Dataset Information

Automated pipeline framework for processing of large-scale building energy time series data.

ABSTRACT: Commercial buildings account for one third of the total electricity consumption in the United States and a significant amount of this energy is wasted. Therefore, there is a need for "virtual" energy audits, to identify energy inefficiencies and their associated savings opportunities using methods that can be non-intrusive and automated for application to large populations of buildings. Here we demonstrate virtual energy audits applied to large populations of buildings' time-series smart-meter data using a systematic approach and a fully automated Building Energy Analytics (BEA) Pipeline that unifies, cleans, stores and analyzes building energy datasets in a non-relational data warehouse for efficient insights and results. This BEA pipeline is based on a custom compute job scheduler for a high performance computing cluster to enable parallel processing of Slurm jobs. Within the analytics pipeline, we introduced a data qualification tool that enhances data quality by fixing common errors, while also detecting abnormalities in a building's daily operation using hierarchical clustering. We analyze the HVAC scheduling of a population of 816 buildings, using this analytics pipeline, as part of a cross-sectional study. With our approach, this sample of 816 buildings is improved in data quality and is efficiently analyzed in 34 minutes, which is 85 times faster than the time taken by a sequential processing. The analytical results for the HVAC operational hours of these buildings show that among 10 building use types, food sales buildings with 17.75 hours of daily HVAC cooling operation are decent targets for HVAC savings. Overall, this analytics pipeline enables the identification of statistically significant results from population based studies of large numbers of building energy time-series datasets with robust results. These types of BEA studies can explore numerous factors impacting building energy efficiency and virtual building energy audits. This approach enables a new generation of data-driven buildings energy analysis at scale.

SUBMITTER: Khalilnejad A

PROVIDER: S-EPMC7707597 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Automated pipeline framework for processing of large-scale building energy time series data.

Khalilnejad Arash A Karimi Ahmad M AM Kamath Shreyas S Haddadian Rojiar R French Roger H RH Abramson Alexis R AR

PloS one 20201201 12

Commercial buildings account for one third of the total electricity consumption in the United States and a significant amount of this energy is wasted. Therefore, there is a need for "virtual" energy audits, to identify energy inefficiencies and their associated savings opportunities using methods that can be non-intrusive and automated for application to large populations of buildings. Here we demonstrate virtual energy audits applied to large populations of buildings' time-series smart-meter d ...[more]

PMID: 33259504

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Project description:BackgroundIn-silico quantitative structure-activity relationship (QSAR) models based tools are widely used to screen huge databases of compounds in order to determine the biological properties of chemical molecules based on their chemical structure. With the passage of time, the exponentially growing amount of synthesized and known chemicals data demands computationally efficient automated QSAR modeling tools, available to researchers that may lack extensive knowledge of machine learning modeling. Thus, a fully automated and advanced modeling platform can be an important addition to the QSAR community.ResultsIn the presented workflow the process from data preparation to model building and validation has been completely automated. The most critical modeling tasks (data curation, data set characteristics evaluation, variable selection and validation) that largely influence the performance of QSAR models were focused. It is also included the ability to quickly evaluate the feasibility of a given data set to be modeled. The developed framework is tested on data sets of thirty different problems. The best-optimized feature selection methodology in the developed workflow is able to remove 62-99% of all redundant data. On average, about 19% of the prediction error was reduced by using feature selection producing an increase of 49% in the percentage of variance explained (PVE) compared to models without feature selection. Selecting only the models with a modelability score above 0.6, average PVE scores were 0.71. A strong correlation was verified between the modelability scores and the PVE of the models produced with variable selection.ConclusionsWe developed an extendable and highly customizable fully automated QSAR modeling framework. This designed workflow does not require any advanced parameterization nor depends on users decisions or expertise in machine learning/programming. With just a given target or problem, the workflow follows an unbiased standard protocol to develop reliable QSAR models by directly accessing online manually curated databases or by using private data sets. The other distinctive features of the workflow include prior estimation of data modelability to avoid time-consuming modeling trials for non modelable data sets, an efficient variable selection procedure and the facility of output availability at each modeling task for the diverse application and reproduction of historical predictions. The results reached on a selection of thirty QSAR problems suggest that the approach is capable of building reliable models even for challenging problems.

Project description:During angiogenesis, growing neovessels must effectively navigate through the tissue space as they elongate and subsequently integrate into a microvascular network. While time series microscopy has provided insight into the cell activities within single growing neovessel sprouts, less is known concerning neovascular dynamics within a large angiogenic tissue bed. Here, we developed a time-lapse imaging technique that allowed visualization and quantification of sprouting neovessels as they form and grow away from adult parent microvessels in three dimensions over cubic millimeters of matrix volume during the course of up to 5 days on the microscope. Using a new image acquisition procedure and novel morphometric analysis tools, we quantified the elongation dynamics of growing neovessels and found an episodic growth pattern accompanied by fluctuations in neovessel diameter. Average elongation rate was 5 ?m/h for individual vessels, but we also observed considerable dynamic variability in growth character including retraction and complete regression of entire neovessels. We observed neovessel-to-neovessel directed growth over tens to hundreds of microns preceding tip-to-tip inosculation. As we have previously described via static 3D imaging at discrete time points, we identified different collagen fibril structures associated with the growing neovessel tip and stalk, and observed the coordinated alignment of growing neovessels in a deforming matrix. Overall analysis of the entire image volumes demonstrated that although individual neovessels exhibited episodic growth and regression, there was a monotonic increase in parameters associated with the entire vascular bed such as total network length and number of branch points. This new time-lapse imaging approach corroborated morphometric changes in individual neovessels described by us and others, as well as captured dynamic neovessel behaviors unique to days-long angiogenesis within the forming neovascular network.

Dataset Information

Automated pipeline framework for processing of large-scale building energy time series data.

Publications

Automated pipeline framework for processing of large-scale building energy time series data.

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