Clowder

Marini, Luigi; Gutierrez-Polo, Indira; Kooper, Rob; Satheesan, Sandeep Puthanveetil; Burnette, Max; Lee, Jong; Nicholson, Todd; Zhao, Yan; McHenry, Kenton

doi:10.1145/3219104.3219159

Cited by 26 publications

(4 citation statements)

References 23 publications

(33 reference statements)

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“…The tail length of a load series of a single household can be explicitly observed when there is unusual load consumption. All the load series exhibited either long or short tails in their PDFs [33,34]. Figure 2 shows two different load series with their corresponding tail characteristics.…”

Section: Reordering Load Series Based On Tail Length For Reducing Shi...mentioning

confidence: 99%

Multihousehold Load Forecasting Based on a Convolutional Neural Network Using Moment Information and Data Augmentation

Acharya,

Yu,

et al. 2024

Energies

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Deep learning (DL) networks are a popular choice for short-term load forecasting (STLF) in the residential sector. Hybrid DL methodologies based on convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) have a higher forecasting accuracy than conventional statistical STLF techniques for different types of single-household load series. However, existing load forecasting methodologies are often inefficient when a high load demand persists for a few hours in a day. Peak load consumption is explicitly depicted as a tail in the probability distribution function (PDF) of the load series. Due to the diverse and uncertain nature of peak load demands, DL methodologies have difficulty maintaining consistent forecasting results, particularly when the PDF of the load series has a longer tail. This paper proposes a multihousehold load forecasting strategy based on the collective moment measure (CMM) (which is obtained from the PDF of the load series), data augmentation, and a CNN. Each load series was compared and ordered through CMM indexing, which helped maintain a minimum or constant shifting variance in the dataset inputted to the CNN. Data augmentation was used to enlarge the input dataset and solve the existing data requirement issues of the CNN. With the ordered load series and data augmentation strategy, the simulation results demonstrated a significant improvement in the performance of both single-household and multihousehold load forecasting. The proposed method predicts day-ahead multihousehold loads simultaneously and compares the results based on a single household. The forecasting performance of the proposed method for six different household groups with 10, 20, 30, 50, 80, and 100 household load series was evaluated and compared with those of existing methodologies. The mean absolute percentage error of the prediction results for each multihousehold load series could be improved by more than 3%. This study can help advance the application of DL methods for household load prediction under high-load-demand conditions.

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Section: Reordering Load Series Based On Tail Length For Reducing Shi...mentioning

confidence: 99%

Multihousehold Load Forecasting Based on a Convolutional Neural Network Using Moment Information and Data Augmentation

Acharya,

Yu,

et al. 2024

Energies

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“…At the onset of this project, three open-source LIMS options were presented: Hyperthought [6] (formerly, ICE) from the Air Force Research Laboratory, 4CeeD [7] (and its underlying infrastructure, Clowder [8]) from University of Illinois-Urbana Champaign, and an LIMS developed specifically by and for the Materials and Chemical Science and Technology directorate at the National Renewable Energy Laboratory [9]. After a rapid trial period of several months, 4CeeD was down-selected as the pilot system because its functionalities were most closely matched with the data needs of electron microscopy for materials science, and because a clone deployment is straightforward.…”

Section: Ceed As a Lims Candidatementioning

confidence: 99%

Laboratory Information Management Systems for Electron Microscopy: Evaluation of the 4CeeD Data Curation Platform

Lau

Devers

Newrock

et al. 2019

J. RES. NATL. INST. STAN.

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An evaluation of the feasibility and the requirements associated with a facility-wide deployment of a laboratory information management system (LIMS) at an electron microscopy facility was conducted. 4CeeD, an open-source LIMS, was selected for the focus study. This report summarizes data infrastructure prerequisites, critical and desirable features, and lessons learned from using and interacting with 4CeeD, and broader LIMS adoption recommendations for this facility.

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“…Clowder [9] is the data management module of Brown Dog. It is a cloud-based open source scalable data management system for research that supports curation of unstructured data and metadata across multiple research domains and diverse data types.…”

Section: Workflow and Data Managementmentioning

confidence: 99%

“…The Brown Dog testbed at UMD is configured to test Brown Dog services, simulating common user interactions with the service at web-scale performance loads. The performance tests are done using Gatling 9 , which leverages the parallelism of Scala to spawn hundreds of simultaneous web clients according to a predefined load scenario. In the testbed, various client-side events are logged to files and then loaded into Elasticsearch after the test, for analysis using the Grafana 10 visualization tool.…”

Section: Performance Testsmentioning

confidence: 99%

Brown Dog

Satheesan¹,

Alameda²,

Bradley³

et al. 2018

Proceedings of the Practice and Experience on Advanced Research Computing

Self Cite

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Brown Dog is a data transformation service for auto-curation of long-tail data. In this digital age, we have more data available for analysis than ever and this trend will only increase. According to most estimates, 70-80% of this data is unstructured, and together with unsupported data formats and inaccessible software tools, in essence, this data is not either easily accessible or usable to its owners in a meaningful way. Brown Dog aims at making this data more * NCSA is the abbreviation for The National Center for Supercomputing Applications.

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Clowder

Cited by 26 publications

References 23 publications

Multihousehold Load Forecasting Based on a Convolutional Neural Network Using Moment Information and Data Augmentation

Multihousehold Load Forecasting Based on a Convolutional Neural Network Using Moment Information and Data Augmentation

Laboratory Information Management Systems for Electron Microscopy: Evaluation of the 4CeeD Data Curation Platform

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