-to-band registration (BBR). Also discussed in this paper are the calibration approaches and strategies developed and implemented in support of MODIS Level 1B data production and re-processing, major challenging issues, and lessons learned. (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Xiong, X.↗
NASA NTRS2019

An Integrated Approach to Weather Radar Calibration and Monitoring Using Ground Clutter and Satellite Comparisons

The stability and accuracy of weather radar reflectivity calibration are imperative for quantitative applications, such as rainfall estimation, severe weather monitoring and nowcasting, and assimilation in numerical weather prediction models. Various radar calibration and monitoring techniques have been developed, but only recently have integrated approaches been proposed, that is, using different calibration techniques in combination. In this paper the following three techniques are used: 1) ground clutter monitoring, 2) comparisons with spaceborne radars, and 3) the self-consistency of polarimetric variables. These techniques are applied to a C-band polarimetric radar (CPOL) located in the Australian tropics since 1998. The ground clutter monitoring technique is applied to each radar volumetric scan and provides a means to reliably detect changes in calibration, relative to a baseline. It is remarkably stable to within a standard deviation of 0.1 dB (decibels). To obtain an absolute calibration value, CPOL observations are compared to spaceborne radars on board TRMM (Tropical Rainfall Measuring Mission) and GPM (Global Precipitation Measurement) using a volume-matching technique. Using an iterative procedure and stable calibration periods identified by the ground echoes technique, we improve the accuracy of this technique to about 1 dB. Finally, we review the self-consistency technique and constrain its assumptions using results from the hybrid TRMM-GPM and ground echo technique. Small changes in the self-consistency parameterization can lead to 5 dB of variation in the reflectivity calibration. We find that the drop-shape model of Brandes et al. with a standard deviation of the canting angle of 12 degrees best matches our dataset.

Louf, Valentin↗
NASA NTRS2020

Use of an Internal Mercury-Argon Lamp for the Calibration of Raman Frequency in Large Raman Images of Astromaterials

Raman imaging is an emerging tool for the analysis of meteorites, as it is capable of describing mineralogy, carbon composition/speciation, and crystal orientation all within a petrographic context. Adequately examining the heterogeneous composition of a meteorite, while maintaining low laser powers needed to prevent damage to meteorite mineralogy and organics, often requires the collection of large Raman images with long collection times on the timescale of days. However, the frequency calibration of the Raman instrument/spectrometer drifts on long timescales resulting in low frequency precision for the Raman spectra comprising a large Raman image. This decrease in frequency precision can compromise the analysis of geological markers that can provide important information on a meteorite sample. We examine, in detail, the change in bandwidth and frequency of the Raman bands and Hg-Ar emission lines in our Raman images as a function of time and laboratory temperature. To overcome the drift in calibration, we utilize a commercial WITec Raman instrument with an internal Hg-Ar calibration lamp to individually calibrate each spectrum in the Raman image. Our instrument, known as “Ratatoskr”, uses a beam splitter in place of the customary mirror to facilitate collection of Hg-Ar calibration lines concurrent with Raman spectra. We show that using the internal calibration we can improve the frequency precision for the spectra in our Raman images from ~±0.15 cm(exp -1) to ~±0.05 cm(exp -1) for Raman spectra collected over multiple days. This is important as it improves measurements of mineral chemistry, latent strain, and other features that are dependent upon accurate peak position determination. We also examine the spectral signal-to-noise ratio needed to minimize the frequency error when fitting bands to Gaussian/Lorentzian profiles. We then use our results to suggest a general method for calibrating the frequency of Raman spectra in large Raman images.

Jakubek, Ryan S.↗
NASA NTRSDate not supplied

Monitoring the Calibration of the DSCOVR EPIC Instrument’s Visible Cchannels Using MODIS and VIIRS as a Reference

The Earth Polychromatic Imaging Camera (EPIC) instrument aboard the Deep Space Climate Observatory (DSCOVR) satellite has a constant unique view of the sunlit disk of the Earth from the Lagrange-1 (L1) point nearly a million miles away from the Earth. Due to EPIC not having any on-board calibration systems, the ten spectral channels of EPIC must be inter-calibrated using vicarious on-orbit methods, such as ray-matching with well-calibrated low Earth orbiting satellites like the Aqua-MODIS and SNPP-VIIRS radiometers. The recently released EPIC version 3 L1B data has greatly enhanced the residual navigation errors found in prior versions, thereby decreasing the calibration uncertainty in the aforementioned ray-matching calibration techniques. The automated navigation correction used by these ray-matching methods for further improving the EPIC geolocation will be compared with a new navigation correction method utilizing optical flow between the EPIC and MODIS/VIIRS images. The DSCOVR satellite went into safe mode on June 27, 2019 due to an anomaly with the satellite’s attitude control system, but operations recommenced on March 2, 2020. The calibration of the EPIC instrument before it went into safe mode and after it resumed will be analyzed in order to investigate any potential calibration shifts or discontinuities caused by the spacecraft anomaly.

Conor Haney↗
NASA NTRS2021

ICESat-2 Pointing Calibration and Geolocation Performance

ICESat-2 science requirements are dependent on the accurate real-time pointing control (i.e. geolocation control) and post-processed geolocation knowledge of the laser altimeter surface returns. Pre-launch pointing alignment errors and post-launch pointing alignment variation result in large geolocation errors that must be calibrated on orbit. In addition, the changing sun-orbit geometry causes thermal-mechanical forced laser frame alignment variations at the orbit period and trends from days, weeks and months. Early mission analysis computed precise post-launch laser beam alignment calibration. The alignment calibration was uploaded to the spacecraft and enabled the pointing control performance to achieve 4.4 ± 6.0 m, a significant improvement over the 45 m (1 σ) mission requirement. Laser frame alignment calibrations are used to reduce the alignment bias and time variation, as well as the orbital variation contributions to geolocation knowledge error from 6 m to 1.7 m (1 σ). Relative beam alignment of the six beams is calibrated and shown to contribute between 0.5 ± 0.1 m and 2.4 ± 0.2 m of remaining geolocation knowledge error. Independent geolocation assessment based on comparison to high-resolution digital elevation models agrees well with the calibration geolocation error estimates. The analysis demonstrates the ICESat-2 mission is performing far better than its geolocation knowledge requirement of 6.5 m (1 σ) after the laser frame alignment bias variation and orbital variation calibrations have been applied. Remaining geolocation error is beam dependent and ranges from 2.5 m for beam 6 to 4.4 m for beam 2 (mean + 1 σ).

S. B. Luthcke↗
NASA NTRS2020

Planning In-Flight Calibration for XRISM

The XRISM X-ray observatory will fly two advanced instruments, the Resolve high-resolution spectrometer and the Xtend wide-field imager. These instruments, particularly Resolve, pose calibration challenges due to the unprecedented combination of spectral resolution, spectral coverage, and effective area, combined with a need to characterize the imaging fidelity of the full instrument system to realize the mission’s ambitious science goals. We present the status of the XRISM in-flight calibration plan, building on lessons from Hitomi and other X-ray missions. We present a discussion of targets and observing strategies to address the needed calibration measurements, with a focus on developing methodologies to plan a thorough and flexible calibration campaign and provide insight on calibration systematic error. We also discuss observations that exploit Resolve’s spectral resolution to calibrate atomic codes, and cross-calibration between the XRISM instruments and with other observatories.

Eric D Miller↗
NASA NTRS2021

ICESat-2 Pointing Calibration and Geolocation Performance

ICESat-2 science requirements are dependent on the accurate real-time pointing control (i.e., geolocation control) and postprocessed geolocation knowledge of the laser altimeter surface returns. Prelaunch pointing alignment errors and postlaunch pointing alignment variation result in large geolocation errors that must be calibrated on orbit. In addition, the changing sun-orbit geometry causes thermal-mechanical forced laser frame alignment variations at the orbit period and trends from days, weeks, and months. Early mission analysis computed precise postlaunch laser beam alignment calibration. The alignment calibration was uploaded to the spacecraft and enabled the pointing control performance to achieve 4.4 ± 6.0 m, a significant improvement over the 45 m (1 σ) mission requirement. Laser frame alignment calibrations are used to reduce the alignment bias and time variation, as well as the orbital variation contributions to geolocation knowledge error from 6 to 1.7 m (1 σ). Relative beam alignment of the six beams is calibrated and shown to contribute between 0.5 ± 0.1 m and 2.4 ± 0.2 m of remaining geolocation knowledge error. Independent geolocation assessment based on comparison to high-resolution digital elevation models agrees well with the calibration geolocation error estimates. The analysis demonstrates the ICESat-2 mission is performing far better than its geolocation knowledge requirement of 6.5 m (1 σ) after the laser frame alignment bias variation and orbital variation calibrations have been applied. Remaining geolocation error is beam dependent and ranges from 2.5 m for beam 6 to 4.4 m for beam 2 (mean + 1 σ).

S B Luthcke↗
NASA NTRS2022

Leveraging Pre-storm Soil Moisture Estimates for Enhanced Land Surface Model Calibration in Ungauged Hydrologic Basins

Despite long-standing efforts, hydrologists still lack robust tools for calibrating land surface model (LSM) streamflow estimates within ungauged basins. Using surface soil moisture estimates from the Soil Moisture Active Passive Level 4 Soil Moisture (L4_SM) product, precipitation observations, and streamflow gauge measurements for 617 medium-scale (200-10,000 km2) basins in the contiguous United States, we measure the temporal (Spearman) rank correlation between antecedent (i.e., pre-storm) surface soil moisture (ASM) and the storm-scale runoff coefficient (RC; the fraction of storm-scale precipitation accumulation converted into streamflow). In humid and semi-humid basins, this rank correlation is shown to be sufficiently strong to allow for the substitution of storm-scale RC observations (available only in basins that are both lightly regulated and gauged) with high-quality ASM values (available quasi-globally from L4_SM) in streamflow calibration procedures. Using this principle, we define a new, basin-wise LSM streamflow calibration approach based on L4_SM alone and successfully apply it to identify LSM configurations that produce a high rank correlation with observed RC. However, since the approach cannot detect RC bias, it is less successful in identifying LSM configurations with low mean-absolute error. Plain Text Summary Accurately forecasting the fraction of rainfall that runs off into streams, as opposed to infiltrates into the soil, is critical for flash-flood prediction, water-resource monitoring, and tracking the transport of nutrients from agricultural fields into local waterways. Such forecasting is typically performed by hydrologic models that attempt to represent the physical processes responsible for surface runoff generation. However, to provide accurate streamflow forecasts, these models typically need to be calibrated against actual streamflow observations. This is problematic given the relatively poor, and declining, global availability of stream gauges. This paper presents a novel model calibration strategy that uses soil moisture from remote sensing and numerical modeling in place of streamflow observations during calibration. This transition has significant practical advantages because, unlike streamflow observations, the soil moisture data are continuously available across space. Our results demonstrate that this new approach can significantly improve hydrologic models within humid and semi-humid basins lacking sufficient ground-based instrumentation for traditional streamflow calibration.

hydrologic models↗
DOE OSTI2026

A Comprehensive Calibration Framework for the Northwest River Forecast Center

We present a comprehensive framework developed by the Northwest River Forecast Center for calibrating hydrologically diverse basins. The framework includes models for snow, soil moisture, routing, channel loss, and consumptive use. Data inputs include a wide range of open-access datasets for meteorology, land use, topography, and land cover. The framework uses conceptual hydrologic models to handle basins with various hydrologic regimes including rain-driven and snowmelt-dominated basins. We also develop a flexible automatic calibration system that can handle numerous unobservable model parameters in a computationally efficient manner. A single-basin automatic calibration run can typically be completed on a modern laptop in under 10 min. We found that model performance metrics for this new approach match the quality of the NWRFC's previous labor-intensive manual calibrations. The model performance also rivals that of a state-of-the-art deep learning model at a fraction of the computational cost. This framework presents a new standard for the quality of calibrations possible with lumped conceptual hydrologic models, combining careful data curation, an objective calibration framework, and expert local knowledge. In addition, we have made software packages available for the entire suite of National Weather Service River Forecast System models, including SAC-SMA, SNOW-17, and Lag-K. These modern interfaces are intended to increase accessibility and facilitate future research.

Forecasting↗
DOE OSTI2025

Elastic Bayesian Model Calibration

Functional data are ubiquitous in scientific modeling. For instance, quantities of interest are modeled as functions of time, space, energy, density, etc. Uncertainty quantification methods for computer models with functional response have resulted in tools for emulation, sensitivity analysis, and calibration that are widely used. However, many of these tools do not perform well when the computer model’s parameters control both the amplitude variation of the functional output and its alignment (or phase variation). This paper introduces a framework for Bayesian model calibration when the model responses are misaligned functional data. The approach generates two types of data out of the misaligned functional responses: (1) aligned functions so that the amplitude variation is isolated and (2) warping functions that isolate the phase variation. These two types of data are created for the computer simulation data (both of which may be emulated) and the experimental data. The calibration approach uses both types so that it seeks to match both the amplitude and phase of the experimental data. The framework is careful to respect constraints that arise, especially when modeling phase variation, and is framed in a way that it can be done with readily available calibration software. In conclusion, we demonstrate the techniques on two simulated data examples and on two dynamic material science problems: a strength model calibration using flyer plate experiments and an equation of state model calibration using experiments performed on the Sandia National Laboratories’ Z-machine.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2015

NASA Glenn Icing Research Tunnel: 2014 and 2015 Cloud Calibration Procedures and Results

This report summarizes the current status of the NASA Glenn Research Center (GRC) Icing Research Tunnel cloud calibration: specifically, the cloud uniformity, liquid water content, and drop-size calibration results from both the January-February 2014 full cloud calibration and the January 2015 interim cloud calibration. Some aspects of the cloud have remained the same as what was reported for the 2014 full calibration, including the cloud uniformity from the Standard nozzles, the drop-size equations for Standard and Mod1 nozzles, and the liquid water content for large-drop conditions. Overall, the tests performed in January 2015 showed good repeatability to 2014, but there is new information to report as well. There have been minor updates to the Mod1 cloud uniformity on the north side of the test section. Also, successful testing with the OAP-230Y has allowed the IRT to re-expand its operating envelopes for large-drop conditions to a maximum median volumetric diameter of 270 microns. Lastly, improvements to the collection-efficiency correction for the SEA multi-wire have resulted in new calibration equations for Standard- and Mod1-nozzle liquid water content.

aircraft icing↗
NASA NTRS2018

Initial Calibration Activities and Performance Assessments of NOAA-20 VIIRS

The second VIIRS instrument was launched on-board the NOAA-20 (formerly JPSS-1) satellite onNovember 18, 2017. It was designed and built with the same performance requirements as the first VIIRSon-board the S-NPP launched on October 28, 2011. Currently, the NOAA-20 is orbiting the Earth in thesame plane as the S-NPP but separated in time and space by 50 minutes. The VIIRS observations are made in22 spectral bands, including a day-night band (DNB) that cover wavelengths from visible to long-waveinfrared. The sensor's on-orbit calibration is provided by a set of on-board calibrators (OBCs), which includea solar diffuser (SD), a solar diffuser stability monitor (SDSM), and a blackbody (BB). After turn-on, theVIIRS instrument conducted a series of post-launch testing (PLT) and intensive calibration and validation(ICV) activities, including those performed via spacecraft maneuvers, designed to verify and establishinstrument on-orbit calibration performance baseline. This paper provides an overview of NOAA-20 VIIRSICV activities and an assessment of its initial on-orbit performance with a focus on several key calibrationparameters, such as the detector response (or gain), dynamic range, and signal-to-noise ratio (SNR). Variousissues identified and lessons learned from initial instrument operation and calibration are also discussed insupport of long-term monitoring (LTM) of NOAA-20 VIIRS calibration and data quality.

Calibration↗
NASA NTRSDate not supplied

Eighteen Years of Aqua MODIS On-orbit Operation, Calibration, and Performance

Aqua MODIS has successfully operated for more than 18 years since its launch in May 2002and has generated numerous science products in support of studies of the Earth’s system and its changes via a set of geophysical and environmental parameters. On-orbit calibration and characterization activities have played a vital role in maintaining the quality of MODIS data products. In addition to data collected from sensor on-board calibrators (OBC), near-monthly lunar observations and select earth view targets have been used to monitor and characterize on-orbit changes in sensor responses and to derive and update the calibration look-up tables. In this paper, we provide an overview of Aqua MODIS on-orbit operations, calibration activities and approaches, and algorithm improvements and also illustrate the sensor on-orbit performance using examples derived from various calibration sources and targets. We will focus on issues identified from instrument operations and calibrations, such as solar diffuser degradation, electronic crosstalk, variations in the cold focal plane temperatures, and changes in response versus scan-angle. Also discussed in this paper are remaining challenges and future improvements.

Aqua↗
NASA NTRSDate not supplied

Overview of NASA's Ocean Color Instrument Solar Calibration Architecture, Pre-Launch Tests and Preliminary On-Orbit Results

Launched in February 2024, the PACE mission represents NASA’s next investment in ocean biology, clouds, and aerosol data records. A key feature of PACE is the inclusion of an advanced satellite radiometer known as the Ocean Color Instrument (OCI), a global mapping radiometer that combines multispectral and hyperspectral remote sensing. Like its predecessors, OCI will provide two day global coverage of TOA radiances. Unlike its predecessors, OCI will cover a spectral range from 340nm to 2260nm. Below 900nm, OCI will include two spectrometers that continuously span the ultraviolet to 600nm and 600nm to near-infrared spectral regions to provide hyperspectral radiances sampled every 2.5 nm, with a bandwidth of 5 nm for each channel. Wavelengths above 900nm are measured in seven discrete multispectral bands of varying bandwidths, six of which are at similar wavelengths to those on heritage missions to support both atmospheric and ocean color applications. Nominal spatial resolution is similar to the SeaWiFS instrument with 1050 m at nadir. As for SeaWiFS, the pixel size increases due to a ~20 degree tilt and as a function of scan angle. Variations in the radiometric sensitivity of each OCI channel over time will be monitored by solar diffuser measurements for short term instrument gain adjustments and independent lunar measurements for trend adjustments of long time periods, similar to the approach employed for the VIIRS instrument [4]. The OCI flight-unit was built at NASA’s Goddard Space Flight Center. At the time of this writing, OCI has completed on-orbit commissioning activities and normal science operations have begun. A key aspect of the OCI architecture is the capability to trend absolute and relative calibration changes over the course of mission life with solar calibration. Every 24 hours, the PACE spacecraft performs an inertial hold as the ground track nears the North Pole which orients a Quasi-Volume Diffuser (QVD) mounted on OCI towards the sun. By knowing the irradiance of the sun and the reflectivity of the target, the absolute radiance at the input to the OCI aperture can be computed as OCI scans the target. The allowable absolute uncertainty budget for each solar calibration measurement is 1.6% 1-sigma below 900nm at beginning of life (BOL) and the allowable relative uncertainty budget is ~0.26% 1-sigma. The Solar Calibration Assembly (SCA) consists of three targets selectable via a single mechanism which also opens a door. The targets consist of a Daily Bright Target (DBT), Monthly Bright Target (MBT), and Daily Dim Target (DDT). The bright targets are quartz QVDs with the monthly target being used to track the degradation of the daily target. The dim target is used to track CCD linearity using Progressive Time-Delay Integration (PTDI). A composite baffle is attached to the SCA housing aperture to block Earth shine and stray light from the spacecraft. The SCA assembly is mounted to a view port ~90° from OCI nadir. This paper provides an overview of driving solar calibration requirements, error-budgets and early trade studies which drove the solar calibration assembly (SCA) architecture and on-orbit maneuver. Measurements of the diffuser Bidirectional Reflectance Distribution Function (BRDF) at TNO, Netherlands and GSFC are briefly described. Optical modelling and test results at the sub-system and instrument level are included. Finally, preliminary measurements on-orbit are compared to pre-launch predictions.

ocean color↗
DOE OSTI2022

Application and evaluation of a pattern-based building energy model calibration method using public building datasets

Building performance simulation has been adopted to support decision making in the building life cycle. An essential issue is to ensure a building energy simulation model can capture the reality and complexity of buildings and their systems in both the static characteristics and dynamic operations. Building energy model calibration is a technique that takes various types of measured performance data (e.g., energy use) and tunes key model parameters to match the simulated results with the actual measurements. This study performed an application and evaluation of an automated pattern-based calibration method on commercial building models that were generated based on characteristics of real buildings. A public building dataset that includes high-level building attributes (e.g., building type, vintage, total floor area, number of stories, zip code) of 111 buildings in San Francisco, California, USA, was used to generate building models in EnergyPlus. Monthly level energy use calibrations were then conducted by comparing building model results against the actual buildings' monthly electricity and natural gas consumption. The results showed 57 out of 111 buildings were successfully calibrated against actual buildings, while the remaining buildings showed opportunities for future calibration improvements. Enhancements to the pattern-based model calibration method are identified to expand its use for: (1) central heating, ventilation and air conditioning (HVAC) systems with chillers, (2) space heating and hot water heating with electricity sources, (3) mixed-use building types, and (4) partially occupied buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Automatic and rapid calibration of urban building energy models by learning from energy performance database

Urban building energy modeling (UBEM) is attracting increasing attention in the energy modeling filed. Unlike modeling a single building using detailed building systems information, UBEM generally uses limited high-level building stock data to infer default assumptions about building characteristics and operations. Additionally, this practice inherently brings uncertainty to UBEM. This study introduced a novel method of automatic and rapid calibration of UBEM based on the annual electricity and natural gas energy use data by learning the correlations between crucial model input parameters and the building energy use from the reference building models. A case study was presented to calibrate 72 large office buildings built before 1978 in San Francisco. Seventeen model parameters were selected and Monte Carlo sampling was used to create 1000 samples that reasonably represent the parameter space. Then 1000 simulations were performed for the reference building model to create an energy performance database. The results showed that by learning from the energy performance database, it took less than four simulation runs on average to calibrate a building model. After the calibration, the distributions of each parameter were obtained to replace their single predefined default values. For example, the default lighting power density of 21.39 W/m 2 was calibrated to be 7.50 W/m 2 on average. The case study successfully demonstrated the effectiveness of the novel calibration method for UBEM in the mild climate. The method will be further tested in future for other climate zones and other building types.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Bayesian calibration of a physics-based crystal plasticity and damage model

In this work, we present a model parameter calibration procedure for a physics-based crystal plasticity model. The calibration process utilizes a powerful statistics-based Bayesian calibration method. Calibration of the crystal plasticity parameters makes use of experimentally-measured data, i.e. compressive stress–strain response, from $\langle$100$\rangle$ and $\langle$123$\rangle$ single crystal copper dynamically loaded via Kolsky bar tests. The calibration of damage parameters is achieved using experimentally-measured free-surface velocity history data from plate impact test on $\langle$100$\rangle$ and $\langle$110$\rangle$ single crystal copper, which generates shock compression followed by dynamic tensile failure. A validation assessment is then carried out by comparing the calibrated model predictions and experimental measurements of the dynamic tensile damage generated in an impacted bicrystal copper plate. Lastly, a model-informed rationale for the experimentally-observed dependence of the spatial distribution of ductile damage (porosity) on crystallography is provided.

42 ENGINEERING↗
DOE OSTI2021

A rigorous physics-based enhanced parameter estimation (EPE) methodology for calibration of building energy simulations

Buildings rarely perform as designed/simulated and there are numerous tangible benefits if this gap is reconciled. A new scientifically rigorous yet pragmatic methodology for calibrating building energy simulations - called Enhanced Parameter Estimation (EPE) - is proposed that allows physically relevant parameter estimation rather than a blind force-fit to energy use data. Starting with a rapidly created simulation model, calibration is performed in two stages: (a) building shell calibration with the HVAC system replaced by an ideal system that meets the loads (b) HVAC system calibration with the building shell and all internal loads replaced by a box with only process loads. In the first stage, EPE identifies a small number of high-level heat flows in the energy balance, calculates them with specifically tailored individual driving functions, introduces physically significant parameters to best accomplish energy balance, and, estimates the parameters and their uncertainty bounds. Calibration is thus done with corrective heat flows without any arbitrary tuning of input parameters. Calibration accuracy is enhanced by machine learning of the residual errors. The EPE methodology is demonstrated by means of: a synthetic building and an actual 75,000 sq. ft. building in Pennsylvania. Future work needed for widespread application is discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗