An official website of the United States government
In this work, generalized polynomial chaos (gPC) expansion for land surface model parameter estimation is evaluated. We perform inverse modeling and compute the posterior distribution of the critical hydrological parameters that are subject to great uncertainty in the Community Land Model (CLM) for a given value of the output LH. The unknown parameters include those that have been identified as the most influential factors on the simulations of surface and subsurface runoff, latent and sensible heat fluxes, and soil moisture in CLM4.0. We set up the inversion problem in the Bayesian framework in two steps: (i) building a surrogate model expressing the input–output mapping, and (ii) performing inverse modeling and computing the posterior distributions of the input parameters using observation data for a given value of the output LH. The development of the surrogate model is carried out with a Bayesian procedure based on the variable selection methods that use gPC expansions. Our approach accounts for bases selection uncertainty and quantifies the importance of the gPC terms, and, hence, all of the input parameters, via the associated posterior probabilities.
more »
« less
A Bayesian Data Analytics Approach to Buildings' Thermal Parameter Estimation
Modeling buildings' heat dynamics is a complex process which depends on various factors including weather, building thermal capacity, insulation preservation, and residents' behavior. Gray-box models offer an explanation of those dynamics, as expressed in a few parameters specific to built environments that can provide compelling insights into the characteristics of building artifacts. In this paper, we present a systematic study of Bayesian approaches to modeling buildings' parameters, and hence their thermal characteristics. We build a Bayesian state-space model that can adapt and incorporate buildings' thermal equations and postulate a generalized solution that can easily adapt prior knowledge regarding the parameters. We then show that a faster approximate approach using Variational Inference for parameter estimation can posit similar parameters' quantification as that of a more time-consuming Markov Chain Monte Carlo (MCMC) approach. We perform extensive evaluations on two datasets to understand the generative process and attest that the Bayesian approach is more interpretable. We further study the effects of prior selection on the model parameters and transfer learning, where we learn parameters from one season and reuse them to fit the model in other seasons. We perform extensive evaluations on controlled and real data traces to enumerate buildings' parameters within a 95% credible interval.
more »
« less
- Award ID(s):
- 1544687
- PAR ID:
- 10113138
- Date Published:
- Journal Name:
- ACM e-Energy '19 Proceedings of the Tenth ACM International Conference on Future Energy Systems
- Page Range / eLocation ID:
- 89 to 99
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Time series behavior of gas consumption is highly irregular, non-stationary, and volatile due to its dependency on the weather, users' habits and lifestyle. This complicates the modeling and forecasting of gas consumption with most of the existing time series modeling techniques, specifically when missing values and outliers are present. To demonstrate and overcome these problems, we investigate two approaches to model the gas consumption, namely Generalized Additive Models (GAM) and Long Short-Term Memory (LSTM). We perform our evaluations on two building datasets from two different continents. We present each selected feature's influence, the tuning parameters, and the characteristics of the gas consumption on their forecasting abilities. We compare the performances of GAM and LSTM with other state-of-the-art forecasting approaches. We show that LSTM outperforms GAM and other existing approaches, however, GAM provides better interpretable results for building management systems (BMS).more » « less
-
null (Ed.)Sensor metadata tagging, akin to the named entity recognition task, provides key contextual information (e.g., measurement type and location) about sensors for running smart building applications. Unfortunately, sensor metadata in different buildings often follows dis- tinct naming conventions. Therefore, learning a tagger currently requires extensive annotations on a per building basis. In this work, we propose a novel framework, SeNsER, which learns a sensor metadata tagger for a new building based on its raw metadata and some existing fully annotated building. It leverages the commonality between different buildings: At the character level, it employs bidirectional neural language models to capture the shared underlying patterns between two buildings and thus regularizes the feature learning process; At the word level, it leverages as features the k-mers existing in the fully annotated building. During inference, we further incorporate the information obtained from sources such as Wikipedia as prior knowledge. As a result, SeNsER shows promising results in extensive experiments on multiple real-world buildings.more » « less
-
Abstract We develop a prior probability model for temporal Poisson process intensities through structured mixtures of Erlang densities with common scale parameter, mixing on the integer shape parameters. The mixture weights are constructed through increments of a cumulative intensity function which is modeled nonparametrically with a gamma process prior. Such model specification provides a novel extension of Erlang mixtures for density estimation to the intensity estimation setting. The prior model structure supports general shapes for the point process intensity function, and it also enables effective handling of the Poisson process likelihood normalizing term resulting in efficient posterior simulation. The Erlang mixture modeling approach is further elaborated to develop an inference method for spatial Poisson processes. The methodology is examined relative to existing Bayesian nonparametric modeling approaches, including empirical comparison with Gaussian process prior based models, and is illustrated with synthetic and real data examples.more » « less
-
A unique paradox exists between the early and modern architectural and mechanical means of dealing with humidity in and around buildings. Traditional architectural conditions of biopolymeric thatch dwellings accommodated human thermal comfort through dehumidification by the materials employed at the building envelope. Original mechanical developments for dehumidification processes were developed specifically for removing moisture from materials in industrial applications. However, today, the modes by which humidity is treated for human comfort and material protection is inverted: buildings now utilize mechanical conditioning for dehumidification cooling functions and moisture protective materials in the building enclosure systems. Emerging multivalent hydrophilic materials are able to process humidity and moisture transport in new ways to allow for a systemic return to dehumidification cooling functions integrated in the building envelope system. The hydrophilic polymers are synthesized with low energy methods and poured into molds and then lyophilized to create macroporous networks that enhance both the sorption and thermal characteristics in the proposed application. The thermal and optical properties of novel hygrothermal materials are identified and used as inputs for simulation modeling for the proposed multivalent building enclosure system. The initial results provide improvements on annual energy loads and consumption for hot-humid climate conditions (Miami and Mumbai). The introduction of a sorption coefficient for the dehumidification function provides a unique contribution to design and performance analyses of double-skin envelopes. In addition, the dynamic modeling for temporal variations of material properties at the envelope also provides a new contribution to the field of building performance simulation. The challenges of these novel modes for moisture processing in the building envelope materials are addressed, with future work required for microbial identification and monitoring. The advantages from initial analytical and simulation modeling convey improvements for building energy conservation, natural daylighting, and water recuperation potential.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3307772.3328316
